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Algorithmic learning theory is a mathematical framework for analyzing
machine learning problems and algorithms. Synonyms include formal learning theory and algorithmic inductive inference. Algorithmic learning theory is different from statistical learning theory in that it does not make use of statistical assumptions and analysis. Both algorithmic and statistical learning theory are concerned with machine learning and can thus be viewed as branches of computational learning theory.
== Distinguishing characteristics ==
Unlike statistical learning theory and most statistical theory in general, algorithmic learning theory does not assume that data are random samples, that is, that data points are independent of each other. This makes the theory suitable for domains where observations are (relatively) noise-free but not random, such as language learning and automated scientific discovery.
The fundamental concept of algorithmic learning theory is learning in the limit: as the number of data points increases, a learning algorithm should converge to a correct hypothesis on every possible data sequence consistent with the problem space. This is a non-probabilistic version of statistical consistency, which also requires convergence to a correct model in the limit, but allows a learner to fail on data sequences with probability measure 0 .
Algorithmic learning theory investigates the learning power of Turing machines. Other frameworks consider a much more restricted class of learning algorithms than Turing machines, for example, learners that compute hypotheses more quickly, for instance in polynomial time. An example of such a framework is probably approximately correct learning .
== Learning in the limit ==
The concept was introduced in E. Mark Gold's seminal paper "Language identification in the limit". The objective of language identification is for a machine running one program to be capable of developing another program by which any given sentence can be tested to determine whether it is "grammatical" or "ungrammatical". The language being learned need not be English or any other natural language - in fact the definition of "grammatical" can be absolutely anything known to the tester.
In Gold's learning model, the tester gives the learner an example sentence at each step, and the learner responds with a hypothesis, which is a suggested program to determine grammatical correctness. It is required of the tester that every possible sentence (grammatical or not) appears in the list eventually, but no particular order is required. It is required of the learner that at each step the hypothesis must be correct for all the sentences so far.
A particular learner is said to be able to "learn a language in the limit" if there is a certain number of steps beyond which its hypothesis no longer changes. At this point it has indeed learned the language, because every possible sentence appears somewhere in the sequence of inputs (past or future), and the hypothesis is correct for all inputs (past or future), so the hypothesis is correct for every sentence. The learner is not required to be able to tell when it has reached a correct hypothesis, all that is required is that it be true.
Gold showed that any language which is defined by a Turing machine program can be learned in the limit by another Turing-complete machine using enumeration. This is done by the learner testing all possible Turing machine programs in turn until one is found which is correct so far - this forms the hypothesis for the current step. Eventually, the correct program will be reached, after which the hypothesis will never change again (but note that the learner does not know that it won't need to change).
Gold also showed that if the learner is given only positive examples (that is, only grammatical sentences appear in the input, not ungrammatical sentences), then the language can only be guaranteed to be learned in the limit if there are only a finite number of possible sentences in the language (this is possible if, for example, sentences are known to be of limited length).
Language identification in the limit is a highly abstract model. It does not allow for limits of runtime or computer memory which can occur in practice, and the enumeration method may fail if there are errors in the input. However the framework is very powerful, because if these strict conditions are maintained, it allows the learning of any program known to be computable. This is because a Turing machine program can be written to mimic any program in any conventional programming language. See Church-Turing thesis.
== Other identification criteria ==
Learning theorists have investigated other learning criteria, such as the following.
Efficiency: minimizing the number of data points required before convergence to a correct hypothesis.
Mind Changes: minimizing the number of hypothesis changes that occur before convergence.
Mind change bounds are closely related to mistake bounds that are studied in statistical learning theory. Kevin Kelly has suggested that minimizing mind changes is closely related to choosing maximally simple hypotheses in the sense of Occam’s Razor.
== Annual conference ==
Since 1990, there is an International Conference on Algorithmic Learning Theory (ALT), called Workshop in its first years (1990–1997). Between 1992 and 2016, proceedings were published in the LNCS series. Starting from 2017, they are published by the Proceedings of Machine Learning Research. The 34th conference will be held in Singapore in Feb 2023. The topics of the conference cover all of theoretical machine learning, including statistical and computational learning theory, online learning, active learning, reinforcement learning, and deep learning.
== See also ==
Formal epistemology
Sample exclusion dimension
== References ==
== External links == | Wikipedia/Algorithmic_learning_theory |
A Bayesian network (also known as a Bayes network, Bayes net, belief network, or decision network) is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). While it is one of several forms of causal notation, causal networks are special cases of Bayesian networks. Bayesian networks are ideal for taking an event that occurred and predicting the likelihood that any one of several possible known causes was the contributing factor. For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases.
Efficient algorithms can perform inference and learning in Bayesian networks. Bayesian networks that model sequences of variables (e.g. speech signals or protein sequences) are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams.
== Graphical model ==
Formally, Bayesian networks are directed acyclic graphs (DAGs) whose nodes represent variables in the Bayesian sense: they may be observable quantities, latent variables, unknown parameters or hypotheses. Each edge represents a direct conditional dependency. Any pair of nodes that are not connected (i.e. no path connects one node to the other) represent variables that are conditionally independent of each other. Each node is associated with a probability function that takes, as input, a particular set of values for the node's parent variables, and gives (as output) the probability (or probability distribution, if applicable) of the variable represented by the node. For example, if
m
{\displaystyle m}
parent nodes represent
m
{\displaystyle m}
Boolean variables, then the probability function could be represented by a table of
2
m
{\displaystyle 2^{m}}
entries, one entry for each of the
2
m
{\displaystyle 2^{m}}
possible parent combinations. Similar ideas may be applied to undirected, and possibly cyclic, graphs such as Markov networks.
== Example ==
Suppose we want to model the dependencies between three variables: the sprinkler (or more appropriately, its state - whether it is on or not), the presence or absence of rain and whether the grass is wet or not. Observe that two events can cause the grass to become wet: an active sprinkler or rain. Rain has a direct effect on the use of the sprinkler (namely that when it rains, the sprinkler usually is not active). This situation can be modeled with a Bayesian network (shown to the right). Each variable has two possible values, T (for true) and F (for false).
The joint probability function is, by the chain rule of probability,
Pr
(
G
,
S
,
R
)
=
Pr
(
G
∣
S
,
R
)
Pr
(
S
∣
R
)
Pr
(
R
)
{\displaystyle \Pr(G,S,R)=\Pr(G\mid S,R)\Pr(S\mid R)\Pr(R)}
where G = "Grass wet (true/false)", S = "Sprinkler turned on (true/false)", and R = "Raining (true/false)".
The model can answer questions about the presence of a cause given the presence of an effect (so-called inverse probability) like "What is the probability that it is raining, given the grass is wet?" by using the conditional probability formula and summing over all nuisance variables:
Pr
(
R
=
T
∣
G
=
T
)
=
Pr
(
G
=
T
,
R
=
T
)
Pr
(
G
=
T
)
=
∑
x
∈
{
T
,
F
}
Pr
(
G
=
T
,
S
=
x
,
R
=
T
)
∑
x
,
y
∈
{
T
,
F
}
Pr
(
G
=
T
,
S
=
x
,
R
=
y
)
{\displaystyle \Pr(R=T\mid G=T)={\frac {\Pr(G=T,R=T)}{\Pr(G=T)}}={\frac {\sum _{x\in \{T,F\}}\Pr(G=T,S=x,R=T)}{\sum _{x,y\in \{T,F\}}\Pr(G=T,S=x,R=y)}}}
Using the expansion for the joint probability function
Pr
(
G
,
S
,
R
)
{\displaystyle \Pr(G,S,R)}
and the conditional probabilities from the conditional probability tables (CPTs) stated in the diagram, one can evaluate each term in the sums in the numerator and denominator. For example,
Pr
(
G
=
T
,
S
=
T
,
R
=
T
)
=
Pr
(
G
=
T
∣
S
=
T
,
R
=
T
)
Pr
(
S
=
T
∣
R
=
T
)
Pr
(
R
=
T
)
=
0.99
×
0.01
×
0.2
=
0.00198.
{\displaystyle {\begin{aligned}\Pr(G=T,S=T,R=T)&=\Pr(G=T\mid S=T,R=T)\Pr(S=T\mid R=T)\Pr(R=T)\\&=0.99\times 0.01\times 0.2\\&=0.00198.\end{aligned}}}
Then the numerical results (subscripted by the associated variable values) are
Pr
(
R
=
T
∣
G
=
T
)
=
0.00198
T
T
T
+
0.1584
T
F
T
0.00198
T
T
T
+
0.288
T
T
F
+
0.1584
T
F
T
+
0.0
T
F
F
=
891
2491
≈
35.77
%
.
{\displaystyle \Pr(R=T\mid G=T)={\frac {0.00198_{TTT}+0.1584_{TFT}}{0.00198_{TTT}+0.288_{TTF}+0.1584_{TFT}+0.0_{TFF}}}={\frac {891}{2491}}\approx 35.77\%.}
To answer an interventional question, such as "What is the probability that it would rain, given that we wet the grass?" the answer is governed by the post-intervention joint distribution function
Pr
(
S
,
R
∣
do
(
G
=
T
)
)
=
Pr
(
S
∣
R
)
Pr
(
R
)
{\displaystyle \Pr(S,R\mid {\text{do}}(G=T))=\Pr(S\mid R)\Pr(R)}
obtained by removing the factor
Pr
(
G
∣
S
,
R
)
{\displaystyle \Pr(G\mid S,R)}
from the pre-intervention distribution. The do operator forces the value of G to be true. The probability of rain is unaffected by the action:
Pr
(
R
∣
do
(
G
=
T
)
)
=
Pr
(
R
)
.
{\displaystyle \Pr(R\mid {\text{do}}(G=T))=\Pr(R).}
To predict the impact of turning the sprinkler on:
Pr
(
R
,
G
∣
do
(
S
=
T
)
)
=
Pr
(
R
)
Pr
(
G
∣
R
,
S
=
T
)
{\displaystyle \Pr(R,G\mid {\text{do}}(S=T))=\Pr(R)\Pr(G\mid R,S=T)}
with the term
Pr
(
S
=
T
∣
R
)
{\displaystyle \Pr(S=T\mid R)}
removed, showing that the action affects the grass but not the rain.
These predictions may not be feasible given unobserved variables, as in most policy evaluation problems. The effect of the action
do
(
x
)
{\displaystyle {\text{do}}(x)}
can still be predicted, however, whenever the back-door criterion is satisfied. It states that, if a set Z of nodes can be observed that d-separates (or blocks) all back-door paths from X to Y then
Pr
(
Y
,
Z
∣
do
(
x
)
)
=
Pr
(
Y
,
Z
,
X
=
x
)
Pr
(
X
=
x
∣
Z
)
.
{\displaystyle \Pr(Y,Z\mid {\text{do}}(x))={\frac {\Pr(Y,Z,X=x)}{\Pr(X=x\mid Z)}}.}
A back-door path is one that ends with an arrow into X. Sets that satisfy the back-door criterion are called "sufficient" or "admissible." For example, the set Z = R is admissible for predicting the effect of S = T on G, because R d-separates the (only) back-door path S ← R → G. However, if S is not observed, no other set d-separates this path and the effect of turning the sprinkler on (S = T) on the grass (G) cannot be predicted from passive observations. In that case P(G | do(S = T)) is not "identified". This reflects the fact that, lacking interventional data, the observed dependence between S and G is due to a causal connection or is spurious
(apparent dependence arising from a common cause, R). (see Simpson's paradox)
To determine whether a causal relation is identified from an arbitrary Bayesian network with unobserved variables, one can use the three rules of "do-calculus" and test whether all do terms can be removed from the expression of that relation, thus confirming that the desired quantity is estimable from frequency data.
Using a Bayesian network can save considerable amounts of memory over exhaustive probability tables, if the dependencies in the joint distribution are sparse. For example, a naive way of storing the conditional probabilities of 10 two-valued variables as a table requires storage space for
2
10
=
1024
{\displaystyle 2^{10}=1024}
values. If no variable's local distribution depends on more than three parent variables, the Bayesian network representation stores at most
10
⋅
2
3
=
80
{\displaystyle 10\cdot 2^{3}=80}
values.
One advantage of Bayesian networks is that it is intuitively easier for a human to understand (a sparse set of) direct dependencies and local distributions than complete joint distributions.
== Inference and learning ==
Bayesian networks perform three main inference tasks:
=== Inferring unobserved variables ===
Because a Bayesian network is a complete model for its variables and their relationships, it can be used to answer probabilistic queries about them. For example, the network can be used to update knowledge of the state of a subset of variables when other variables (the evidence variables) are observed. This process of computing the posterior distribution of variables given evidence is called probabilistic inference. The posterior gives a universal sufficient statistic for detection applications, when choosing values for the variable subset that minimize some expected loss function, for instance the probability of decision error. A Bayesian network can thus be considered a mechanism for automatically applying Bayes' theorem to complex problems.
The most common exact inference methods are: variable elimination, which eliminates (by integration or summation) the non-observed non-query variables one by one by distributing the sum over the product; clique tree propagation, which caches the computation so that many variables can be queried at one time and new evidence can be propagated quickly; and recursive conditioning and AND/OR search, which allow for a space–time tradeoff and match the efficiency of variable elimination when enough space is used. All of these methods have complexity that is exponential in the network's treewidth. The most common approximate inference algorithms are importance sampling, stochastic MCMC simulation, mini-bucket elimination, loopy belief propagation, generalized belief propagation and variational methods.
=== Parameter learning ===
In order to fully specify the Bayesian network and thus fully represent the joint probability distribution, it is necessary to specify for each node X the probability distribution for X conditional upon X's parents. The distribution of X conditional upon its parents may have any form. It is common to work with discrete or Gaussian distributions since that simplifies calculations. Sometimes only constraints on distribution are known; one can then use the principle of maximum entropy to determine a single distribution, the one with the greatest entropy given the constraints. (Analogously, in the specific context of a dynamic Bayesian network, the conditional distribution for the hidden state's temporal evolution is commonly specified to maximize the entropy rate of the implied stochastic process.)
Often these conditional distributions include parameters that are unknown and must be estimated from data, e.g., via the maximum likelihood approach. Direct maximization of the likelihood (or of the posterior probability) is often complex given unobserved variables. A classical approach to this problem is the expectation-maximization algorithm, which alternates computing expected values of the unobserved variables conditional on observed data, with maximizing the complete likelihood (or posterior) assuming that previously computed expected values are correct. Under mild regularity conditions, this process converges on maximum likelihood (or maximum posterior) values for parameters.
A more fully Bayesian approach to parameters is to treat them as additional unobserved variables and to compute a full posterior distribution over all nodes conditional upon observed data, then to integrate out the parameters. This approach can be expensive and lead to large dimension models, making classical parameter-setting approaches more tractable.
=== Structure learning ===
In the simplest case, a Bayesian network is specified by an expert and is then used to perform inference. In other applications, the task of defining the network is too complex for humans. In this case, the network structure and the parameters of the local distributions must be learned from data.
Automatically learning the graph structure of a Bayesian network (BN) is a challenge pursued within machine learning. The basic idea goes back to a recovery algorithm developed by Rebane and Pearl and rests on the distinction between the three possible patterns allowed in a 3-node DAG:
The first 2 represent the same dependencies (
X
{\displaystyle X}
and
Z
{\displaystyle Z}
are independent given
Y
{\displaystyle Y}
) and are, therefore, indistinguishable. The collider, however, can be uniquely identified, since
X
{\displaystyle X}
and
Z
{\displaystyle Z}
are marginally independent and all other pairs are dependent. Thus, while the skeletons (the graphs stripped of arrows) of these three triplets are identical, the directionality of the arrows is partially identifiable. The same distinction applies when
X
{\displaystyle X}
and
Z
{\displaystyle Z}
have common parents, except that one must first condition on those parents. Algorithms have been developed to systematically determine the skeleton of the underlying graph and, then, orient all arrows whose directionality is dictated by the conditional independences observed.
An alternative method of structural learning uses optimization-based search. It requires a scoring function and a search strategy. A common scoring function is posterior probability of the structure given the training data, like the BIC or the BDeu. The time requirement of an exhaustive search returning a structure that maximizes the score is superexponential in the number of variables. A local search strategy makes incremental changes aimed at improving the score of the structure. A global search algorithm like Markov chain Monte Carlo can avoid getting trapped in local minima. Friedman et al. discuss using mutual information between variables and finding a structure that maximizes this. They do this by restricting the parent candidate set to k nodes and exhaustively searching therein.
A particularly fast method for exact BN learning is to cast the problem as an optimization problem, and solve it using integer programming. Acyclicity constraints are added to the integer program (IP) during solving in the form of cutting planes. Such method can handle problems with up to 100 variables.
In order to deal with problems with thousands of variables, a different approach is necessary. One is to first sample one ordering, and then find the optimal BN structure with respect to that ordering. This implies working on the search space of the possible orderings, which is convenient as it is smaller than the space of network structures. Multiple orderings are then sampled and evaluated. This method has been proven to be the best available in literature when the number of variables is huge.
Another method consists of focusing on the sub-class of decomposable models, for which the MLE have a closed form. It is then possible to discover a consistent structure for hundreds of variables.
Learning Bayesian networks with bounded treewidth is necessary to allow exact, tractable inference, since the worst-case inference complexity is exponential in the treewidth k (under the exponential time hypothesis). Yet, as a global property of the graph, it considerably increases the difficulty of the learning process. In this context it is possible to use K-tree for effective learning.
== Statistical introduction ==
Given data
x
{\displaystyle x\,\!}
and parameter
θ
{\displaystyle \theta }
, a simple Bayesian analysis starts with a prior probability (prior)
p
(
θ
)
{\displaystyle p(\theta )}
and likelihood
p
(
x
∣
θ
)
{\displaystyle p(x\mid \theta )}
to compute a posterior probability
p
(
θ
∣
x
)
∝
p
(
x
∣
θ
)
p
(
θ
)
{\displaystyle p(\theta \mid x)\propto p(x\mid \theta )p(\theta )}
.
Often the prior on
θ
{\displaystyle \theta }
depends in turn on other parameters
φ
{\displaystyle \varphi }
that are not mentioned in the likelihood. So, the prior
p
(
θ
)
{\displaystyle p(\theta )}
must be replaced by a likelihood
p
(
θ
∣
φ
)
{\displaystyle p(\theta \mid \varphi )}
, and a prior
p
(
φ
)
{\displaystyle p(\varphi )}
on the newly introduced parameters
φ
{\displaystyle \varphi }
is required, resulting in a posterior probability
p
(
θ
,
φ
∣
x
)
∝
p
(
x
∣
θ
)
p
(
θ
∣
φ
)
p
(
φ
)
.
{\displaystyle p(\theta ,\varphi \mid x)\propto p(x\mid \theta )p(\theta \mid \varphi )p(\varphi ).}
This is the simplest example of a hierarchical Bayes model.
The process may be repeated; for example, the parameters
φ
{\displaystyle \varphi }
may depend in turn on additional parameters
ψ
{\displaystyle \psi \,\!}
, which require their own prior. Eventually the process must terminate, with priors that do not depend on unmentioned parameters.
=== Introductory examples ===
Given the measured quantities
x
1
,
…
,
x
n
{\displaystyle x_{1},\dots ,x_{n}\,\!}
each with normally distributed errors of known standard deviation
σ
{\displaystyle \sigma \,\!}
,
x
i
∼
N
(
θ
i
,
σ
2
)
{\displaystyle x_{i}\sim N(\theta _{i},\sigma ^{2})}
Suppose we are interested in estimating the
θ
i
{\displaystyle \theta _{i}}
. An approach would be to estimate the
θ
i
{\displaystyle \theta _{i}}
using a maximum likelihood approach; since the observations are independent, the likelihood factorizes and the maximum likelihood estimate is simply
θ
i
=
x
i
.
{\displaystyle \theta _{i}=x_{i}.}
However, if the quantities are related, so that for example the individual
θ
i
{\displaystyle \theta _{i}}
have themselves been drawn from an underlying distribution, then this relationship destroys the independence and suggests a more complex model, e.g.,
x
i
∼
N
(
θ
i
,
σ
2
)
,
{\displaystyle x_{i}\sim N(\theta _{i},\sigma ^{2}),}
θ
i
∼
N
(
φ
,
τ
2
)
,
{\displaystyle \theta _{i}\sim N(\varphi ,\tau ^{2}),}
with improper priors
φ
∼
flat
{\displaystyle \varphi \sim {\text{flat}}}
,
τ
∼
flat
∈
(
0
,
∞
)
{\displaystyle \tau \sim {\text{flat}}\in (0,\infty )}
. When
n
≥
3
{\displaystyle n\geq 3}
, this is an identified model (i.e. there exists a unique solution for the model's parameters), and the posterior distributions of the individual
θ
i
{\displaystyle \theta _{i}}
will tend to move, or shrink away from the maximum likelihood estimates towards their common mean. This shrinkage is a typical behavior in hierarchical Bayes models.
=== Restrictions on priors ===
Some care is needed when choosing priors in a hierarchical model, particularly on scale variables at higher levels of the hierarchy such as the variable
τ
{\displaystyle \tau \,\!}
in the example. The usual priors such as the Jeffreys prior often do not work, because the posterior distribution will not be normalizable and estimates made by minimizing the expected loss will be inadmissible.
== Definitions and concepts ==
Several equivalent definitions of a Bayesian network have been offered. For the following, let G = (V,E) be a directed acyclic graph (DAG) and let X = (Xv), v ∈ V be a set of random variables indexed by V.
=== Factorization definition ===
X is a Bayesian network with respect to G if its joint probability density function (with respect to a product measure) can be written as a product of the individual density functions, conditional on their parent variables:
p
(
x
)
=
∏
v
∈
V
p
(
x
v
|
x
pa
(
v
)
)
{\displaystyle p(x)=\prod _{v\in V}p\left(x_{v}\,{\big |}\,x_{\operatorname {pa} (v)}\right)}
where pa(v) is the set of parents of v (i.e. those vertices pointing directly to v via a single edge).
For any set of random variables, the probability of any member of a joint distribution can be calculated from conditional probabilities using the chain rule (given a topological ordering of X) as follows:
P
(
X
1
=
x
1
,
…
,
X
n
=
x
n
)
=
∏
v
=
1
n
P
(
X
v
=
x
v
∣
X
v
+
1
=
x
v
+
1
,
…
,
X
n
=
x
n
)
{\displaystyle \operatorname {P} (X_{1}=x_{1},\ldots ,X_{n}=x_{n})=\prod _{v=1}^{n}\operatorname {P} \left(X_{v}=x_{v}\mid X_{v+1}=x_{v+1},\ldots ,X_{n}=x_{n}\right)}
Using the definition above, this can be written as:
P
(
X
1
=
x
1
,
…
,
X
n
=
x
n
)
=
∏
v
=
1
n
P
(
X
v
=
x
v
∣
X
j
=
x
j
for each
X
j
that is a parent of
X
v
)
{\displaystyle \operatorname {P} (X_{1}=x_{1},\ldots ,X_{n}=x_{n})=\prod _{v=1}^{n}\operatorname {P} (X_{v}=x_{v}\mid X_{j}=x_{j}{\text{ for each }}X_{j}\,{\text{ that is a parent of }}X_{v}\,)}
The difference between the two expressions is the conditional independence of the variables from any of their non-descendants, given the values of their parent variables.
=== Local Markov property ===
X is a Bayesian network with respect to G if it satisfies the local Markov property: each variable is conditionally independent of its non-descendants given its parent variables:
X
v
⊥
⊥
X
V
∖
de
(
v
)
∣
X
pa
(
v
)
for all
v
∈
V
{\displaystyle X_{v}\perp \!\!\!\perp X_{V\,\smallsetminus \,\operatorname {de} (v)}\mid X_{\operatorname {pa} (v)}\quad {\text{for all }}v\in V}
where de(v) is the set of descendants and V \ de(v) is the set of non-descendants of v.
This can be expressed in terms similar to the first definition, as
P
(
X
v
=
x
v
∣
X
i
=
x
i
for each
X
i
that is not a descendant of
X
v
)
=
P
(
X
v
=
x
v
∣
X
j
=
x
j
for each
X
j
that is a parent of
X
v
)
{\displaystyle {\begin{aligned}&\operatorname {P} (X_{v}=x_{v}\mid X_{i}=x_{i}{\text{ for each }}X_{i}{\text{ that is not a descendant of }}X_{v}\,)\\[6pt]={}&P(X_{v}=x_{v}\mid X_{j}=x_{j}{\text{ for each }}X_{j}{\text{ that is a parent of }}X_{v}\,)\end{aligned}}}
The set of parents is a subset of the set of non-descendants because the graph is acyclic.
=== Marginal independence structure ===
In general, learning a Bayesian network from data is known to be NP-hard. This is due in part to the combinatorial explosion of enumerating DAGs as the number of variables increases. Nevertheless, insights about an underlying Bayesian network can be learned from data in polynomial time by focusing on its marginal independence structure: while the conditional independence statements of a distribution modeled by a Bayesian network are encoded by a DAG (according to the factorization and Markov properties above), its marginal independence statements—the conditional independence statements in which the conditioning set is empty—are encoded by a simple undirected graph with special properties such as equal intersection and independence numbers.
=== Developing Bayesian networks ===
Developing a Bayesian network often begins with creating a DAG G such that X satisfies the local Markov property with respect to G. Sometimes this is a causal DAG. The conditional probability distributions of each variable given its parents in G are assessed. In many cases, in particular in the case where the variables are discrete, if the joint distribution of X is the product of these conditional distributions, then X is a Bayesian network with respect to G.
=== Markov blanket ===
The Markov blanket of a node is the set of nodes consisting of its parents, its children, and any other parents of its children. The Markov blanket renders the node independent of the rest of the network; the joint distribution of the variables in the Markov blanket of a node is sufficient knowledge for calculating the distribution of the node. X is a Bayesian network with respect to G if every node is conditionally independent of all other nodes in the network, given its Markov blanket.
==== d-separation ====
This definition can be made more general by defining the "d"-separation of two nodes, where d stands for directional. We first define the "d"-separation of a trail and then we will define the "d"-separation of two nodes in terms of that.
Let P be a trail from node u to v. A trail is a loop-free, undirected (i.e. all edge directions are ignored) path between two nodes. Then P is said to be d-separated by a set of nodes Z if any of the following conditions holds:
P contains (but does not need to be entirely) a directed chain,
u
⋯
←
m
←
⋯
v
{\displaystyle u\cdots \leftarrow m\leftarrow \cdots v}
or
u
⋯
→
m
→
⋯
v
{\displaystyle u\cdots \rightarrow m\rightarrow \cdots v}
, such that the middle node m is in Z,
P contains a fork,
u
⋯
←
m
→
⋯
v
{\displaystyle u\cdots \leftarrow m\rightarrow \cdots v}
, such that the middle node m is in Z, or
P contains an inverted fork (or collider),
u
⋯
→
m
←
⋯
v
{\displaystyle u\cdots \rightarrow m\leftarrow \cdots v}
, such that the middle node m is not in Z and no descendant of m is in Z.
The nodes u and v are d-separated by Z if all trails between them are d-separated. If u and v are not d-separated, they are d-connected.
X is a Bayesian network with respect to G if, for any two nodes u, v:
X
u
⊥
⊥
X
v
∣
X
Z
{\displaystyle X_{u}\perp \!\!\!\perp X_{v}\mid X_{Z}}
where Z is a set which d-separates u and v. (The Markov blanket is the minimal set of nodes which d-separates node v from all other nodes.)
=== Causal networks ===
Although Bayesian networks are often used to represent causal relationships, this need not be the case: a directed edge from u to v does not require that Xv be causally dependent on Xu. This is demonstrated by the fact that Bayesian networks on the graphs:
a
→
b
→
c
and
a
←
b
←
c
{\displaystyle a\rightarrow b\rightarrow c\qquad {\text{and}}\qquad a\leftarrow b\leftarrow c}
are equivalent: that is they impose exactly the same conditional independence requirements.
A causal network is a Bayesian network with the requirement that the relationships be causal. The additional semantics of causal networks specify that if a node X is actively caused to be in a given state x (an action written as do(X = x)), then the probability density function changes to that of the network obtained by cutting the links from the parents of X to X, and setting X to the caused value x. Using these semantics, the impact of external interventions from data obtained prior to intervention can be predicted.
== Inference complexity and approximation algorithms ==
In 1990, while working at Stanford University on large bioinformatic applications, Cooper proved that exact inference in Bayesian networks is NP-hard. This result prompted research on approximation algorithms with the aim of developing a tractable approximation to probabilistic inference. In 1993, Paul Dagum and Michael Luby proved two surprising results on the complexity of approximation of probabilistic inference in Bayesian networks. First, they proved that no tractable deterministic algorithm can approximate probabilistic inference to within an absolute error ɛ < 1/2. Second, they proved that no tractable randomized algorithm can approximate probabilistic inference to within an absolute error ɛ < 1/2 with confidence probability greater than 1/2.
At about the same time, Roth proved that exact inference in Bayesian networks is in fact #P-complete (and thus as hard as counting the number of satisfying assignments of a conjunctive normal form formula (CNF)) and that approximate inference within a factor 2n1−ɛ for every ɛ > 0, even for Bayesian networks with restricted architecture, is NP-hard.
In practical terms, these complexity results suggested that while Bayesian networks were rich representations for AI and machine learning applications, their use in large real-world applications would need to be tempered by either topological structural constraints, such as naïve Bayes networks, or by restrictions on the conditional probabilities. The bounded variance algorithm developed by Dagum and Luby was the first provable fast approximation algorithm to efficiently approximate probabilistic inference in Bayesian networks with guarantees on the error approximation. This powerful algorithm required the minor restriction on the conditional probabilities of the Bayesian network to be bounded away from zero and one by
1
/
p
(
n
)
{\displaystyle 1/p(n)}
where
p
(
n
)
{\displaystyle p(n)}
was any polynomial of the number of nodes in the network,
n
{\displaystyle n}
.
== Software ==
Notable software for Bayesian networks include:
Just another Gibbs sampler (JAGS) – Open-source alternative to WinBUGS. Uses Gibbs sampling.
OpenBUGS – Open-source development of WinBUGS.
SPSS Modeler – Commercial software that includes an implementation for Bayesian networks.
Stan (software) – Stan is an open-source package for obtaining Bayesian inference using the No-U-Turn sampler (NUTS), a variant of Hamiltonian Monte Carlo.
PyMC – A Python library implementing an embedded domain specific language to represent bayesian networks, and a variety of samplers (including NUTS)
WinBUGS – One of the first computational implementations of MCMC samplers. No longer maintained.
== History ==
The term Bayesian network was coined by Judea Pearl in 1985 to emphasize:
the often subjective nature of the input information
the reliance on Bayes' conditioning as the basis for updating information
the distinction between causal and evidential modes of reasoning
In the late 1980s Pearl's Probabilistic Reasoning in Intelligent Systems and Neapolitan's Probabilistic Reasoning in Expert Systems summarized their properties and established them as a field of study.
== See also ==
== Notes ==
== References ==
== Further reading ==
Conrady S, Jouffe L (2015-07-01). Bayesian Networks and BayesiaLab – A practical introduction for researchers. Franklin, Tennessee: Bayesian USA. ISBN 978-0-9965333-0-0.
Charniak E (Winter 1991). "Bayesian networks without tears" (PDF). AI Magazine.
Kruse R, Borgelt C, Klawonn F, Moewes C, Steinbrecher M, Held P (2013). Computational Intelligence A Methodological Introduction. London: Springer-Verlag. ISBN 978-1-4471-5012-1.
Borgelt C, Steinbrecher M, Kruse R (2009). Graphical Models – Representations for Learning, Reasoning and Data Mining (Second ed.). Chichester: Wiley. ISBN 978-0-470-74956-2.
== External links ==
An Introduction to Bayesian Networks and their Contemporary Applications
On-line Tutorial on Bayesian nets and probability
Web-App to create Bayesian nets and run it with a Monte Carlo method
Continuous Time Bayesian Networks
Bayesian Networks: Explanation and Analogy
A live tutorial on learning Bayesian networks
A hierarchical Bayes Model for handling sample heterogeneity in classification problems, provides a classification model taking into consideration the uncertainty associated with measuring replicate samples.
Hierarchical Naive Bayes Model for handling sample uncertainty Archived 2007-09-28 at the Wayback Machine, shows how to perform classification and learning with continuous and discrete variables with replicated measurements. | Wikipedia/Belief_networks |
In the mathematical discipline of graph theory, a graph labeling is the assignment of labels, traditionally represented by integers, to edges and/or vertices of a graph.
Formally, given a graph G = (V, E), a vertex labeling is a function of V to a set of labels; a graph with such a function defined is called a vertex-labeled graph. Likewise, an edge labeling is a function of E to a set of labels. In this case, the graph is called an edge-labeled graph.
When the edge labels are members of an ordered set (e.g., the real numbers), it may be called a weighted graph.
When used without qualification, the term labeled graph generally refers to a vertex-labeled graph with all labels distinct. Such a graph may equivalently be labeled by the consecutive integers { 1, …, |V| } , where |V| is the number of vertices in the graph. For many applications, the edges or vertices are given labels that are meaningful in the associated domain. For example, the edges may be assigned weights representing the "cost" of traversing between the incident vertices.
In the above definition a graph is understood to be a finite undirected simple graph. However, the notion of labeling may be applied to all extensions and generalizations of graphs. For example, in automata theory and formal language theory it is convenient to consider labeled multigraphs, i.e., a pair of vertices may be connected by several labeled edges.
== History ==
Most graph labelings trace their origins to labelings presented by Alexander Rosa in his 1967 paper. Rosa identified three types of labelings, which he called α-, β-, and ρ-labelings. β-labelings were later renamed as "graceful" by Solomon Golomb, and the name has been popular since.
== Special cases ==
=== Graceful labeling ===
A graph is known as graceful if its vertices are labeled from 0 to |E|, the size of the graph, and if this vertex labeling induces an edge labeling from 1 to |E|. For any edge e, the label of e is the positive difference between the labels of the two vertices incident with e. In other words, if e is incident with vertices labeled i and j, then e will be labeled |i − j|. Thus, a graph G = (V, E) is graceful if and only if there exists an injection from V to {0, ..., |E|} that induces a bijection from E to {1, ..., |E|}.
In his original paper, Rosa proved that all Eulerian graphs with size equivalent to 1 or 2 (mod 4) are not graceful. Whether or not certain families of graphs are graceful is an area of graph theory under extensive study. Arguably, the largest unproven conjecture in graph labeling is the Ringel–Kotzig conjecture, which hypothesizes that all trees are graceful. This has been proven for all paths, caterpillars, and many other infinite families of trees. Anton Kotzig himself has called the effort to prove the conjecture a "disease".
=== Edge-graceful labeling ===
An edge-graceful labeling on a simple graph without loops or multiple edges on p vertices and q edges is a labeling of the edges by distinct integers in {1, …, q} such that the labeling on the vertices induced by labeling a vertex with the sum of the incident edges taken modulo p assigns all values from 0 to p − 1 to the vertices. A graph G is said to be "edge-graceful" if it admits an edge-graceful labeling.
Edge-graceful labelings were first introduced by Sheng-Ping Lo in 1985.
A necessary condition for a graph to be edge-graceful is "Lo's condition":
q
(
q
+
1
)
=
p
(
p
−
1
)
2
mod
p
.
{\displaystyle q(q+1)={\frac {p(p-1)}{2}}\mod p.}
=== Harmonious labeling ===
A "harmonious labeling" on a graph G is an injection from the vertices of G to the group of integers modulo k, where k is the number of edges of G, that induces a bijection between the edges of G and the numbers modulo k by taking the edge label for an edge (x, y) to be the sum of the labels of the two vertices x, y (mod k). A "harmonious graph" is one that has a harmonious labeling. Odd cycles are harmonious, as are Petersen graphs. It is conjectured that trees are all harmonious if one vertex label is allowed to be reused. The seven-page book graph K1,7 × K2 provides an example of a graph that is not harmonious.
=== Graph coloring ===
A graph coloring is a subclass of graph labelings. Vertex colorings assign different labels to adjacent vertices, while edge colorings assign different labels to adjacent edges.
=== Lucky labeling ===
A lucky labeling of a graph G is an assignment of positive integers to the vertices of G such that if S(v) denotes the sum of the labels on the neighbors of v, then S is a vertex coloring of G. The "lucky number" of G is the least k such that G has a lucky labeling with the integers {1, …, k}.
== References == | Wikipedia/Labelled_graph |
A semantic network, or frame network is a knowledge base that represents semantic relations between concepts in a network. This is often used as a form of knowledge representation. It is a directed or undirected graph consisting of vertices, which represent concepts, and edges, which represent semantic relations between concepts, mapping or connecting semantic fields. A semantic network may be instantiated as, for example, a graph database or a concept map. Typical standardized semantic networks are expressed as semantic triples.
Semantic networks are used in neurolinguistics and natural language processing applications such as semantic parsing and word-sense disambiguation. Semantic networks can also be used as a method to analyze large texts and identify the main themes and topics (e.g., of social media posts), to reveal biases (e.g., in news coverage), or even to map an entire research field.
== History ==
Examples of the use of semantic networks in logic, directed acyclic graphs as a mnemonic tool, dates back centuries, the earliest documented use being the Greek philosopher Porphyry's commentary on Aristotle's categories in the third century AD.
In computing history, "Semantic Nets" for the propositional calculus were first implemented for computers by Richard H. Richens of the Cambridge Language Research Unit in 1956 as an "interlingua" for machine translation of natural languages, although the importance of this work and the Cambridge Language Research Unit was only belatedly realized.
Semantic networks were also independently implemented by Robert F. Simmons and Sheldon Klein, using the first-order predicate calculus as a base, after being inspired by a demonstration of Victor Yngve. The "line of research was originated by the first President of the Association for Computational Linguistics, Victor Yngve, who in 1960 had published descriptions of algorithms for using a phrase structure grammar to generate syntactically well-formed nonsense sentences. Sheldon Klein and I about 1962–1964 were fascinated by the technique and generalized it to a method for controlling the sense of what was generated by respecting the semantic dependencies of words as they occurred in text." Other researchers, most notably M. Ross Quillian and others at System Development Corporation helped contribute to their work in the early 1960s as part of the SYNTHEX project. It's these publications at System Development Corporation that most modern derivatives of the term "semantic network" cite as their background. Later prominent works were done by Allan M. Collins and Quillian (e.g., Collins and Quillian; Collins and Loftus Quillian). Still later in 2006, Hermann Helbig fully described MultiNet.
In the late 1980s, two universities in the Netherlands, Groningen and Twente, jointly began a project called Knowledge Graphs, which are semantic networks but with the added constraint that edges are restricted to be from a limited set of possible relations, to facilitate algebras on the graph. In the subsequent decades, the distinction between semantic networks and knowledge graphs was blurred. In 2012, Google gave their knowledge graph the name Knowledge Graph.
The semantic link network was systematically studied as a semantic social networking method. Its basic model consists of semantic nodes, semantic links between nodes, and a semantic space that defines the semantics of nodes and links and reasoning rules on semantic links. The systematic theory and model was published in 2004. This research direction can trace to the definition of inheritance rules for efficient model retrieval in 1998 and the Active Document Framework ADF. Since 2003, research has developed toward social semantic networking. This work is a systematic innovation at the age of the World Wide Web and global social networking rather than an application or simple extension of the Semantic Net (Network). Its purpose and scope are different from that of the Semantic Net (or network). The rules for reasoning and evolution and automatic discovery of implicit links play an important role in the Semantic Link Network. Recently it has been developed to support Cyber-Physical-Social Intelligence. It was used for creating a general summarization method. The self-organised Semantic Link Network was integrated with a multi-dimensional category space to form a semantic space to support advanced applications with multi-dimensional abstractions and self-organised semantic links It has been verified that Semantic Link Network play an important role in understanding and representation through text summarisation applications. Semantic Link Network has been extended from cyberspace to cyber-physical-social space. Competition relation and symbiosis relation as well as their roles in evolving society were studied in the emerging topic: Cyber-Physical-Social Intelligence
More specialized forms of semantic networks has been created for specific use. For example, in 2008, Fawsy Bendeck's PhD thesis formalized the Semantic Similarity Network (SSN) that contains specialized relationships and propagation algorithms to simplify the semantic similarity representation and calculations.
== Basics of semantic networks ==
A semantic network is used when one has knowledge that is best understood as a set of concepts that are related to one another.
Most semantic networks are cognitively based. They consist of arcs (spokes) and nodes (hubs) which can be organized into a taxonomic hierarchy. Different semantic networks can also be connected by bridge nodes. Semantic networks contributed to the ideas of spreading activation, inheritance, and nodes as proto-objects.
One process of constructing semantic networks, known also as co-occurrence networks, includes identifying keywords in the text, calculating the frequencies of co-occurrences, and analyzing the networks to find central words and clusters of themes in the network.
== In linguistics ==
In the field of linguistics, semantic networks represent how the human mind handles associated concepts. Typically, concepts in a semantic network can have one of two different relationships: either semantic or associative.
If semantic in relation, the two concepts are linked by any of the following semantic relationships: synonymy, antonymy, hypernymy, hyponymy, holonymy, meronymy, metonymy, or polysemy. These are not the only semantic relationships, but some of the most common.
If associative in relation, the two concepts are linked based on their frequency to occur together. These associations are accidental, meaning that nothing about their individual meanings requires them to be associated with one another, only that they typically are. Examples of this would be pig and farm, pig and trough, or pig and mud. While nothing about the meaning of pig forces it to be associated with farms, as pigs can be wild, the fact that pigs are so frequently found on farms creates an accidental associated relationship. These thematic relationships are common within semantic networks and are notable results in free association tests.
As the initial word is given, activation of the most closely related concepts begin, spreading outward to the lesser associated concepts. An example of this would be the initial word pig prompting mammal, then animal, and then breathes. This example shows that taxonomic relationships are inherent within semantic networks. The most closely related concepts typically share semantic features, which are determinants of semantic similarity scores. Words with higher similarity scores are more closely related, thus have higher probability of being a close word in the semantic network.
These relationships can be suggested into the brain through priming, where previous examples of the same relationship are shown before the target word is shown. The effect of priming on a semantic network linking can be seen through the speed of the reaction time to the word. Priming can help to reveal the structure of a semantic network and which words are most closely associated with the original word.
Disruption of a semantic network can lead to a semantic deficit (not to be confused with as semantic dementia).
=== In the brain ===
There exists physical manifestation of semantic relationships in the brain as well. Category-specific semantic circuits show that words belonging to different categories are processed in circuits differently located throughout the brain. For example, the semantic circuits for a word associated with the face or mouth (such as lick) is located in a different place of the brain than a word associated with the leg or foot (such as kick). This is a primary result of a 2013 study published by Friedemann Pulvermüller. These semantic circuits are directly tied to their sensorimotor areas of the brain. This is known as embodied semantics, a subtopic of embodied language processing.
If brain damage occurs, the normal processing of semantic networks could be disrupted, leading to preference into what kind of relationships dominate the semantic network in the mind.
== Examples ==
=== In Lisp ===
The following code shows an example of a semantic network in the Lisp programming language using an association list.
To extract all the information about the "canary" type, one would use the assoc function with a key of "canary".
=== WordNet ===
An example of a semantic network is WordNet, a lexical database of English. It groups English words into sets of synonyms called synsets, provides short, general definitions, and records the various semantic relations between these synonym sets. Some of the most common semantic relations defined are meronymy (A is a meronym of B if A is part of B), holonymy (B is a holonym of A if B contains A), hyponymy (or troponymy) (A is subordinate of B; A is kind of B), hypernymy (A is superordinate of B), synonymy (A denotes the same as B) and antonymy (A denotes the opposite of B).
WordNet properties have been studied from a network theory perspective and compared to other semantic networks created from Roget's Thesaurus and word association tasks. From this perspective the three of them are a small world structure.
=== Other examples ===
It is also possible to represent logical descriptions using semantic networks such as the existential graphs of Charles Sanders Peirce or the related conceptual graphs of John F. Sowa. These have expressive power equal to or exceeding standard first-order predicate logic. Unlike WordNet or other lexical or browsing networks, semantic networks using these representations can be used for reliable automated logical deduction. Some automated reasoners exploit the graph-theoretic features of the networks during processing.
Other examples of semantic networks are Gellish models. Gellish English with its Gellish English dictionary, is a formal language that is defined as a network of relations between concepts and names of concepts. Gellish English is a formal subset of natural English, just as Gellish Dutch is a formal subset of Dutch, whereas multiple languages share the same concepts. Other Gellish networks consist of knowledge models and information models that are expressed in the Gellish language. A Gellish network is a network of (binary) relations between things. Each relation in the network is an expression of a fact that is classified by a relation type. Each relation type itself is a concept that is defined in the Gellish language dictionary. Each related thing is either a concept or an individual thing that is classified by a concept. The definitions of concepts are created in the form of definition models (definition networks) that together form a Gellish Dictionary. A Gellish network can be documented in a Gellish database and is computer interpretable.
SciCrunch is a collaboratively edited knowledge base for scientific resources. It provides unambiguous identifiers (Research Resource IDentifiers or RRIDs) for software, lab tools etc. and it also provides options to create links between RRIDs and from communities.
Another example of semantic networks, based on category theory, is ologs. Here each type is an object, representing a set of things, and each arrow is a morphism, representing a function. Commutative diagrams also are prescribed to constrain the semantics.
In the social sciences people sometimes use the term semantic network to refer to co-occurrence networks. The basic idea is that words that co-occur in a unit of text, e.g. a sentence, are semantically related to one another. Ties based on co-occurrence can then be used to construct semantic networks. This process includes identifying keywords in the text, constructing co-occurrence networks, and analyzing the networks to find central words and clusters of themes in the network. It is a particularly useful method to analyze large text and big data.
== Software tools ==
There are also elaborate types of semantic networks connected with corresponding sets of software tools used for lexical knowledge engineering, like the Semantic Network Processing System (SNePS) of Stuart C. Shapiro or the MultiNet paradigm of Hermann Helbig, especially suited for the semantic representation of natural language expressions and used in several NLP applications.
Semantic networks are used in specialized information retrieval tasks, such as plagiarism detection. They provide information on hierarchical relations in order to employ semantic compression to reduce language diversity and enable the system to match word meanings, independently from sets of words used.
The Knowledge Graph proposed by Google in 2012 is actually an application of semantic network in search engine.
Modeling multi-relational data like semantic networks in low-dimensional spaces through forms of embedding has benefits in expressing entity relationships as well as extracting relations from mediums like text. There are many approaches to learning these embeddings, notably using Bayesian clustering frameworks or energy-based frameworks, and more recently, TransE (NeurIPS 2013). Applications of embedding knowledge base data include Social network analysis and Relationship extraction.
== See also ==
=== Other examples ===
Cognition Network Technology
Lexipedia
OpenCog
Open Mind Common Sense (OMCS)
Schema.org
Semantic computing
SNOMED CT
Universal Networking Language (UNL)
Wikidata
Freebase
== References ==
== Further reading ==
Allen, J. and A. Frisch (1982). "What's in a Semantic Network". In: Proceedings of the 20th. annual meeting of ACL, Toronto, pp. 19–27.
John F. Sowa, Alexander Borgida (1991). Principles of Semantic Networks: Explorations in the Representation of Knowledge.
Segev, E. (Ed.) (2022). Semantic Network Analysis in Social Sciences. New York: Routledge.
== External links ==
"Semantic Networks" by John F. Sowa
"Semantic Link Network" by Hai Zhuge | Wikipedia/Semantic_networks |
In information science and ontology, a classification scheme is an arrangement of classes or groups of classes. The activity of developing the schemes bears similarity to taxonomy, but with perhaps a more theoretical bent, as a single classification scheme can be applied over a wide semantic spectrum while taxonomies tend to be devoted to a single topic.
In the abstract, the resulting structures are a crucial aspect of metadata, often represented as a hierarchical structure and accompanied by descriptive information of the classes or groups. Such a classification scheme is intended to be used for the classification of individual objects into the classes or groups, and the classes or groups are based on characteristics which the objects (members) have in common.
The ISO/IEC 11179 metadata registry standard uses classification schemes as a way to classify administered items, such as data elements, in a metadata registry.
Some quality criteria for classification schemes are:
Whether different kinds are grouped together. In other words, whether it is a grouping system or a pure classification system. In case of grouping, a subset (subgroup) does not have (inherit) all the characteristics of the superset, which makes that the knowledge and requirements about the superset are not applicable for the members of the subset.
Whether the classes have overlaps.
Whether subordinates (may) have multiple superordinates. Some classification schemes allow that a kind of thing has more than one superordinate others do not. Multiple supertypes for one subtype implies that the subordinate has the combined characteristics of all its superordinates. This is called multiple inheritance (of characteristics from multiple superordinates to their subordinates).
Whether the criteria for belonging to a class or group are well defined.
Whether the kinds of relations between the concepts are made explicit and well defined.
Whether subtype-supertype relations are distinguished from composition relations (part-whole relations) and from object-role relations.
== In linguistics ==
In linguistics, subordinate concepts are described as hyponyms of their respective superordinates; typically, a hyponym is 'a kind of' its superordinate.
== Benefits of using classification schemes ==
Using one or more classification schemes for the classification of a collection of objects has many benefits. Some of these include:
It allows a user to find an individual object quickly on the basis of its kind or group.
It makes it easier to detect duplicate objects.
It conveys semantics (meaning) of an object from the definition of its kind, which meaning is not conveyed by the name of the individual object or its way of spelling.
Knowledge and requirements about a kind of thing can be applied to other objects of that kind.
== Kinds of classification schemes ==
The following are examples of different kinds of classification schemes. This list is in approximate order from informal to more formal:
thesaurus – a collection of categorized concepts, denoted by words or phrases, that are related to each other by narrower term, wider term and related term relations.
taxonomy – a formal list of concepts, denoted by controlled words or phrases, arranged from abstract to specific, related by subtype-supertype relations or by superset-subset relations.
data model – an arrangement of concepts (entity types), denoted by words or phrases, that have various kinds of relationships. Typically, but not necessarily, representing requirements and capabilities for a specific scope (application area).
network (mathematics) – an arrangement of objects in a random graph.
ontology – an arrangement of concepts that are related by various well defined kinds of relations. The arrangement can be visualized in a directed acyclic graph.
One example of a classification scheme for data elements is a representation term.
== See also ==
ISO/IEC 11179
Faceted classification
Metadata
Ontology (computer science)
Representation class
Representation term
Simple Knowledge Organisation System
Semantic spectrum
== References ==
== External links ==
OECD Glossary of Statistical Terms – Classification Schemes
ISO/IEC 11179-2:2005 Metadata registries (MDR) – Part 2: Classification
Nancy Lawler's presentation on Classification Schemes Archived 2007-09-28 at the Wayback Machine | Wikipedia/Classification_scheme_(information_science) |
The Web Science Trust (WST) is a UK Charitable Trust with the aim of supporting the global development of Web science. It was originally started in 2006 as a joint effort between MIT and University of Southampton to formalise the social and technical aspects of the World Wide Web. The trust coordinates a set of international "WSTNet Laboratories" that include academic research groups in the emerging area of Web science.
It was first announced at MIT on 2 November 2006 as the Web Science Research Initiative (WSRI), changing its name in 2009 to the Web Science Trust. Tim Berners-Lee originally led this program, now run by a Board of Trustees, which aims to attract government and private funds to support their many activities. The Web Science Trust supports curriculum development in universities and research institutions to train future generations of Web Scientists. Given the similarities between Web Science and Information Science, Web Science overlaps with the interests of the ISchool movement, particularly in the United States, but focuses more specifically on the Web itself. The annual Web Science conference brings together participants from many fields including those studying both the social and the computational aspects of the World Wide Web.
Areas of interest include:
Social networks
Social machine
Collaboration
Understanding online community
Analyzing the human interactions inherent in social media
Web observatories
Developing "accountability" and other mechanisms for enhancing privacy and trust on the Web.
== Key personnel ==
Directors/trustees
Wendy Hall (managing director)
Nigel Shadbolt
James Hendler
Noshir Contractor
JP Rangaswami (chairman)
George Metakides
Steffen Staab
Anni Rowland-Campbell
Bill Thompson
Fellows
Tim Berners-Lee (also Founding Director)
Sir John Taylor
Patron
Rennie Fritchie, Baroness Fritchie
== Conferences ==
The first Web Science conference (WebSci09: Society on Line) was sponsored in part by WSRI and was held in Greece in March 2009. The conference had over 300 registrants from a number of fields including computing, social science, law, economics, philosophy, psychology. The conference has since continued as a yearly event. The first fully virtual Web Science conference was held in July 2020 as a result of travel restrictions arising from the COVID-19 pandemic.
== See also ==
List of I-Schools
World Wide Web
Webometrics
Web Engineering
== Bibliography ==
Lohr, Steve (2 November 2006). "Group of University Researchers to Make Web Science a Field of Study". The New York Times.
Tim Berners-Lee, Wendy Hall, James Hendler, Nigel Shadbolt, Daniel J. Weitzner (August 2006). "Creating a Science of the Web". Science. 313 (11): 769–71. doi:10.1126/science.1126902. PMID 16902115. S2CID 5104030.{{cite journal}}: CS1 maint: multiple names: authors list (link)
Julià Minguillon, Daniel Riera, Kieron O'Hara and Wendy Hall (October 2008). "Web Science (dossier)". UOC Papers (7): 25.{{cite journal}}: CS1 maint: multiple names: authors list (link)
James Hendler, Nigel Shadbolt, Wendy Hall, Tim Berners-Lee, Daniel J. Weitzner (July 2008). "Web science: an interdisciplinary approach to understanding the web". Communications of the ACM. 51 (7): 60–69. doi:10.1145/1364782.1364798.{{cite journal}}: CS1 maint: multiple names: authors list (link)
Web Science: Studying the Internet to Protect Our Future, an article by Tim Berners-Lee.
== References ==
== External links ==
Official website
Press release
Audio: Web Science: A Conversation with the Inventor of the Web | Wikipedia/Web_Science_Trust |
BORO (Business Objects Reference Ontology) is an approach to developing ontological or semantic models for large complex operational applications that consists of a top ontology as well as a process for constructing the ontology. It was originally developed as a method for mining ontologies from multiple legacy systems – as the first stage in an architectural transformation or software modernization. It has also been used to enable semantic interoperability between legacy systems. It is described in detail in (Partridge 1996, 2005). It is the analysis method used in the development and maintenance of the U.S. Department of Defense Architecture Framework (DoDAF) Meta Model (DM2), where a data modeling working group of over 350 members was able to systematically resolve a broad spectrum of knowledge representation issues.
== History ==
The approach was developed in the late 1980s and early 1990s by a team of KPMG consultants led by Chris Partridge. The team was working on a complex legacy systems re-engineering project and needed a new approach. The prime challenge of the re-engineering work was to clarify the underlying ontology of the systems and the work focussed on developing a process for mining ontologies and a top ontology that formed the foundation for the analysis. The top ontology was tailored to meet the needs of the re-engineering. Early work established that a key factor was to make a series of clear metaphysical choices to provide a solid (metaphysical) foundation.
A key choice was for an extensional (and hence, four-dimensional) ontology which provided neat Criterion of identity. Using this top ontology as a basis, a systematic process for re-engineering legacy systems was developed. From a software engineering perspective, a key feature of this process was the identification of common general patterns, under which the legacy system was subsumed. It has been substantially developed since then.
Much of the approach and the associated tools are proprietary, but some aspects have been delivered to the public domain and elements of it have appeared in a number of standards. For example, the ISO standard, ISO 15926 – Industrial automation systems and integration – was heavily influenced by an early version. The IDEAS (International Defence Enterprise Architecture Specification for exchange) standard is based upon BORO, which in turn was used to develop DODAF 2.0. From 2003 to 2008, the start-up company 42 Objects, funded by private equity company 3i worked on developing systems based upon BORO.
== Description ==
The BORO approach is designed to be a simple, repeatable process for developing formal ontologies. The method takes an extensional approach to ontology development. The method aims to be grounded in physical reality so that if followed to the letter the method should consistently produce the same ontology given the same inputs. It can then be used for comparing multiple data-sources for semantic matches/mismatches and for re-engineering multiple legacy systems into a coherent whole (either as a new monolithic system, or as a method for designing federation of existing systems).
BORO's purpose is to improve the quality of information and information models, to integrate multiple information sources and extract hidden semantics. The purpose of the method is to re-engineer disparate data sources into a common model. It is meant to be focused on semantic analysis – establishing whether two concepts are the same, if they overlap, or if they are unrelated. This is based on using resources from higher order logic, mathematics and philosophy.
For example in the case of Criterion of identity, the method adopts an extensional approach. As an example, take “Waterloo Bridge” as a term. The first thing we ask is “does it refer to an object that has a spatial and temporal extent ?”. It has spatial extent; it spans the River Thames. However, when we examine the temporal extent we realise there have been two bridges at that site. The first, built in 1817 (two years after the battle of Waterloo) was demolished in 1920. The bridge that stands there now was built in 1942. This analysis has immediately highlighted a problem with a name-based approach – there are two bridges of that name, which one are we referring to? At this point, the analyst can add one or both of the bridges to the ontology, then apply the appropriate names to each.
The process also works for types of things. Take “bridges” as a concept. It doesn’t have spatiotemporal extent, so we go to the next question “does it have members ?”. It does – the members are all the bridges in the world. We then identify some exemplar members – e.g. Waterloo Bridge. At this stage, it is advisable to identify exemplars that are “on the edge” of the set – e.g. things that may or may not be bridges – e.g. pontoons, bridging vehicles, etc. so as to accurately identify the extent of the type.
The final concept covered by the process is the tuple. A tuple is a relationship between things. If the concept under analysis is neither a type nor an individual, then it must be a tuple. We identify the things at the end of the tuple then add it to the ontology.
Traditional methods of data analysis tend to be linguistic; comparison of concepts is based on the names these concepts have. More modern methods have introduced a semantic approach, where the analyst will tend to analyse the underlying senses of the word (meaning). A lot of it depends on the analyst’s domain knowledge and linguistic interpretation. Although BORO produces an ontology (information science) in the very strictest sense of the term, it is not intended to produce the type of ontology (information science) that computer scientists would use for reasoning and inference. BORO is different to many other data analysis techniques in that treats the names of things as a secondary concern. With BORO, the analyst is forced to identify individual concepts by their extent.
The BORO methodology is best summarised as a flowchart:
== Presentations ==
The method has been presented several times, including a tutorial at the Integrated Enterprise Architecture Conference in London in 2008. It was also presented at the UK Ministry of Defence's EKIG conference in October 2009.
== Notes ==
== References ==
== External links ==
Boro Research Resources
EKIG Conference
DODAF Ontology Foundation site
IDEAS Ontology site
Ian Bailey's BORO Presentation from EKIG
Integrated Enterprise Architecture Conference
Cutter Consortium Article on Using BORO for forensic data analysis
Analysis of Information Assets - using BORO to unpick documents and representations | Wikipedia/BORO_method |
Knowledge representation (KR) aims to model information in a structured manner to formally represent it as knowledge in knowledge-based systems whereas knowledge representation and reasoning (KRR, KR&R, or KR²) also aims to understand, reason, and interpret knowledge. KRR is widely used in the field of artificial intelligence (AI) with the goal to represent information about the world in a form that a computer system can use to solve complex tasks, such as diagnosing a medical condition or having a natural-language dialog. KR incorporates findings from psychology about how humans solve problems and represent knowledge, in order to design formalisms that make complex systems easier to design and build. KRR also incorporates findings from logic to automate various kinds of reasoning.
Traditional KRR focuses more on the declarative representation of knowledge. Related knowledge representation formalisms mainly include vocabularies, thesaurus, semantic networks, axiom systems, frames, rules, logic programs, and ontologies. Examples of automated reasoning engines include inference engines, theorem provers, model generators, and classifiers.
In a broader sense, parameterized models in machine learning — including neural network architectures such as convolutional neural networks and transformers — can also be regarded as a family of knowledge representation formalisms. The question of which formalism is most appropriate for knowledge-based systems has long been a subject of extensive debate. For instance, Frank van Harmelen et al. discussed the suitability of logic as a knowledge representation formalism and reviewed arguments presented by anti-logicists. Paul Smolensky criticized the limitations of symbolic formalisms and explored the possibilities of integrating it with connectionist approaches.
More recently, Heng Zhang et al. have demonstrated that all universal (or equally expressive and natural) knowledge representation formalisms are recursively isomorphic. This finding indicates a theoretical equivalence among mainstream knowledge representation formalisms with respect to their capacity for supporting artificial general intelligence (AGI). They further argue that while diverse technical approaches may draw insights from one another via recursive isomorphisms, the fundamental challenges remain inherently shared.
== History ==
The earliest work in computerized knowledge representation was focused on general problem-solvers such as the General Problem Solver (GPS) system developed by Allen Newell and Herbert A. Simon in 1959 and the Advice Taker proposed by John McCarthy also in 1959. GPS featured data structures for planning and decomposition. The system would begin with a goal. It would then decompose that goal into sub-goals and then set out to construct strategies that could accomplish each subgoal. The Advisor Taker, on the other hand, proposed the use of the predicate calculus to implement common sense reasoning.
Many of the early approaches to knowledge representation in Artificial Intelligence (AI) used graph representations and semantic networks, similar to knowledge graphs today. In such approaches, problem solving was a form of graph traversal or path-finding, as in the A* search algorithm. Typical applications included robot plan-formation and game-playing.
Other researchers focused on developing automated theorem-provers for first-order logic, motivated by the use of mathematical logic to formalise mathematics and to automate the proof of mathematical theorems. A major step in this direction was the development of the resolution method by John Alan Robinson.
In the meanwhile, John McCarthy and Pat Hayes developed the situation calculus as a logical representation of common sense knowledge about the laws of cause and effect. Cordell Green, in turn, showed how to do robot plan-formation by applying resolution to the situation calculus. He also showed how to use resolution for question-answering and automatic programming.
In contrast, researchers at Massachusetts Institute of Technology (MIT) rejected the resolution uniform proof procedure paradigm and advocated the procedural embedding of knowledge instead. The resulting conflict between the use of logical representations and the use of procedural representations was resolved in the early 1970s with the development of logic programming and Prolog, using SLD resolution to treat Horn clauses as goal-reduction procedures.
The early development of logic programming was largely a European phenomenon. In North America, AI researchers such as Ed Feigenbaum and Frederick Hayes-Roth advocated the representation of domain-specific knowledge rather than general-purpose reasoning.
These efforts led to the cognitive revolution in psychology and to the phase of AI focused on knowledge representation that resulted in expert systems in the 1970s and 80s, production systems, frame languages, etc. Rather than general problem solvers, AI changed its focus to expert systems that could match human competence on a specific task, such as medical diagnosis.
Expert systems gave us the terminology still in use today where AI systems are divided into a knowledge base, which includes facts and rules about a problem domain, and an inference engine, which applies the knowledge in the knowledge base to answer questions and solve problems in the domain. In these early systems the facts in the knowledge base tended to be a fairly flat structure, essentially assertions about the values of variables used by the rules.
Meanwhile, Marvin Minsky developed the concept of frame in the mid-1970s. A frame is similar to an object class: It is an abstract description of a category describing things in the world, problems, and potential solutions. Frames were originally used on systems geared toward human interaction, e.g. understanding natural language and the social settings in which various default expectations such as ordering food in a restaurant narrow the search space and allow the system to choose appropriate responses to dynamic situations.
It was not long before the frame communities and the rule-based researchers realized that there was a synergy between their approaches. Frames were good for representing the real world, described as classes, subclasses, slots (data values) with various constraints on possible values. Rules were good for representing and utilizing complex logic such as the process to make a medical diagnosis. Integrated systems were developed that combined frames and rules. One of the most powerful and well known was the 1983 Knowledge Engineering Environment (KEE) from Intellicorp. KEE had a complete rule engine with forward and backward chaining. It also had a complete frame-based knowledge base with triggers, slots (data values), inheritance, and message passing. Although message passing originated in the object-oriented community rather than AI it was quickly embraced by AI researchers as well in environments such as KEE and in the operating systems for Lisp machines from Symbolics, Xerox, and Texas Instruments.
The integration of frames, rules, and object-oriented programming was significantly driven by commercial ventures such as KEE and Symbolics spun off from various research projects. At the same time, there was another strain of research that was less commercially focused and was driven by mathematical logic and automated theorem proving. One of the most influential languages in this research was the KL-ONE language of the mid-'80s. KL-ONE was a frame language that had a rigorous semantics, formal definitions for concepts such as an Is-A relation. KL-ONE and languages that were influenced by it such as Loom had an automated reasoning engine that was based on formal logic rather than on IF-THEN rules. This reasoner is called the classifier. A classifier can analyze a set of declarations and infer new assertions, for example, redefine a class to be a subclass or superclass of some other class that wasn't formally specified. In this way the classifier can function as an inference engine, deducing new facts from an existing knowledge base. The classifier can also provide consistency checking on a knowledge base (which in the case of KL-ONE languages is also referred to as an Ontology).
Another area of knowledge representation research was the problem of common-sense reasoning. One of the first realizations learned from trying to make software that can function with human natural language was that humans regularly draw on an extensive foundation of knowledge about the real world that we simply take for granted but that is not at all obvious to an artificial agent, such as basic principles of common-sense physics, causality, intentions, etc. An example is the frame problem, that in an event driven logic there need to be axioms that state things maintain position from one moment to the next unless they are moved by some external force. In order to make a true artificial intelligence agent that can converse with humans using natural language and can process basic statements and questions about the world, it is essential to represent this kind of knowledge. In addition to McCarthy and Hayes' situation calculus, one of the most ambitious programs to tackle this problem was Doug Lenat's Cyc project. Cyc established its own Frame language and had large numbers of analysts document various areas of common-sense reasoning in that language. The knowledge recorded in Cyc included common-sense models of time, causality, physics, intentions, and many others.
The starting point for knowledge representation is the knowledge representation hypothesis first formalized by Brian C. Smith in 1985:
Any mechanically embodied intelligent process will be comprised of structural ingredients that a) we as external observers naturally take to represent a propositional account of the knowledge that the overall process exhibits, and b) independent of such external semantic attribution, play a formal but causal and essential role in engendering the behavior that manifests that knowledge.
One of the most active areas of knowledge representation research is the Semantic Web. The Semantic Web seeks to add a layer of semantics (meaning) on top of the current Internet. Rather than indexing web sites and pages via keywords, the Semantic Web creates large ontologies of concepts. Searching for a concept will be more effective than traditional text only searches. Frame languages and automatic classification play a big part in the vision for the future Semantic Web. The automatic classification gives developers technology to provide order on a constantly evolving network of knowledge. Defining ontologies that are static and incapable of evolving on the fly would be very limiting for Internet-based systems. The classifier technology provides the ability to deal with the dynamic environment of the Internet.
Recent projects funded primarily by the Defense Advanced Research Projects Agency (DARPA) have integrated frame languages and classifiers with markup languages based on XML. The Resource Description Framework (RDF) provides the basic capability to define classes, subclasses, and properties of objects. The Web Ontology Language (OWL) provides additional levels of semantics and enables integration with classification engines.
== Overview ==
Knowledge-representation is a field of artificial intelligence that focuses on designing computer representations that capture information about the world that can be used for solving complex problems.
The justification for knowledge representation is that conventional procedural code is not the best formalism to use to solve complex problems. Knowledge representation makes complex software easier to define and maintain than procedural code and can be used in expert systems.
For example, talking to experts in terms of business rules rather than code lessens the semantic gap between users and developers and makes development of complex systems more practical.
Knowledge representation goes hand in hand with automated reasoning because one of the main purposes of explicitly representing knowledge is to be able to reason about that knowledge, to make inferences, assert new knowledge, etc. Virtually all knowledge representation languages have a reasoning or inference engine as part of the system.
A key trade-off in the design of knowledge representation formalisms is that between expressivity and tractability. First Order Logic (FOL), with its high expressive power and ability to formalise much of mathematics, is a standard for comparing the expressibility of knowledge representation languages.
Arguably, FOL has two drawbacks as a knowledge representation formalism in its own right, namely ease of use and efficiency of implementation. Firstly, because of its high expressive power, FOL allows many ways of expressing the same information, and this can make it hard for users to formalise or even to understand knowledge expressed in complex, mathematically-oriented ways. Secondly, because of its complex proof procedures, it can be difficult for users to understand complex proofs and explanations, and it can be hard for implementations to be efficient. As a consequence, unrestricted FOL can be intimidating for many software developers.
One of the key discoveries of AI research in the 1970s was that languages that do not have the full expressive power of FOL can still provide close to the same expressive power of FOL, but can be easier for both the average developer and for the computer to understand. Many of the early AI knowledge representation formalisms, from databases to semantic nets to production systems, can be viewed as making various design decisions about how to balance expressive power with naturalness of expression and efficiency. In particular, this balancing act was a driving motivation for the development of IF-THEN rules in rule-based expert systems.
A similar balancing act was also a motivation for the development of logic programming (LP) and the logic programming language Prolog. Logic programs have a rule-based syntax, which is easily confused with the IF-THEN syntax of production rules. But logic programs have a well-defined logical semantics, whereas production systems do not.
The earliest form of logic programming was based on the Horn clause subset of FOL. But later extensions of LP included the negation as failure inference rule, which turns LP into a non-monotonic logic for default reasoning. The resulting extended semantics of LP is a variation of the standard semantics of Horn clauses and FOL, and is a form of database semantics, which includes the unique name assumption and a form of closed world assumption. These assumptions are much harder to state and reason with explicitly using the standard semantics of FOL.
In a key 1993 paper on the topic, Randall Davis of MIT outlined five distinct roles to analyze a knowledge representation framework:
"A knowledge representation (KR) is most fundamentally a surrogate, a substitute for the thing itself, used to enable an entity to determine consequences by thinking rather than acting," i.e., "by reasoning about the world rather than taking action in it."
"It is a set of ontological commitments", i.e., "an answer to the question: In what terms should I think about the world?"
"It is a fragmentary theory of intelligent reasoning, expressed in terms of three components: (i) the representation's fundamental conception of intelligent reasoning; (ii) the set of inferences the representation sanctions; and (iii) the set of inferences it recommends."
"It is a medium for pragmatically efficient computation", i.e., "the computational environment in which thinking is accomplished. One contribution to this pragmatic efficiency is supplied by the guidance a representation provides for organizing information" so as "to facilitate making the recommended inferences."
"It is a medium of human expression", i.e., "a language in which we say things about the world."
Knowledge representation and reasoning are a key enabling technology for the Semantic Web. Languages based on the Frame model with automatic classification provide a layer of semantics on top of the existing Internet. Rather than searching via text strings as is typical today, it will be possible to define logical queries and find pages that map to those queries. The automated reasoning component in these systems is an engine known as the classifier. Classifiers focus on the subsumption relations in a knowledge base rather than rules. A classifier can infer new classes and dynamically change the ontology as new information becomes available. This capability is ideal for the ever-changing and evolving information space of the Internet.
The Semantic Web integrates concepts from knowledge representation and reasoning with markup languages based on XML. The Resource Description Framework (RDF) provides the basic capabilities to define knowledge-based objects on the Internet with basic features such as Is-A relations and object properties. The Web Ontology Language (OWL) adds additional semantics and integrates with automatic classification reasoners.
== Characteristics ==
In 1985, Ron Brachman categorized the core issues for knowledge representation as follows:
Primitives. What is the underlying framework used to represent knowledge? Semantic networks were one of the first knowledge representation primitives. Also, data structures and algorithms for general fast search. In this area, there is a strong overlap with research in data structures and algorithms in computer science. In early systems, the Lisp programming language, which was modeled after the lambda calculus, was often used as a form of functional knowledge representation. Frames and Rules were the next kind of primitive. Frame languages had various mechanisms for expressing and enforcing constraints on frame data. All data in frames are stored in slots. Slots are analogous to relations in entity-relation modeling and to object properties in object-oriented modeling. Another technique for primitives is to define languages that are modeled after First Order Logic (FOL). The most well known example is Prolog, but there are also many special-purpose theorem-proving environments. These environments can validate logical models and can deduce new theories from existing models. Essentially they automate the process a logician would go through in analyzing a model. Theorem-proving technology had some specific practical applications in the areas of software engineering. For example, it is possible to prove that a software program rigidly adheres to a formal logical specification.
Meta-representation. This is also known as the issue of reflection in computer science. It refers to the ability of a formalism to have access to information about its own state. An example is the meta-object protocol in Smalltalk and CLOS that gives developers runtime access to the class objects and enables them to dynamically redefine the structure of the knowledge base even at runtime. Meta-representation means the knowledge representation language is itself expressed in that language. For example, in most Frame based environments all frames would be instances of a frame class. That class object can be inspected at runtime, so that the object can understand and even change its internal structure or the structure of other parts of the model. In rule-based environments, the rules were also usually instances of rule classes. Part of the meta protocol for rules were the meta rules that prioritized rule firing.
Incompleteness. Traditional logic requires additional axioms and constraints to deal with the real world as opposed to the world of mathematics. Also, it is often useful to associate degrees of confidence with a statement, i.e., not simply say "Socrates is Human" but rather "Socrates is Human with confidence 50%". This was one of the early innovations from expert systems research which migrated to some commercial tools, the ability to associate certainty factors with rules and conclusions. Later research in this area is known as fuzzy logic.
Definitions and universals vs. facts and defaults. Universals are general statements about the world such as "All humans are mortal". Facts are specific examples of universals such as "Socrates is a human and therefore mortal". In logical terms definitions and universals are about universal quantification while facts and defaults are about existential quantifications. All forms of knowledge representation must deal with this aspect and most do so with some variant of set theory, modeling universals as sets and subsets and definitions as elements in those sets.
Non-monotonic reasoning. Non-monotonic reasoning allows various kinds of hypothetical reasoning. The system associates facts asserted with the rules and facts used to justify them and as those facts change updates the dependent knowledge as well. In rule based systems this capability is known as a truth maintenance system.
Expressive adequacy. The standard that Brachman and most AI researchers use to measure expressive adequacy is usually First Order Logic (FOL). Theoretical limitations mean that a full implementation of FOL is not practical. Researchers should be clear about how expressive (how much of full FOL expressive power) they intend their representation to be.
Reasoning efficiency. This refers to the runtime efficiency of a system: The ability of the knowledge base to be updated and the reasoner to develop new inferences in a reasonable time. In some ways, this is the flip side of expressive adequacy. In general, the more powerful a representation, the more it has expressive adequacy, the less efficient its automated reasoning engine will be. Efficiency was often an issue, especially for early applications of knowledge representation technology. They were usually implemented in interpreted environments such as Lisp, which were slow compared to more traditional platforms of the time.
== Ontology engineering ==
In the early years of knowledge-based systems the knowledge-bases were fairly small. The knowledge-bases that were meant to actually solve real problems rather than do proof of concept demonstrations needed to focus on well defined problems. So for example, not just medical diagnosis as a whole topic, but medical diagnosis of certain kinds of diseases.
As knowledge-based technology scaled up, the need for larger knowledge bases and for modular knowledge bases that could communicate and integrate with each other became apparent. This gave rise to the discipline of ontology engineering, designing and building large knowledge bases that could be used by multiple projects. One of the leading research projects in this area was the Cyc project. Cyc was an attempt to build a huge encyclopedic knowledge base that would contain not just expert knowledge but common-sense knowledge. In designing an artificial intelligence agent, it was soon realized that representing common-sense knowledge, knowledge that humans simply take for granted, was essential to make an AI that could interact with humans using natural language. Cyc was meant to address this problem. The language they defined was known as CycL.
After CycL, a number of ontology languages have been developed. Most are declarative languages, and are either frame languages, or are based on first-order logic. Modularity—the ability to define boundaries around specific domains and problem spaces—is essential for these languages because as stated by Tom Gruber, "Every ontology is a treaty–a social agreement among people with common motive in sharing." There are always many competing and differing views that make any general-purpose ontology impossible. A general-purpose ontology would have to be applicable in any domain and different areas of knowledge need to be unified.
There is a long history of work attempting to build ontologies for a variety of task domains, e.g., an ontology for liquids, the lumped element model widely used in representing electronic circuits (e.g.), as well as ontologies for time, belief, and even programming itself. Each of these offers a way to see some part of the world.
The lumped element model, for instance, suggests that we think of circuits in terms of components with connections between them, with signals flowing instantaneously along the connections. This is a useful view, but not the only possible one. A different ontology arises if we need to attend to the electrodynamics in the device: Here signals propagate at finite speed and an object (like a resistor) that was previously viewed as a single component with an I/O behavior may now have to be thought of as an extended medium through which an electromagnetic wave flows.
Ontologies can of course be written down in a wide variety of languages and notations (e.g., logic, LISP, etc.); the essential information is not the form of that language but the content, i.e., the set of concepts offered as a way of thinking about the world. Simply put, the important part is notions like connections and components, not the choice between writing them as predicates or LISP constructs.
The commitment made selecting one or another ontology can produce a sharply different view of the task at hand. Consider the difference that arises in selecting the lumped element view of a circuit rather than the electrodynamic view of the same device. As a second example, medical diagnosis viewed in terms of rules (e.g., MYCIN) looks substantially different from the same task viewed in terms of frames (e.g., INTERNIST). Where MYCIN sees the medical world as made up of empirical associations connecting symptom to disease, INTERNIST sees a set of prototypes, in particular prototypical diseases, to be matched against the case at hand.
== See also ==
Alphabet of human thought – Hypothetical language created by Gottfried Wilhelm Leibniz
Belief revision – Process of changing beliefs to take into account a new piece of information
Chunking (psychology) – Cognitive psychology process
Commonsense knowledge base – Facts assumed to be known to all humans
Conceptual graph – Formalism for knowledge representation
DIKW pyramid – Data, information, knowledge, wisdom hierarchy
DATR, a language for lexical knowledge representation
FO(.), a KR language based on first-order logic
Knowledge graph – Type of knowledge base
Knowledge management – Processing of knowledge to accomplish organizational goals
Logic programming – Programming paradigm based on formal logic
Logico-linguistic modeling
Mind map – Diagram to visually organize information
Semantic technology – Technology to help machines understand data
Valuation-based system
== References ==
== Further reading ==
Ronald J. Brachman; What IS-A is and isn't. An Analysis of Taxonomic Links in Semantic Networks; IEEE Computer, 16 (10); October 1983
Ronald J. Brachman, Hector J. Levesque Knowledge Representation and Reasoning, Morgan Kaufmann, 2004 ISBN 978-1-55860-932-7
Ronald J. Brachman, Hector J. Levesque (eds) Readings in Knowledge Representation, Morgan Kaufmann, 1985, ISBN 0-934613-01-X
Chein, M., Mugnier, M.-L. (2009),Graph-based Knowledge Representation: Computational Foundations of Conceptual Graphs, Springer, 2009,ISBN 978-1-84800-285-2.
Randall Davis, Howard Shrobe, and Peter Szolovits; What Is a Knowledge Representation? AI Magazine, 14(1):17-33,1993
Ronald Fagin, Joseph Y. Halpern, Yoram Moses, Moshe Y. Vardi Reasoning About Knowledge, MIT Press, 1995, ISBN 0-262-06162-7
Jean-Luc Hainaut, Jean-Marc Hick, Vincent Englebert, Jean Henrard, Didier Roland: Understanding Implementations of IS-A Relations. ER 1996: 42-57
Hermann Helbig: Knowledge Representation and the Semantics of Natural Language, Springer, Berlin, Heidelberg, New York 2006
Frank van Harmelen, Vladimir Lifschitz and Bruce Porter: Handbook of Knowledge Representation 2007.
Arthur B. Markman: Knowledge Representation Lawrence Erlbaum Associates, 1998
John F. Sowa: Knowledge Representation: Logical, Philosophical, and Computational Foundations. Brooks/Cole: New York, 2000
Adrian Walker, Michael McCord, John F. Sowa, and Walter G. Wilson: Knowledge Systems and Prolog, Second Edition, Addison-Wesley, 1990
Mary-Anne Williams and Hans Rott: "Frontiers in Belief Revision, Kluwer", 2001.
== External links ==
What is a Knowledge Representation? by Randall Davis and others
Introduction to Knowledge Modeling by Pejman Makhfi
Introduction to Description Logics course by Enrico Franconi, Faculty of Computer Science, Free University of Bolzano, Italy
DATR Lexical knowledge representation language
Loom Project Home Page
Principles of Knowledge Representation and Reasoning Incorporated
Description Logic in Practice: A CLASSIC Application
The Rule Markup Initiative
Nelements KOS - a non-free 3d knowledge representation system | Wikipedia/Knowledge_model |
The Systems Biology Ontology (SBO) is a set of controlled, relational vocabularies of terms commonly used in systems biology, and in particular in computational modeling.
== Motivation ==
The rise of systems biology, seeking to comprehend biological processes as a whole, highlighted the need to not only develop corresponding quantitative models but also to create standards allowing their exchange and integration. This concern drove the community to design common data formats, such as SBML and CellML. SBML is now largely accepted and used in the field. However, as important as the definition of a common syntax is, it is also necessary to make clear the semantics of models. SBO tries to give us a way to label models with words that describe how they should be used in a large group of models that are commonly used in computational systems biology. The development of SBO was first discussed at the 9th SBML Forum Meeting in Heidelberg on October 14–15, 2004. During the forum, Pedro Mendes mentioned that modellers possessed a lot of knowledge that was necessary to understand the model and, more importantly, to simulate it, but this knowledge was not encoded in SBML. Nicolas Le Novère proposed to create a controlled vocabulary to store the content of Pedro Mendes' mind before he wandered out of the community. The development of the ontology was announced more officially in a message from Le Novère to Michael Hucka and Andrew Finney on October 19.
== Structure ==
SBO is currently made up of seven different vocabularies:
systems description parameter (catalytic constant, thermodynamic temperature...)
participant role (substrate, product, catalyst...)
modelling framework (discrete, continuous...)
mathematical expression (mass-action rate law, Hill-type rate law...)
occurring entity representation (biochemical process, molecular or genetic interaction...)
physical entity representation (transporter, physical compartment, observable...)
metadata representation (annotation)
== Resources ==
To curate and maintain SBO, a dedicated resource has been developed and the public interface of the SBO browser can be accessed at http://www.ebi.ac.uk/sbo.
A relational database management system (MySQL) at the back-end is
accessed through a web interface based on Java Server Pages (JSP) and JavaBeans. Its
content is encoded in UTF-8, therefore supporting a large set of
characters in the definitions of terms. Distributed curation is made possible
by using a custom-tailored locking system allowing concurrent access.
This system allows a continuous update of the ontology with immediate
availability and suppress merging problems.
Several exports formats (OBO flat file, SBO-XML and OWL) are generated daily or on request and can be downloaded from the web interface.
To allow programmatic access to the resource, Web Services have been implemented based on Apache Axis for the communication layer and Castor for the validation. The libraries, full documentation, samples and tutorial are available online.
The SourceForge project can be accessed at http://sourceforge.net/projects/sbo/.
== SBO and SBML ==
Since Level 2 Version 2 SBML provides a mechanism to annotate model components with SBO terms, therefore increasing the semantics of the
model beyond the sole topology of interaction and mathematical expression. Modelling tools such as SBMLsqueezer interpret SBO terms to augment the mathematics in the SBML file. Simulation tools can check the consistency of a rate law, convert reaction from one modelling framework to another (e.g., continuous to discrete), or distinguish between identical mathematical expressions based on different assumptions (e.g., Michaelis–Menten vs. Briggs–Haldane). To add missing SBO terms to models, software such as SBOannotator can be used. Other tools such as semanticSBML can use the SBO annotation to integrate individual models into a larger one. The use of SBO is not restricted to the development of models. Resources providing quantitative experimental information such as SABIO Reaction Kinetics will be able to annotate the parameters (what do they mean exactly, how were they calculated) and determine relationships between them.
== SBO and SBGN ==
All the graphical symbols used in the SBGN languages are associated with an SBO term. This permits, for instance, to help generate SBGN maps from SBML models.
== SBO and BioPAX ==
The Systems Biology Pathway Exchange (SBPAX) allows SBO terms to be added to Biological Pathway Exchange (BioPAX). This links BioPAX to information useful for modelling, especially by adding quantitative descriptions described by SBO.
== Organization of SBO development ==
SBO is built in collaboration by the Computational Neurobiology Group (Nicolas Le Novère, EMBL-EBI, United-Kingdom) and the SBMLTeam (Michael Hucka, Caltech, USA).
== Funding for SBO ==
SBO has benefited from the funds of the European Molecular Biology Laboratory and the National Institute of General Medical Sciences.
== References ==
== External links ==
www.biomodels.net
Hucka M, Finney A, Sauro HM, et al. (March 2003). "The systems biology markup language (SBML): A medium for representation and exchange of Biochemical Network Models". Bioinformatics. 19 (4): 524–31. CiteSeerX 10.1.1.562.1085. doi:10.1093/bioinformatics/btg015. PMID 12611808.
Lloyd CM, Halstead MD, Nielsen PF (2004). "CellML: its future, present and past". Prog. Biophys. Mol. Biol. 85 (2–3): 433–50. CiteSeerX 10.1.1.460.934. doi:10.1016/j.pbiomolbio.2004.01.004. PMID 15142756. | Wikipedia/Systems_Biology_Ontology |
In knowledge representation and reasoning, a knowledge graph is a knowledge base that uses a graph-structured data model or topology to represent and operate on data. Knowledge graphs are often used to store interlinked descriptions of entities – objects, events, situations or abstract concepts – while also encoding the free-form semantics or relationships underlying these entities.
Since the development of the Semantic Web, knowledge graphs have often been associated with linked open data projects, focusing on the connections between concepts and entities. They are also historically associated with and used by search engines such as Google, Bing, Yext and Yahoo; knowledge-engines and question-answering services such as WolframAlpha, Apple's Siri, and Amazon Alexa; and social networks such as LinkedIn and Facebook.
Recent developments in data science and machine learning, particularly in graph neural networks and representation learning and also in machine learning, have broadened the scope of knowledge graphs beyond their traditional use in search engines and recommender systems. They are increasingly used in scientific research, with notable applications in fields such as genomics, proteomics, and systems biology.
== History ==
The term was coined as early as 1972 by the Austrian linguist Edgar W. Schneider, in a discussion of how to build modular instructional systems for courses. In the late 1980s, the University of Groningen and University of Twente jointly began a project called Knowledge Graphs, focusing on the design of semantic networks with edges restricted to a limited set of relations, to facilitate algebras on the graph. In subsequent decades, the distinction between semantic networks and knowledge graphs was blurred.
Some early knowledge graphs were topic-specific. In 1985, Wordnet was founded, capturing semantic relationships between words and meanings – an application of this idea to language itself. In 2005, Marc Wirk founded Geonames to capture relationships between different geographic names and locales and associated entities. In 1998 Andrew Edmonds of Science in Finance Ltd in the UK created a system called ThinkBase that offered fuzzy-logic based reasoning in a graphical context. ThinkBase LLC
In 2007, both DBpedia and Freebase were founded as graph-based knowledge repositories for general-purpose knowledge. DBpedia focused exclusively on data extracted from Wikipedia, while Freebase also included a range of public datasets. Neither described themselves as a 'knowledge graph' but developed and described related concepts.
In 2012, Google introduced their Knowledge Graph, building on DBpedia and Freebase among other sources. They later incorporated RDFa, Microdata, JSON-LD content extracted from indexed web pages, including the CIA World Factbook, Wikidata, and Wikipedia. Entity and relationship types associated with this knowledge graph have been further organized using terms from the schema.org vocabulary. The Google Knowledge Graph became a successful complement to string-based search within Google, and its popularity online brought the term into more common use.
Since then, several large multinationals have advertised their knowledge graphs use, further popularising the term. These include Facebook, LinkedIn, Airbnb, Microsoft, Amazon, Uber and eBay.
In 2019, IEEE combined its annual international conferences on "Big Knowledge" and "Data Mining and Intelligent Computing" into the International Conference on Knowledge Graph.
== Definitions ==
There is no single commonly accepted definition of a knowledge graph. Most definitions view the topic through a Semantic Web lens and include these features:
Flexible relations among knowledge in topical domains: A knowledge graph (i) defines abstract classes and relations of entities in a schema, (ii) mainly describes real world entities and their interrelations, organized in a graph, (iii) allows for potentially interrelating arbitrary entities with each other, and (iv) covers various topical domains.
General structure: A network of entities, their semantic types, properties, and relationships. To represent properties, categorical or numerical values are often used.
Supporting reasoning over inferred ontologies: A knowledge graph acquires and integrates information into an ontology and applies a reasoner to derive new knowledge.
There are, however, many knowledge graph representations for which some of these features are not relevant. For those knowledge graphs, this simpler definition may be more useful:
A digital structure that represents knowledge as concepts and the relationships between them (facts). A knowledge graph can include an ontology that allows both humans and machines to understand and reason about its contents.
=== Implementations ===
In addition to the above examples, the term has been used to describe open knowledge projects such as YAGO and Wikidata; federations like the Linked Open Data cloud; a range of commercial search tools, including Yahoo's semantic search assistant Spark, Google's Knowledge Graph, and Microsoft's Satori; and the LinkedIn and Facebook entity graphs.
The term is also used in the context of note-taking software applications that allow a user to build a personal knowledge graph.
The popularization of knowledge graphs and their accompanying methods have led to the development of graph databases such as Neo4j, GraphDB and AgensGraph. These graph databases allow users to easily store data as entities and their interrelationships, and facilitate operations such as data reasoning, node embedding, and ontology development on knowledge bases.
In contrast, virtual knowledge graphs do not store information in specialized databases. They rely on an underlying relational database or data lake to answer queries on the graph. Such a virtual knowledge graph system must be properly configured in order to answer the queries correctly. This specific configuration is done through a set of mappings that define the relationship between the elements of the data source and the structure and ontology of the virtual knowledge graph.
== Using a knowledge graph for reasoning over data ==
A knowledge graph formally represents semantics by describing entities and their relationships. Knowledge graphs may make use of ontologies as a schema layer. By doing this, they allow logical inference for retrieving implicit knowledge rather than only allowing queries requesting explicit knowledge.
In order to allow the use of knowledge graphs in various machine learning tasks, several methods for deriving latent feature representations of entities and relations have been devised. These knowledge graph embeddings allow them to be connected to machine learning methods that require feature vectors like word embeddings. This can complement other estimates of conceptual similarity.
Models for generating useful knowledge graph embeddings are commonly the domain of graph neural networks (GNNs). GNNs are deep learning architectures that comprise edges and nodes, which correspond well to the entities and relationships of knowledge graphs. The topology and data structures afforded by GNNs provides a convenient domain for semi-supervised learning, wherein the network is trained to predict the value of a node embedding (provided a group of adjacent nodes and their edges) or edge (provided a pair of nodes). These tasks serve as fundamental abstractions for more complex tasks such as knowledge graph reasoning and alignment.
=== Entity alignment ===
As new knowledge graphs are produced across a variety of fields and contexts, the same entity will inevitably be represented in multiple graphs. However, because no single standard for the construction or representation of knowledge graph exists, resolving which entities from disparate graphs correspond to the same real world subject is a non-trivial task. This task is known as knowledge graph entity alignment, and is an active area of research.
Strategies for entity alignment generally seek to identify similar substructures, semantic relationships, shared attributes, or combinations of all three between two distinct knowledge graphs. Entity alignment methods use these structural similarities between generally non-isomorphic graphs to predict which nodes corresponds to the same entity.
The recent successes of large language models (LLMs), in particular their effectiveness at producing syntactically meaningful embeddings, has spurred the use of LLMs in the task of entity alignment.
As the amount of data stored in knowledge graphs grows, developing dependable methods for knowledge graph entity alignment becomes an increasingly crucial step in the integration and cohesion of knowledge graph data.
== See also ==
Concept map – Diagram showing relationships among concepts
Formal semantics (natural language) – Study of meaning in natural languages
Graph database – Database using graph structures for queries
Knowledge base – Information repository with multiple applications
Knowledge graph embedding – Dimensionality reduction of graph-based semantic data objects [machine learning task]
Logical graph – Type of diagrammatic notation for propositional logicPages displaying short descriptions of redirect targets
Semantic integration – Interrelating info from diverse sources
Semantic technology – Technology to help machines understand data
Topic map – Knowledge organization system
Vadalog – Type of Knowledge Graph Management System
Wikibase- Mediawiki Software extensions for creating knowledge bases
Wikidata - Free Knowledge Database Project
YAGO (database) – Open-source information repository
== References ==
== External links ==
Will Douglas Heaven (4 September 2020). "This know-it-all AI learns by reading the entire web nonstop". MIT Technology Review. Retrieved 5 September 2020. Diffbot is building the biggest-ever knowledge graph by applying image recognition and natural-language processing to billions of web pages. | Wikipedia/Knowledge_graph |
Controlled vocabularies provide a way to organize knowledge for subsequent retrieval. They are used in subject indexing schemes, subject headings, thesauri, taxonomies and other knowledge organization systems. Controlled vocabulary schemes mandate the use of predefined, preferred terms that have been preselected by the designers of the schemes, in contrast to natural language vocabularies, which have no such restriction.
== In library and information science ==
In library and information science, controlled vocabulary is a carefully selected list of words and phrases, which are used to tag units of information (document or work) so that they may be more easily retrieved by a search. Controlled vocabularies solve the problems of homographs, synonyms and polysemes by a bijection between concepts and preferred terms. In short, controlled vocabularies reduce unwanted ambiguity inherent in normal human languages where the same concept can be given different names and ensure consistency.
For example, in the Library of Congress Subject Headings (a subject heading system that uses a controlled vocabulary), preferred terms—subject headings in this case—have to be chosen to handle choices between variant spellings of the same word (American versus British), choice among scientific and popular terms (cockroach versus Periplaneta americana), and choices between synonyms (automobile versus car), among other difficult issues.
Choices of preferred terms are based on the principles of user warrant (what terms users are likely to use), literary warrant (what terms are generally used in the literature and documents), and structural warrant (terms chosen by considering the structure, scope of the controlled vocabulary).
Controlled vocabularies also typically handle the problem of homographs with qualifiers. For example, the term pool has to be qualified to refer to either swimming pool or the game pool to ensure that each preferred term or heading refers to only one concept.
=== Types used in libraries ===
There are two main kinds of controlled vocabulary tools used in libraries: subject headings and thesauri. While the differences between the two are diminishing, there are still some minor differences.
Historically, subject headings were designed to describe books in library catalogs by catalogers while thesauri were used by indexers to apply index terms to documents and articles.
Subject headings tend to be broader in scope describing whole books, while thesauri tend to be more specialized covering very specific disciplines.
Because of the card catalog system, subject headings tend to have terms that are in indirect order (though with the rise of automated systems this is being removed), while thesaurus terms are always in direct order.
Subject headings tend to use more pre-coordination of terms such that the designer of the controlled vocabulary will combine various concepts together to form one preferred subject heading. (e.g., children and terrorism) while thesauri tend to use singular direct terms. Thesauri list not only equivalent terms but also narrower, broader terms and related terms among various preferred and non-preferred (but potentially synonymous) terms, while historically most subject headings did not. For example, the Library of Congress Subject Heading itself did not have much syndetic structure until 1943, and it was not until 1985 when it began to adopt the thesauri type term "Broader term" and "Narrow term".
The terms are chosen and organized by trained professionals (including librarians and information scientists) who possess expertise in the subject area. Controlled vocabulary terms can accurately describe what a given document is actually about, even if the terms themselves do not occur within the document's text. Well known subject heading systems include the Library of Congress system, Medical Subject Headings (MeSH) created by the United States National Library of Medicine, and Sears. Well known thesauri include the Art and Architecture Thesaurus and the ERIC Thesaurus.
When selecting terms for a controlled vocabulary, the designer has to consider the specificity of the term chosen, whether to use direct entry, inter consistency and stability of the language.
Lastly the amount of pre-coordination (in which case the degree of enumeration versus synthesis becomes an issue) and post-coordination in the system is another important issue. Controlled vocabulary elements (terms/phrases) employed as tags, to aid in the content identification process of documents, or other information system entities (e.g. DBMS, Web Services) qualifies as metadata.
== Indexing languages ==
There are three main types of indexing languages.
Controlled indexing language – only approved terms can be used by the indexer to describe the document
Natural language indexing language – any term from the document in question can be used to describe the document
Free indexing language – any term (not only from the document) can be used to describe the document
When indexing a document, the indexer also has to choose the level of indexing exhaustivity, the level of detail in which the document is described. For example, using low indexing exhaustivity, minor aspects of the work will not be described with index terms. In general the higher the indexing exhaustivity, the more terms indexed for each document.
In recent years free text search as a means of access to documents has become popular. This involves using natural language indexing with an indexing exhaustively set to maximum (every word in the text is indexed). These methods have been compared in some studies, such as the 2007 article, "A Comparative Evaluation of Full-text, Concept-based, and Context-sensitive Search".
=== Advantages ===
Controlled vocabularies are often claimed to improve the accuracy of free text searching, such as to reduce irrelevant items in the retrieval list. These irrelevant items (false positives) are often caused by the inherent ambiguity of natural language. Take the English word football for example. Football is the name given to a number of different team sports. Worldwide the most popular of these team sports is association football, which also happens to be called soccer in several countries. The word football is also applied to rugby football (rugby union and rugby league), American football, Australian rules football, Gaelic football, and Canadian football. A search for football therefore will retrieve documents that are about several completely different sports. Controlled vocabulary solves this problem by tagging the documents in such a way that the ambiguities are eliminated.
Compared to free text searching, the use of a controlled vocabulary can dramatically increase the performance of an information retrieval system, if performance is measured by precision (the percentage of documents in the retrieval list that are actually relevant to the search topic).
In some cases controlled vocabulary can enhance recall as well, because unlike natural language schemes, once the correct preferred term is searched, there is no need to search for other terms that might be synonyms of that term.
=== Problems ===
A controlled vocabulary search may lead to unsatisfactory recall, in that it will fail to retrieve some documents that are actually relevant to the search question.
This is particularly problematic when the search question involves terms that are sufficiently tangential to the subject area such that the indexer might have decided to tag it using a different term (but the searcher might consider the same). Essentially, this can be avoided only by an experienced user of controlled vocabulary whose understanding of the vocabulary coincides with that of the indexer.
Another possibility is that the article is just not tagged by the indexer because indexing exhaustivity is low. For example, an article might mention football as a secondary focus, and the indexer might decide not to tag it with "football" because it is not important enough compared to the main focus. But it turns out that for the searcher that article is relevant and hence recall fails. A free text search would automatically pick up that article regardless.
On the other hand, free text searches have high exhaustivity (every word is searched) so although it has much lower precision, it has potential for high recall as long as the searcher overcome the problem of synonyms by entering every combination.
Controlled vocabularies may become outdated rapidly in fast developing fields of knowledge, unless the preferred terms are updated regularly. Even in an ideal scenario, a controlled vocabulary is often less specific than the words of the text itself. Indexers trying to choose the appropriate index terms might misinterpret the author, while this precise problem is not a factor in a free text, as it uses the author's own words.
The use of controlled vocabularies can be costly compared to free text searches because human experts or expensive automated systems are necessary to index each entry. Furthermore, the user has to be familiar with the controlled vocabulary scheme to make best use of the system. But as already mentioned, the control of synonyms, homographs can help increase precision.
Numerous methodologies have been developed to assist in the creation of controlled vocabularies, including faceted classification, which enables a given data record or document to be described in multiple ways.
Word choice in chosen vocabularies is not neutral, and the indexer must carefully consider the ethics of their word choices. For example, traditionally colonialist terms have often been the preferred terms in chosen vocabularies when discussing First Nations issues, which has caused controversy.
== Applications ==
Controlled vocabularies, such as the Library of Congress Subject Headings, are an essential component of bibliography, the study and classification of books. They were initially developed in library and information science. In the 1950s, government agencies began to develop controlled vocabularies for the burgeoning journal literature in specialized fields; an example is the Medical Subject Headings (MeSH) developed by the U.S. National Library of Medicine. Subsequently, for-profit firms (called Abstracting and indexing services) emerged to index the fast-growing literature in every field of knowledge. In the 1960s, an online bibliographic database industry developed based on dialup X.25 networking. These services were seldom made available to the public because they were difficult to use; specialist librarians called search intermediaries handled the searching job. In the 1980s, the first full text databases appeared; these databases contain the full text of the index articles as well as the bibliographic information. Online bibliographic databases have migrated to the Internet and are now publicly available; however, most are proprietary and can be expensive to use. Students enrolled in colleges and universities may be able to access some of these services without charge; some of these services may be accessible without charge at a public library.
=== Technical communication ===
In large organizations, controlled vocabularies may be introduced to improve technical communication. The use of controlled vocabulary ensures that everyone is using the same word to mean the same thing. This consistency of terms is one of the most important concepts in technical writing and knowledge management, where effort is expended to use the same word throughout a document or organization instead of slightly different ones to refer to the same thing.
=== Semantic web and structured data ===
Web searching could be dramatically improved by the development of a controlled vocabulary for describing Web pages; the use of such a vocabulary could culminate in a Semantic Web, in which the content of Web pages is described using a machine-readable metadata scheme. One of the first proposals for such a scheme is the Dublin Core Initiative. An example of a controlled vocabulary which is usable for indexing web pages is PSH.
It is unlikely that a single metadata scheme will ever succeed in describing the content of the entire Web. To create a Semantic Web, it may be necessary to draw from two or more metadata systems to describe a Web page's contents. The eXchangeable Faceted Metadata Language (XFML) is designed to enable controlled vocabulary creators to publish and share metadata systems. XFML is designed on faceted classification principles.
Controlled vocabularies of the Semantic Web define the concepts and relationships (terms) used to describe a field of interest or area of concern. For instance, to declare a person in a machine-readable format, a vocabulary is needed that has the formal definition of "Person", such as the Friend of a Friend (FOAF) vocabulary, which has a Person class that defines typical properties of a person including, but not limited to, name, honorific prefix, affiliation, email address, and homepage, or the Person vocabulary of Schema.org. Similarly, a book can be described using the Book vocabulary of Schema.org and general publication terms from the Dublin Core vocabulary, an event with the Event vocabulary of Schema.org, and so on.
To use machine-readable terms from any controlled vocabulary, web designers can choose from a variety of annotation formats, including RDFa, HTML5 Microdata, or JSON-LD in the markup, or RDF serializations (RDF/XML, Turtle, N3, TriG, TriX) in external files.
== See also ==
== References ==
== External links ==
Directory of Linked Open Vocabularies (LOV) | Wikipedia/Controlled_vocabularies |
First-order logic, also called predicate logic, predicate calculus, or quantificational logic, is a collection of formal systems used in mathematics, philosophy, linguistics, and computer science. First-order logic uses quantified variables over non-logical objects, and allows the use of sentences that contain variables. Rather than propositions such as "all humans are mortal", in first-order logic one can have expressions in the form "for all x, if x is a human, then x is mortal", where "for all x" is a quantifier, x is a variable, and "... is a human" and "... is mortal" are predicates. This distinguishes it from propositional logic, which does not use quantifiers or relations;: 161 in this sense, propositional logic is the foundation of first-order logic.
A theory about a topic, such as set theory, a theory for groups, or a formal theory of arithmetic, is usually a first-order logic together with a specified domain of discourse (over which the quantified variables range), finitely many functions from that domain to itself, finitely many predicates defined on that domain, and a set of axioms believed to hold about them. "Theory" is sometimes understood in a more formal sense as just a set of sentences in first-order logic.
The term "first-order" distinguishes first-order logic from higher-order logic, in which there are predicates having predicates or functions as arguments, or in which quantification over predicates, functions, or both, are permitted.: 56 In first-order theories, predicates are often associated with sets. In interpreted higher-order theories, predicates may be interpreted as sets of sets.
There are many deductive systems for first-order logic which are both sound, i.e. all provable statements are true in all models; and complete, i.e. all statements which are true in all models are provable. Although the logical consequence relation is only semidecidable, much progress has been made in automated theorem proving in first-order logic. First-order logic also satisfies several metalogical theorems that make it amenable to analysis in proof theory, such as the Löwenheim–Skolem theorem and the compactness theorem.
First-order logic is the standard for the formalization of mathematics into axioms, and is studied in the foundations of mathematics. Peano arithmetic and Zermelo–Fraenkel set theory are axiomatizations of number theory and set theory, respectively, into first-order logic. No first-order theory, however, has the strength to uniquely describe a structure with an infinite domain, such as the natural numbers or the real line. Axiom systems that do fully describe these two structures, i.e. categorical axiom systems, can be obtained in stronger logics such as second-order logic.
The foundations of first-order logic were developed independently by Gottlob Frege and Charles Sanders Peirce. For a history of first-order logic and how it came to dominate formal logic, see José Ferreirós (2001).
== Introduction ==
While propositional logic deals with simple declarative propositions, first-order logic additionally covers predicates and quantification. A predicate evaluates to true or false for an entity or entities in the domain of discourse.
Consider the two sentences "Socrates is a philosopher" and "Plato is a philosopher". In propositional logic, these sentences themselves are viewed as the individuals of study, and might be denoted, for example, by variables such as p and q. They are not viewed as an application of a predicate, such as
isPhil
{\displaystyle {\text{isPhil}}}
, to any particular objects in the domain of discourse, instead viewing them as purely an utterance which is either true or false. However, in first-order logic, these two sentences may be framed as statements that a certain individual or non-logical object has a property. In this example, both sentences happen to have the common form
isPhil
(
x
)
{\displaystyle {\text{isPhil}}(x)}
for some individual
x
{\displaystyle x}
, in the first sentence the value of the variable x is "Socrates", and in the second sentence it is "Plato". Due to the ability to speak about non-logical individuals along with the original logical connectives, first-order logic includes propositional logic.: 29–30
The truth of a formula such as "x is a philosopher" depends on which object is denoted by x and on the interpretation of the predicate "is a philosopher". Consequently, "x is a philosopher" alone does not have a definite truth value of true or false, and is akin to a sentence fragment. Relationships between predicates can be stated using logical connectives. For example, the first-order formula "if x is a philosopher, then x is a scholar", is a conditional statement with "x is a philosopher" as its hypothesis, and "x is a scholar" as its conclusion, which again needs specification of x in order to have a definite truth value.
Quantifiers can be applied to variables in a formula. The variable x in the previous formula can be universally quantified, for instance, with the first-order sentence "For every x, if x is a philosopher, then x is a scholar". The universal quantifier "for every" in this sentence expresses the idea that the claim "if x is a philosopher, then x is a scholar" holds for all choices of x.
The negation of the sentence "For every x, if x is a philosopher, then x is a scholar" is logically equivalent to the sentence "There exists x such that x is a philosopher and x is not a scholar". The existential quantifier "there exists" expresses the idea that the claim "x is a philosopher and x is not a scholar" holds for some choice of x.
The predicates "is a philosopher" and "is a scholar" each take a single variable. In general, predicates can take several variables. In the first-order sentence "Socrates is the teacher of Plato", the predicate "is the teacher of" takes two variables.
An interpretation (or model) of a first-order formula specifies what each predicate means, and the entities that can instantiate the variables. These entities form the domain of discourse or universe, which is usually required to be a nonempty set. For example, consider the sentence "There exists x such that x is a philosopher." This sentence is seen as being true in an interpretation such that the domain of discourse consists of all human beings, and that the predicate "is a philosopher" is understood as "was the author of the Republic." It is true, as witnessed by Plato in that text.
There are two key parts of first-order logic. The syntax determines which finite sequences of symbols are well-formed expressions in first-order logic, while the semantics determines the meanings behind these expressions.
== Syntax ==
Unlike natural languages, such as English, the language of first-order logic is completely formal, so that it can be mechanically determined whether a given expression is well formed. There are two key types of well-formed expressions: terms, which intuitively represent objects, and formulas, which intuitively express statements that can be true or false. The terms and formulas of first-order logic are strings of symbols, where all the symbols together form the alphabet of the language.
=== Alphabet ===
As with all formal languages, the nature of the symbols themselves is outside the scope of formal logic; they are often regarded simply as letters and punctuation symbols.
It is common to divide the symbols of the alphabet into logical symbols, which always have the same meaning, and non-logical symbols, whose meaning varies by interpretation. For example, the logical symbol
∧
{\displaystyle \land }
always represents "and"; it is never interpreted as "or", which is represented by the logical symbol
∨
{\displaystyle \lor }
. However, a non-logical predicate symbol such as Phil(x) could be interpreted to mean "x is a philosopher", "x is a man named Philip", or any other unary predicate depending on the interpretation at hand.
==== Logical symbols ====
Logical symbols are a set of characters that vary by author, but usually include the following:
Quantifier symbols: ∀ for universal quantification, and ∃ for existential quantification
Logical connectives: ∧ for conjunction, ∨ for disjunction, → for implication, ↔ for biconditional, ¬ for negation. Some authors use Cpq instead of → and Epq instead of ↔, especially in contexts where → is used for other purposes. Moreover, the horseshoe ⊃ may replace →; the triple-bar ≡ may replace ↔; a tilde (~), Np, or Fp may replace ¬; a double bar
‖
{\displaystyle \|}
,
+
{\displaystyle +}
, or Apq may replace ∨; and an ampersand &, Kpq, or the middle dot ⋅ may replace ∧, especially if these symbols are not available for technical reasons.
Parentheses, brackets, and other punctuation symbols. The choice of such symbols varies depending on context.
An infinite set of variables, often denoted by lowercase letters at the end of the alphabet x, y, z, ... . Subscripts are often used to distinguish variables: x0, x1, x2, ... .
An equality symbol (sometimes, identity symbol) = (see § Equality and its axioms below).
Not all of these symbols are required in first-order logic. Either one of the quantifiers along with negation, conjunction (or disjunction), variables, brackets, and equality suffices.
Other logical symbols include the following:
Truth constants: T, or ⊤ for "true" and F, or ⊥ for "false". Without any such logical operators of valence 0, these two constants can only be expressed using quantifiers.
Additional logical connectives such as the Sheffer stroke, Dpq (NAND), and exclusive or, Jpq.
==== Non-logical symbols ====
Non-logical symbols represent predicates (relations), functions and constants. It used to be standard practice to use a fixed, infinite set of non-logical symbols for all purposes:
For every integer n ≥ 0, there is a collection of n-ary, or n-place, predicate symbols. Because they represent relations between n elements, they are also called relation symbols. For each arity n, there is an infinite supply of them:
Pn0, Pn1, Pn2, Pn3, ...
For every integer n ≥ 0, there are infinitely many n-ary function symbols:
f n0, f n1, f n2, f n3, ...
When the arity of a predicate symbol or function symbol is clear from context, the superscript n is often omitted.
In this traditional approach, there is only one language of first-order logic. This approach is still common, especially in philosophically oriented books.
A more recent practice is to use different non-logical symbols according to the application one has in mind. Therefore, it has become necessary to name the set of all non-logical symbols used in a particular application. This choice is made via a signature.
Typical signatures in mathematics are {1, ×} or just {×} for groups, or {0, 1, +, ×, <} for ordered fields. There are no restrictions on the number of non-logical symbols. The signature can be empty, finite, or infinite, even uncountable. Uncountable signatures occur for example in modern proofs of the Löwenheim–Skolem theorem.
Though signatures might in some cases imply how non-logical symbols are to be interpreted, interpretation of the non-logical symbols in the signature is separate (and not necessarily fixed). Signatures concern syntax rather than semantics.
In this approach, every non-logical symbol is of one of the following types:
A predicate symbol (or relation symbol) with some valence (or arity, number of arguments) greater than or equal to 0. These are often denoted by uppercase letters such as P, Q and R. Examples:
In P(x), P is a predicate symbol of valence 1. One possible interpretation is "x is a man".
In Q(x,y), Q is a predicate symbol of valence 2. Possible interpretations include "x is greater than y" and "x is the father of y".
Relations of valence 0 can be identified with propositional variables, which can stand for any statement. One possible interpretation of R is "Socrates is a man".
A function symbol, with some valence greater than or equal to 0. These are often denoted by lowercase roman letters such as f, g and h. Examples:
f(x) may be interpreted as "the father of x". In arithmetic, it may stand for "-x". In set theory, it may stand for "the power set of x".
In arithmetic, g(x,y) may stand for "x+y". In set theory, it may stand for "the union of x and y".
Function symbols of valence 0 are called constant symbols, and are often denoted by lowercase letters at the beginning of the alphabet such as a, b and c. The symbol a may stand for Socrates. In arithmetic, it may stand for 0. In set theory, it may stand for the empty set.
The traditional approach can be recovered in the modern approach, by simply specifying the "custom" signature to consist of the traditional sequences of non-logical symbols.
=== Formation rules ===
The formation rules define the terms and formulas of first-order logic. When terms and formulas are represented as strings of symbols, these rules can be used to write a formal grammar for terms and formulas. These rules are generally context-free (each production has a single symbol on the left side), except that the set of symbols may be allowed to be infinite and there may be many start symbols, for example the variables in the case of terms.
==== Terms ====
The set of terms is inductively defined by the following rules:
Variables. Any variable symbol is a term.
Functions. If f is an n-ary function symbol, and t1, ..., tn are terms, then f(t1,...,tn) is a term. In particular, symbols denoting individual constants are nullary function symbols, and thus are terms.
Only expressions which can be obtained by finitely many applications of rules 1 and 2 are terms. For example, no expression involving a predicate symbol is a term.
==== Formulas ====
The set of formulas (also called well-formed formulas or WFFs) is inductively defined by the following rules:
Predicate symbols. If P is an n-ary predicate symbol and t1, ..., tn are terms then P(t1,...,tn) is a formula.
Equality. If the equality symbol is considered part of logic, and t1 and t2 are terms, then t1 = t2 is a formula.
Negation. If
φ
{\displaystyle \varphi }
is a formula, then
¬
φ
{\displaystyle \lnot \varphi }
is a formula.
Binary connectives. If
φ
{\displaystyle \varphi }
and
ψ
{\displaystyle \psi }
are formulas, then (
φ
→
ψ
{\displaystyle \varphi \rightarrow \psi }
) is a formula. Similar rules apply to other binary logical connectives.
Quantifiers. If
φ
{\displaystyle \varphi }
is a formula and x is a variable, then
∀
x
φ
{\displaystyle \forall x\varphi }
(for all x,
φ
{\displaystyle \varphi }
holds) and
∃
x
φ
{\displaystyle \exists x\varphi }
(there exists x such that
φ
{\displaystyle \varphi }
) are formulas.
Only expressions which can be obtained by finitely many applications of rules 1–5 are formulas. The formulas obtained from the first two rules are said to be atomic formulas.
For example:
∀
x
∀
y
(
P
(
f
(
x
)
)
→
¬
(
P
(
x
)
→
Q
(
f
(
y
)
,
x
,
z
)
)
)
{\displaystyle \forall x\forall y(P(f(x))\rightarrow \neg (P(x)\rightarrow Q(f(y),x,z)))}
is a formula, if f is a unary function symbol, P a unary predicate symbol, and Q a ternary predicate symbol. However,
∀
x
x
→
{\displaystyle \forall x\,x\rightarrow }
is not a formula, although it is a string of symbols from the alphabet.
The role of the parentheses in the definition is to ensure that any formula can only be obtained in one way—by following the inductive definition (i.e., there is a unique parse tree for each formula). This property is known as unique readability of formulas. There are many conventions for where parentheses are used in formulas. For example, some authors use colons or full stops instead of parentheses, or change the places in which parentheses are inserted. Each author's particular definition must be accompanied by a proof of unique readability.
==== Notational conventions ====
For convenience, conventions have been developed about the precedence of the logical operators, to avoid the need to write parentheses in some cases. These rules are similar to the order of operations in arithmetic. A common convention is:
¬
{\displaystyle \lnot }
is evaluated first
∧
{\displaystyle \land }
and
∨
{\displaystyle \lor }
are evaluated next
Quantifiers are evaluated next
→
{\displaystyle \to }
is evaluated last.
Moreover, extra punctuation not required by the definition may be inserted—to make formulas easier to read. Thus the formula:
¬
∀
x
P
(
x
)
→
∃
x
¬
P
(
x
)
{\displaystyle \lnot \forall xP(x)\to \exists x\lnot P(x)}
might be written as:
(
¬
[
∀
x
P
(
x
)
]
)
→
∃
x
[
¬
P
(
x
)
]
.
{\displaystyle (\lnot [\forall xP(x)])\to \exists x[\lnot P(x)].}
=== Free and bound variables ===
In a formula, a variable may occur free or bound (or both). One formalization of this notion is due to Quine, first the concept of a variable occurrence is defined, then whether a variable occurrence is free or bound, then whether a variable symbol overall is free or bound. In order to distinguish different occurrences of the identical symbol x, each occurrence of a variable symbol x in a formula φ is identified with the initial substring of φ up to the point at which said instance of the symbol x appears.p.297 Then, an occurrence of x is said to be bound if that occurrence of x lies within the scope of at least one of either
∃
x
{\displaystyle \exists x}
or
∀
x
{\displaystyle \forall x}
. Finally, x is bound in φ if all occurrences of x in φ are bound.pp.142--143
Intuitively, a variable symbol is free in a formula if at no point is it quantified:pp.142--143 in ∀y P(x, y), the sole occurrence of variable x is free while that of y is bound. The free and bound variable occurrences in a formula are defined inductively as follows.
Atomic formulas
If φ is an atomic formula, then x occurs free in φ if and only if x occurs in φ. Moreover, there are no bound variables in any atomic formula.
Negation
x occurs free in ¬φ if and only if x occurs free in φ. x occurs bound in ¬φ if and only if x occurs bound in φ
Binary connectives
x occurs free in (φ → ψ) if and only if x occurs free in either φ or ψ. x occurs bound in (φ → ψ) if and only if x occurs bound in either φ or ψ. The same rule applies to any other binary connective in place of →.
Quantifiers
x occurs free in ∀y φ, if and only if x occurs free in φ and x is a different symbol from y. Also, x occurs bound in ∀y φ, if and only if x is y or x occurs bound in φ. The same rule holds with ∃ in place of ∀.
For example, in ∀x ∀y (P(x) → Q(x,f(x),z)), x and y occur only bound, z occurs only free, and w is neither because it does not occur in the formula.
Free and bound variables of a formula need not be disjoint sets: in the formula P(x) → ∀x Q(x), the first occurrence of x, as argument of P, is free while the second one, as argument of Q, is bound.
A formula in first-order logic with no free variable occurrences is called a first-order sentence. These are the formulas that will have well-defined truth values under an interpretation. For example, whether a formula such as Phil(x) is true must depend on what x represents. But the sentence ∃x Phil(x) will be either true or false in a given interpretation.
=== Example: ordered abelian groups ===
In mathematics, the language of ordered abelian groups has one constant symbol 0, one unary function symbol −, one binary function symbol +, and one binary relation symbol ≤. Then:
The expressions +(x, y) and +(x, +(y, −(z))) are terms. These are usually written as x + y and x + y − z.
The expressions +(x, y) = 0 and ≤(+(x, +(y, −(z))), +(x, y)) are atomic formulas. These are usually written as x + y = 0 and x + y − z ≤ x + y.
The expression
(
∀
x
∀
y
[
≤
(
+
(
x
,
y
)
,
z
)
→
∀
x
∀
y
+
(
x
,
y
)
=
0
)
]
{\displaystyle (\forall x\forall y\,[\mathop {\leq } (\mathop {+} (x,y),z)\to \forall x\,\forall y\,\mathop {+} (x,y)=0)]}
is a formula, which is usually written as
∀
x
∀
y
(
x
+
y
≤
z
)
→
∀
x
∀
y
(
x
+
y
=
0
)
.
{\displaystyle \forall x\forall y(x+y\leq z)\to \forall x\forall y(x+y=0).}
This formula has one free variable, z.
The axioms for ordered abelian groups can be expressed as a set of sentences in the language. For example, the axiom stating that the group is commutative is usually written
(
∀
x
)
(
∀
y
)
[
x
+
y
=
y
+
x
]
.
{\displaystyle (\forall x)(\forall y)[x+y=y+x].}
== Semantics ==
An interpretation of a first-order language assigns a denotation to each non-logical symbol (predicate symbol, function symbol, or constant symbol) in that language. It also determines a domain of discourse that specifies the range of the quantifiers. The result is that each term is assigned an object that it represents, each predicate is assigned a property of objects, and each sentence is assigned a truth value. In this way, an interpretation provides semantic meaning to the terms, predicates, and formulas of the language. The study of the interpretations of formal languages is called formal semantics. What follows is a description of the standard or Tarskian semantics for first-order logic. (It is also possible to define game semantics for first-order logic, but aside from requiring the axiom of choice, game semantics agree with Tarskian semantics for first-order logic, so game semantics will not be elaborated herein.)
=== First-order structures ===
The most common way of specifying an interpretation (especially in mathematics) is to specify a structure (also called a model; see below). The structure consists of a domain of discourse D and an interpretation function I mapping non-logical symbols to predicates, functions, and constants.
The domain of discourse D is a nonempty set of "objects" of some kind. Intuitively, given an interpretation, a first-order formula becomes a statement about these objects; for example,
∃
x
P
(
x
)
{\displaystyle \exists xP(x)}
states the existence of some object in D for which the predicate P is true (or, more precisely, for which the predicate assigned to the predicate symbol P by the interpretation is true). For example, one can take D to be the set of integers.
Non-logical symbols are interpreted as follows:
The interpretation of an n-ary function symbol is a function from Dn to D. For example, if the domain of discourse is the set of integers, a function symbol f of arity 2 can be interpreted as the function that gives the sum of its arguments. In other words, the symbol f is associated with the function
I
(
f
)
{\displaystyle I(f)}
which, in this interpretation, is addition.
The interpretation of a constant symbol (a function symbol of arity 0) is a function from D0 (a set whose only member is the empty tuple) to D, which can be simply identified with an object in D. For example, an interpretation may assign the value
I
(
c
)
=
10
{\displaystyle I(c)=10}
to the constant symbol
c
{\displaystyle c}
.
The interpretation of an n-ary predicate symbol is a set of n-tuples of elements of D, giving the arguments for which the predicate is true. For example, an interpretation
I
(
P
)
{\displaystyle I(P)}
of a binary predicate symbol P may be the set of pairs of integers such that the first one is less than the second. According to this interpretation, the predicate P would be true if its first argument is less than its second argument. Equivalently, predicate symbols may be assigned Boolean-valued functions from Dn to
{
t
r
u
e
,
f
a
l
s
e
}
{\displaystyle \{\mathrm {true,false} \}}
.
=== Evaluation of truth values ===
A formula evaluates to true or false given an interpretation and a variable assignment μ that associates an element of the domain of discourse with each variable. The reason that a variable assignment is required is to give meanings to formulas with free variables, such as
y
=
x
{\displaystyle y=x}
. The truth value of this formula changes depending on the values that x and y denote.
First, the variable assignment μ can be extended to all terms of the language, with the result that each term maps to a single element of the domain of discourse. The following rules are used to make this assignment:
Variables. Each variable x evaluates to μ(x)
Functions. Given terms
t
1
,
…
,
t
n
{\displaystyle t_{1},\ldots ,t_{n}}
that have been evaluated to elements
d
1
,
…
,
d
n
{\displaystyle d_{1},\ldots ,d_{n}}
of the domain of discourse, and a n-ary function symbol f, the term
f
(
t
1
,
…
,
t
n
)
{\displaystyle f(t_{1},\ldots ,t_{n})}
evaluates to
(
I
(
f
)
)
(
d
1
,
…
,
d
n
)
{\displaystyle (I(f))(d_{1},\ldots ,d_{n})}
.
Next, each formula is assigned a truth value. The inductive definition used to make this assignment is called the T-schema.
Atomic formulas (1). A formula
P
(
t
1
,
…
,
t
n
)
{\displaystyle P(t_{1},\ldots ,t_{n})}
is associated the value true or false depending on whether
⟨
v
1
,
…
,
v
n
⟩
∈
I
(
P
)
{\displaystyle \langle v_{1},\ldots ,v_{n}\rangle \in I(P)}
, where
v
1
,
…
,
v
n
{\displaystyle v_{1},\ldots ,v_{n}}
are the evaluation of the terms
t
1
,
…
,
t
n
{\displaystyle t_{1},\ldots ,t_{n}}
and
I
(
P
)
{\displaystyle I(P)}
is the interpretation of
P
{\displaystyle P}
, which by assumption is a subset of
D
n
{\displaystyle D^{n}}
.
Atomic formulas (2). A formula
t
1
=
t
2
{\displaystyle t_{1}=t_{2}}
is assigned true if
t
1
{\displaystyle t_{1}}
and
t
2
{\displaystyle t_{2}}
evaluate to the same object of the domain of discourse (see the section on equality below).
Logical connectives. A formula in the form
¬
φ
{\displaystyle \neg \varphi }
,
φ
→
ψ
{\displaystyle \varphi \rightarrow \psi }
, etc. is evaluated according to the truth table for the connective in question, as in propositional logic.
Existential quantifiers. A formula
∃
x
φ
(
x
)
{\displaystyle \exists x\varphi (x)}
is true according to M and
μ
{\displaystyle \mu }
if there exists an evaluation
μ
′
{\displaystyle \mu '}
of the variables that differs from
μ
{\displaystyle \mu }
at most regarding the evaluation of x and such that φ is true according to the interpretation M and the variable assignment
μ
′
{\displaystyle \mu '}
. This formal definition captures the idea that
∃
x
φ
(
x
)
{\displaystyle \exists x\varphi (x)}
is true if and only if there is a way to choose a value for x such that φ(x) is satisfied.
Universal quantifiers. A formula
∀
x
φ
(
x
)
{\displaystyle \forall x\varphi (x)}
is true according to M and
μ
{\displaystyle \mu }
if φ(x) is true for every pair composed by the interpretation M and some variable assignment
μ
′
{\displaystyle \mu '}
that differs from
μ
{\displaystyle \mu }
at most on the value of x. This captures the idea that
∀
x
φ
(
x
)
{\displaystyle \forall x\varphi (x)}
is true if every possible choice of a value for x causes φ(x) to be true.
If a formula does not contain free variables, and so is a sentence, then the initial variable assignment does not affect its truth value. In other words, a sentence is true according to M and
μ
{\displaystyle \mu }
if and only if it is true according to M and every other variable assignment
μ
′
{\displaystyle \mu '}
.
There is a second common approach to defining truth values that does not rely on variable assignment functions. Instead, given an interpretation M, one first adds to the signature a collection of constant symbols, one for each element of the domain of discourse in M; say that for each d in the domain the constant symbol cd is fixed. The interpretation is extended so that each new constant symbol is assigned to its corresponding element of the domain. One now defines truth for quantified formulas syntactically, as follows:
Existential quantifiers (alternate). A formula
∃
x
φ
(
x
)
{\displaystyle \exists x\varphi (x)}
is true according to M if there is some d in the domain of discourse such that
φ
(
c
d
)
{\displaystyle \varphi (c_{d})}
holds. Here
φ
(
c
d
)
{\displaystyle \varphi (c_{d})}
is the result of substituting cd for every free occurrence of x in φ.
Universal quantifiers (alternate). A formula
∀
x
φ
(
x
)
{\displaystyle \forall x\varphi (x)}
is true according to M if, for every d in the domain of discourse,
φ
(
c
d
)
{\displaystyle \varphi (c_{d})}
is true according to M.
This alternate approach gives exactly the same truth values to all sentences as the approach via variable assignments.
=== Validity, satisfiability, and logical consequence ===
If a sentence φ evaluates to true under a given interpretation M, one says that M satisfies φ; this is denoted
M
⊨
φ
{\displaystyle M\vDash \varphi }
. A sentence is satisfiable if there is some interpretation under which it is true. This is a bit different from the symbol
⊨
{\displaystyle \vDash }
from model theory, where
M
⊨
ϕ
{\displaystyle M\vDash \phi }
denotes satisfiability in a model, i.e. "there is a suitable assignment of values in
M
{\displaystyle M}
's domain to variable symbols of
ϕ
{\displaystyle \phi }
".
Satisfiability of formulas with free variables is more complicated, because an interpretation on its own does not determine the truth value of such a formula. The most common convention is that a formula φ with free variables
x
1
{\displaystyle x_{1}}
, ...,
x
n
{\displaystyle x_{n}}
is said to be satisfied by an interpretation if the formula φ remains true regardless which individuals from the domain of discourse are assigned to its free variables
x
1
{\displaystyle x_{1}}
, ...,
x
n
{\displaystyle x_{n}}
. This has the same effect as saying that a formula φ is satisfied if and only if its universal closure
∀
x
1
…
∀
x
n
ϕ
(
x
1
,
…
,
x
n
)
{\displaystyle \forall x_{1}\dots \forall x_{n}\phi (x_{1},\dots ,x_{n})}
is satisfied.
A formula is logically valid (or simply valid) if it is true in every interpretation. These formulas play a role similar to tautologies in propositional logic.
A formula φ is a logical consequence of a formula ψ if every interpretation that makes ψ true also makes φ true. In this case one says that φ is logically implied by ψ.
=== Algebraizations ===
An alternate approach to the semantics of first-order logic proceeds via abstract algebra. This approach generalizes the Lindenbaum–Tarski algebras of propositional logic. There are three ways of eliminating quantified variables from first-order logic that do not involve replacing quantifiers with other variable binding term operators:
Cylindric algebra, by Alfred Tarski, et al.;
Polyadic algebra, by Paul Halmos;
Predicate functor logic, primarily by Willard Quine.
These algebras are all lattices that properly extend the two-element Boolean algebra.
Tarski and Givant (1987) showed that the fragment of first-order logic that has no atomic sentence lying in the scope of more than three quantifiers has the same expressive power as relation algebra.: 32–33 This fragment is of great interest because it suffices for Peano arithmetic and most axiomatic set theory, including the canonical Zermelo–Fraenkel set theory (ZFC). They also prove that first-order logic with a primitive ordered pair is equivalent to a relation algebra with two ordered pair projection functions.: 803
=== First-order theories, models, and elementary classes ===
A first-order theory of a particular signature is a set of axioms, which are sentences consisting of symbols from that signature. The set of axioms is often finite or recursively enumerable, in which case the theory is called effective. Some authors require theories to also include all logical consequences of the axioms. The axioms are considered to hold within the theory and from them other sentences that hold within the theory can be derived.
A first-order structure that satisfies all sentences in a given theory is said to be a model of the theory. An elementary class is the set of all structures satisfying a particular theory. These classes are a main subject of study in model theory.
Many theories have an intended interpretation, a certain model that is kept in mind when studying the theory. For example, the intended interpretation of Peano arithmetic consists of the usual natural numbers with their usual operations. However, the Löwenheim–Skolem theorem shows that most first-order theories will also have other, nonstandard models.
A theory is consistent (within a deductive system) if it is not possible to prove a contradiction from the axioms of the theory. A theory is complete if, for every formula in its signature, either that formula or its negation is a logical consequence of the axioms of the theory. Gödel's incompleteness theorem shows that effective first-order theories that include a sufficient portion of the theory of the natural numbers can never be both consistent and complete.
=== Empty domains ===
The definition above requires that the domain of discourse of any interpretation must be nonempty. There are settings, such as inclusive logic, where empty domains are permitted. Moreover, if a class of algebraic structures includes an empty structure (for example, there is an empty poset), that class can only be an elementary class in first-order logic if empty domains are permitted or the empty structure is removed from the class.
There are several difficulties with empty domains, however:
Many common rules of inference are valid only when the domain of discourse is required to be nonempty. One example is the rule stating that
φ
∨
∃
x
ψ
{\displaystyle \varphi \lor \exists x\psi }
implies
∃
x
(
φ
∨
ψ
)
{\displaystyle \exists x(\varphi \lor \psi )}
when x is not a free variable in
φ
{\displaystyle \varphi }
. This rule, which is used to put formulas into prenex normal form, is sound in nonempty domains, but unsound if the empty domain is permitted.
The definition of truth in an interpretation that uses a variable assignment function cannot work with empty domains, because there are no variable assignment functions whose range is empty. (Similarly, one cannot assign interpretations to constant symbols.) This truth definition requires that one must select a variable assignment function (μ above) before truth values for even atomic formulas can be defined. Then the truth value of a sentence is defined to be its truth value under any variable assignment, and it is proved that this truth value does not depend on which assignment is chosen. This technique does not work if there are no assignment functions at all; it must be changed to accommodate empty domains.
Thus, when the empty domain is permitted, it must often be treated as a special case. Most authors, however, simply exclude the empty domain by definition.
== Deductive systems ==
A deductive system is used to demonstrate, on a purely syntactic basis, that one formula is a logical consequence of another formula. There are many such systems for first-order logic, including Hilbert-style deductive systems, natural deduction, the sequent calculus, the tableaux method, and resolution. These share the common property that a deduction is a finite syntactic object; the format of this object, and the way it is constructed, vary widely. These finite deductions themselves are often called derivations in proof theory. They are also often called proofs but are completely formalized unlike natural-language mathematical proofs.
A deductive system is sound if any formula that can be derived in the system is logically valid. Conversely, a deductive system is complete if every logically valid formula is derivable. All of the systems discussed in this article are both sound and complete. They also share the property that it is possible to effectively verify that a purportedly valid deduction is actually a deduction; such deduction systems are called effective.
A key property of deductive systems is that they are purely syntactic, so that derivations can be verified without considering any interpretation. Thus, a sound argument is correct in every possible interpretation of the language, regardless of whether that interpretation is about mathematics, economics, or some other area.
In general, logical consequence in first-order logic is only semidecidable: if a sentence A logically implies a sentence B then this can be discovered (for example, by searching for a proof until one is found, using some effective, sound, complete proof system). However, if A does not logically imply B, this does not mean that A logically implies the negation of B. There is no effective procedure that, given formulas A and B, always correctly decides whether A logically implies B.
=== Rules of inference ===
A rule of inference states that, given a particular formula (or set of formulas) with a certain property as a hypothesis, another specific formula (or set of formulas) can be derived as a conclusion. The rule is sound (or truth-preserving) if it preserves validity in the sense that whenever any interpretation satisfies the hypothesis, that interpretation also satisfies the conclusion.
For example, one common rule of inference is the rule of substitution. If t is a term and φ is a formula possibly containing the variable x, then φ[t/x] is the result of replacing all free instances of x by t in φ. The substitution rule states that for any φ and any term t, one can conclude φ[t/x] from φ provided that no free variable of t becomes bound during the substitution process. (If some free variable of t becomes bound, then to substitute t for x it is first necessary to change the bound variables of φ to differ from the free variables of t.)
To see why the restriction on bound variables is necessary, consider the logically valid formula φ given by
∃
x
(
x
=
y
)
{\displaystyle \exists x(x=y)}
, in the signature of (0,1,+,×,=) of arithmetic. If t is the term "x + 1", the formula φ[t/y] is
∃
x
(
x
=
x
+
1
)
{\displaystyle \exists x(x=x+1)}
, which will be false in many interpretations. The problem is that the free variable x of t became bound during the substitution. The intended replacement can be obtained by renaming the bound variable x of φ to something else, say z, so that the formula after substitution is
∃
z
(
z
=
x
+
1
)
{\displaystyle \exists z(z=x+1)}
, which is again logically valid.
The substitution rule demonstrates several common aspects of rules of inference. It is entirely syntactical; one can tell whether it was correctly applied without appeal to any interpretation. It has (syntactically defined) limitations on when it can be applied, which must be respected to preserve the correctness of derivations. Moreover, as is often the case, these limitations are necessary because of interactions between free and bound variables that occur during syntactic manipulations of the formulas involved in the inference rule.
=== Hilbert-style systems and natural deduction ===
A deduction in a Hilbert-style deductive system is a list of formulas, each of which is a logical axiom, a hypothesis that has been assumed for the derivation at hand or follows from previous formulas via a rule of inference. The logical axioms consist of several axiom schemas of logically valid formulas; these encompass a significant amount of propositional logic. The rules of inference enable the manipulation of quantifiers. Typical Hilbert-style systems have a small number of rules of inference, along with several infinite schemas of logical axioms. It is common to have only modus ponens and universal generalization as rules of inference.
Natural deduction systems resemble Hilbert-style systems in that a deduction is a finite list of formulas. However, natural deduction systems have no logical axioms; they compensate by adding additional rules of inference that can be used to manipulate the logical connectives in formulas in the proof.
=== Sequent calculus ===
The sequent calculus was developed to study the properties of natural deduction systems. Instead of working with one formula at a time, it uses sequents, which are expressions of the form:
A
1
,
…
,
A
n
⊢
B
1
,
…
,
B
k
,
{\displaystyle A_{1},\ldots ,A_{n}\vdash B_{1},\ldots ,B_{k},}
where A1, ..., An, B1, ..., Bk are formulas and the turnstile symbol
⊢
{\displaystyle \vdash }
is used as punctuation to separate the two halves. Intuitively, a sequent expresses the idea that
(
A
1
∧
⋯
∧
A
n
)
{\displaystyle (A_{1}\land \cdots \land A_{n})}
implies
(
B
1
∨
⋯
∨
B
k
)
{\displaystyle (B_{1}\lor \cdots \lor B_{k})}
.
=== Tableaux method ===
Unlike the methods just described the derivations in the tableaux method are not lists of formulas. Instead, a derivation is a tree of formulas. To show that a formula A is provable, the tableaux method attempts to demonstrate that the negation of A is unsatisfiable. The tree of the derivation has
¬
A
{\displaystyle \lnot A}
at its root; the tree branches in a way that reflects the structure of the formula. For example, to show that
C
∨
D
{\displaystyle C\lor D}
is unsatisfiable requires showing that C and D are each unsatisfiable; this corresponds to a branching point in the tree with parent
C
∨
D
{\displaystyle C\lor D}
and children C and D.
=== Resolution ===
The resolution rule is a single rule of inference that, together with unification, is sound and complete for first-order logic. As with the tableaux method, a formula is proved by showing that the negation of the formula is unsatisfiable. Resolution is commonly used in automated theorem proving.
The resolution method works only with formulas that are disjunctions of atomic formulas; arbitrary formulas must first be converted to this form through Skolemization. The resolution rule states that from the hypotheses
A
1
∨
⋯
∨
A
k
∨
C
{\displaystyle A_{1}\lor \cdots \lor A_{k}\lor C}
and
B
1
∨
⋯
∨
B
l
∨
¬
C
{\displaystyle B_{1}\lor \cdots \lor B_{l}\lor \lnot C}
, the conclusion
A
1
∨
⋯
∨
A
k
∨
B
1
∨
⋯
∨
B
l
{\displaystyle A_{1}\lor \cdots \lor A_{k}\lor B_{1}\lor \cdots \lor B_{l}}
can be obtained.
=== Provable identities ===
Many identities can be proved, which establish equivalences between particular formulas. These identities allow for rearranging formulas by moving quantifiers across other connectives and are useful for putting formulas in prenex normal form. Some provable identities include:
¬
∀
x
P
(
x
)
⇔
∃
x
¬
P
(
x
)
{\displaystyle \lnot \forall x\,P(x)\Leftrightarrow \exists x\,\lnot P(x)}
¬
∃
x
P
(
x
)
⇔
∀
x
¬
P
(
x
)
{\displaystyle \lnot \exists x\,P(x)\Leftrightarrow \forall x\,\lnot P(x)}
∀
x
∀
y
P
(
x
,
y
)
⇔
∀
y
∀
x
P
(
x
,
y
)
{\displaystyle \forall x\,\forall y\,P(x,y)\Leftrightarrow \forall y\,\forall x\,P(x,y)}
∃
x
∃
y
P
(
x
,
y
)
⇔
∃
y
∃
x
P
(
x
,
y
)
{\displaystyle \exists x\,\exists y\,P(x,y)\Leftrightarrow \exists y\,\exists x\,P(x,y)}
∀
x
P
(
x
)
∧
∀
x
Q
(
x
)
⇔
∀
x
(
P
(
x
)
∧
Q
(
x
)
)
{\displaystyle \forall x\,P(x)\land \forall x\,Q(x)\Leftrightarrow \forall x\,(P(x)\land Q(x))}
∃
x
P
(
x
)
∨
∃
x
Q
(
x
)
⇔
∃
x
(
P
(
x
)
∨
Q
(
x
)
)
{\displaystyle \exists x\,P(x)\lor \exists x\,Q(x)\Leftrightarrow \exists x\,(P(x)\lor Q(x))}
P
∧
∃
x
Q
(
x
)
⇔
∃
x
(
P
∧
Q
(
x
)
)
{\displaystyle P\land \exists x\,Q(x)\Leftrightarrow \exists x\,(P\land Q(x))}
(where
x
{\displaystyle x}
must not occur free in
P
{\displaystyle P}
)
P
∨
∀
x
Q
(
x
)
⇔
∀
x
(
P
∨
Q
(
x
)
)
{\displaystyle P\lor \forall x\,Q(x)\Leftrightarrow \forall x\,(P\lor Q(x))}
(where
x
{\displaystyle x}
must not occur free in
P
{\displaystyle P}
)
== Equality and its axioms ==
There are several different conventions for using equality (or identity) in first-order logic. The most common convention, known as first-order logic with equality, includes the equality symbol as a primitive logical symbol which is always interpreted as the real equality relation between members of the domain of discourse, such that the "two" given members are the same member. This approach also adds certain axioms about equality to the deductive system employed. These equality axioms are:: 198–200
Reflexivity. For each variable x, x = x.
Substitution for functions. For all variables x and y, and any function symbol f,
x = y → f(..., x, ...) = f(..., y, ...).
Substitution for formulas. For any variables x and y and any formula φ(z) with a free variable z, then:
x = y → (φ(x) → φ(y)).
These are axiom schemas, each of which specifies an infinite set of axioms. The third schema is known as Leibniz's law, "the principle of substitutivity", "the indiscernibility of identicals", or "the replacement property". The second schema, involving the function symbol f, is (equivalent to) a special case of the third schema, using the formula:
φ(z): f(..., x, ...) = f(..., z, ...)
Then
x = y → (f(..., x, ...) = f(..., x, ...) → f(..., x, ...) = f(..., y, ...)).
Since x = y is given, and f(..., x, ...) = f(..., x, ...) true by reflexivity, we have f(..., x, ...) = f(..., y, ...)
Many other properties of equality are consequences of the axioms above, for example:
Symmetry. If x = y then y = x.
Transitivity. If x = y and y = z then x = z.
=== First-order logic without equality ===
An alternate approach considers the equality relation to be a non-logical symbol. This convention is known as first-order logic without equality. If an equality relation is included in the signature, the axioms of equality must now be added to the theories under consideration, if desired, instead of being considered rules of logic. The main difference between this method and first-order logic with equality is that an interpretation may now interpret two distinct individuals as "equal" (although, by Leibniz's law, these will satisfy exactly the same formulas under any interpretation). That is, the equality relation may now be interpreted by an arbitrary equivalence relation on the domain of discourse that is congruent with respect to the functions and relations of the interpretation.
When this second convention is followed, the term normal model is used to refer to an interpretation where no distinct individuals a and b satisfy a = b. In first-order logic with equality, only normal models are considered, and so there is no term for a model other than a normal model. When first-order logic without equality is studied, it is necessary to amend the statements of results such as the Löwenheim–Skolem theorem so that only normal models are considered.
First-order logic without equality is often employed in the context of second-order arithmetic and other higher-order theories of arithmetic, where the equality relation between sets of natural numbers is usually omitted.
=== Defining equality within a theory ===
If a theory has a binary formula A(x,y) which satisfies reflexivity and Leibniz's law, the theory is said to have equality, or to be a theory with equality. The theory may not have all instances of the above schemas as axioms, but rather as derivable theorems. For example, in theories with no function symbols and a finite number of relations, it is possible to define equality in terms of the relations, by defining the two terms s and t to be equal if any relation is unchanged by changing s to t in any argument.
Some theories allow other ad hoc definitions of equality:
In the theory of partial orders with one relation symbol ≤, one could define s = t to be an abbreviation for s ≤ t
∧
{\displaystyle \wedge }
t ≤ s.
In set theory with one relation ∈, one may define s = t to be an abbreviation for ∀x (s ∈ x ↔ t ∈ x)
∧
{\displaystyle \wedge }
∀x (x ∈ s ↔ x ∈ t). This definition of equality then automatically satisfies the axioms for equality. In this case, one should replace the usual axiom of extensionality, which can be stated as
∀
x
∀
y
[
∀
z
(
z
∈
x
⇔
z
∈
y
)
⇒
x
=
y
]
{\displaystyle \forall x\forall y[\forall z(z\in x\Leftrightarrow z\in y)\Rightarrow x=y]}
, with an alternative formulation
∀
x
∀
y
[
∀
z
(
z
∈
x
⇔
z
∈
y
)
⇒
∀
z
(
x
∈
z
⇔
y
∈
z
)
]
{\displaystyle \forall x\forall y[\forall z(z\in x\Leftrightarrow z\in y)\Rightarrow \forall z(x\in z\Leftrightarrow y\in z)]}
, which says that if sets x and y have the same elements, then they also belong to the same sets.
== Metalogical properties ==
One motivation for the use of first-order logic, rather than higher-order logic, is that first-order logic has many metalogical properties that stronger logics do not have. These results concern general properties of first-order logic itself, rather than properties of individual theories. They provide fundamental tools for the construction of models of first-order theories.
=== Completeness and undecidability ===
Gödel's completeness theorem, proved by Kurt Gödel in 1929, establishes that there are sound, complete, effective deductive systems for first-order logic, and thus the first-order logical consequence relation is captured by finite provability. Naively, the statement that a formula φ logically implies a formula ψ depends on every model of φ; these models will in general be of arbitrarily large cardinality, and so logical consequence cannot be effectively verified by checking every model. However, it is possible to enumerate all finite derivations and search for a derivation of ψ from φ. If ψ is logically implied by φ, such a derivation will eventually be found. Thus first-order logical consequence is semidecidable: it is possible to make an effective enumeration of all pairs of sentences (φ,ψ) such that ψ is a logical consequence of φ.
Unlike propositional logic, first-order logic is undecidable (although semidecidable), provided that the language has at least one predicate of arity at least 2 (other than equality). This means that there is no decision procedure that determines whether arbitrary formulas are logically valid. This result was established independently by Alonzo Church and Alan Turing in 1936 and 1937, respectively, giving a negative answer to the Entscheidungsproblem posed by David Hilbert and Wilhelm Ackermann in 1928. Their proofs demonstrate a connection between the unsolvability of the decision problem for first-order logic and the unsolvability of the halting problem.
There are systems weaker than full first-order logic for which the logical consequence relation is decidable. These include propositional logic and monadic predicate logic, which is first-order logic restricted to unary predicate symbols and no function symbols. Other logics with no function symbols which are decidable are the guarded fragment of first-order logic, as well as two-variable logic. The Bernays–Schönfinkel class of first-order formulas is also decidable. Decidable subsets of first-order logic are also studied in the framework of description logics.
=== The Löwenheim–Skolem theorem ===
The Löwenheim–Skolem theorem shows that if a first-order theory of cardinality λ has an infinite model, then it has models of every infinite cardinality greater than or equal to λ. One of the earliest results in model theory, it implies that it is not possible to characterize countability or uncountability in a first-order language with a countable signature. That is, there is no first-order formula φ(x) such that an arbitrary structure M satisfies φ if and only if the domain of discourse of M is countable (or, in the second case, uncountable).
The Löwenheim–Skolem theorem implies that infinite structures cannot be categorically axiomatized in first-order logic. For example, there is no first-order theory whose only model is the real line: any first-order theory with an infinite model also has a model of cardinality larger than the continuum. Since the real line is infinite, any theory satisfied by the real line is also satisfied by some nonstandard models. When the Löwenheim–Skolem theorem is applied to first-order set theories, the nonintuitive consequences are known as Skolem's paradox.
=== The compactness theorem ===
The compactness theorem states that a set of first-order sentences has a model if and only if every finite subset of it has a model. This implies that if a formula is a logical consequence of an infinite set of first-order axioms, then it is a logical consequence of some finite number of those axioms. This theorem was proved first by Kurt Gödel as a consequence of the completeness theorem, but many additional proofs have been obtained over time. It is a central tool in model theory, providing a fundamental method for constructing models.
The compactness theorem has a limiting effect on which collections of first-order structures are elementary classes. For example, the compactness theorem implies that any theory that has arbitrarily large finite models has an infinite model. Thus, the class of all finite graphs is not an elementary class (the same holds for many other algebraic structures).
There are also more subtle limitations of first-order logic that are implied by the compactness theorem. For example, in computer science, many situations can be modeled as a directed graph of states (nodes) and connections (directed edges). Validating such a system may require showing that no "bad" state can be reached from any "good" state. Thus, one seeks to determine if the good and bad states are in different connected components of the graph. However, the compactness theorem can be used to show that connected graphs are not an elementary class in first-order logic, and there is no formula φ(x,y) of first-order logic, in the logic of graphs, that expresses the idea that there is a path from x to y. Connectedness can be expressed in second-order logic, however, but not with only existential set quantifiers, as
Σ
1
1
{\displaystyle \Sigma _{1}^{1}}
also enjoys compactness.
=== Lindström's theorem ===
Per Lindström showed that the metalogical properties just discussed actually characterize first-order logic in the sense that no stronger logic can also have those properties (Ebbinghaus and Flum 1994, Chapter XIII). Lindström defined a class of abstract logical systems, and a rigorous definition of the relative strength of a member of this class. He established two theorems for systems of this type:
A logical system satisfying Lindström's definition that contains first-order logic and satisfies both the Löwenheim–Skolem theorem and the compactness theorem must be equivalent to first-order logic.
A logical system satisfying Lindström's definition that has a semidecidable logical consequence relation and satisfies the Löwenheim–Skolem theorem must be equivalent to first-order logic.
== Limitations ==
Although first-order logic is sufficient for formalizing much of mathematics and is commonly used in computer science and other fields, it has certain limitations. These include limitations on its expressiveness and limitations of the fragments of natural languages that it can describe.
For instance, first-order logic is undecidable, meaning a sound, complete and terminating decision algorithm for provability is impossible. This has led to the study of interesting decidable fragments, such as C2: first-order logic with two variables and the counting quantifiers
∃
≥
n
{\displaystyle \exists ^{\geq n}}
and
∃
≤
n
{\displaystyle \exists ^{\leq n}}
.
=== Expressiveness ===
The Löwenheim–Skolem theorem shows that if a first-order theory has any infinite model, then it has infinite models of every cardinality. In particular, no first-order theory with an infinite model can be categorical. Thus, there is no first-order theory whose only model has the set of natural numbers as its domain, or whose only model has the set of real numbers as its domain. Many extensions of first-order logic, including infinitary logics and higher-order logics, are more expressive in the sense that they do permit categorical axiomatizations of the natural numbers or real numbers. This expressiveness comes at a metalogical cost, however: by Lindström's theorem, the compactness theorem and the downward Löwenheim–Skolem theorem cannot hold in any logic stronger than first-order.
=== Formalizing natural languages ===
First-order logic is able to formalize many simple quantifier constructions in natural language, such as "every person who lives in Perth lives in Australia". Hence, first-order logic is used as a basis for knowledge representation languages, such as FO(.).
Still, there are complicated features of natural language that cannot be expressed in first-order logic. "Any logical system which is appropriate as an instrument for the analysis of natural language needs a much richer structure than first-order predicate logic".
== Restrictions, extensions, and variations ==
There are many variations of first-order logic. Some of these are inessential in the sense that they merely change notation without affecting the semantics. Others change the expressive power more significantly, by extending the semantics through additional quantifiers or other new logical symbols. For example, infinitary logics permit formulas of infinite size, and modal logics add symbols for possibility and necessity.
=== Restricted languages ===
First-order logic can be studied in languages with fewer logical symbols than were described above:
Because
∃
x
φ
(
x
)
{\displaystyle \exists x\varphi (x)}
can be expressed as
¬
∀
x
¬
φ
(
x
)
{\displaystyle \neg \forall x\neg \varphi (x)}
, and
∀
x
φ
(
x
)
{\displaystyle \forall x\varphi (x)}
can be expressed as
¬
∃
x
¬
φ
(
x
)
{\displaystyle \neg \exists x\neg \varphi (x)}
, either of the two quantifiers
∃
{\displaystyle \exists }
and
∀
{\displaystyle \forall }
can be dropped.
Since
φ
∨
ψ
{\displaystyle \varphi \lor \psi }
can be expressed as
¬
(
¬
φ
∧
¬
ψ
)
{\displaystyle \lnot (\lnot \varphi \land \lnot \psi )}
and
φ
∧
ψ
{\displaystyle \varphi \land \psi }
can be expressed as
¬
(
¬
φ
∨
¬
ψ
)
{\displaystyle \lnot (\lnot \varphi \lor \lnot \psi )}
, either
∨
{\displaystyle \vee }
or
∧
{\displaystyle \wedge }
can be dropped. In other words, it is sufficient to have
¬
{\displaystyle \neg }
and
∨
{\displaystyle \vee }
, or
¬
{\displaystyle \neg }
and
∧
{\displaystyle \wedge }
, as the only logical connectives.
Similarly, it is sufficient to have only
¬
{\displaystyle \neg }
and
→
{\displaystyle \rightarrow }
as logical connectives, or to have only the Sheffer stroke (NAND) or the Peirce arrow (NOR) operator.
It is possible to entirely avoid function symbols and constant symbols, rewriting them via predicate symbols in an appropriate way. For example, instead of using a constant symbol
0
{\displaystyle \;0}
one may use a predicate
0
(
x
)
{\displaystyle \;0(x)}
(interpreted as
x
=
0
{\displaystyle \;x=0}
) and replace every predicate such as
P
(
0
,
y
)
{\displaystyle \;P(0,y)}
with
∀
x
(
0
(
x
)
→
P
(
x
,
y
)
)
{\displaystyle \forall x\;(0(x)\rightarrow P(x,y))}
. A function such as
f
(
x
1
,
x
2
,
.
.
.
,
x
n
)
{\displaystyle f(x_{1},x_{2},...,x_{n})}
will similarly be replaced by a predicate
F
(
x
1
,
x
2
,
.
.
.
,
x
n
,
y
)
{\displaystyle F(x_{1},x_{2},...,x_{n},y)}
interpreted as
y
=
f
(
x
1
,
x
2
,
.
.
.
,
x
n
)
{\displaystyle y=f(x_{1},x_{2},...,x_{n})}
. This change requires adding additional axioms to the theory at hand, so that interpretations of the predicate symbols used have the correct semantics.
Restrictions such as these are useful as a technique to reduce the number of inference rules or axiom schemas in deductive systems, which leads to shorter proofs of metalogical results. The cost of the restrictions is that it becomes more difficult to express natural-language statements in the formal system at hand, because the logical connectives used in the natural language statements must be replaced by their (longer) definitions in terms of the restricted collection of logical connectives. Similarly, derivations in the limited systems may be longer than derivations in systems that include additional connectives. There is thus a trade-off between the ease of working within the formal system and the ease of proving results about the formal system.
It is also possible to restrict the arities of function symbols and predicate symbols, in sufficiently expressive theories. One can in principle dispense entirely with functions of arity greater than 2 and predicates of arity greater than 1 in theories that include a pairing function. This is a function of arity 2 that takes pairs of elements of the domain and returns an ordered pair containing them. It is also sufficient to have two predicate symbols of arity 2 that define projection functions from an ordered pair to its components. In either case it is necessary that the natural axioms for a pairing function and its projections are satisfied.
=== Many-sorted logic ===
Ordinary first-order interpretations have a single domain of discourse over which all quantifiers range. Many-sorted first-order logic allows variables to have different sorts, which have different domains. This is also called typed first-order logic, and the sorts called types (as in data type), but it is not the same as first-order type theory. Many-sorted first-order logic is often used in the study of second-order arithmetic.
When there are only finitely many sorts in a theory, many-sorted first-order logic can be reduced to single-sorted first-order logic.: 296–299
One introduces into the single-sorted theory a unary predicate symbol for each sort in the many-sorted theory and adds an axiom saying that these unary predicates partition the domain of discourse. For example, if there are two sorts, one adds predicate symbols
P
1
(
x
)
{\displaystyle P_{1}(x)}
and
P
2
(
x
)
{\displaystyle P_{2}(x)}
and the axiom:
∀
x
(
P
1
(
x
)
∨
P
2
(
x
)
)
∧
¬
∃
x
(
P
1
(
x
)
∧
P
2
(
x
)
)
{\displaystyle \forall x(P_{1}(x)\lor P_{2}(x))\land \lnot \exists x(P_{1}(x)\land P_{2}(x))}
.
Then the elements satisfying
P
1
{\displaystyle P_{1}}
are thought of as elements of the first sort, and elements satisfying
P
2
{\displaystyle P_{2}}
as elements of the second sort. One can quantify over each sort by using the corresponding predicate symbol to limit the range of quantification. For example, to say there is an element of the first sort satisfying formula
φ
(
x
)
{\displaystyle \varphi (x)}
, one writes:
∃
x
(
P
1
(
x
)
∧
φ
(
x
)
)
{\displaystyle \exists x(P_{1}(x)\land \varphi (x))}
.
=== Additional quantifiers ===
Additional quantifiers can be added to first-order logic.
Sometimes it is useful to say that "P(x) holds for exactly one x", which can be expressed as ∃!x P(x). This notation, called uniqueness quantification, may be taken to abbreviate a formula such as ∃x (P(x)
∧
{\displaystyle \wedge }
∀y (P(y) → (x = y))).
First-order logic with extra quantifiers has new quantifiers Qx,..., with meanings such as "there are many x such that ...". Also see branching quantifiers and the plural quantifiers of George Boolos and others.
Bounded quantifiers are often used in the study of set theory or arithmetic.
=== Infinitary logics ===
Infinitary logic allows infinitely long sentences. For example, one may allow a conjunction or disjunction of infinitely many formulas, or quantification over infinitely many variables. Infinitely long sentences arise in areas of mathematics including topology and model theory.
Infinitary logic generalizes first-order logic to allow formulas of infinite length. The most common way in which formulas can become infinite is through infinite conjunctions and disjunctions. However, it is also possible to admit generalized signatures in which function and relation symbols are allowed to have infinite arities, or in which quantifiers can bind infinitely many variables. Because an infinite formula cannot be represented by a finite string, it is necessary to choose some other representation of formulas; the usual representation in this context is a tree. Thus, formulas are, essentially, identified with their parse trees, rather than with the strings being parsed.
The most commonly studied infinitary logics are denoted Lαβ, where α and β are each either cardinal numbers or the symbol ∞. In this notation, ordinary first-order logic is Lωω.
In the logic L∞ω, arbitrary conjunctions or disjunctions are allowed when building formulas, and there is an unlimited supply of variables. More generally, the logic that permits conjunctions or disjunctions with less than κ constituents is known as Lκω. For example, Lω1ω permits countable conjunctions and disjunctions.
The set of free variables in a formula of Lκω can have any cardinality strictly less than κ, yet only finitely many of them can be in the scope of any quantifier when a formula appears as a subformula of another. In other infinitary logics, a subformula may be in the scope of infinitely many quantifiers. For example, in Lκ∞, a single universal or existential quantifier may bind arbitrarily many variables simultaneously. Similarly, the logic Lκλ permits simultaneous quantification over fewer than λ variables, as well as conjunctions and disjunctions of size less than κ.
=== Non-classical and modal logics ===
Intuitionistic first-order logic uses intuitionistic rather than classical reasoning; for example, ¬¬φ need not be equivalent to φ and ¬ ∀x.φ is in general not equivalent to ∃ x.¬φ .
First-order modal logic allows one to describe other possible worlds as well as this contingently true world which we inhabit. In some versions, the set of possible worlds varies depending on which possible world one inhabits. Modal logic has extra modal operators with meanings which can be characterized informally as, for example "it is necessary that φ" (true in all possible worlds) and "it is possible that φ" (true in some possible world). With standard first-order logic we have a single domain, and each predicate is assigned one extension. With first-order modal logic we have a domain function that assigns each possible world its own domain, so that each predicate gets an extension only relative to these possible worlds. This allows us to model cases where, for example, Alex is a philosopher, but might have been a mathematician, and might not have existed at all. In the first possible world P(a) is true, in the second P(a) is false, and in the third possible world there is no a in the domain at all.
First-order fuzzy logics are first-order extensions of propositional fuzzy logics rather than classical propositional calculus.
=== Fixpoint logic ===
Fixpoint logic extends first-order logic by adding the closure under the least fixed points of positive operators.
=== Higher-order logics ===
The characteristic feature of first-order logic is that individuals can be quantified, but not predicates. Thus
∃
a
(
Phil
(
a
)
)
{\displaystyle \exists a({\text{Phil}}(a))}
is a legal first-order formula, but
∃
Phil
(
Phil
(
a
)
)
{\displaystyle \exists {\text{Phil}}({\text{Phil}}(a))}
is not, in most formalizations of first-order logic. Second-order logic extends first-order logic by adding the latter type of quantification. Other higher-order logics allow quantification over even higher types than second-order logic permits. These higher types include relations between relations, functions from relations to relations between relations, and other higher-type objects. Thus the "first" in first-order logic describes the type of objects that can be quantified.
Unlike first-order logic, for which only one semantics is studied, there are several possible semantics for second-order logic. The most commonly employed semantics for second-order and higher-order logic is known as full semantics. The combination of additional quantifiers and the full semantics for these quantifiers makes higher-order logic stronger than first-order logic. In particular, the (semantic) logical consequence relation for second-order and higher-order logic is not semidecidable; there is no effective deduction system for second-order logic that is sound and complete under full semantics.
Second-order logic with full semantics is more expressive than first-order logic. For example, it is possible to create axiom systems in second-order logic that uniquely characterize the natural numbers and the real line. The cost of this expressiveness is that second-order and higher-order logics have fewer attractive metalogical properties than first-order logic. For example, the Löwenheim–Skolem theorem and compactness theorem of first-order logic become false when generalized to higher-order logics with full semantics.
== Automated theorem proving and formal methods ==
Automated theorem proving refers to the development of computer programs that search and find derivations (formal proofs) of mathematical theorems. Finding derivations is a difficult task because the search space can be very large; an exhaustive search of every possible derivation is theoretically possible but computationally infeasible for many systems of interest in mathematics. Thus complicated heuristic functions are developed to attempt to find a derivation in less time than a blind search.
The related area of automated proof verification uses computer programs to check that human-created proofs are correct. Unlike complicated automated theorem provers, verification systems may be small enough that their correctness can be checked both by hand and through automated software verification. This validation of the proof verifier is needed to give confidence that any derivation labeled as "correct" is actually correct.
Some proof verifiers, such as Metamath, insist on having a complete derivation as input. Others, such as Mizar and Isabelle, take a well-formatted proof sketch (which may still be very long and detailed) and fill in the missing pieces by doing simple proof searches or applying known decision procedures: the resulting derivation is then verified by a small core "kernel". Many such systems are primarily intended for interactive use by human mathematicians: these are known as proof assistants. They may also use formal logics that are stronger than first-order logic, such as type theory. Because a full derivation of any nontrivial result in a first-order deductive system will be extremely long for a human to write, results are often formalized as a series of lemmas, for which derivations can be constructed separately.
Automated theorem provers are also used to implement formal verification in computer science. In this setting, theorem provers are used to verify the correctness of programs and of hardware such as processors with respect to a formal specification. Because such analysis is time-consuming and thus expensive, it is usually reserved for projects in which a malfunction would have grave human or financial consequences.
For the problem of model checking, efficient algorithms are known to decide whether an input finite structure satisfies a first-order formula, in addition to computational complexity bounds: see Model checking § First-order logic.
== See also ==
== Notes ==
== References ==
Rautenberg, Wolfgang (2010), A Concise Introduction to Mathematical Logic (3rd ed.), New York, NY: Springer Science+Business Media, doi:10.1007/978-1-4419-1221-3, ISBN 978-1-4419-1220-6
Andrews, Peter B. (2002); An Introduction to Mathematical Logic and Type Theory: To Truth Through Proof, 2nd ed., Berlin: Kluwer Academic Publishers. Available from Springer.
Avigad, Jeremy; Donnelly, Kevin; Gray, David; and Raff, Paul (2007); "A formally verified proof of the prime number theorem", ACM Transactions on Computational Logic, vol. 9 no. 1 doi:10.1145/1297658.1297660
Barwise, Jon (1977). "An Introduction to First-Order Logic". In Barwise, Jon (ed.). Handbook of Mathematical Logic. Studies in Logic and the Foundations of Mathematics. Amsterdam, NL: North-Holland (published 1982). ISBN 978-0-444-86388-1.
Monk, J. Donald (1976). Mathematical Logic. New York, NY: Springer New York. doi:10.1007/978-1-4684-9452-5. ISBN 978-1-4684-9454-9.
Barwise, Jon; and Etchemendy, John (2000); Language Proof and Logic, Stanford, CA: CSLI Publications (Distributed by the University of Chicago Press)
Bocheński, Józef Maria (2007); A Précis of Mathematical Logic, Dordrecht, NL: D. Reidel, translated from the French and German editions by Otto Bird
Ferreirós, José (2001); The Road to Modern Logic — An Interpretation, Bulletin of Symbolic Logic, Volume 7, Issue 4, 2001, pp. 441–484, doi:10.2307/2687794, JSTOR 2687794
Gamut, L. T. F. (1991), Logic, Language, and Meaning, Volume 2: Intensional Logic and Logical Grammar, Chicago, Illinois: University of Chicago Press, ISBN 0-226-28088-8
Hilbert, David; and Ackermann, Wilhelm (1950); Principles of Mathematical Logic, Chelsea (English translation of Grundzüge der theoretischen Logik, 1928 German first edition)
Hodges, Wilfrid (2001); "Classical Logic I: First-Order Logic", in Goble, Lou (ed.); The Blackwell Guide to Philosophical Logic, Blackwell
Ebbinghaus, Heinz-Dieter; Flum, Jörg; and Thomas, Wolfgang (1994); Mathematical Logic, Undergraduate Texts in Mathematics, Berlin, DE/New York, NY: Springer-Verlag, Second Edition, ISBN 978-0-387-94258-2
Tarski, Alfred and Givant, Steven (1987); A Formalization of Set Theory without Variables. Vol.41 of American Mathematical Society colloquium publications, Providence RI: American Mathematical Society, ISBN 978-0821810415
== External links ==
"Predicate calculus", Encyclopedia of Mathematics, EMS Press, 2001 [1994]
Stanford Encyclopedia of Philosophy (2000): Shapiro, S., "Classical Logic". Covers syntax, model theory, and metatheory for first-order logic in the natural deduction style.
Magnus, P. D.; forall x: an introduction to formal logic. Covers formal semantics and proof theory for first-order logic.
Metamath: an ongoing online project to reconstruct mathematics as a huge first-order theory, using first-order logic and the axiomatic set theory ZFC. Principia Mathematica modernized.
Podnieks, Karl; Introduction to mathematical logic
Cambridge Mathematical Tripos notes (typeset by John Fremlin). These notes cover part of a past Cambridge Mathematical Tripos course taught to undergraduate students (usually) within their third year. The course is entitled "Logic, Computation and Set Theory" and covers Ordinals and cardinals, Posets and Zorn's Lemma, Propositional logic, Predicate logic, Set theory and Consistency issues related to ZFC and other set theories.
Tree Proof Generator can validate or invalidate formulas of first-order logic through the semantic tableaux method. | Wikipedia/First-order_predicate_calculus |
Knowledge modeling is a process of creating a computer interpretable model of knowledge or standard specifications about a kind of process and/or about a kind of facility or product. The resulting knowledge model can only be computer interpretable when it is expressed in some knowledge representation language or data structure that enables the knowledge to be interpreted by software and to be stored in a database or data exchange file.
Knowledge-based engineering or knowledge-aided design is a process of computer-aided usage of such knowledge models for the design of products, facilities or processes. The design of products or facilities then uses the knowledge model to guide the creation of the facility or product that need to be designed. In other words, it used knowledge about a kind of object to create a product model of an (imaginary) individual object.
Similarly, the design of a particular process implies the creation of a process model, which design activity can be guided by the knowledge that is contained in a knowledge model about such a kind of process. The resulting process model, product model or facility model is typically also stored in a database.
Usually the knowledge representation language only allows to represent knowledge (about kinds of things), whereas another language or data structure is required to represent and store the information models about individual things. If the knowledge representation language enables to express both, then the knowledge model and the information model can be expressed in the same language (or data structure).
An example of a language that enables the expression of knowledge as well as information about individual things is Gellish English.
The basis of a knowledge model of an assembly physical object is a decomposition structure that specifies the components of the assembly and possible the sub-components of the components.
For example, knowledge about a compressor system includes that a compressor system consists of a compressor, a lubrication system, etc., whereas a lubrication system consists of a pump system, etc. Assume that this knowledge is expressed in a knowledge representation language that expresses knowledge as a collection of relations between two kinds of things, whereas in that language a relation type is defined that is called <shall have as part a>. Then a part of a knowledge model about a compressor system will consist of the following expressions of knowledge facts:
compressor system shall have as part a compressor
compressor system shall have as part a lubrication system
lubrication system shall have as part a pump system
pump system shall have as part a pump.
Such a knowledge model will be further extended with knowledge and specifications about the properties of the components, their fabrications and possibly testing and maintenance requirements.
Similarly, a knowledge model of a process is basically a specification of the sequence of process stages. This sequence is determined by the fact that a kind of stream is output of a kind of process stage, whereas that same type of stream in input in the next process stage. So the defined streams have roles as inputs to process stages, whereas the same streams are outputs of other process stages. For example:
water shall be input in a boiler
steam shall be output of a boiler
steam shall be input in a heater
condensate shall be output of a heater
etc.
== Explicitation of document content ==
Knowledge modeling includes the explicitation of knowledge and requirements that is available in documents, such as design manuals, (international) standard specifications and standard data sheets.
In order to make such knowledge computer interpretable it needs to be expressed in a formal knowledge representation language and thus transformed into a computer interpretable form.
For example, in the form of an expressions Gellish English. This enables that the knowledge and requirements are related to the objects in the knowledge model, whereas the whole model is again stored in a Database.The knowledge that is contained in documents can be modeled at various levels of explicitation.
A low level of explicitation keeps large parts of the specifications in the form of natural language text. This means that the text is only human interpretable, but is nevertheless related to the objects in the knowledge model.
Thus software can still present the information to users when knowledge about that object is requested. The other extreme is that the content of each sentence in a documents is converted in the formal knowledge representation language and thus the objects that are mentioned in those sentences become an integral part of the computer interpretable knowledge model.
For example, the knowledge that the API 617 standard contains a standard specification for compressors can be linked to the concept compressor in the knowledge model of a compressor system.
This can be expressed in a knowledge representation language (using the relation type ⟨is specified in⟩ as follows:
compressor ⟨is specified in⟩ API 617
A higher level of explicitation means that paragraphs or sentences in natural language are related to components in the knowledge model. A full explicit model means that the natural language sentences are completely transformed into data in a database structure.
For example, a specification of a minimum shaft diameter might be included in the knowledge model as follows:
shaft diameter ⟨shall have on scale a value greater than⟩ 20 mm
The above described explicitation process results in Knowledge Models and Standard Specifications Models that enable their use for computer supported knowledge-aided design as well as for automated verification of designs.
== See also ==
Knowledge base
Knowledge-based systems
Knowledge representation and reasoning | Wikipedia/Knowledge_modeling |
In computer science and artificial intelligence, ontology languages are formal languages used to construct ontologies. They allow the encoding of knowledge about specific domains and often include reasoning rules that support the processing of that knowledge. Ontology languages are usually declarative languages, are almost always generalizations of frame languages, and are commonly based on either first-order logic or on description logic.
== Classification of ontology languages ==
=== Classification by syntax ===
==== Traditional syntax ontology languages ====
Common Logic - and its dialects
CycL
DOGMA (Developing Ontology-Grounded Methods and Applications)
F-Logic (Frame Logic)
FO-dot (First-order logic extended with types, arithmetic, aggregates and inductive definitions)
KIF (Knowledge Interchange Format)
Ontolingua based on KIF
KL-ONE
KM programming language
LOOM (ontology)
OCML (Operational Conceptual Modelling Language)
OKBC (Open Knowledge Base Connectivity)
PLIB (Parts LIBrary)
RACER
==== Markup ontology languages ====
These languages use a markup scheme to encode knowledge, most commonly with XML.
DAML+OIL
Ontology Inference Layer (OIL)
Web Ontology Language (OWL)
Resource Description Framework (RDF)
RDF Schema (RDFS)
SHOE
==== Controlled natural languages ====
Attempto Controlled English
==== Open vocabulary natural languages ====
Executable English
=== Classification by structure (logic type) ===
==== Frame-based ====
Three languages are completely or partially frame-based languages.
F-Logic
OKBC
KM
==== Description logic-based ====
Description logic provides an extension of frame languages, without going so far as to take the leap to first-order logic and support for arbitrary predicates.
KL-ONE
RACER
OWL.
Gellish is an example of a combined ontology language and ontology that is description logic-based. It distinguishes between the semantic differences among others of:
relation types for relations between concepts (classes)
relation types for relations between individuals
relation types for relations between individuals and classes
It also contains constructs to express queries and communicative intent.
==== First-order logic-based ====
Several ontology languages support expressions in first-order logic and allow general predicates.
Common Logic
CycL
FO-dot (first-order logic extended with types, arithmetic, aggregates and inductive definitions)
KIF
== See also ==
Domain theory
Formal concept analysis
Galois connection
Lattice (order)
Modeling language
OntoUML
== Notes ==
== References ==
Oscar Corcho, Asuncion Gomez-Perez, A Roadmap to Ontology Specification Languages (2000)
Introduction to Description Logics – DL course by Enrico Franconi, Faculty of Computer Science, Free University of Bolzano, Italy | Wikipedia/Ontology_language_(computer_science) |
The Disease Ontology (DO) is a formal ontology of human disease. The Disease Ontology project is hosted at the Institute for Genome Sciences at the University of Maryland School of Medicine.
The Disease Ontology project was initially developed in 2003 at Northwestern University to address the need for a purpose-built ontology that covers the full spectrum of disease concepts annotated within biomedical repositories within an ontological framework that is extensible to meet community needs.
The Disease Ontology is an OBO (Open Biomedical Ontologies) Foundry ontology.
Disease Ontology Identifiers (DOIDs) consist of the prefix DOID: followed by number, for example, Alzheimer's disease has the stable identifier DOID:10652. DO is cross-referenced in several resources such as UniProt.
== Example term ==
The Disease Ontology entry for motor neuron disease in OBO format is given below, showing the links to other classification schemes, including ICD-9, ICD-10, MeSH, SNOMED and UMLS.
id: DOID:231
name: motor neuron disease
def: "A neurodegenerative disease that is located_in the motor neurones." Motor neuron disease
xref: ICD10CM:G12.2
xref: ICD10CM:G12.20
xref: ICD9CM:335.2
xref: MSH:D016472
xref: SNOMEDCT_US_2016_03_01:155015007
xref: SNOMEDCT_US_2016_03_01:192888001
xref: SNOMEDCT_US_2016_03_01:192889009
xref: SNOMEDCT_US_2016_03_01:192890000
xref: SNOMEDCT_US_2016_03_01:37340000
xref: UMLS_CUI:C0085084
is_a: DOID:1289 ! Neurodegenerative disease
== See also ==
Disease Ontology project
== References ==
== External links ==
Disease Ontology web interface
DO Wiki Archived 2012-04-08 at the Wayback Machine
sourceforge SVN trunk
DO request tracker
OBO Foundry | Wikipedia/Disease_Ontology |
The Computer Science Ontology (CSO) is an automatically generated taxonomy of research topics in the field of Computer Science. It was produced by the Open University in collaboration with Springer Nature by running an information extraction system over a large corpus of scientific articles. Several branches were manually improved by domain experts. The current version (CSO 3.2) includes about 14K research topics and 160K semantic relationships.
CSO is available in OWL, Turtle, and N-Triples.
It is aligned with several other knowledge graphs, including DBpedia, Wikidata, YAGO, Freebase, and Cyc. New versions of CSO are regularly released on the CSO Portal.
CSO is mostly used to characterise scientific papers and other documents according to their research areas, in order to enable different kinds of analytics. The CSO Classifier is an open-source python tool for automatically annotating documents with CSO.
== Applications ==
Recommender Systems.
Computing the semantic similarity of documents.
Extracting metadata from video lecture subtitles.
Performing bibliometrics analysis.
== See also ==
== References ==
== External links ==
Official website | Wikipedia/Computer_Science_Ontology |
Semantic Application Design Language (SADL) is an English-like open source language for building formal models composed of an OWL ontology, rules expressed in terms of the ontological concepts, queries for retrieving information from the model, and tests to validate and re-validate model content and entailments (implications).
The SADL-IDE is an Eclipse-based integrated development environment(IDE) which facilitates authoring and maintaining models expressed in the SADL language.
== Motivation ==
The Semantic Technology stack offers significant potential for knowledge capture and usage in many domains. However, native representations (OWL, SWRL, Jena Rules, SPARQL) are unfriendly to domain experts who are not computer scientists and not knowledgeable in the intricacies of artificial intelligence and formal logic. Furthermore, in the opinion of the creator, the tools available to build, test, maintain, and apply knowledge bases (models) over their life cycle are inadequate. SADL attempts to bridge these gaps.
== How ==
SADL attempts to meet the needs identified above in several ways.
The SADL grammar tries to use common words to express formal model relationships. These key words and phrases are mapped unambiguously into the formalisms of OWL, SWRL or Jena Rules, and SPARQL.
SADL allows statement combinations for more concise and understandable groupings. Examples include listing the properties that "describe" a class as part of the class definition, identifying multiple subclasses in a single statement, chaining triple patterns in rules and queries to eliminate variables and make the overall pattern more readable, listing the attributes and relationships of an instance together with a single subject, and optionally named instances.
The SADL-IDE provides templates, content assistance, quick fixes, hyper linking of concepts to their definitions, folding, and other aids to make it easier for domain experts to view and understand, create, and maintain models.
The close integration of Eclipse with source code control systems such as CVS, SVN, or GIT allow SADL models to be versioned and more easily managed over their life cycle. The sequential nature of the language makes differences between model versions easy to compute and view. A set of models may be easily tagged as a particular release and retrieved as a compete set at any time.
Integration of reasoners/rules engines with the SADL-IDE allows the model developer to exercise the model, query results and create test cases for validation and regression testing.
== Sources ==
== References ==
SADL on SourceForge
TOWARD A UNIFIED ENGLISH-LIKE REPRESENTATION OF SEMANTIC MODELS, DATA, AND GRAPH PATTERNS FOR SUBJECT MATTER EXPERTS | Wikipedia/Semantic_Application_Design_Language |
Controlled vocabularies provide a way to organize knowledge for subsequent retrieval. They are used in subject indexing schemes, subject headings, thesauri, taxonomies and other knowledge organization systems. Controlled vocabulary schemes mandate the use of predefined, preferred terms that have been preselected by the designers of the schemes, in contrast to natural language vocabularies, which have no such restriction.
== In library and information science ==
In library and information science, controlled vocabulary is a carefully selected list of words and phrases, which are used to tag units of information (document or work) so that they may be more easily retrieved by a search. Controlled vocabularies solve the problems of homographs, synonyms and polysemes by a bijection between concepts and preferred terms. In short, controlled vocabularies reduce unwanted ambiguity inherent in normal human languages where the same concept can be given different names and ensure consistency.
For example, in the Library of Congress Subject Headings (a subject heading system that uses a controlled vocabulary), preferred terms—subject headings in this case—have to be chosen to handle choices between variant spellings of the same word (American versus British), choice among scientific and popular terms (cockroach versus Periplaneta americana), and choices between synonyms (automobile versus car), among other difficult issues.
Choices of preferred terms are based on the principles of user warrant (what terms users are likely to use), literary warrant (what terms are generally used in the literature and documents), and structural warrant (terms chosen by considering the structure, scope of the controlled vocabulary).
Controlled vocabularies also typically handle the problem of homographs with qualifiers. For example, the term pool has to be qualified to refer to either swimming pool or the game pool to ensure that each preferred term or heading refers to only one concept.
=== Types used in libraries ===
There are two main kinds of controlled vocabulary tools used in libraries: subject headings and thesauri. While the differences between the two are diminishing, there are still some minor differences.
Historically, subject headings were designed to describe books in library catalogs by catalogers while thesauri were used by indexers to apply index terms to documents and articles.
Subject headings tend to be broader in scope describing whole books, while thesauri tend to be more specialized covering very specific disciplines.
Because of the card catalog system, subject headings tend to have terms that are in indirect order (though with the rise of automated systems this is being removed), while thesaurus terms are always in direct order.
Subject headings tend to use more pre-coordination of terms such that the designer of the controlled vocabulary will combine various concepts together to form one preferred subject heading. (e.g., children and terrorism) while thesauri tend to use singular direct terms. Thesauri list not only equivalent terms but also narrower, broader terms and related terms among various preferred and non-preferred (but potentially synonymous) terms, while historically most subject headings did not. For example, the Library of Congress Subject Heading itself did not have much syndetic structure until 1943, and it was not until 1985 when it began to adopt the thesauri type term "Broader term" and "Narrow term".
The terms are chosen and organized by trained professionals (including librarians and information scientists) who possess expertise in the subject area. Controlled vocabulary terms can accurately describe what a given document is actually about, even if the terms themselves do not occur within the document's text. Well known subject heading systems include the Library of Congress system, Medical Subject Headings (MeSH) created by the United States National Library of Medicine, and Sears. Well known thesauri include the Art and Architecture Thesaurus and the ERIC Thesaurus.
When selecting terms for a controlled vocabulary, the designer has to consider the specificity of the term chosen, whether to use direct entry, inter consistency and stability of the language.
Lastly the amount of pre-coordination (in which case the degree of enumeration versus synthesis becomes an issue) and post-coordination in the system is another important issue. Controlled vocabulary elements (terms/phrases) employed as tags, to aid in the content identification process of documents, or other information system entities (e.g. DBMS, Web Services) qualifies as metadata.
== Indexing languages ==
There are three main types of indexing languages.
Controlled indexing language – only approved terms can be used by the indexer to describe the document
Natural language indexing language – any term from the document in question can be used to describe the document
Free indexing language – any term (not only from the document) can be used to describe the document
When indexing a document, the indexer also has to choose the level of indexing exhaustivity, the level of detail in which the document is described. For example, using low indexing exhaustivity, minor aspects of the work will not be described with index terms. In general the higher the indexing exhaustivity, the more terms indexed for each document.
In recent years free text search as a means of access to documents has become popular. This involves using natural language indexing with an indexing exhaustively set to maximum (every word in the text is indexed). These methods have been compared in some studies, such as the 2007 article, "A Comparative Evaluation of Full-text, Concept-based, and Context-sensitive Search".
=== Advantages ===
Controlled vocabularies are often claimed to improve the accuracy of free text searching, such as to reduce irrelevant items in the retrieval list. These irrelevant items (false positives) are often caused by the inherent ambiguity of natural language. Take the English word football for example. Football is the name given to a number of different team sports. Worldwide the most popular of these team sports is association football, which also happens to be called soccer in several countries. The word football is also applied to rugby football (rugby union and rugby league), American football, Australian rules football, Gaelic football, and Canadian football. A search for football therefore will retrieve documents that are about several completely different sports. Controlled vocabulary solves this problem by tagging the documents in such a way that the ambiguities are eliminated.
Compared to free text searching, the use of a controlled vocabulary can dramatically increase the performance of an information retrieval system, if performance is measured by precision (the percentage of documents in the retrieval list that are actually relevant to the search topic).
In some cases controlled vocabulary can enhance recall as well, because unlike natural language schemes, once the correct preferred term is searched, there is no need to search for other terms that might be synonyms of that term.
=== Problems ===
A controlled vocabulary search may lead to unsatisfactory recall, in that it will fail to retrieve some documents that are actually relevant to the search question.
This is particularly problematic when the search question involves terms that are sufficiently tangential to the subject area such that the indexer might have decided to tag it using a different term (but the searcher might consider the same). Essentially, this can be avoided only by an experienced user of controlled vocabulary whose understanding of the vocabulary coincides with that of the indexer.
Another possibility is that the article is just not tagged by the indexer because indexing exhaustivity is low. For example, an article might mention football as a secondary focus, and the indexer might decide not to tag it with "football" because it is not important enough compared to the main focus. But it turns out that for the searcher that article is relevant and hence recall fails. A free text search would automatically pick up that article regardless.
On the other hand, free text searches have high exhaustivity (every word is searched) so although it has much lower precision, it has potential for high recall as long as the searcher overcome the problem of synonyms by entering every combination.
Controlled vocabularies may become outdated rapidly in fast developing fields of knowledge, unless the preferred terms are updated regularly. Even in an ideal scenario, a controlled vocabulary is often less specific than the words of the text itself. Indexers trying to choose the appropriate index terms might misinterpret the author, while this precise problem is not a factor in a free text, as it uses the author's own words.
The use of controlled vocabularies can be costly compared to free text searches because human experts or expensive automated systems are necessary to index each entry. Furthermore, the user has to be familiar with the controlled vocabulary scheme to make best use of the system. But as already mentioned, the control of synonyms, homographs can help increase precision.
Numerous methodologies have been developed to assist in the creation of controlled vocabularies, including faceted classification, which enables a given data record or document to be described in multiple ways.
Word choice in chosen vocabularies is not neutral, and the indexer must carefully consider the ethics of their word choices. For example, traditionally colonialist terms have often been the preferred terms in chosen vocabularies when discussing First Nations issues, which has caused controversy.
== Applications ==
Controlled vocabularies, such as the Library of Congress Subject Headings, are an essential component of bibliography, the study and classification of books. They were initially developed in library and information science. In the 1950s, government agencies began to develop controlled vocabularies for the burgeoning journal literature in specialized fields; an example is the Medical Subject Headings (MeSH) developed by the U.S. National Library of Medicine. Subsequently, for-profit firms (called Abstracting and indexing services) emerged to index the fast-growing literature in every field of knowledge. In the 1960s, an online bibliographic database industry developed based on dialup X.25 networking. These services were seldom made available to the public because they were difficult to use; specialist librarians called search intermediaries handled the searching job. In the 1980s, the first full text databases appeared; these databases contain the full text of the index articles as well as the bibliographic information. Online bibliographic databases have migrated to the Internet and are now publicly available; however, most are proprietary and can be expensive to use. Students enrolled in colleges and universities may be able to access some of these services without charge; some of these services may be accessible without charge at a public library.
=== Technical communication ===
In large organizations, controlled vocabularies may be introduced to improve technical communication. The use of controlled vocabulary ensures that everyone is using the same word to mean the same thing. This consistency of terms is one of the most important concepts in technical writing and knowledge management, where effort is expended to use the same word throughout a document or organization instead of slightly different ones to refer to the same thing.
=== Semantic web and structured data ===
Web searching could be dramatically improved by the development of a controlled vocabulary for describing Web pages; the use of such a vocabulary could culminate in a Semantic Web, in which the content of Web pages is described using a machine-readable metadata scheme. One of the first proposals for such a scheme is the Dublin Core Initiative. An example of a controlled vocabulary which is usable for indexing web pages is PSH.
It is unlikely that a single metadata scheme will ever succeed in describing the content of the entire Web. To create a Semantic Web, it may be necessary to draw from two or more metadata systems to describe a Web page's contents. The eXchangeable Faceted Metadata Language (XFML) is designed to enable controlled vocabulary creators to publish and share metadata systems. XFML is designed on faceted classification principles.
Controlled vocabularies of the Semantic Web define the concepts and relationships (terms) used to describe a field of interest or area of concern. For instance, to declare a person in a machine-readable format, a vocabulary is needed that has the formal definition of "Person", such as the Friend of a Friend (FOAF) vocabulary, which has a Person class that defines typical properties of a person including, but not limited to, name, honorific prefix, affiliation, email address, and homepage, or the Person vocabulary of Schema.org. Similarly, a book can be described using the Book vocabulary of Schema.org and general publication terms from the Dublin Core vocabulary, an event with the Event vocabulary of Schema.org, and so on.
To use machine-readable terms from any controlled vocabulary, web designers can choose from a variety of annotation formats, including RDFa, HTML5 Microdata, or JSON-LD in the markup, or RDF serializations (RDF/XML, Turtle, N3, TriG, TriX) in external files.
== See also ==
== References ==
== External links ==
Directory of Linked Open Vocabularies (LOV) | Wikipedia/Controlled_vocabulary |
The CIDOC Conceptual Reference Model (CRM) provides an extensible ontology for concepts and information in cultural heritage and museum documentation. It is the international standard (ISO 21127:2023) for the controlled exchange of cultural heritage information. Galleries, libraries, archives, museums (GLAMs), and other cultural institutions are encouraged to use the CIDOC CRM to enhance accessibility to museum-related information and knowledge.
== History ==
The CIDOC CRM emerged from the CIDOC Documentation Standards Group in the International Committee for Documentation of the International Council of Museums. Initially, until 1994, the work focused on developing an entity-relationship model for museum information, however, in 1996, the approach shifted to object-oriented modeling methodologies, resulting in the first "CIDOC Conceptual Reference Model (CRM)" in 1999. The process of standardizing the CIDOC CRM began in 2000 and was completed in 2006 with its acceptance as the ISO 21127 standard. The standard was updated and a revised edition was published in 2023.
== Aims ==
The overall aim of the CIDOC CRM is to provide a reference model and information standard that museums, and other cultural heritage institutions, can use to describe their collections, and related business entities, to improve information sharing.
The CIDOC Conceptual Reference Model (CRM) provides definitions and a formal structure for describing the implicit and explicit concepts and relationships used in cultural heritage documentation...to promote a shared understanding of cultural heritage information by providing a common and extensible semantic framework that any cultural heritage information can be mapped to. It is intended to be a common language for domain experts and implementers to formulate requirements for information systems and to serve as a guide for good practice of conceptual modelling. In this way, it can provide the "semantic glue" needed to mediate between different sources of cultural heritage information, such as that published by museums, libraries and archives.
By adopting formal semantics for the CIDOC CRM, the pre-conditions for machine-to-machine interoperability and integration have been established. Thus, CIDOC CRM is well placed to become an important information standard and reference model for Semantic Web initiatives, and serves as a guide for data, or database, modeling more generally. Technically speaking, CIDOC CRM lends itself to software applications that extensively use XML and RDF. Many cultural heritage institutions are investigating or building applications that use CIDOC CRM.
Following the successful standardization of the CIDOC CRM, a new initiative, FRBRoo, was begun in 2006 to harmonize it with the Functional Requirements for Bibliographic Records (FRBR). The aim of this initiative is to "provide a formal ontology intended to capture and represent the underlying semantics of bibliographic information and to facilitate the integration, mediation, and interchange of bibliographic and museum information."
== Ontology ==
The "CIDOC object-oriented Conceptual Reference Model" (CRM) is a domain ontology, but includes its own version of an upper ontology.
The core classes cover:
Space-Time
includes title/identifier, place, era/period, time-span, and relationship to persistent items
Events
includes title/identifier, beginning/ending of existence, participants (people, either individually or in groups), creation/modification of things (physical or conceptional), and relationship to persistent items
Material Things
includes title/identifier, place, the information object the material thing carries, part-of relationships, and relationship to persistent items
Immaterial Things
includes title/identifier, information objects (propositional or symbolic), conceptional things, and part-of relationships
Examples of definitions:
Persistent Item
a physical or conceptional item that has a persistent identity recognized within the duration of its existence by its identification rather than by its continuity or by observation. A Persistent Item is comparable to an endurant.
Temporal Entity
includes events, eras/periods, and condition states which happen over a limited extent in time, and is disjoint with Persistent Item. A Temporal Entity is comparable to a perdurant.
Propositional Object
a set of statements about real or imaginary things.
Symbolic Object
a sign/symbol or an aggregation of signs/symbols.
== CIDOC CRM Implementations and Systems ==
The CIDOC CRM has been implemented in OWL DL as Erlangen CRM/OWL (ECRM)
The ECRM (and thus CIDOC CRM) is used extensively in the WissKI system, an ontology based virtual research environment for managing primary research data in the area of cultural heritage as linked data.
== References ==
== Further reading ==
Doerr M., "The CIDOC CRM – An Ontological Approach to Semantic Interoperability of Metadata", AI Magazine, Volume.24, Number 3 pp. 75–92 (2003)
Martin Doerr, Dolores Iorizzo, The Dream of a Global Knowledge Network – A New Approach, ACM Journal for Computing and Cultural Heritage, Vol. 1, No. 1, Article 5, Publication date: June 2008
Nick Crofts, Martin Doerr, Tony Gill, Stephen Stead, Matthew Stiff (editors), Definition of the CIDOC Conceptual Reference Model, October 2006. Version 4.2.1
Martin Doerr, Nicholas Crofts: Electronic Communication on Diverse Data. The Role of the oo CIDOC Reference Model doi:10.1145/1367080.1367085
T. Gill: Making sense of cultural infodiversity: The CIDOC-CRM. 2002
Regine Stein, Jürgen Gottschewski u.a.: Das CIDOC Conceptual Reference Model: Eine Hilfe für den Datenaustausch? Berlin, 2005 (German)
Görz, G.; Schiemann, B.; Oischinger, M.: An Implementation of the CIDOC Conceptual Reference Model (4.2.4) in OWL-DL); Proceedings CIDOC 2008 - The Digital Curation of Cultural Heritage
== External links ==
The CIDOC CRM Website
Erlangen CRM/OWL web site and OWL sources | Wikipedia/CIDOC_Conceptual_Reference_Model |
Big design up front (BDUF) is a software development approach in which the program's design is to be completed and perfected before that program's implementation is started. It is often associated with the waterfall model of software development.
Synonyms for big design up front (BDUF) are big modeling up front (BMUF) and big requirements up front (BRUF). These are viewed as anti-patterns within agile software development.
== Arguments for ==
Proponents of the waterfall model argue that time spent in designing is a worthwhile investment, with the hope that less time and effort will be spent fixing a bug in the early stages of a software product's lifecycle than when that same bug is found and must be fixed later. That is, it is much easier to fix a requirements bug in the requirements phase than to fix that same bug in the implementation phase, as to fix a requirements bug in the implementation phase requires scrapping at least some of the implementation and design work which has already been completed.
Joel Spolsky, a popular online commentator on software development, has argued strongly in favor of big design up front:
"Many times, thinking things out in advance saved us serious development headaches later on. ... [on making a particular specification change] ... Making this change in the spec took an hour or two. If we had made this change in code, it would have added weeks to the schedule. I can’t tell you how strongly I believe in Big Design Up Front, which the proponents of Extreme Programming consider anathema. I have consistently saved time and made better products by using BDUF and I’m proud to use it, no matter what the XP fanatics claim. They’re just wrong on this point and I can’t be any clearer than that."
However, several commentators have argued that what Spolsky has called big design up front doesn't resemble the BDUF criticized by advocates of XP and other agile software development methodologies because he himself says his example was neither recognizably the full program design nor completed entirely upfront:
"This specification is simply a starting point for the design of Aardvark 1.0, not a final blueprint. As we start to build the product, we'll discover a lot of things that won't work exactly as planned. We'll invent new features, we'll change things, we'll refine the wording, etc. We'll try to keep the spec up to date as things change. By no means should you consider this spec to be some kind of holy, cast-in-stone law."
== Arguments against ==
Critics (notably those who practice agile software development) argue that BDUF is poorly adaptable to changing requirements and that BDUF assumes that designers are able to foresee problem areas without extensive prototyping and at least some investment into implementation. For substantial projects, the requirements from users need refinement in light of initial deliverables, and the needs of the business evolve at a pace faster than large projects are completed in - making the Big Design outdated by the time the system is completed.
They also assert that there is an overhead to be balanced between the time spent planning and the time that fixing a defect would actually cost. This is sometimes termed analysis paralysis.
If the cost of planning is greater than the cost of fixing then time spent planning is wasted.
Continuous deployment, automatic updates, and related ideas seek to substantially reduce the cost of defects in production so that they become cheaper to fix at run-time than to plan out at the beginning. In reality, run-time fixes are vastly more costly than design fixes, so it is critical to use Agile methods such as frequent demonstrations and user feedback during development to fix issues during the development cycle. Improving software with the benefit of user feedback is generally less expensive than trying to anticipate and document every aspect of a system with BDUF.
Also, in most projects there is a significant lack of comprehensive written (or even well known) requirements. So in BDUF a lot of assumptions are made that later prove to be false but are designed and possibly already coded.
== Alternatives ==
An alternative approach is rough design up front (RDUF) in which 'sufficient' design is completed up front to provide a framework on which to build in the design detail as the project progresses.
A similar approach has been called sufficient design by Joshua Kerievsky:
"I'm saying that we need high design quality for stuff that is critical to our products and less design quality for stuff that isn't critical."
Advocates of Scrum refer to the concept of emergent design:
"The difference on a Scrum project is not that intentional design is thrown out, but that it is done (like everything else on a Scrum project) incrementally."
== See also ==
List of software development philosophies
== References == | Wikipedia/Big_design_up_front |
In software development, the V-model represents a development process that may be considered an extension of the waterfall model and is an example of the more general V-model. Instead of moving down linearly, the process steps are bent upwards after the coding phase, to form the typical V shape. The V-Model demonstrates the relationships between each phase of the development life cycle and its associated phase of testing. The horizontal and vertical axes represent time or project completeness (left-to-right) and level of abstraction (coarsest-grain abstraction uppermost), respectively.
== Project definition phases ==
=== Requirements analysis ===
In the requirements analysis phase, the first step in the verification process, the requirements of the system are collected by analyzing the needs of the user(s). This phase is concerned with establishing what the ideal system has to perform. However, it does not determine how the software will be designed or built. Usually, the users are interviewed and a document called the user requirements document is generated.
The user requirements document will typically describe the system's functional, interface, performance, data, security, etc. requirements as expected by the user. It is used by business analysts to communicate their understanding of the system to the users. The users carefully review this document as this document would serve as the guideline for the system designers in the system design phase. The user acceptance tests are designed in this phase. See also Functional requirements.
There are different methods for gathering requirements of both soft and hard methodologies including; interviews, questionnaires, document analysis, observation, throw-away prototypes, use case, and static and dynamic views with users.
=== System design ===
Systems design is the phase where system engineers analyze and understand the business of the proposed system by studying the user requirements document. They figure out possibilities and techniques by which the user requirements can be implemented. If any of the requirements are not feasible, the user is informed of the issue. A resolution is found and the user requirement document is edited accordingly.
The software specification document which serves as a blueprint for the development phase is generated. This document contains the general system organization, menu structures, data structures etc. It may also hold example business scenarios, sample windows, and reports to aid understanding. Other technical documentation like entity diagrams, and data dictionaries will also be produced in this phase. The documents for system testing are prepared.
=== Architecture design ===
The phase of the design of computer architecture and software architecture can also be referred to as high-level design. The baseline in selecting the architecture is that it should realize all which typically consists of the list of modules, brief functionality of each module, their interface relationships, dependencies, database tables, architecture diagrams, technology details, etc. The integration testing design is carried out in the particular phase.
=== Module design ===
The module design phase can also be referred to as low-level design. The designed system is broken up into smaller units or modules and each of them is explained so that the programmer can start coding directly.
The low-level design document or program specifications will contain a detailed functional logic of the module, in pseudocode:
database tables, with all elements, including their type and size
all interface details with complete API references
all dependency issues
error message listings
complete input and outputs for a module.
The unit test design is developed in this stage.
== Validation phases ==
In the V-model, each stage of the design phase has a corresponding stage in the validation phase. The following are the typical phases of validation in the V-Model, though they may be known by other names.
=== Unit testing ===
In the V-Model, Unit Test Plans (UTPs) are developed during the module design phase. These UTPs are executed to eliminate bugs at the code level or unit level. A unit is the smallest entity that can independently exist, e.g. a program module. Unit testing verifies that the smallest entity can function correctly when isolated from the rest of the codes/units.
=== Integration testing ===
Integration Test Plans are developed during the Architectural Design Phase. These tests verify that units created and tested independently can coexist and communicate among themselves. Test results are shared with the customer's team.
=== System testing ===
System Tests Plans are developed during the System Design Phase. Unlike Unit and Integration Test Plans, System Test Plans are composed by the client's business team. System Test ensures that expectations from the application developed are met. The whole application is tested for its functionality, interdependency, and communication. System Testing verifies that functional and non-functional requirements have been met. Load and performance testing, stress testing, regression testing, etc., are subsets of system testing.
=== User acceptance testing ===
User Acceptance Test (UAT) Plans are developed during the Requirements Analysis phase. Test Plans are composed by business users. UAT is performed in a user environment that resembles the production environment, using realistic data. UAT verifies that the delivered system meets the user's requirement and the system is ready for use in real-time.
== Criticism ==
The V-Model has been criticized by Agile advocates and others as an inadequate model of software development for numerous reasons. Criticisms include:
It is too simple to accurately reflect the software development process, and can lead managers into a false sense of security. The V-Model reflects a project management view of software development and fits the needs of project managers, accountants and lawyers rather than software developers or users.
Although it is easily understood by novices, that early understanding is useful only if the novice goes on to acquire a deeper understanding of the development process and how the V-Model must be adapted and extended in practice. If practitioners persist with their naive view of the V-Model they will have great difficulty applying it successfully.
It is inflexible and encourages a rigid and linear view of software development and has no inherent ability to respond to change.
It provides only a slight variant on the waterfall model and is therefore subject to the same criticisms as that model. It provides greater emphasis on testing, and particularly the importance of early test planning. However, a common practical criticism of the V-Model is that it leads to testing being squeezed into tight windows at the end of development when earlier stages have overrun but the implementation date remains fixed.
It is consistent with, and therefore implicitly encourages, inefficient and ineffective approaches to testing. It implicitly promotes writing test scripts in advance rather than exploratory testing; it encourages testers to look for what they expect to find, rather than discover what is truly there. It also encourages a rigid link between the equivalent levels of either leg (e.g. user acceptance test plans being derived from user requirements documents), rather than encouraging testers to select the most effective and efficient way to plan and execute testing.
It lacks coherence and precision. There is widespread confusion about what exactly the V-Model is. If one boils it down to those elements that most people would agree upon it becomes a trite and unhelpful representation of software development. Disagreement about the merits of the V-Model often reflects a lack of shared understanding of its definition.
== Current state ==
Supporters of the V-Model argue that it has evolved and supports flexibility and agility throughout the development process. They argue that in addition to being a highly disciplined approach, it promotes meticulous design, development, and documentation necessary to build stable software products. Lately, it is being adopted by the medical device industry.
== See also ==
Product lifecycle management
Systems development life cycle
== References ==
== Further reading ==
Roger S. Pressman:Software Engineering: A Practitioner's Approach, The McGraw-Hill Companies, ISBN 0-07-301933-X
Mark Hoffman & Ted Beaumont: Application Development: Managing the Project Life Cycle, Mc Press, ISBN 1-883884-45-4
Boris Beizer: Software Testing Techniques. Second Edition, International Thomson Computer Press, 1990, ISBN 1-85032-880-3 | Wikipedia/V-Model_(software_development) |
In software engineering, a software development process or software development life cycle (SDLC) is a process of planning and managing software development. It typically involves dividing software development work into smaller, parallel, or sequential steps or sub-processes to improve design and/or product management. The methodology may include the pre-definition of specific deliverables and artifacts that are created and completed by a project team to develop or maintain an application.
Most modern development processes can be vaguely described as agile. Other methodologies include waterfall, prototyping, iterative and incremental development, spiral development, rapid application development, and extreme programming.
A life-cycle "model" is sometimes considered a more general term for a category of methodologies and a software development "process" is a particular instance as adopted by a specific organization. For example, many specific software development processes fit the spiral life-cycle model. The field is often considered a subset of the systems development life cycle.
== History ==
The software development methodology framework did not emerge until the 1960s. According to Elliott (2004), the systems development life cycle can be considered to be the oldest formalized methodology framework for building information systems. The main idea of the software development life cycle has been "to pursue the development of information systems in a very deliberate, structured and methodical way, requiring each stage of the life cycle––from the inception of the idea to delivery of the final system––to be carried out rigidly and sequentially" within the context of the framework being applied. The main target of this methodology framework in the 1960s was "to develop large scale functional business systems in an age of large scale business conglomerates. Information systems activities revolved around heavy data processing and number crunching routines."
Requirements gathering and analysis:
The first phase of the custom software development process involves understanding the client's requirements and objectives. This stage typically involves engaging in thorough discussions and conducting interviews with stakeholders to identify the desired features, functionalities, and overall scope of the software. The development team works closely with the client to analyze existing systems and workflows, determine technical feasibility, and define project milestones.
Planning and design:
Once the requirements are understood, the custom software development team proceeds to create a comprehensive project plan. This plan outlines the development roadmap, including timelines, resource allocation, and deliverables. The software architecture and design are also established during this phase. User interface (UI) and user experience (UX) design elements are considered to ensure the software's usability, intuitiveness, and visual appeal.
Development:
With the planning and design in place, the development team begins the coding process. This phase involves writing, testing, and debugging the software code. Agile methodologies, such as scrum or kanban, are often employed to promote flexibility, collaboration, and iterative development. Regular communication between the development team and the client ensures transparency and enables quick feedback and adjustments.
Testing and quality assurance:
To ensure the software's reliability, performance, and security, rigorous testing and quality assurance (QA) processes are carried out. Different testing techniques, including unit testing, integration testing, system testing, and user acceptance testing, are employed to identify and rectify any issues or bugs. QA activities aim to validate the software against the predefined requirements, ensuring that it functions as intended.
Deployment and implementation:
Once the software passes the testing phase, it is ready for deployment and implementation. The development team assists the client in setting up the software environment, migrating data if necessary, and configuring the system. User training and documentation are also provided to ensure a smooth transition and enable users to maximize the software's potential.
Maintenance and support:
After the software is deployed, ongoing maintenance and support become crucial to address any issues, enhance performance, and incorporate future enhancements. Regular updates, bug fixes, and security patches are released to keep the software up-to-date and secure. This phase also involves providing technical support to end users and addressing their queries or concerns.
Methodologies, processes, and frameworks range from specific prescriptive steps that can be used directly by an organization in day-to-day work, to flexible frameworks that an organization uses to generate a custom set of steps tailored to the needs of a specific project or group. In some cases, a "sponsor" or "maintenance" organization distributes an official set of documents that describe the process. Specific examples include:
1970s
Structured programming since 1969
Cap Gemini SDM, originally from PANDATA, the first English translation was published in 1974. SDM stands for System Development Methodology
1980s
Structured systems analysis and design method (SSADM) from 1980 onwards
Information Requirement Analysis/Soft systems methodology
1990s
Object-oriented programming (OOP) developed in the early 1960s and became a dominant programming approach during the mid-1990s
Rapid application development (RAD), since 1991
Dynamic systems development method (DSDM), since 1994
Scrum, since 1995
Team software process, since 1998
Rational Unified Process (RUP), maintained by IBM since 1998
Extreme programming, since 1999
2000s
Agile Unified Process (AUP) maintained since 2005 by Scott Ambler
Disciplined agile delivery (DAD) Supersedes AUP
2010s
Scaled Agile Framework (SAFe)
Large-Scale Scrum (LeSS)
DevOps
Since DSDM in 1994, all of the methodologies on the above list except RUP have been agile methodologies - yet many organizations, especially governments, still use pre-agile processes (often waterfall or similar). Software process and software quality are closely interrelated; some unexpected facets and effects have been observed in practice.
Among these, another software development process has been established in open source. The adoption of these best practices known and established processes within the confines of a company is called inner source.
== Prototyping ==
Software prototyping is about creating prototypes, i.e. incomplete versions of the software program being developed.
The basic principles are:
Prototyping is not a standalone, complete development methodology, but rather an approach to try out particular features in the context of a full methodology (such as incremental, spiral, or rapid application development (RAD)).
Attempts to reduce inherent project risk by breaking a project into smaller segments and providing more ease of change during the development process.
The client is involved throughout the development process, which increases the likelihood of client acceptance of the final implementation.
While some prototypes are developed with the expectation that they will be discarded, it is possible in some cases to evolve from prototype to working system.
A basic understanding of the fundamental business problem is necessary to avoid solving the wrong problems, but this is true for all software methodologies.
== Methodologies ==
=== Agile development ===
"Agile software development" refers to a group of software development frameworks based on iterative development, where requirements and solutions evolve via collaboration between self-organizing cross-functional teams. The term was coined in the year 2001 when the Agile Manifesto was formulated.
Agile software development uses iterative development as a basis but advocates a lighter and more people-centric viewpoint than traditional approaches. Agile processes fundamentally incorporate iteration and the continuous feedback that it provides to successively refine and deliver a software system.
The Agile model also includes the following software development processes:
Dynamic systems development method (DSDM)
Kanban
Scrum
Lean software development
=== Continuous integration ===
Continuous integration is the practice of merging all developer working copies to a shared mainline several times a day.
Grady Booch first named and proposed CI in his 1991 method, although he did not advocate integrating several times a day. Extreme programming (XP) adopted the concept of CI and did advocate integrating more than once per day – perhaps as many as tens of times per day.
=== Incremental development ===
Various methods are acceptable for combining linear and iterative systems development methodologies, with the primary objective of each being to reduce inherent project risk by breaking a project into smaller segments and providing more ease-of-change during the development process.
There are three main variants of incremental development:
A series of mini-waterfalls are performed, where all phases of the waterfall are completed for a small part of a system, before proceeding to the next increment, or
Overall requirements are defined before proceeding to evolutionary, mini-waterfall development of individual increments of a system, or
The initial software concept, requirements analysis, and design of architecture and system core are defined via waterfall, followed by incremental implementation, which culminates in installing the final version, a working system.
=== Rapid application development ===
Rapid application development (RAD) is a software development methodology, which favors iterative development and the rapid construction of prototypes instead of large amounts of up-front planning. The "planning" of software developed using RAD is interleaved with writing the software itself. The lack of extensive pre-planning generally allows software to be written much faster and makes it easier to change requirements.
The rapid development process starts with the development of preliminary data models and business process models using structured techniques. In the next stage, requirements are verified using prototyping, eventually to refine the data and process models. These stages are repeated iteratively; further development results in "a combined business requirements and technical design statement to be used for constructing new systems".
The term was first used to describe a software development process introduced by James Martin in 1991. According to Whitten (2003), it is a merger of various structured techniques, especially data-driven information technology engineering, with prototyping techniques to accelerate software systems development.
The basic principles of rapid application development are:
Key objective is for fast development and delivery of a high-quality system at a relatively low investment cost.
Attempts to reduce inherent project risk by breaking a project into smaller segments and providing more ease of change during the development process.
Aims to produce high-quality systems quickly, primarily via iterative Prototyping (at any stage of development), active user involvement, and computerized development tools. These tools may include graphical user interface (GUI) builders, Computer Aided Software Engineering (CASE) tools, Database Management Systems (DBMS), fourth-generation programming languages, code generators, and object-oriented techniques.
Key emphasis is on fulfilling the business need, while technological or engineering excellence is of lesser importance.
Project control involves prioritizing development and defining delivery deadlines or “timeboxes”. If the project starts to slip, the emphasis is on reducing requirements to fit the timebox, not on increasing the deadline.
Generally includes joint application design (JAD), where users are intensely involved in system design, via consensus building in either structured workshops, or electronically facilitated interaction.
Active user involvement is imperative.
Iteratively produces production software, as opposed to a throwaway prototype.
Produces documentation necessary to facilitate future development and maintenance.
Standard systems analysis and design methods can be fitted into this framework.
=== Waterfall development ===
The waterfall model is a sequential development approach, in which development is seen as flowing steadily downwards (like a waterfall) through several phases, typically:
Requirements analysis resulting in a software requirements specification
Software design
Implementation
Testing
Integration, if there are multiple subsystems
Deployment (or Installation)
Maintenance
The first formal description of the method is often cited as an article published by Winston W. Royce in 1970, although Royce did not use the term "waterfall" in this article. Royce presented this model as an example of a flawed, non-working model.
The basic principles are:
The Project is divided into sequential phases, with some overlap and splashback acceptable between phases.
Emphasis is on planning, time schedules, target dates, budgets, and implementation of an entire system at one time.
Tight control is maintained over the life of the project via extensive written documentation, formal reviews, and approval/signoff by the user and information technology management occurring at the end of most phases before beginning the next phase. Written documentation is an explicit deliverable of each phase.
The waterfall model is a traditional engineering approach applied to software engineering. A strict waterfall approach discourages revisiting and revising any prior phase once it is complete. This "inflexibility" in a pure waterfall model has been a source of criticism by supporters of other more "flexible" models. It has been widely blamed for several large-scale government projects running over budget, over time and sometimes failing to deliver on requirements due to the big design up front approach. Except when contractually required, the waterfall model has been largely superseded by more flexible and versatile methodologies developed specifically for software development. See Criticism of waterfall model.
=== Spiral development ===
In 1988, Barry Boehm published a formal software system development "spiral model," which combines some key aspects of the waterfall model and rapid prototyping methodologies, in an effort to combine advantages of top-down and bottom-up concepts. It provided emphasis on a key area many felt had been neglected by other methodologies: deliberate iterative risk analysis, particularly suited to large-scale complex systems.
The basic principles are:
Focus is on risk assessment and on minimizing project risk by breaking a project into smaller segments and providing more ease-of-change during the development process, as well as providing the opportunity to evaluate risks and weigh consideration of project continuation throughout the life cycle.
"Each cycle involves a progression through the same sequence of steps, for each part of the product and for each of its levels of elaboration, from an overall concept-of-operation document down to the coding of each individual program."
Each trip around the spiral traverses four basic quadrants: (1) determine objectives, alternatives, and constraints of the iteration, and (2) evaluate alternatives; Identify and resolve risks; (3) develop and verify deliverables from the iteration; and (4) plan the next iteration.
Begin each cycle with an identification of stakeholders and their "win conditions", and end each cycle with review and commitment.
=== Shape Up ===
Shape Up is a software development approach introduced by Basecamp in 2018. It is a set of principles and techniques that Basecamp developed internally to overcome the problem of projects dragging on with no clear end. Its primary target audience is remote teams. Shape Up has no estimation and velocity tracking, backlogs, or sprints, unlike waterfall, agile, or scrum. Instead, those concepts are replaced with appetite, betting, and cycles. As of 2022, besides Basecamp, notable organizations that have adopted Shape Up include UserVoice and Block.
=== Advanced methodologies ===
Other high-level software project methodologies include:
Behavior-driven development and business process management.
Chaos model - The main rule always resolves the most important issue first.
Incremental funding methodology - an iterative approach
Lightweight methodology - a general term for methods that only have a few rules and practices
Structured systems analysis and design method - a specific version of waterfall
Slow programming, as part of the larger Slow Movement, emphasizes careful and gradual work without (or minimal) time pressures. Slow programming aims to avoid bugs and overly quick release schedules.
V-Model (software development) - an extension of the waterfall model
Unified Process (UP) is an iterative software development methodology framework, based on Unified Modeling Language (UML). UP organizes the development of software into four phases, each consisting of one or more executable iterations of the software at that stage of development: inception, elaboration, construction, and guidelines.
== Process meta-models ==
Some "process models" are abstract descriptions for evaluating, comparing, and improving the specific process adopted by an organization.
ISO/IEC 12207 is the international standard describing the method to select, implement, and monitor the life cycle for software.
The Capability Maturity Model Integration (CMMI) is one of the leading models and is based on best practices. Independent assessments grade organizations on how well they follow their defined processes, not on the quality of those processes or the software produced. CMMI has replaced CMM.
ISO 9000 describes standards for a formally organized process to manufacture a product and the methods of managing and monitoring progress. Although the standard was originally created for the manufacturing sector, ISO 9000 standards have been applied to software development as well. Like CMMI, certification with ISO 9000 does not guarantee the quality of the end result, only that formalized business processes have been followed.
ISO/IEC 15504 Information technology—Process assessment is also known as Software Process Improvement Capability Determination (SPICE), is a "framework for the assessment of software processes". This standard is aimed at setting out a clear model for process comparison. SPICE is used much like CMMI. It models processes to manage, control, guide, and monitor software development. This model is then used to measure what a development organization or project team actually does during software development. This information is analyzed to identify weaknesses and drive improvement. It also identifies strengths that can be continued or integrated into common practice for that organization or team.
ISO/IEC 24744 Software Engineering—Metamodel for Development Methodologies, is a power type-based metamodel for software development methodologies.
Soft systems methodology - a general method for improving management processes.
Method engineering - a general method for improving information system processes.
== See also ==
Systems development life cycle
Computer-aided software engineering (some of these tools support specific methodologies)
List of software development philosophies
Outline of software engineering
Software Project Management
Software development
Software development effort estimation
Software documentation
Software release life cycle
Top-down and bottom-up design#Computer science
== References ==
== External links ==
Selecting a development approach Archived January 2, 2019, at the Wayback Machine at cms.hhs.gov.
Gerhard Fischer, "The Software Technology of the 21st Century: From Software Reuse to Collaborative Software Design" Archived September 15, 2009, at the Wayback Machine, 2001 | Wikipedia/Software_development_model |
In computing, the chaos model is a structure of software development. Its creator, who used the pseudonym L.B.S. Raccoon, noted that project management models such as the spiral model and waterfall model, while good at managing schedules and staff, didn't provide methods to fix bugs or solve other technical problems. At the same time, programming methodologies, while effective at fixing bugs and solving technical problems, do not help in managing deadlines or responding to customer requests. The structure attempts to bridge this gap. Chaos theory was used as a tool to help understand these issues.
== Software development life cycle ==
The chaos model notes that the phases of the life cycle apply to all levels of projects, from the whole project to individual lines of code.
The whole project must be defined, implemented, and integrated.
Systems must be defined, implemented, and integrated.
Modules must be defined, implemented, and integrated.
Functions must be defined, implemented, and integrated.
Lines of code are defined, implemented and integrated.
One important change in perspective is whether projects can be thought of as whole units, or must be thought of in pieces. Nobody writes tens of thousands of lines of code in one sitting. They write small pieces, one line at a time, verifying that the small pieces work. Then they build up from there. The behavior of a complex system emerges from the combined behavior of the smaller building blocks.
== Chaos strategy ==
The chaos strategy is a strategy of software development based on the chaos model. The main rule is always resolve the most important issue first.
An issue is an incomplete programming task.
The most important issue is a combination of big, urgent, and robust.
Big issues provide value to users as working functionality.
Urgent issues are timely in that they would otherwise hold up other work.
Robust issues are trusted and tested when resolved. Developers can then safely focus their attention elsewhere.
To resolve means to bring it to a point of stability.
The chaos strategy resembles the way that programmers work toward the end of a project, when they have a list of bugs to fix and features to create. Usually someone prioritizes the remaining tasks, and the programmers fix them one at a time. The chaos strategy states that this is the only valid
way to do the work.
The chaos strategy was inspired by Go strategy.
== Connections with chaos theory ==
There are several tie-ins with chaos theory.
The chaos model may help explain why software tends to be so unpredictable.
It explains why high-level concepts like architecture cannot be treated independently of low-level lines of code.
It provides a hook for explaining what to do next, in terms of the chaos strategy.
== See also ==
V-model
== References ==
== Further reading ==
Roger Pressman (1997) Software Engineering: A Practitioner's Approach 4th edition, pages 29–30, McGraw Hill.
Raccoon (1995) The Chaos Model and the Chaos Life Cycle, in ACM Software Engineering Notes, Volume 20, Number 1, Pages 55 to 66, January 1995, ACM Press. | Wikipedia/Chaos_model |
In software engineering, a software development process or software development life cycle (SDLC) is a process of planning and managing software development. It typically involves dividing software development work into smaller, parallel, or sequential steps or sub-processes to improve design and/or product management. The methodology may include the pre-definition of specific deliverables and artifacts that are created and completed by a project team to develop or maintain an application.
Most modern development processes can be vaguely described as agile. Other methodologies include waterfall, prototyping, iterative and incremental development, spiral development, rapid application development, and extreme programming.
A life-cycle "model" is sometimes considered a more general term for a category of methodologies and a software development "process" is a particular instance as adopted by a specific organization. For example, many specific software development processes fit the spiral life-cycle model. The field is often considered a subset of the systems development life cycle.
== History ==
The software development methodology framework did not emerge until the 1960s. According to Elliott (2004), the systems development life cycle can be considered to be the oldest formalized methodology framework for building information systems. The main idea of the software development life cycle has been "to pursue the development of information systems in a very deliberate, structured and methodical way, requiring each stage of the life cycle––from the inception of the idea to delivery of the final system––to be carried out rigidly and sequentially" within the context of the framework being applied. The main target of this methodology framework in the 1960s was "to develop large scale functional business systems in an age of large scale business conglomerates. Information systems activities revolved around heavy data processing and number crunching routines."
Requirements gathering and analysis:
The first phase of the custom software development process involves understanding the client's requirements and objectives. This stage typically involves engaging in thorough discussions and conducting interviews with stakeholders to identify the desired features, functionalities, and overall scope of the software. The development team works closely with the client to analyze existing systems and workflows, determine technical feasibility, and define project milestones.
Planning and design:
Once the requirements are understood, the custom software development team proceeds to create a comprehensive project plan. This plan outlines the development roadmap, including timelines, resource allocation, and deliverables. The software architecture and design are also established during this phase. User interface (UI) and user experience (UX) design elements are considered to ensure the software's usability, intuitiveness, and visual appeal.
Development:
With the planning and design in place, the development team begins the coding process. This phase involves writing, testing, and debugging the software code. Agile methodologies, such as scrum or kanban, are often employed to promote flexibility, collaboration, and iterative development. Regular communication between the development team and the client ensures transparency and enables quick feedback and adjustments.
Testing and quality assurance:
To ensure the software's reliability, performance, and security, rigorous testing and quality assurance (QA) processes are carried out. Different testing techniques, including unit testing, integration testing, system testing, and user acceptance testing, are employed to identify and rectify any issues or bugs. QA activities aim to validate the software against the predefined requirements, ensuring that it functions as intended.
Deployment and implementation:
Once the software passes the testing phase, it is ready for deployment and implementation. The development team assists the client in setting up the software environment, migrating data if necessary, and configuring the system. User training and documentation are also provided to ensure a smooth transition and enable users to maximize the software's potential.
Maintenance and support:
After the software is deployed, ongoing maintenance and support become crucial to address any issues, enhance performance, and incorporate future enhancements. Regular updates, bug fixes, and security patches are released to keep the software up-to-date and secure. This phase also involves providing technical support to end users and addressing their queries or concerns.
Methodologies, processes, and frameworks range from specific prescriptive steps that can be used directly by an organization in day-to-day work, to flexible frameworks that an organization uses to generate a custom set of steps tailored to the needs of a specific project or group. In some cases, a "sponsor" or "maintenance" organization distributes an official set of documents that describe the process. Specific examples include:
1970s
Structured programming since 1969
Cap Gemini SDM, originally from PANDATA, the first English translation was published in 1974. SDM stands for System Development Methodology
1980s
Structured systems analysis and design method (SSADM) from 1980 onwards
Information Requirement Analysis/Soft systems methodology
1990s
Object-oriented programming (OOP) developed in the early 1960s and became a dominant programming approach during the mid-1990s
Rapid application development (RAD), since 1991
Dynamic systems development method (DSDM), since 1994
Scrum, since 1995
Team software process, since 1998
Rational Unified Process (RUP), maintained by IBM since 1998
Extreme programming, since 1999
2000s
Agile Unified Process (AUP) maintained since 2005 by Scott Ambler
Disciplined agile delivery (DAD) Supersedes AUP
2010s
Scaled Agile Framework (SAFe)
Large-Scale Scrum (LeSS)
DevOps
Since DSDM in 1994, all of the methodologies on the above list except RUP have been agile methodologies - yet many organizations, especially governments, still use pre-agile processes (often waterfall or similar). Software process and software quality are closely interrelated; some unexpected facets and effects have been observed in practice.
Among these, another software development process has been established in open source. The adoption of these best practices known and established processes within the confines of a company is called inner source.
== Prototyping ==
Software prototyping is about creating prototypes, i.e. incomplete versions of the software program being developed.
The basic principles are:
Prototyping is not a standalone, complete development methodology, but rather an approach to try out particular features in the context of a full methodology (such as incremental, spiral, or rapid application development (RAD)).
Attempts to reduce inherent project risk by breaking a project into smaller segments and providing more ease of change during the development process.
The client is involved throughout the development process, which increases the likelihood of client acceptance of the final implementation.
While some prototypes are developed with the expectation that they will be discarded, it is possible in some cases to evolve from prototype to working system.
A basic understanding of the fundamental business problem is necessary to avoid solving the wrong problems, but this is true for all software methodologies.
== Methodologies ==
=== Agile development ===
"Agile software development" refers to a group of software development frameworks based on iterative development, where requirements and solutions evolve via collaboration between self-organizing cross-functional teams. The term was coined in the year 2001 when the Agile Manifesto was formulated.
Agile software development uses iterative development as a basis but advocates a lighter and more people-centric viewpoint than traditional approaches. Agile processes fundamentally incorporate iteration and the continuous feedback that it provides to successively refine and deliver a software system.
The Agile model also includes the following software development processes:
Dynamic systems development method (DSDM)
Kanban
Scrum
Lean software development
=== Continuous integration ===
Continuous integration is the practice of merging all developer working copies to a shared mainline several times a day.
Grady Booch first named and proposed CI in his 1991 method, although he did not advocate integrating several times a day. Extreme programming (XP) adopted the concept of CI and did advocate integrating more than once per day – perhaps as many as tens of times per day.
=== Incremental development ===
Various methods are acceptable for combining linear and iterative systems development methodologies, with the primary objective of each being to reduce inherent project risk by breaking a project into smaller segments and providing more ease-of-change during the development process.
There are three main variants of incremental development:
A series of mini-waterfalls are performed, where all phases of the waterfall are completed for a small part of a system, before proceeding to the next increment, or
Overall requirements are defined before proceeding to evolutionary, mini-waterfall development of individual increments of a system, or
The initial software concept, requirements analysis, and design of architecture and system core are defined via waterfall, followed by incremental implementation, which culminates in installing the final version, a working system.
=== Rapid application development ===
Rapid application development (RAD) is a software development methodology, which favors iterative development and the rapid construction of prototypes instead of large amounts of up-front planning. The "planning" of software developed using RAD is interleaved with writing the software itself. The lack of extensive pre-planning generally allows software to be written much faster and makes it easier to change requirements.
The rapid development process starts with the development of preliminary data models and business process models using structured techniques. In the next stage, requirements are verified using prototyping, eventually to refine the data and process models. These stages are repeated iteratively; further development results in "a combined business requirements and technical design statement to be used for constructing new systems".
The term was first used to describe a software development process introduced by James Martin in 1991. According to Whitten (2003), it is a merger of various structured techniques, especially data-driven information technology engineering, with prototyping techniques to accelerate software systems development.
The basic principles of rapid application development are:
Key objective is for fast development and delivery of a high-quality system at a relatively low investment cost.
Attempts to reduce inherent project risk by breaking a project into smaller segments and providing more ease of change during the development process.
Aims to produce high-quality systems quickly, primarily via iterative Prototyping (at any stage of development), active user involvement, and computerized development tools. These tools may include graphical user interface (GUI) builders, Computer Aided Software Engineering (CASE) tools, Database Management Systems (DBMS), fourth-generation programming languages, code generators, and object-oriented techniques.
Key emphasis is on fulfilling the business need, while technological or engineering excellence is of lesser importance.
Project control involves prioritizing development and defining delivery deadlines or “timeboxes”. If the project starts to slip, the emphasis is on reducing requirements to fit the timebox, not on increasing the deadline.
Generally includes joint application design (JAD), where users are intensely involved in system design, via consensus building in either structured workshops, or electronically facilitated interaction.
Active user involvement is imperative.
Iteratively produces production software, as opposed to a throwaway prototype.
Produces documentation necessary to facilitate future development and maintenance.
Standard systems analysis and design methods can be fitted into this framework.
=== Waterfall development ===
The waterfall model is a sequential development approach, in which development is seen as flowing steadily downwards (like a waterfall) through several phases, typically:
Requirements analysis resulting in a software requirements specification
Software design
Implementation
Testing
Integration, if there are multiple subsystems
Deployment (or Installation)
Maintenance
The first formal description of the method is often cited as an article published by Winston W. Royce in 1970, although Royce did not use the term "waterfall" in this article. Royce presented this model as an example of a flawed, non-working model.
The basic principles are:
The Project is divided into sequential phases, with some overlap and splashback acceptable between phases.
Emphasis is on planning, time schedules, target dates, budgets, and implementation of an entire system at one time.
Tight control is maintained over the life of the project via extensive written documentation, formal reviews, and approval/signoff by the user and information technology management occurring at the end of most phases before beginning the next phase. Written documentation is an explicit deliverable of each phase.
The waterfall model is a traditional engineering approach applied to software engineering. A strict waterfall approach discourages revisiting and revising any prior phase once it is complete. This "inflexibility" in a pure waterfall model has been a source of criticism by supporters of other more "flexible" models. It has been widely blamed for several large-scale government projects running over budget, over time and sometimes failing to deliver on requirements due to the big design up front approach. Except when contractually required, the waterfall model has been largely superseded by more flexible and versatile methodologies developed specifically for software development. See Criticism of waterfall model.
=== Spiral development ===
In 1988, Barry Boehm published a formal software system development "spiral model," which combines some key aspects of the waterfall model and rapid prototyping methodologies, in an effort to combine advantages of top-down and bottom-up concepts. It provided emphasis on a key area many felt had been neglected by other methodologies: deliberate iterative risk analysis, particularly suited to large-scale complex systems.
The basic principles are:
Focus is on risk assessment and on minimizing project risk by breaking a project into smaller segments and providing more ease-of-change during the development process, as well as providing the opportunity to evaluate risks and weigh consideration of project continuation throughout the life cycle.
"Each cycle involves a progression through the same sequence of steps, for each part of the product and for each of its levels of elaboration, from an overall concept-of-operation document down to the coding of each individual program."
Each trip around the spiral traverses four basic quadrants: (1) determine objectives, alternatives, and constraints of the iteration, and (2) evaluate alternatives; Identify and resolve risks; (3) develop and verify deliverables from the iteration; and (4) plan the next iteration.
Begin each cycle with an identification of stakeholders and their "win conditions", and end each cycle with review and commitment.
=== Shape Up ===
Shape Up is a software development approach introduced by Basecamp in 2018. It is a set of principles and techniques that Basecamp developed internally to overcome the problem of projects dragging on with no clear end. Its primary target audience is remote teams. Shape Up has no estimation and velocity tracking, backlogs, or sprints, unlike waterfall, agile, or scrum. Instead, those concepts are replaced with appetite, betting, and cycles. As of 2022, besides Basecamp, notable organizations that have adopted Shape Up include UserVoice and Block.
=== Advanced methodologies ===
Other high-level software project methodologies include:
Behavior-driven development and business process management.
Chaos model - The main rule always resolves the most important issue first.
Incremental funding methodology - an iterative approach
Lightweight methodology - a general term for methods that only have a few rules and practices
Structured systems analysis and design method - a specific version of waterfall
Slow programming, as part of the larger Slow Movement, emphasizes careful and gradual work without (or minimal) time pressures. Slow programming aims to avoid bugs and overly quick release schedules.
V-Model (software development) - an extension of the waterfall model
Unified Process (UP) is an iterative software development methodology framework, based on Unified Modeling Language (UML). UP organizes the development of software into four phases, each consisting of one or more executable iterations of the software at that stage of development: inception, elaboration, construction, and guidelines.
== Process meta-models ==
Some "process models" are abstract descriptions for evaluating, comparing, and improving the specific process adopted by an organization.
ISO/IEC 12207 is the international standard describing the method to select, implement, and monitor the life cycle for software.
The Capability Maturity Model Integration (CMMI) is one of the leading models and is based on best practices. Independent assessments grade organizations on how well they follow their defined processes, not on the quality of those processes or the software produced. CMMI has replaced CMM.
ISO 9000 describes standards for a formally organized process to manufacture a product and the methods of managing and monitoring progress. Although the standard was originally created for the manufacturing sector, ISO 9000 standards have been applied to software development as well. Like CMMI, certification with ISO 9000 does not guarantee the quality of the end result, only that formalized business processes have been followed.
ISO/IEC 15504 Information technology—Process assessment is also known as Software Process Improvement Capability Determination (SPICE), is a "framework for the assessment of software processes". This standard is aimed at setting out a clear model for process comparison. SPICE is used much like CMMI. It models processes to manage, control, guide, and monitor software development. This model is then used to measure what a development organization or project team actually does during software development. This information is analyzed to identify weaknesses and drive improvement. It also identifies strengths that can be continued or integrated into common practice for that organization or team.
ISO/IEC 24744 Software Engineering—Metamodel for Development Methodologies, is a power type-based metamodel for software development methodologies.
Soft systems methodology - a general method for improving management processes.
Method engineering - a general method for improving information system processes.
== See also ==
Systems development life cycle
Computer-aided software engineering (some of these tools support specific methodologies)
List of software development philosophies
Outline of software engineering
Software Project Management
Software development
Software development effort estimation
Software documentation
Software release life cycle
Top-down and bottom-up design#Computer science
== References ==
== External links ==
Selecting a development approach Archived January 2, 2019, at the Wayback Machine at cms.hhs.gov.
Gerhard Fischer, "The Software Technology of the 21st Century: From Software Reuse to Collaborative Software Design" Archived September 15, 2009, at the Wayback Machine, 2001 | Wikipedia/System_development_methodology |
Structured systems analysis and design method (SSADM) is a systems approach to the analysis and design of information systems. SSADM was produced for the Central Computer and Telecommunications Agency, a UK government office concerned with the use of technology in government, from 1980 onwards.
== Overview ==
SSADM is a waterfall method for the analysis and design of information systems. SSADM can be thought to represent a pinnacle of the rigorous document-led approach to system design, and contrasts with more contemporary agile methods such as DSDM or Scrum.
SSADM is one particular implementation and builds on the work of different schools of structured analysis and development methods, such as Peter Checkland's soft systems methodology, Larry Constantine's structured design, Edward Yourdon's Yourdon Structured Method, Michael A. Jackson's Jackson Structured Programming, and Tom DeMarco's structured analysis.
The names "Structured Systems Analysis and Design Method" and "SSADM" are registered trademarks of the Office of Government Commerce (OGC), which is an office of the United Kingdom's Treasury.
== History ==
The principal stages of the development of Structured System Analysing And Design Method were:
1980: Central Computer and Telecommunications Agency (CCTA) evaluate analysis and design methods.
1981: Consultants working for Learmonth & Burchett Management Systems, led by John Hall, chosen to develop SSADM v1.
1982: John Hall and Keith Robinson left to found Model Systems Ltd, LBMS later developed LSDM, their proprietary version.
1983: SSADM made mandatory for all new information system developments
1984: Version 2 of SSADM released
1986: Version 3 of SSADM released, adopted by NCC
1988: SSADM Certificate of Proficiency launched, SSADM promoted as 'open' standard
1989: Moves towards Euromethod, launch of CASE products certification scheme
1990: Version 4 launched
1993: SSADM V4 Standard and Tools Conformance Scheme
1995: SSADM V4+ announced, V4.2 launched
2000: CCTA renamed SSADM as "Business System Development". The method was repackaged into 15 modules and another 6 modules were added.
== SSADM techniques ==
The three most important techniques that are used in SSADM are as follows:
Logical Data Modelling
The process of identifying, modelling and documenting the data requirements of the system being designed. The result is a data model containing entities (things about which a business needs to record information), attributes (facts about the entities) and relationships (associations between the entities).
Data Flow Modelling
The process of identifying, modelling and documenting how data moves around an information system. Data Flow Modeling examines processes (activities that transform data from one form to another), data stores (the holding areas for data), external entities (what sends data into a system or receives data from a system), and data flows (routes by which data can flow).
Entity Event Modelling
A two-stranded process: Entity Behavior Modelling, identifying, modelling and documenting the events that affect each entity and the sequence (or life history) in which these events occur, and Event Modelling, designing for each event the process to coordinate entity life histories.
== Stages ==
The SSADM method involves the application of a sequence of analysis, documentation and design tasks concerned with the following.
=== Stage 0 – Feasibility study ===
In order to determine whether or not a given project is feasible, there must be some form of investigation into the goals and implications of the project. For very small scale projects this may not be necessary at all as the scope of the project is easily understood. In larger projects, the feasibility may be done but in an informal sense, either because there is no time for a formal study or because the project is a "must-have" and will have to be done one way or the other. A data flow Diagram is used to describe how the current system works and to visualize the known problems.
When a feasibility study is carried out, there are four main areas of consideration:
Technical – is the project technically possible?
Financial – can the business afford to carry out the project?
Organizational – will the new system be compatible with existing practices?
Ethical – is the impact of the new system socially acceptable?
To answer these questions, the feasibility study is effectively a condensed version of a comprehensive systems analysis and design. The requirements and usages are analyzed to some extent, some business options are drawn up and even some details of the technical implementation.
The product of this stage is a formal feasibility study document. SSADM specifies the sections that the study should contain including any preliminary models that have been constructed and also details of rejected options and the reasons for their rejection.
=== Stage 1 – Investigation of the current environment ===
The developers of SSADM understood that in almost all cases there is some form of current system even if it is entirely composed of people and paper. Through a combination of interviewing employees, circulating questionnaires, observations and existing documentation, the analyst comes to full understanding of the system as it is at the start of the project. This serves many purposes (Like examples?).
=== Stage 2 – Business system options ===
Having investigated the current system, the analyst must decide on the overall design of the new system. To do this, he or she, using the outputs of the previous stage, develops a set of business system options. These are different ways in which the new system could be produced varying from doing nothing to throwing out the old system entirely and building an entirely new one. The analyst may hold a brainstorming session so that as many and various ideas as possible are generated.
The ideas are then collected to options which are presented to the user. The options consider the following:
the degree of automation
the boundary between the system and the users
the distribution of the system, for example, is it centralized to one office or spread out across several?
cost/benefit
impact of the new system
Where necessary, the option will be documented with a logical data structure and a level 1 data-flow diagram.
The users and analyst together choose a single business option. This may be one of the ones already defined or may be a synthesis of different aspects of the existing options. The output of this stage is the single selected business option together with all the outputs of the feasibility stage.
=== Stage 3 – Requirements specification ===
This is probably the most complex stage in SSADM. Using the requirements developed in stage 1 and working within the framework of the selected business option, the analyst must develop a full logical specification of what the new system must do. The specification must be free from error, ambiguity and inconsistency. By logical, we mean that the specification does not say how the system will be implemented but rather describes what the system will do.
To produce the logical specification, the analyst builds the required logical models for both the data-flow diagrams (DFDs) and the Logical Data Model (LDM), consisting of the Logical Data Structure (referred to in other methods as entity relationship diagrams) and full descriptions of the data and its relationships. These are used to produce function definitions of every function which the users will require of the system, Entity Life-Histories (ELHs) which describe all events through the life of an entity, and Effect Correspondence Diagrams (ECDs) which describe how each event interacts with all relevant entities. These are continually matched against the requirements and where necessary, the requirements are added to and completed.
The product of this stage is a complete requirements specification document which is made up of:
the updated data catalogue
the updated requirements catalogue
the processing specification which in turn is made up of
user role/function matrix
function definitions
required logical data model
entity life-histories
effect correspondence diagrams
=== Stage 4 – Technical system options ===
This stage is the first towards a physical implementation of the new system application. Like the Business System Options, in this stage a large number of options for the implementation of the new system are generated. This is narrowed down to two or three to present to the user from which the final option is chosen or synthesized.
However, the considerations are quite different being:
the hardware architectures
the software to use
the cost of the implementation
the staffing required
the physical limitations such as a space occupied by the system
the distribution including any networks which that may require
the overall format of the human computer interface
All of these aspects must also conform to any constraints imposed by the business such as available money and standardization of hardware and software.
The output of this stage is a chosen technical system option.
=== Stage 5 – Logical design ===
Though the previous level specifies details of the implementation, the outputs of this stage are implementation-independent and concentrate on the requirements for the human computer interface. The logical design specifies the main methods of interaction in terms of menu structures and command structures.
One area of activity is the definition of the user dialogues. These are the main interfaces with which the users will interact with the system. Other activities are concerned with analyzing both the effects of events in updating the system and the need to make inquiries about the data on the system. Both of these use the events, function descriptions and effect correspondence diagrams produced in stage 3 to determine precisely how to update and read data in a consistent and secure way.
The product of this stage is the logical design which is made up of:
Data catalogue
Required logical data structure
Logical process model – includes dialogues and model for the update and inquiry processes
Stress & Bending moment.
=== Stage 6 – Physical design ===
This is the final stage where all the logical specifications of the system are converted to descriptions of the system in terms of real hardware and software. This is a very technical stage and a simple overview is presented here.
The logical data structure is converted into a physical architecture in terms of database structures. The exact structure of the functions and how they are implemented is specified. The physical data structure is optimized where necessary to meet size and performance requirements.
The product is a complete Physical Design which could tell software engineers how to build the system in specific details of hardware and software and to the appropriate standards.
== References ==
5. Keith Robinson, Graham Berrisford: Object-oriented SSADM, Prentice Hall International (UK), Hemel Hempstead, ISBN 0-13-309444-8
== External links ==
What is SSADM? at webopedia.com
Introduction to Methodologies and SSADM
Case study using pragmatic SSADM
Structured Analysis Wiki | Wikipedia/Structured_Systems_Analysis_and_Design_Method |
Model-driven engineering (MDE) is a software development methodology that focuses on creating and exploiting domain models, which are conceptual models of all the topics related to a specific problem. Hence, it highlights and aims at abstract representations of the knowledge and activities that govern a particular application domain, rather than the computing (i.e. algorithmic) concepts.
MDE is a subfield of a software design approach referred as round-trip engineering. The scope of the MDE is much wider than that of the Model-Driven Architecture.
== Overview ==
The MDE approach is meant to increase productivity by maximizing compatibility between systems (via reuse of standardized models), simplifying the process of design (via models of recurring design patterns in the application domain), and promoting communication between individuals and teams working on the system (via a standardization of the terminology and the best practices used in the application domain). For instance, in model-driven development, technical artifacts such as source code, documentation, tests, and more are generated algorithmically from a domain model.
A modeling paradigm for MDE is considered effective if its models make sense from the point of view of a user that is familiar with the domain, and if they can serve as a basis for implementing systems. The models are developed through extensive communication among product managers, designers, developers and users of the application domain. As the models approach completion, they enable the development of software and systems.
Some of the better known MDE initiatives are:
The Object Management Group (OMG) initiative Model-Driven Architecture (MDA) which is leveraged by several of their standards such as Meta-Object Facility, XMI, CWM, CORBA, Unified Modeling Language (to be more precise, the OMG currently promotes the use of a subset of UML called fUML together with its action language, ALF, for model-driven architecture; a former approach relied on Executable UML and OCL, instead), and QVT.
The Eclipse "eco-system" of programming and modelling tools represented in general terms by the (Eclipse Modeling Framework). This framework allows the creation of tools implementing the MDA standards of the OMG; but, it is also possible to use it to implement other modeling-related tools.
== History ==
The first tools to support MDE were the Computer-Aided Software Engineering (CASE) tools developed in the 1980s. Companies like Integrated Development Environments (IDE – StP), Higher Order Software (now Hamilton Technologies, Inc., HTI), Cadre Technologies, Bachman Information Systems, and Logic Works (BP-Win and ER-Win) were pioneers in the field.
The US government got involved in the modeling definitions creating the IDEF specifications. With several variations of the modeling definitions (see Booch, Rumbaugh, Jacobson, Gane and Sarson, Harel, Shlaer and Mellor, and others) they were eventually joined creating the Unified Modeling Language (UML). Rational Rose, a product for UML implementation, was done by Rational Corporation (Booch) responding automation yield higher levels of abstraction in software development. This abstraction promotes simpler models with a greater focus on problem space. Combined with executable semantics this elevates the total level of automation possible. The Object Management Group (OMG) has developed a set of standards called Model-Driven Architecture (MDA), building a foundation for this advanced architecture-focused approach.
== Advantages ==
According to Douglas C. Schmidt, model-driven engineering technologies offer a promising approach to address the inability of third-generation languages to alleviate the complexity of platforms and express domain concepts effectively.
== Tools ==
Notable software tools for model-driven engineering include:
== See also ==
Application lifecycle management (ALM)
Business Process Model and Notation (BPMN)
Business-driven development (BDD)
Domain-driven design (DDD)
Domain-specific language (DSL)
Domain-specific modeling (DSM)
Domain-specific multimodeling
Language-oriented programming (LOP)
List of Unified Modeling Language tools
Model transformation (e.g. using QVT)
Model-based testing (MBT)
Modeling Maturity Level (MML)
Model-based systems engineering (MBSE)
Service-oriented modeling Framework (SOMF)
Software factory (SF)
Story-driven modeling (SDM)
Open API, open source specification for description of models and operations for HTTP interoperation and REST APIc
== References ==
== Further reading ==
David S. Frankel, Model Driven Architecture: Applying MDA to Enterprise Computing, John Wiley & Sons, ISBN 0-471-31920-1
Marco Brambilla, Jordi Cabot, Manuel Wimmer, Model Driven Software Engineering in Practice, foreword by Richard Soley (OMG Chairman), Morgan & Claypool, USA, 2012, Synthesis Lectures on Software Engineering #1. 182 pages. ISBN 9781608458820 (paperback), ISBN 9781608458837 (ebook). https://www.mdse-book.com
da Silva, Alberto Rodrigues (2015). "Model-Driven Engineering: A Survey Supported by a Unified Conceptual Model". Computer Languages, Systems & Structures. 43 (43): 139–155. doi:10.1016/j.cl.2015.06.001.
== External links ==
Model-Driven Architecture: Vision, Standards And Emerging Technologies at omg.org | Wikipedia/Model-driven_software_development |
Rapid application development (RAD), also called rapid application building (RAB), is both a general term for adaptive software development approaches, and the name for James Martin's method of rapid development. In general, RAD approaches to software development put less emphasis on planning and more emphasis on an adaptive process. Prototypes are often used in addition to or sometimes even instead of design specifications.
RAD is especially well suited for (although not limited to) developing software that is driven by user interface requirements. Graphical user interface builders are often called rapid application development tools. Other approaches to rapid development include the adaptive, agile, spiral, and unified models.
== History ==
Rapid application development was a response to plan-driven waterfall processes, developed in the 1970s and 1980s, such as the Structured Systems Analysis and Design Method (SSADM). One of the problems with these methods is that they were based on a traditional engineering model used to design and build things like bridges and buildings. Software is an inherently different kind of artifact. Software can radically change the entire process used to solve a problem. As a result, knowledge gained from the development process itself can feed back to the requirements and design of the solution. Plan-driven approaches attempt to rigidly define the requirements, the solution, and the plan to implement it, and have a process that discourages changes. RAD approaches, on the other hand, recognize that software development is a knowledge intensive process and provide flexible processes that help take advantage of knowledge gained during the project to improve or adapt the solution.
The first such RAD alternative was developed by Barry Boehm and was known as the spiral model. Boehm and other subsequent RAD approaches emphasized developing prototypes as well as or instead of rigorous design specifications. Prototypes had several advantages over traditional specifications:
Risk reduction. A prototype could test some of the most difficult potential parts of the system early on in the life-cycle. This can provide valuable information as to the feasibility of a design and can prevent the team from pursuing solutions that turn out to be too complex or time-consuming to implement. This benefit of finding problems earlier in the life-cycle rather than later was a key benefit of the RAD approach. The earlier a problem can be found the cheaper it is to address.
Users are better at using and reacting than at creating specifications. In the waterfall model it was common for a user to sign off on a set of requirements but then when presented with an implemented system to suddenly realize that a given design lacked some critical features or was too complex. In general most users give much more useful feedback when they can experience a prototype of the running system rather than abstractly define what that system should be.
Prototypes can be usable and can evolve into the completed product. One approach used in some RAD methods was to build the system as a series of prototypes that evolve from minimal functionality to moderately useful to the final completed system. The advantage of this besides the two advantages above was that the users could get useful business functionality much earlier in the process.
Starting with the ideas of Barry Boehm and others, James Martin developed the rapid application development approach during the 1980s at IBM and finally formalized it by publishing a book in 1991, Rapid Application Development. This has resulted in some confusion over the term RAD even among IT professionals. It is important to distinguish between RAD as a general alternative to the waterfall model and RAD as the specific method created by Martin. The Martin method was tailored toward knowledge intensive and UI intensive business systems.
These ideas were further developed and improved upon by RAD pioneers like James Kerr and Richard Hunter, who together wrote the seminal book on the subject, Inside RAD, which followed the journey of a RAD project manager as he drove and refined the RAD Methodology in real-time on an actual RAD project. These practitioners, and those like them, helped RAD gain popularity as an alternative to traditional systems project life cycle approaches.
The RAD approach also matured during the period of peak interest in business re-engineering. The idea of business process re-engineering was to radically rethink core business processes such as sales and customer support with the new capabilities of Information Technology in mind. RAD was often an essential part of larger business re engineering programs. The rapid prototyping approach of RAD was a key tool to help users and analysts "think out of the box" about innovative ways that technology might radically reinvent a core business process.
Much of James Martin's comfort with RAD stemmed from Dupont's Information Engineering division and its leader Scott Schultz and their respective relationships with John Underwood who headed up a bespoke RAD development company that pioneered many successful RAD projects in Australia and Hong Kong.
Successful projects that included ANZ Bank, Lend Lease, BHP, Coca-Cola Amatil, Alcan, Hong Kong Jockey Club and numerous others.
Success that led to both Scott Shultz and James Martin both spending time in Australia with John Underwood to understand the methods and details of why Australia was disproportionately successful in implementing significant mission critical RAD projects.
== James Martin approach ==
The James Martin approach to RAD divides the process into four distinct phases:
Requirements planning phase – combines elements of the system planning and systems analysis phases of the systems development life cycle (SDLC). Users, managers, and IT staff members discuss and agree on business needs, project scope, constraints, and system requirements. It ends when the team agrees on the key issues and obtains management authorization to continue.
User design phase – during this phase, users interact with systems analysts and develop models and prototypes that represent all system processes, inputs, and outputs. The RAD groups or subgroups typically use a combination of joint application design (JAD) techniques and CASE tools to translate user needs into working models. User design is a continuous interactive process that allows users to understand, modify, and eventually approve a working model of the system that meets their needs.
Construction phase – focuses on program and application development task similar to the SDLC. In RAD, however, users continue to participate and can still suggest changes or improvements as actual screens or reports are developed. Its tasks are programming and application development, coding, unit-integration and system testing.
Cutover phase – resembles the final tasks in the SDLC implementation phase, including data conversion, testing, changeover to the new system, and user training. Compared with traditional methods, the entire process is compressed. As a result, the new system is built, delivered, and placed in operation much sooner.
== Advantages ==
In modern Information Technology environments, many systems are now built using some degree of Rapid Application Development (not necessarily the James Martin approach). In addition to Martin's method, agile methods and the Rational Unified Process are often used for RAD development.
The purported advantages of RAD include:
Better quality. By having users interact with evolving prototypes the business functionality from a RAD project can often be much higher than that achieved via a waterfall model. The software can be more usable and has a better chance to focus on business problems that are critical to end users rather than technical problems of interest to developers. However, this excludes other categories of what are usually known as Non-functional requirements (AKA constraints or quality attributes) including security and portability.
Risk control. Although much of the literature on RAD focuses on speed and user involvement a critical feature of RAD done correctly is risk mitigation. It's worth remembering that Boehm initially characterized the spiral model as a risk based approach. A RAD approach can focus in early on the key risk factors and adjust to them based on empirical evidence collected in the early part of the process. E.g., the complexity of prototyping some of the most complex parts of the system.
More projects completed on time and within budget. By focusing on the development of incremental units the chances for catastrophic failures that have dogged large waterfall projects is reduced. In the Waterfall model it was common to come to a realization after six months or more of analysis and development that required a radical rethinking of the entire system. With RAD this kind of information can be discovered and acted upon earlier in the process.
== Disadvantages ==
The purported disadvantages of RAD include:
The risk of a new approach. For most IT shops RAD was a new approach that required experienced professionals to rethink the way they worked. Humans are virtually always averse to change and any project undertaken with new tools or methods will be more likely to fail the first time simply due to the requirement for the team to learn.
Lack of emphasis on Non-functional requirements, which are often not visible to the end user in normal operation.
Requires time of scarce resources. One thing virtually all approaches to RAD have in common is that there is much more interaction throughout the entire life-cycle between users and developers. In the waterfall model, users would define requirements and then mostly go away as developers created the system. In RAD users are involved from the beginning and through virtually the entire project. This requires that the business is willing to invest the time of application domain experts. The paradox is that the better the expert, the more they are familiar with their domain, the more they are required to actually run the business and it may be difficult to convince their supervisors to invest their time. Without such commitments RAD projects will not succeed.
Less control. One of the advantages of RAD is that it provides a flexible adaptable process. The ideal is to be able to adapt quickly to both problems and opportunities. There is an inevitable trade-off between flexibility and control, more of one means less of the other. If a project (e.g. life-critical software) values control more than agility RAD is not appropriate.
Poor design. The focus on prototypes can be taken too far in some cases resulting in a "hack and test" methodology where developers are constantly making minor changes to individual components and ignoring system architecture issues that could result in a better overall design. This can especially be an issue for methodologies such as Martin's that focus so heavily on the user interface of the system.
Lack of scalability. RAD typically focuses on small to medium-sized project teams. The other issues cited above (less design and control) present special challenges when using a RAD approach for very large scale systems.
== See also ==
Practical concepts to implement RAD:
Graphical user interface builder, where main software tools for RAD are represented
Fourth-generation programming language, e.g. FileMaker, 4th Dimension, dBase and Visual FoxPro
Other similar concepts:
Flow-based programming
Lean software development
Platform as a service
Low-code development platforms
No-code development platform
== References ==
== Further reading ==
Steve McConnell (1996). Rapid Development: Taming Wild Software Schedules, Microsoft Press Books, ISBN 978-1-55615-900-8
Kerr, James M.; Hunter, Richard (1993). Inside RAD: How to Build a Fully Functional System in 90 Days or Less. McGraw-Hill. ISBN 0-07-034223-7.
Ellen Gottesdiener (1995). "RAD Realities: Beyond the Hype to How RAD Really Works" Application Development Trends
Ken Schwaber (1996). Agile Project Management with Scrum, Microsoft Press Books, ISBN 978-0-7356-1993-7
Steve McConnell (2003). Professional Software Development: Shorter Schedules, Higher Quality Products, More Successful Projects, Enhanced Careers, Addison-Wesley, ISBN 978-0-321-19367-4
Dean Leffingwell (2007). Scaling Software Agility: Best Practices for Large Enterprises, Addison-Wesley Professional, ISBN 978-0-321-45819-3
Scott Stiner (2016). Forbes List: "Rapid Application Development (RAD): A Smart, Quick And Valuable Process For Software Developers" | Wikipedia/Rapid_Application_Development_Tool |
Joint application design is a term originally used to describe a software development process pioneered and deployed during the mid-1970s by the New York Telephone Company's Systems Development Center under the direction of Dan Gielan. Following a series of implementations of this methodology, Gielan lectured extensively in various forums on the methodology and its practices. Arnie Lind, then a Senior Systems Engineer at IBM Canada in Regina, Saskatchewan created and named joint application design in 1974. Existing methods, however, entailed application developers spending months learning the specifics of a particular department or job function, and then developing an application for the function or department. In addition to development backlog delays, this process resulted in applications taking years to develop, and often not being fully accepted by the application users.
Arnie Lind's idea was that rather than have application developers learn about people's jobs, people doing the work could be taught how to write an application. Arnie pitched the concept to IBM Canada's Vice President Carl Corcoran (later President of IBM Canada), and Carl approved a pilot project. Arnie and Carl together named the methodology JAD, an acronym for joint application design, after Carl Corcoran rejected the acronym JAL, or joint application logistics, upon realizing that Arnie Lind's initials were JAL (John Arnold Lind).
The pilot project was an emergency room project for the Saskatchewan Government. Arnie developed the JAD methodology, and put together a one-week seminar, involving primarily nurses and administrators from the emergency room, but also including some application development personnel. The one-week seminar produced an application framework, which was then coded and implemented in less than one month, versus an average of 18 months for traditional application development. And because the users themselves designed the system, they immediately adopted and liked the application. After the pilot project, IBM was very supportive of the JAD methodology, as they saw it as a way to more quickly implement computing applications, running on IBM hardware.
Arnie Lind spent the next 13 years at IBM Canada continuing to develop the JAD methodology, and traveling around the world performing JAD seminars, and training IBM employees in the methods and techniques of JAD. JADs were performed extensively throughout IBM Canada, and the technique also spread to IBM in the United States. Arnie Lind trained several people at IBM Canada to perform JADs, including Tony Crawford and Chuck Morris. Arnie Lind retired from IBM in 1987, and continued to teach and perform JADs on a consulting basis, throughout Canada, the United States, and Asia.
The JAD process was formalized by Tony Crawford and Chuck Morris of IBM in the late 1970s. It was then deployed at Canadian International Paper. JAD was used in IBM Canada for a while before being brought back to the US. Initially, IBM used JAD to help sell and implement a software program they sold, called COPICS. It was widely adapted to many uses (system requirements, grain elevator design, problem-solving, etc.). Tony Crawford later developed JAD-Plan and then JAR (joint application requirements). In 1985, Gary Rush wrote about JAD and its derivations – Facilitated Application Specification Techniques (FAST) – in Computerworld.
Originally, JAD was designed to bring system developers and users of varying backgrounds and opinions together in a productive as well as creative environment. The meetings were a way of obtaining quality requirements and specifications. The structured approach provides a good alternative to traditional serial interviews by system analysts. JAD has since expanded to cover broader IT work as well as non-IT work (read about Facilitated Application Specification Techniques – FAST – created by Gary Rush in 1985 to expand JAD applicability.
== Key participants ==
Executive Sponsor
The executive who charters the project, the system owner. They must be high enough in the organization to be able to make decisions and provide the necessary strategy, planning, and direction.
Subject Matter Experts
These are the business users, the IS professionals, and the outside experts that will be needed for a successful workshop. This group is the backbone of the meeting; they will drive the changes.
Facilitator/Session Leader
meeting and directs traffic by keeping the group on the meeting agenda. The facilitator is responsible for identifying those issues that can be solved as part of the meeting and those which need to be assigned at the end of the meeting for follow-up investigation and resolution. The facilitator serves the participants and does not contribute information to the meeting.
Scribe/Modeller/Recorder/Documentation Expert
Records and publish the proceedings of the meeting and does not contribute information to the meeting.
Observers
Generally members of the application development team assigned to the project. They are to sit behind the participants and are to silently observe the proceedings.
== 9 key steps ==
Identify project objectives and limitations: It is vital to have clear objectives for the workshop and for the project as a whole. The pre-workshop activities, the planning and scoping, set the expectations of the workshop sponsors and participants. Scoping identifies the business functions that are within the scope of the project. It also tries to assess both the project design and implementation complexity. The political sensitivity of the project should be assessed. Has this been tried in the past? How many false starts were there? How many implementation failures were there? Sizing is important. For best results, systems projects should be sized so that a complete design – right down to screens and menus – can be designed in 8 to 10 workshop days.
Identify critical success factors: It is important to identify the critical success factors for both the development project and the business function being studied. How will we know that the planned changes have been effective? How will success be measured? Planning for outcomes assessment helps to judge the effectiveness and the quality of the implemented system over its entire operational life.
Define project deliverables: In general, the deliverables from a workshop are documentation and a design. It is important to define the form and level of detail of the workshop documentation. What types of diagrams will be provided? What type or form of narrative will be supplied? It is a good idea to start using a CASE tool for diagramming support right from the start. Most of the available tools have good to great diagramming capabilities but their narrative support is generally weak. The narrative is best produced with your standard word processing software.
Define the schedule of workshop activities: Workshops vary in length from one to five days. The initial workshop for a project should not be less than three days. It takes the participants most of the first day to get comfortable with their roles, with each other, and with the environment. The second day is spent learning to understand each other and developing a common language with which to communicate issues and concerns. By the third day, everyone is working together on the problem and real productivity is achieved. After the initial workshop, the team-building has been done. Shorter workshops can be scheduled for subsequent phases of the project, for instance, to verify a prototype. However, it will take the participants from one to three hours to re-establish the team psychology of the initial workshop.
Select the participants: These are the business users, the IT professionals, and the outside experts that will be needed for a successful workshop. These are the true "back bones" of the meeting who will drive the changes.
Prepare the workshop material: Before the workshop, the project manager and the facilitator perform an analysis and build a preliminary design or straw man to focus the workshop. The workshop material consists of documentation, worksheets, diagrams, and even props that will help the participants understand the business function under investigation.
Organize workshop activities and exercises: The facilitator must design workshop exercises and activities to provide interim deliverables that build towards the final output of the workshop. The pre-workshop activities help design those workshop exercises. For example, for a Business Area Analysis, what's in it? A decomposition diagram? A high-level entity-relationship diagram? A normalized data model? A state transition diagram? A dependency diagram? All of the above? None of the above? It is important to define the level of technical diagramming that is appropriate to the environment. The most important thing about a diagram is that it must be understood by the users. Once the diagram choice is made, the facilitator designs exercises into the workshop agenda to get the group to develop those diagrams. A workshop combines exercises that are serially oriented to build on one another, and parallel exercises, with each sub-team working on a piece of the problem or working on the same thing for a different functional area. High-intensity exercises led by the facilitator energize the group and direct it towards a specific goal. Low-intensity exercises allow for detailed discussions before decisions. The discussions can involve the total group or teams can work out the issues and present a limited number of suggestions for the whole group to consider. To integrate the participants, the facilitator can match people with similar expertise from different departments. To help participants learn from each other, the facilitator can mix the expertise. It's up to the facilitator to mix and match the sub-team members to accomplish the organizational, cultural, and political objectives of the workshop. A workshop operates on both the technical level and the political level. It is the facilitator's job to build consensus and communications, to force issues out early in the process. There is no need to worry about the technical implementation of a system if the underlying business issues cannot be resolved.
Prepare, inform, educate the workshop participants: All of the participants in the workshop must be made aware of the objectives and limitations of the project and the expected deliverables of the workshop. Briefing of participants should take place 1 to 5 days before the workshop. This briefing may be teleconferenced if participants are widely dispersed. The briefing document might be called the Familiarization Guide, Briefing Guide, Project Scope Definition, or the Management Definition Guide – or anything else that seems appropriate. It is a document of eight to twelve pages, and it provides a clear definition of the scope of the project for the participants. The briefing itself lasts two to four hours. It provides the psychological preparation everyone needs to move forward into the workshop.
Coordinate workshop logistics: Workshops should be held off-site to avoid interruptions. Projectors, screens, PCs, tables, markers, masking tape, Post-It notes, and many other props should be prepared. What specific facilities and props are needed is up to the facilitator. They can vary from simple flip charts to electronic white boards. In any case, the layout of the room must promote the communication and interaction of the participants.
== Advantages ==
JAD decreases time and costs associated with requirements elicitation process. During 2-4 weeks information not only is collected, but requirements, agreed upon by various system users, are identified. Experience with JAD allows companies to customize their systems analysis process into even more dynamic ones like Double Helix, a methodology for mission-critical work.
JAD sessions help bring experts together giving them a chance to share their views, understand views of others, and develop the sense of project ownership.
The methods of JAD implementation are well-known, as it is "the first accelerated design technique available on the market and probably best known", and can easily be applied by any organization.
Easy integration of CASE tools into JAD workshops improves session productivity and provides systems analysts with discussed and ready to use models.
== Challenges ==
Without multifaceted preparation for a JAD session, professionals' valuable time can be easily wasted. If JAD session organizers do not study the elements of the system being evaluated, an incorrect problem could be addressed, incorrect people could be invited to participate, and inadequate problem-solving resources could be used.
JAD workshop participants should include employees able to provide input on most, if not all, of the pertinent areas of the problem. This is why particular attention should be paid during participant selection. The group should consist not only of employees from various departments who will interact with the new system, but from different hierarchies of the organizational ladder. The participants may have conflicting points of view, but meeting will allow participants to see issues from different viewpoints. JAD brings to light a better model outline with better understanding of underlying processes.
The facilitator has an obligation to ensure all participants – not only the most vocal ones – have a chance to offer their opinions, ideas, and thoughts.
== References ==
== Bibliography ==
Yatco, Mei C. (1999). "Joint Application Design/Development". University of Missouri-St. Louis. Retrieved 2009-02-06.
Soltys, Roman; Anthony Crawford (1999). "JAD for business plans and designs". Archived from the original on 2009-03-13. Retrieved 2009-02-06.
Dennis, Alan R.; Hayes, Glenda S.; Daniels, Robert M. Jr. (Spring 1999). "Business Process Modeling with Group Support Systems". Journal of Management Information Systems. 15 (4): 115–142. doi:10.1080/07421222.1999.11518224. Retrieved 2015-05-14.
Botkin, John C. "Customer Involved Participation as Part of the Application Development Process". Archived from the original on 1998-12-01.
Moeller, Walter E. "Facilitated Information Gathering Sessions: An Information Engineering Technique". Retrieved 2010-03-22.
Bill Jennerich "Joint Application Design -- Business Requirements Analysis for Successful Re-engineering." 18:50, 26 June 2006 (UTC)[2] Last update time unknown. Accessed on Nov. 14, 1999.
Gary Rush "JAD - Its History and Evolution -- MGR Consulting Newsletter." July 2006 [3]
Gary Rush, "JAD Project Aids Design", Computerworld, Volume 18 Number 52, pages 31 and 38, December 24, 1984. [4]
Davidson, E.J (1999). "Joint application design (JAD) in practice". Journal of Systems and Software. 45 (3). Elsevier BV: 215–223. doi:10.1016/s0164-1212(98)10080-8. ISSN 0164-1212.
Gottesdiener, Ellen; Requirements by Collaboration: Workshops for Defining Needs, Addison-Wesley, 2002, ISBN 0-201-78606-0.
Wood, Jane and Silver, Denise; Joint Application Development, John Wiley & Sons Inc, ISBN 0-471-04299-4 | Wikipedia/Joint_application_design |
Agile software development is an umbrella term for approaches to developing software that reflect the values and principles agreed upon by The Agile Alliance, a group of 17 software practitioners, in 2001. As documented in their Manifesto for Agile Software Development the practitioners value:
Individuals and interactions over processes and tools
Working software over comprehensive documentation
Customer collaboration over contract negotiation
Responding to change over following a plan
The practitioners cite inspiration from new practices at the time including extreme programming, scrum, dynamic systems development method, adaptive software development and being sympathetic to the need for an alternative to documentation driven, heavyweight software development processes.
Many software development practices emerged from the agile mindset. These agile-based practices, sometimes called Agile (with a capital A) include requirements, discovery and solutions improvement through the collaborative effort of self-organizing and cross-functional teams with their customer(s)/end user(s).
While there is much anecdotal evidence that the agile mindset and agile-based practices improve the software development process, the empirical evidence is limited and less than conclusive.
== History ==
Iterative and incremental software development methods can be traced back as early as 1957, with evolutionary project management and adaptive software development emerging in the early 1970s.
During the 1990s, a number of lightweight software development methods evolved in reaction to the prevailing heavyweight methods (often referred to collectively as waterfall) that critics described as overly regulated, planned, and micromanaged. These lightweight methods included: rapid application development (RAD), from 1991; the unified process (UP) and dynamic systems development method (DSDM), both from 1994; Scrum, from 1995; Crystal Clear and extreme programming (XP), both from 1996; and feature-driven development (FDD), from 1997. Although these all originated before the publication of the Agile Manifesto, they are now collectively referred to as agile software development methods.
Already since 1991 similar changes had been underway in manufacturing and management thinking derived from Lean management.
In 2001, seventeen software developers met at a resort in Snowbird, Utah to discuss lightweight development methods. They were: Kent Beck (Extreme Programming), Ward Cunningham (Extreme Programming), Dave Thomas (Pragmatic Programming, Ruby), Jeff Sutherland (Scrum), Ken Schwaber (Scrum), Jim Highsmith (Adaptive Software Development), Alistair Cockburn (Crystal), Robert C. Martin (SOLID), Mike Beedle (Scrum), Arie van Bennekum, Martin Fowler (OOAD and UML), James Grenning, Andrew Hunt (Pragmatic Programming, Ruby), Ron Jeffries (Extreme Programming), Jon Kern, Brian Marick (Ruby, Test-driven development), and Steve Mellor (OOA). The group, The Agile Alliance, published the Manifesto for Agile Software Development.
In 2005, a group headed by Cockburn and Highsmith wrote an addendum of project management principles, the PM Declaration of Interdependence, to guide software project management according to agile software development methods.
In 2009, a group working with Martin wrote an extension of software development principles, the Software Craftsmanship Manifesto, to guide agile software development according to professional conduct and mastery.
In 2011, the Agile Alliance created the Guide to Agile Practices (renamed the Agile Glossary in 2016), an evolving open-source compendium of the working definitions of agile practices, terms, and elements, along with interpretations and experience guidelines from the worldwide community of agile practitioners.
== Values and principles ==
=== Values ===
The agile manifesto reads:
We are uncovering better ways of developing software by doing it and helping others do it. Through this work we have come to value:
Individuals and interactions over processes and tools
Working software over comprehensive documentation
Customer collaboration over contract negotiation
Responding to change over following a plan
That is, while there is value in the items on the right, we value the items on the left more.
Scott Ambler explained:
Tools and processes are important, but it is more important to have competent people working together effectively.
Good documentation is useful in helping people to understand how the software is built and how to use it, but the main point of development is to create software, not documentation.
A contract is important but is not a substitute for working closely with customers to discover what they need.
A project plan is important, but it must not be too rigid to accommodate changes in technology or the environment, stakeholders' priorities, and people's understanding of the problem and its solution.
Introducing the manifesto on behalf of the Agile Alliance, Jim Highsmith said,
The Agile movement is not anti-methodology, in fact many of us want to restore credibility to the word methodology. We want to restore a balance. We embrace modeling, but not in order to file some diagram in a dusty corporate repository. We embrace documentation, but not hundreds of pages of never-maintained and rarely-used tomes. We plan, but recognize the limits of planning in a turbulent environment. Those who would brand proponents of XP or SCRUM or any of the other Agile Methodologies as "hackers" are ignorant of both the methodologies and the original definition of the term hacker.
=== Principles ===
The values are based on these principles:
Customer satisfaction by early and continuous delivery of valuable software.
Welcome changing requirements, even in late development.
Deliver working software frequently (weeks rather than months).
Close, daily cooperation between business people and developers.
Projects are built around motivated individuals, who should be trusted.
Face-to-face conversation is the best form of communication (co-location).
Working software is the primary measure of progress.
Sustainable development, able to maintain a constant pace.
Continuous attention to technical excellence and good design.
Simplicity—the art of maximizing the amount of work not done—is essential.
Best architectures, requirements, and designs emerge from self-organizing teams.
Regularly, the team reflects on how to become more effective, and adjusts accordingly.
== Overview ==
=== Iterative, incremental, and evolutionary ===
Most agile development methods break product development work into small increments that minimize the amount of up-front planning and design. Iterations, or sprints, are short time frames (timeboxes) that typically last from one to four weeks.: 20 Each iteration involves a cross-functional team working in all functions: planning, analysis, design, coding, unit testing, and acceptance testing. At the end of the iteration a working product is demonstrated to stakeholders. This minimizes overall risk and allows the product to adapt to changes quickly. An iteration might not add enough functionality to warrant a market release, but the goal is to have an available release (with minimal bugs) at the end of each iteration. Through incremental development, products have room to "fail often and early" throughout each iterative phase instead of drastically on a final release date. Multiple iterations might be required to release a product or new features. Working software is the primary measure of progress.
A key advantage of agile approaches is speed to market and risk mitigation. Smaller increments are typically released to market, reducing the time and cost risks of engineering a product that doesn't meet user requirements.
=== Efficient and face-to-face communication ===
The 6th principle of the agile manifesto for software development states "The most efficient and effective method of conveying information to and within a development team is face-to-face conversation". The manifesto, written in 2001 when video conferencing was not widely used, states this in relation to the communication of information, not necessarily that a team should be co-located.
The principle of co-location is that co-workers on the same team should be situated together to better establish the identity as a team and to improve communication. This enables face-to-face interaction, ideally in front of a whiteboard, that reduces the cycle time typically taken when questions and answers are mediated through phone, persistent chat, wiki, or email. With the widespread adoption of remote working during the COVID-19 pandemic and changes to tooling, more studies have been conducted around co-location and distributed working which show that co-location is increasingly less relevant.
No matter which development method is followed, every team should include a customer representative (known as product owner in Scrum). This representative is agreed by stakeholders to act on their behalf and makes a personal commitment to being available for developers to answer questions throughout the iteration. At the end of each iteration, the project stakeholders together with the customer representative review progress and re-evaluate priorities with a view to optimizing the return on investment (ROI) and ensuring alignment with customer needs and company goals. The importance of stakeholder satisfaction, detailed by frequent interaction and review at the end of each phase, is why the approach is often denoted as a customer-centered methodology.
==== Information radiator ====
In agile software development, an information radiator is a (normally large) physical display, board with sticky notes or similar, located prominently near the development team, where passers-by can see it. It presents an up-to-date summary of the product development status. A build light indicator may also be used to inform a team about the current status of their product development.
=== Very short feedback loop and adaptation cycle ===
A common characteristic in agile software development is the daily stand-up (known as daily scrum in the Scrum framework). In a brief session (e.g., 15 minutes), team members review collectively how they are progressing toward their goal and agree whether they need to adapt their approach. To keep to the agreed time limit, teams often use simple coded questions (such as what they completed the previous day, what they aim to complete that day, and whether there are any impediments or risks to progress), and delay detailed discussions and problem resolution until after the stand-up.
=== Quality focus ===
Specific tools and techniques, such as continuous integration, automated unit testing, pair programming, test-driven development, design patterns, behavior-driven development, domain-driven design, code refactoring and other techniques are often used to improve quality and enhance product development agility. This is predicated on designing and building quality in from the beginning and being able to demonstrate software for customers at any point, or at least at the end of every iteration.
== Philosophy ==
Compared to traditional software engineering, agile software development mainly targets complex systems and product development with dynamic, indeterministic and non-linear properties. Accurate estimates, stable plans, and predictions are often hard to get in early stages, and confidence in them is likely to be low. Agile practitioners use their free will to reduce the "leap of faith" that is needed before any evidence of value can be obtained. Requirements and design are held to be emergent. Big up-front specifications would probably cause a lot of waste in such cases, i.e., are not economically sound. These basic arguments and previous industry experiences, learned from years of successes and failures, have helped shape agile development's favor of adaptive, iterative and evolutionary development.
=== Adaptive vs. predictive ===
Development methods exist on a continuum from adaptive to predictive. Agile software development methods lie on the adaptive side of this continuum. One key of adaptive development methods is a rolling wave approach to schedule planning, which identifies milestones but leaves flexibility in the path to reach them, and also allows for the milestones themselves to change.
Adaptive methods focus on adapting quickly to changing realities. When the needs of a project change, an adaptive team changes as well. An adaptive team has difficulty describing exactly what will happen in the future. The further away a date is, the more vague an adaptive method is about what will happen on that date. An adaptive team cannot report exactly what tasks they will do next week, but only which features they plan for next month. When asked about a release six months from now, an adaptive team might be able to report only the mission statement for the release, or a statement of expected value vs. cost.
Predictive methods, in contrast, focus on analyzing and planning the future in detail and cater for known risks. In the extremes, a predictive team can report exactly what features and tasks are planned for the entire length of the development process. Predictive methods rely on effective early phase analysis, and if this goes very wrong, the project may have difficulty changing direction. Predictive teams often institute a change control board to ensure they consider only the most valuable changes.
Risk analysis can be used to choose between adaptive (agile or value-driven) and predictive (plan-driven) methods. Barry Boehm and Richard Turner suggest that each side of the continuum has its own home ground, as follows:
=== Agile vs. waterfall ===
One of the differences between agile software development methods and waterfall is the approach to quality and testing. In the waterfall model, work moves through software development life cycle (SDLC) phases—with one phase being completed before another can start—hence the testing phase is separate and follows a build phase. In agile software development, however, testing is completed in the same iteration as programming.
Because testing is done in every iteration—which develops a small piece of the software—users can frequently use those new pieces of software and validate the value. After the users know the real value of the updated piece of software, they can make better decisions about the software's future. Having a value retrospective and software re-planning session in each iteration—Scrum typically has iterations of just two weeks—helps the team continuously adapt its plans so as to maximize the value it delivers. This follows a pattern similar to the plan-do-check-act (PDCA) cycle, as the work is planned, done, checked (in the review and retrospective), and any changes agreed are acted upon.
This iterative approach supports a product rather than a project mindset. This provides greater flexibility throughout the development process; whereas on projects the requirements are defined and locked down from the very beginning, making it difficult to change them later. Iterative product development allows the software to evolve in response to changes in business environment or market requirements.
=== Code vs. documentation ===
In a letter to IEEE Computer, Steven Rakitin expressed cynicism about agile software development, calling it "yet another attempt to undermine the discipline of software engineering" and translating "working software over comprehensive documentation" as "we want to spend all our time coding. Remember, real programmers don't write documentation."
This is disputed by proponents of agile software development, who state that developers should write documentation if that is the best way to achieve the relevant goals, but that there are often better ways to achieve those goals than writing static documentation.
Scott Ambler states that documentation should be "just barely good enough" (JBGE), that too much or comprehensive documentation would usually cause waste, and developers rarely trust detailed documentation because it's usually out of sync with code, while too little documentation may also cause problems for maintenance, communication, learning and knowledge sharing. Alistair Cockburn wrote of the Crystal Clear method:
Crystal considers development a series of co-operative games, and intends that the documentation is enough to help the next win at the next game. The work products for Crystal include use cases, risk list, iteration plan, core domain models, and design notes to inform on choices...however there are no templates for these documents and descriptions are necessarily vague, but the objective is clear, just enough documentation for the next game. I always tend to characterize this to my team as: what would you want to know if you joined the team tomorrow.
== Methods ==
Agile software development methods support a broad range of the software development life cycle. Some methods focus on the practices (e.g., XP, pragmatic programming, agile modeling), while some focus on managing the flow of work (e.g., Scrum, Kanban). Some support activities for requirements specification and development (e.g., FDD), while some seek to cover the full development life cycle (e.g., DSDM, RUP).
Notable agile software development frameworks include:
=== Agile software development practices ===
Agile software development is supported by a number of concrete practices, covering areas like requirements, design, modeling, coding, testing, planning, risk management, process, quality, etc. Some notable agile software development practices include:
==== Acceptance test-driven development ====
==== Agile modeling ====
==== Agile testing ====
==== Backlogs ====
==== Behavior-driven development ====
==== Continuous integration ====
==== Cross-functional team ====
==== Daily stand-up ====
=== Method tailoring ===
In the literature, different terms refer to the notion of method adaptation, including 'method tailoring', 'method fragment adaptation' and 'situational method engineering'. Method tailoring is defined as:
A process or capability in which human agents determine a system development approach for a specific project situation through responsive changes in, and dynamic interplays between contexts, intentions, and method fragments.
Situation-appropriateness should be considered as a distinguishing characteristic between agile methods and more plan-driven software development methods, with agile methods allowing product development teams to adapt working practices according to the needs of individual products. Potentially, most agile methods could be suitable for method tailoring, such as DSDM tailored in a CMM context. and XP tailored with the Rule Description Practices (RDP) technique. Not all agile proponents agree, however, with Schwaber noting "that is how we got into trouble in the first place, thinking that the problem was not having a perfect methodology. Efforts [should] center on the changes [needed] in the enterprise". Bas Vodde reinforced this viewpoint, suggesting that unlike traditional, large methodologies that require you to pick and choose elements, Scrum provides the basics on top of which you add additional elements to localize and contextualize its use. Practitioners seldom use system development methods, or agile methods specifically, by the book, often choosing to omit or tailor some of the practices of a method in order to create an in-house method.
In practice, methods can be tailored using various tools. Generic process modeling languages such as Unified Modeling Language can be used to tailor software development methods. However, dedicated tools for method engineering such as the Essence Theory of Software Engineering of SEMAT also exist.
=== Large-scale, offshore and distributed ===
Agile software development has been widely seen as highly suited to certain types of environments, including small teams of experts working on greenfield projects, and the challenges and limitations encountered in the adoption of agile software development methods in a large organization with legacy infrastructure are well-documented and understood.
In response, a range of strategies and patterns has evolved for overcoming challenges with large-scale development efforts (>20 developers) or distributed (non-colocated) development teams, amongst other challenges; and there are now several recognized frameworks that seek to mitigate or avoid these challenges.
There are many conflicting viewpoints on whether all of these are effective or indeed fit the definition of agile development, and this remains an active and ongoing area of research.
When agile software development is applied in a distributed setting (with teams dispersed across multiple business locations), it is commonly referred to as distributed agile software development. The goal is to leverage the unique benefits offered by each approach. Distributed development allows organizations to build software by strategically setting up teams in different parts of the globe, virtually building software round-the-clock (more commonly referred to as follow-the-sun model). On the other hand, agile development provides increased transparency, continuous feedback, and more flexibility when responding to changes.
=== Regulated domains ===
Agile software development methods were initially seen as best suitable for non-critical product developments, thereby excluded from use in regulated domains such as medical devices, pharmaceutical, financial, nuclear systems, automotive, and avionics sectors, etc. However, in the last several years, there have been several initiatives for the adaptation of agile methods for these domains.
There are numerous standards that may apply in regulated domains, including ISO 26262, ISO 9000, ISO 9001, and ISO/IEC 15504.
A number of key concerns are of particular importance in regulated domains:
Quality assurance (QA): Systematic and inherent quality management underpinning a controlled professional process and reliability and correctness of product.
Safety and security: Formal planning and risk management to mitigate safety risks for users and securely protecting users from unintentional and malicious misuse.
Traceability: Documentation providing auditable evidence of regulatory compliance and facilitating traceability and investigation of problems.
Verification and validation (V&V): Embedded throughout the software development process (e.g. user requirements specification, functional specification, design specification, code review, unit tests, integration tests, system tests).
== Experience and adoption ==
Although agile software development methods can be used with any programming paradigm or language in practice, they were originally closely associated with object-oriented environments such as Smalltalk, Lisp and later Java, C#. The initial adopters of agile methods were usually small to medium-sized teams working on unprecedented systems with requirements that were difficult to finalize and likely to change as the system was being developed. This section describes common problems that organizations encounter when they try to adopt agile software development methods as well as various techniques to measure the quality and performance of agile teams.
=== Measuring agility ===
==== Internal assessments ====
The Agility measurement index, amongst others, rates developments against five dimensions of product development (duration, risk, novelty, effort, and interaction). Other techniques are based on measurable goals and one study suggests that velocity can be used as a metric of agility. There are also agile self-assessments to determine whether a team is using agile software development practices (Nokia test, Karlskrona test, 42 points test).
==== Public surveys ====
One of the early studies reporting gains in quality, productivity, and business satisfaction by using agile software developments methods was a survey conducted by Shine Technologies from November 2002 to January 2003.
A similar survey, the State of Agile, is conducted every year starting in 2006 with thousands of participants from around the software development community. This tracks trends on the perceived benefits of agility, lessons learned, and good practices. Each survey has reported increasing numbers saying that agile software development helps them deliver software faster; improves their ability to manage changing customer priorities; and increases their productivity. Surveys have also consistently shown better results with agile product development methods compared to classical project management. In balance, there are reports that some feel that agile development methods are still too young to enable extensive academic research of their success.
=== Common agile software development pitfalls ===
Organizations and teams implementing agile software development often face difficulties transitioning from more traditional methods such as waterfall development, such as teams having an agile process forced on them. These are often termed agile anti-patterns or more commonly agile smells. Below are some common examples:
==== Lack of overall product design ====
A goal of agile software development is to focus more on producing working software and less on documentation. This is in contrast to waterfall models where the process is often highly controlled and minor changes to the system require significant revision of supporting documentation. However, this does not justify completely doing without any analysis or design at all. Failure to pay attention to design can cause a team to proceed rapidly at first, but then to require significant rework as they attempt to scale up the system. One of the key features of agile software development is that it is iterative. When done correctly, agile software development allows the design to emerge as the system is developed and helps the team discover commonalities and opportunities for re-use.
==== Adding stories to an iteration in progress ====
In agile software development, stories (similar to use case descriptions) are typically used to define requirements and an iteration is a short period of time during which the team commits to specific goals. Adding stories to an iteration in progress is detrimental to a good flow of work. These should be added to the product backlog and prioritized for a subsequent iteration or in rare cases the iteration could be cancelled.
This does not mean that a story cannot expand. Teams must deal with new information, which may produce additional tasks for a story. If the new information prevents the story from being completed during the iteration, then it should be carried over to a subsequent iteration. However, it should be prioritized against all remaining stories, as the new information may have changed the story's original priority.
==== Lack of sponsor support ====
Agile software development is often implemented as a grassroots effort in organizations by software development teams trying to optimize their development processes and ensure consistency in the software development life cycle. By not having sponsor support, teams may face difficulties and resistance from business partners, other development teams and management. Additionally, they may suffer without appropriate funding and resources. This increases the likelihood of failure.
==== Insufficient training ====
A survey performed by VersionOne found respondents cited insufficient training as the most significant cause for failed agile implementations Teams have fallen into the trap of assuming the reduced processes of agile software development compared to other approaches such as waterfall means that there are no actual rules for agile software development.
==== Product owner role is not properly filled ====
The product owner is responsible for representing the business in the development activity and is often the most demanding role.
A common mistake is to fill the product owner role with someone from the development team. This requires the team to make its own decisions on prioritization without real feedback from the business. They try to solve business issues internally or delay work as they reach outside the team for direction. This often leads to distraction and a breakdown in collaboration.
==== Teams are not focused ====
Agile software development requires teams to meet product commitments, which means they should focus on work for only that product. However, team members who appear to have spare capacity are often expected to take on other work, which makes it difficult for them to help complete the work to which their team had committed.
==== Excessive preparation/planning ====
Teams may fall into the trap of spending too much time preparing or planning. This is a common trap for teams less familiar with agile software development where the teams feel obliged to have a complete understanding and specification of all stories. Teams should be prepared to move forward with only those stories in which they have confidence, then during the iteration continue to discover and prepare work for subsequent iterations (often referred to as backlog refinement or grooming).
==== Problem-solving in the daily standup ====
A daily standup should be a focused, timely meeting where all team members disseminate information. If problem-solving occurs, it often can involve only certain team members and potentially is not the best use of the entire team's time. If during the daily standup the team starts diving into problem-solving, it should be set aside until a sub-team can discuss, usually immediately after the standup completes.
==== Assigning tasks ====
One of the intended benefits of agile software development is to empower the team to make choices, as they are closest to the problem. Additionally, they should make choices as close to implementation as possible, to use more timely information in the decision. If team members are assigned tasks by others or too early in the process, the benefits of localized and timely decision making can be lost.
Being assigned work also constrains team members into certain roles (for example, team member A must always do the database work), which limits opportunities for cross-training. Team members themselves can choose to take on tasks that stretch their abilities and provide cross-training opportunities.
==== Scrum master as a contributor ====
In the Scrum framework, which claims to be consistent with agile values and principles, the scrum master role is accountable for ensuring the scrum process is followed and for coaching the scrum team through that process. A common pitfall is for a scrum master to act as a contributor. While not prohibited by the Scrum framework, the scrum master needs to ensure they have the capacity to act in the role of scrum master first and not work on development tasks. A scrum master's role is to facilitate the process rather than create the product.
Having the scrum master also multitasking may result in too many context switches to be productive. Additionally, as a scrum master is responsible for ensuring roadblocks are removed so that the team can make forward progress, the benefit gained by individual tasks moving forward may not outweigh roadblocks that are deferred due to lack of capacity.
==== Lack of test automation ====
Due to the iterative nature of agile development, multiple rounds of testing are often needed. Automated testing helps reduce the impact of repeated unit, integration, and regression tests and frees developers and testers to focus on higher value work.
Test automation also supports continued refactoring required by iterative software development. Allowing a developer to quickly run tests to confirm refactoring has not modified the functionality of the application may reduce the workload and increase confidence that cleanup efforts have not introduced new defects.
==== Allowing technical debt to build up ====
Focusing on delivering new functionality may result in increased technical debt. The team must allow themselves time for defect remediation and refactoring. Technical debt hinders planning abilities by increasing the amount of unscheduled work as production defects distract the team from further progress.
As the system evolves it is important to refactor. Over time the lack of constant maintenance causes increasing defects and development costs.
==== Attempting to take on too much in an iteration ====
A common misconception is that agile software development allows continuous change, however an iteration backlog is an agreement of what work can be completed during an iteration. Having too much work-in-progress (WIP) results in inefficiencies such as context-switching and queueing. The team must avoid feeling pressured into taking on additional work.
==== Fixed time, resources, scope, and quality ====
Agile software development fixes time (iteration duration), quality, and ideally resources in advance (though maintaining fixed resources may be difficult if developers are often pulled away from tasks to handle production incidents), while the scope remains variable. The customer or product owner often pushes for a fixed scope for an iteration. However, teams should be reluctant to commit to the locked time, resources and scope (commonly known as the project management triangle). Efforts to add scope to the fixed time and resources of agile software development may result in decreased quality.
==== Developer burnout ====
Due to the focused pace and continuous nature of agile practices, there is a heightened risk of burnout among members of the delivery team.
== Agile management ==
Agile project management is an iterative development process, where feedback is continuously gathered from users and stakeholders to create the right user experience. Different methods can be used to perform an agile process, these include scrum, extreme programming, lean and kanban.
The term agile management is applied to an iterative, incremental method of managing the design and build activities of engineering, information technology and other business areas that aim to provide new product or service development in a highly flexible and interactive manner, based on the principles expressed in the Manifesto for Agile Software Development.
Agile project management metrics help reduce confusion, identify weak points, and measure team's performance throughout the development cycle. Supply chain agility is the ability of a supply chain to cope with uncertainty and variability on offer and demand. An agile supply chain can increase and reduce its capacity rapidly, so it can adapt to a fast-changing customer demand. Finally, strategic agility is the ability of an organisation to change its course of action as its environment is evolving. The key for strategic agility is to recognize external changes early enough and to allocate resources to adapt to these changing environments.
Agile X techniques may also be called extreme project management. It is a variant of iterative life cycle where deliverables are submitted in stages. The main difference between agile and iterative development is that agile methods complete small portions of the deliverables in each delivery cycle (iteration), while iterative methods evolve the entire set of deliverables over time, completing them near the end of the project. Both iterative and agile methods were developed as a reaction to various obstacles that developed in more sequential forms of project organization. For example, as technology projects grow in complexity, end users tend to have difficulty defining the long-term requirements without being able to view progressive prototypes. Projects that develop in iterations can constantly gather feedback to help refine those requirements.
Agile management also offers a simple framework promoting communication and reflection on past work amongst team members. Teams who were using traditional waterfall planning and adopted the agile way of development typically go through a transformation phase and often take help from agile coaches who help guide the teams through a smoother transformation. There are typically two styles of agile coaching: push-based and pull-based agile coaching. Here a "push-system" can refer to an upfront estimation of what tasks can be fitted into a sprint (pushing work) e.g. typical with scrum; whereas a "pull system" can refer to an environment where tasks are only performed when capacity is available. Agile management approaches have also been employed and adapted to the business and government sectors. For example, within the federal government of the United States, the United States Agency for International Development (USAID) is employing a collaborative project management approach that focuses on incorporating collaborating, learning and adapting (CLA) strategies to iterate and adapt programming.
Agile methods are mentioned in the Guide to the Project Management Body of Knowledge (PMBOK Guide 6th Edition) under the Product Development Lifecycle definition:
Within a project life cycle, there are generally one or more phases
that are associated with the development of the product, service, or result. These are called a development life cycle (...) Adaptive life cycles are agile, iterative, or incremental. The detailed scope is defined and approved before the start of an iteration. Adaptive life cycles are also referred to as agile or change-driven life cycles.
=== Applications outside software development ===
According to Jean-Loup Richet (research fellow at ESSEC Institute for Strategic Innovation & Services) "this approach can be leveraged effectively for non-software products and for project management in general, especially in areas of innovation and uncertainty." The result is a product or project that best meets current customer needs and is delivered with minimal costs, waste, and time, enabling companies to achieve bottom line gains earlier than via traditional approaches.
Agile software development methods have been extensively used for development of software products and some of them use certain characteristics of software, such as object technologies. However, these techniques can be applied to the development of non-software products, such as computers, medical devices, food, clothing, and music. Agile software development methods have been used in non-development IT infrastructure deployments and migrations. Some of the wider principles of agile software development have also found application in general management (e.g., strategy, governance, risk, finance) under the terms business agility or agile business management. Agile software methodologies have also been adopted for use with the learning engineering process, an iterative data-informed process that applies human-centered design, and data informed decision-making to support learners and their development.
Agile software development paradigms can be used in other areas of life such as raising children. Its success in child development might be founded on some basic management principles; communication, adaptation, and awareness. In a TED Talk, Bruce Feiler shared how he applied basic agile paradigms to household management and raising children.
== Criticism ==
Agile practices have been cited as potentially inefficient in large organizations and certain types of development. Many organizations believe that agile software development methodologies are too extreme and adopt a hybrid approach that mixes elements of agile software development and plan-driven approaches. Some methods, such as dynamic systems development method (DSDM) attempt this in a disciplined way, without sacrificing fundamental principles.
The increasing adoption of agile practices has also been criticized as being a management fad that simply describes existing good practices under new jargon, promotes a one size fits all mindset towards development strategies, and wrongly emphasizes method over results.
Alistair Cockburn organized a celebration of the 10th anniversary of the Manifesto for Agile Software Development in Snowbird, Utah on 12 February 2011, gathering some 30+ people who had been involved at the original meeting and since. A list of about 20 elephants in the room ('undiscussable' agile topics/issues) were collected, including aspects: the alliances, failures and limitations of agile software development practices and context (possible causes: commercial interests, decontextualization, no obvious way to make progress based on failure, limited objective evidence, cognitive biases and reasoning fallacies), politics and culture. As Philippe Kruchten wrote:
The agile movement is in some ways a bit like a teenager: very self-conscious, checking constantly its appearance in a mirror, accepting few criticisms, only interested in being with its peers, rejecting en bloc all wisdom from the past, just because it is from the past, adopting fads and new jargon, at times cocky and arrogant. But I have no doubts that it will mature further, become more open to the outside world, more reflective, and therefore, more effective.
The "Manifesto" may have had a negative impact on higher education management and leadership, where it suggested to administrators that slower traditional and deliberative processes should be replaced with more "nimble" ones. The concept rarely found acceptance among university faculty.
Another criticism is that in many ways, agile management and traditional management practices end up being in opposition to one another. A common criticism of this practice is that the time spent attempting to learn and implement the practice is too costly, despite potential benefits. A transition from traditional management to agile management requires total submission to agile and a firm commitment from all members of the organization to seeing the process through. Issues like unequal results across the organization, too much change for employees' ability to handle, or a lack of guarantees at the end of the transformation are just a few examples.
== See also ==
Cross-functional team
Scrum (software development)
Fail fast (business), a related subject in business management
Kanban
Agile leadership
Agile contracts
Rational unified process
== References ==
== Further reading ==
== External links ==
Agile Manifesto
Agile Glossary of the Agile Alliance
The New Methodology - Martin Fowler's description of the background to agile methods
AgilePatterns.org | Wikipedia/Agile_methods |
The Computer Science Tripos (CST) is the undergraduate course in computer science offered by the University of Cambridge Computer Laboratory. It evolved out of the Diploma in Computer Science, the world's first taught course in computer science, which started in 1953. Successful candidates are awarded a Bachelor of Arts (BA) honours degree after three years or, a combined BA + Master of Engineering (MEng) honours degree after four years of study, though admission to the fourth year is usually contingent on attaining a first-class result in the third year.
== Notable alumni ==
Aubrey de Grey
Demis Hassabis
Simon Tatham
== References == | Wikipedia/Computer_Science_Tripos |
Anchor modeling is an agile database modeling technique suited for information that changes over time both in structure and content. It provides a graphical notation used for conceptual modeling similar to that of entity-relationship modeling, with extensions for working with temporal data. The modeling technique involves four modeling constructs: the anchor, attribute, tie and knot, each capturing different aspects of the domain being modeled. The resulting models can be translated to physical database designs using formalized rules. When such a translation is done the tables in the relational database will mostly be in the sixth normal form.
Unlike the star schema (dimensional modelling) and the classical relational model (3NF), data vault and anchor modeling are well-suited for capturing changes that occur when a source system is changed or added, but are considered advanced techniques which require experienced data architects. Both data vaults and anchor models are entity-based models, but anchor models have a more normalized approach.
== Philosophy ==
Anchor modeling was created in order to take advantage of the benefits from a high degree of normalization while avoiding its drawbacks which higher normal forms have with regards to human readability. Advantages such as being able to non-destructively evolve the model, avoid null values, and keep the information free from redundancies are gained. Performance issues due to extra joins are largely avoided thanks to a feature in modern database engines called join elimination or table elimination. In order to handle changes in the information content, anchor modeling emulates aspects of a temporal database in the resulting relational database schema.
== History ==
The earliest installations using anchor modeling were made 2004 in Sweden when a data warehouse for an insurance company was built using the technique.
In 2007 the technique was being used in a few data warehouses and one online transaction processing (OLTP) system, and it was presented internationally by Lars Rönnbäck at the 2007 Transforming Data with Intelligence (TDWI) conference in Amsterdam. This stirred enough interest for the technique to warrant a more formal description. Since then research concerning anchor modeling is being done in a collaboration between the creators Olle Regardt and Lars Rönnbäck and a team at the Department of Computer and Systems Sciences, Stockholm University.
The first paper, in which anchor modeling is formalized, was presented in 2008 at the 28th International Conference on Conceptual Modeling and won the best paper award.
A commercial web site provides material on anchor modeling which is free to use under a Creative Commons license. An online modeling tool is also available, which is free to use and is open source.
== Basic notions ==
Anchor modeling has four basic modeling concepts: anchors, attributes, ties, and knots. Anchors are used to model entities and events, attributes are used to model properties of anchors, ties model the relationships between anchors, and knots are used to model shared properties, such as states. Attributes and ties can be historized when changes in the information they model need to be kept.
An example model showing the different graphical symbols for all the concepts can be seen below. The symbols resemble those used in entity–relationship modeling, with a couple of extensions. A double outline on an attribute or tie indicates that a history of changes is kept. The knot symbol (an outlined square with rounded edges) is also available, but knots cannot be historized. The anchor symbol is a solid square.
== Temporal aspects ==
Anchor modeling handles two types of informational evolution, which are structural changes and content changes. Changes to the structure of information is represented through extensions. The high degree of normalization makes it possible to non-destructively add the necessary modeling concepts needed to capture a change, in such a way that every previous schema always remains as a subset of the current schema. Since the existing schema is not touched, this gives the benefit of being able to evolve the database in a highly iterative manner and without causing any downtime.
Changes in the content of information is done by emulating similar features of a temporal database in a relational database. In anchor modeling, pieces of information can be tied to points in time or to intervals of time (both open and closed). The time points when events occur are modeled using attributes, e g the birth dates of persons or the time of a purchase. The intervals of time in which a value is valid are captured through the historization of attributes and ties, e g the changes of hair color of a person or the period of time during which a person was married. In a relational database this is achieved by adding a single column, with a data type granular enough to capture the speed of the changes, to the table corresponding to the historized attribute or tie. This adds a slight complexity as more than one row in the table have to be examined in order to know if an interval is closed or not.
Points or intervals of time not directly related to the domain being modeled, such as the points of time information entered the database, are handled through the use of metadata in anchor modeling, rather than any of the above-mentioned constructs. If information about such changes to the database needs to be kept then bitemporal anchor modeling can be used, where in addition to updates, also delete statements become non-destructive.
== Relational representation ==
In anchor modeling there is a one-to-one mapping between the symbols used in the conceptual model and tables in the relational database. Every anchor, attribute, tie, and knot have a corresponding table in the database with an unambiguously defined structure. A conceptual model can thereby be translated to a relational database schema using simple automated rules, and vice versa. This is different from many other modeling techniques in which there are complex and sometimes subjective translation steps between the conceptual, logical, and physical levels.
Anchor tables contain a single column in which identities are stored. An identity is assumed to be the only property of an entity that is always present and immutable. As identities are rarely available from the domain being modeled, they are instead technically generated, e g from an incrementing number sequence.
An example of an anchor for the identities of the nephews of Donald Duck is a set of 1-tuples:
{⟨#42⟩, ⟨#43⟩, ⟨#44⟩}
Knots can be thought of as the combination of an anchor and a single attribute. Knot tables contain two columns, one for an identity and one for a value. Due to storing identities and values together, knots cannot be historized. Their usefulness comes from being able to reduce storage requirements and improve performance, since tables referencing knots can store a short value rather than a long string.
An example of a knot for genders is a set of 2-tuples:
{⟨#1, 'Male'⟩, ⟨#2, 'Female'⟩}
Static attribute tables contain two columns, one for the identity of the entity to which the value belongs and one for the actual property value. Historized attribute tables have an extra column for storing the starting point of a time interval. In a knotted attribute table, the value column is an identity that references a knot table.
An example of a static attribute for their names is a set of 2-tuples:
{⟨#42, 'Huey'⟩, ⟨#43, 'Dewey'⟩, ⟨#44, 'Louie'⟩}
An example of a knotted static attribute for their genders is a set of 2-tuples:
{⟨#42, #1⟩, ⟨#43, #1⟩, ⟨#44, #1⟩}
An example of a historized attribute for the (changing) colors of their outfits is a set of 3-tuples:
{⟨#44, 'Orange', 1938-04-15⟩, ⟨#44, 'Green', 1939-04-28⟩, ⟨#44, 'Blue', 1940-12-13⟩}
Static tie tables relate two or more anchors to each other, and contain two or more columns for storing the identities. Historized tie tables have an extra column for storing the starting point of a time interval. Knotted tie tables have an additional column for each referenced knot.
An example of a static tie for the sibling relationship is a set of 2-tuples:
{⟨#42, #43⟩, ⟨#42, #44⟩, ⟨#43, #42⟩, ⟨#43, #44⟩, ⟨#44, #42⟩, ⟨#44, #43⟩}
The resulting tables will all be in sixth normal form except for ties in which not all columns are part of the primary key.
== Compared to other approaches ==
In the 2000s, several data warehouse data modeling patterns have been introduced with the goal of achieving agile data warehouses, including ensemble modeling forms such as anchor modeling, data vault modeling, focal point modeling, and others.
=== Data vault comparison ===
In 2013 at the data modeling conference BI Podium in the Netherlands, Lars Rönnbäck presented a comparison of anchor modeling and data vault modeling.
== References ==
== External links ==
Anchor modeling blog, with video tutorials and research information
Online anchor modeling tool | Wikipedia/Anchor_Modeling |
A conceptual schema or conceptual data model is a high-level description of informational needs underlying the design of a database. It typically includes only the core concepts and the main relationships among them. This is a high-level model with insufficient detail to build a complete, functional database. It describes the structure of the whole database for a group of users. The conceptual model is also known as the data model that can be used to describe the conceptual schema when a database system is implemented. It hides the internal details of physical storage and targets the description of entities, datatypes, relationships and constraints.
== Overview ==
A conceptual schema is a map of concepts and their relationships used for databases. This describes the semantics of an organization and represents a series of assertions about its nature. Specifically, it describes the things of significance to an organization (entity classes), about which it is inclined to collect information, and their characteristics (attributes) and the associations between pairs of those things of significance (relationships).
Because a conceptual schema represents the semantics of an organization, and not a database design, it may exist on various levels of abstraction. The original ANSI four-schema architecture began with the set of external schemata that each represents one person's view of the world around him or her. These are consolidated into a single conceptual schema that is the superset of all of those external views. A data model can be as concrete as each person's perspective, but this tends to make it inflexible. If that person's world changes, the model must change. Conceptual data models take a more abstract perspective, identifying the fundamental things, of which the things an individual deals with are just examples.
The model does allow for what is called inheritance in object oriented terms. The set of instances of an entity class may be subdivided into entity classes in their own right. Thus, each instance of a sub-type entity class is also an instance of the entity class's super-type. Each instance of the super-type entity class, then is also an instance of one of the sub-type entity classes.
Super-type/sub-type relationships may be exclusive or not. A methodology may require that each instance of a super-type may only be an instance of one sub-type. Similarly, a super-type/sub-type relationship may be exhaustive or not. It is exhaustive if the methodology requires that each instance of a super-type must be an instance of a sub-type. A sub-type named "Other" is often necessary.
== Example relationships ==
Each PERSON may be the vendor in one or more ORDERS.
Each ORDER must be from one and only one PERSON.
PERSON is a sub-type of PARTY. (Meaning that every instance of PERSON is also an instance of PARTY.)
Each EMPLOYEE may have a supervisor who is also an EMPLOYEE.
== Data structure diagram ==
A data structure diagram (DSD) is a data model or diagram used to describe conceptual data models by providing graphical notations which document entities and their relationships, and the constraints that bind them.
== See also ==
== References ==
== Further reading ==
Perez, Sandra K., & Anthony K. Sarris, eds. (1995) Technical Report for IRDS Conceptual Schema, Part 1: Conceptual Schema for IRDS, Part 2: Modeling Language Analysis, X3/TR-14:1995, American National Standards Institute, New York, NY.
Halpin T, Morgan T (2008) Information Modeling and Relational Databases, 2nd edn., San Francisco, CA: Morgan Kaufmann.
== External links ==
A different point of view, as described by the agile community | Wikipedia/Conceptual_data_model |
The SERM (structured entity relationship model) is an amplification of the ERM which is commonly used for data modeling. It was first proposed from Prof. Dr. Elmar J. Sinz in 1988. The SERM is commonly used in the SAP-world for the data modeling.
== Aims ==
structuring of large schemes
visualization of existence dependency
avoidance of inconsistencies
avoidance of unnecessary relationshiptypes
== SERM symbols ==
== SERM example ==
Customer and article are independent entities
Every order is referred to one customer. Orders without customers are illegal (order is an ER type). Customers without any orders are legal because they are independent Entities.
To every order there is belonging at least one order item.
Every order item is related to exactly one order.
Every invoice is referred to one customer, as well. Invoices without customers are illegal. Customers without any invoice are legal.
To every invoice there is belonging at least one invoice line item.
Every invoice line item is related to exactly order item. An order item could be calculated or not.
SERM is already in the third normal form
== References == | Wikipedia/Structured_entity_relationship_model |
The Arm Advanced Microcontroller Bus Architecture (AMBA) is an open-standard, on-chip interconnect specification for the connection and management of functional blocks in system-on-a-chip (SoC) designs. It facilitates development of multi-processor designs with large numbers of controllers and components with a bus architecture. Since its inception, the scope of AMBA has, despite its name, gone far beyond microcontroller devices. Today, AMBA is widely used on a range of ASIC and SoC parts including applications processors used in modern portable mobile devices like smartphones. AMBA is a registered trademark of Arm Ltd.
AMBA was introduced by Arm in 1996. The first AMBA buses were the Advanced System Bus (ASB) and the Advanced Peripheral Bus (APB). In its second version, AMBA 2 in 1999, Arm added AMBA High-performance Bus (AHB) that is a single clock-edge protocol. In 2003, Arm introduced the third generation, AMBA 3, including Advanced eXtensible Interface (AXI) to reach even higher performance interconnect and the Advanced Trace Bus (ATB) as part of the CoreSight on-chip debug and trace solution. In 2010 the AMBA 4 specifications were introduced starting with AMBA 4 AXI4, then in 2011 extending system-wide coherency with AMBA 4 AXI Coherency Extensions (ACE). In 2013 the AMBA 5 Coherent Hub Interface (CHI) specification was introduced, with a re-designed high-speed transport layer and features designed to reduce congestion. These protocols are today the de facto standard for embedded processor bus architectures because they are well documented and can be used without royalties.
== Design principles ==
An important aspect of an SoC is not only which components or blocks it houses, but also how they interconnect. AMBA is a solution for the blocks to interface with each other.
The objective of the AMBA specification is to:
facilitate right-first-time development of embedded microcontroller products with one or more CPUs, GPUs or signal processors,
be technology independent, to allow reuse of IP cores, peripheral and system macrocells across diverse IC processes,
encourage modular system design to improve processor independence, and the development of reusable peripheral and system IP libraries
minimize silicon infrastructure while supporting high performance and low power on-chip communication.
== AMBA protocol specifications ==
The AMBA specification defines an on-chip communications standard for designing high-performance embedded microcontrollers. It is supported by Arm Limited with wide cross-industry participation.
The AMBA 5 specification defines the following buses/interfaces:
AXI5, AXI5-Lite and ACE5 Protocol Specification
Advanced High-performance Bus (AHB5, AHB-Lite)
Coherent Hub Interface (CHI)
Distributed Translation Interface (DTI)
Generic Flash Bus (GFB)
The AMBA 4 specification defines following buses/interfaces:
AXI Coherency Extensions (ACE) - widely used on the latest Arm Cortex-A processors including Cortex-A7 and Cortex-A15
AXI Coherency Extensions Lite (ACE-Lite)
Advanced Extensible Interface 4 (AXI4)
Advanced Extensible Interface 4 Lite (AXI4-Lite)
Advanced Extensible Interface 4 Stream (AXI4-Stream v1.0)
Advanced Trace Bus (ATB v1.1)
Advanced Peripheral Bus (APB4 v2.0)
AMBA Low Power Interfaces (Q-Channel and P-Channel)
AMBA 3 specification defines four buses/interfaces:
Advanced eXtensible Interface (AXI3 or AXI v1.0) - widely used on Arm Cortex-A processors including Cortex-A9
Advanced High-performance Bus Lite (AHB-Lite v1.0)
Advanced Peripheral Bus (APB3 v1.0)
Advanced Trace Bus (ATB v1.0)
AMBA 2 specification defines three buses/interfaces:
Advanced High-performance Bus (AHB) - widely used on ARM7, ARM9 and Arm Cortex-M based designs
Advanced System Bus (ASB)
Advanced Peripheral Bus (APB2 or APB)
AMBA specification (First version) defines two buses/interfaces:
Advanced System Bus (ASB)
Advanced Peripheral Bus (APB)
The timing aspects and the voltage levels on the bus are not dictated by the specifications.
=== AXI Coherency Extensions (ACE and ACE-Lite) ===
ACE, defined as part of the AMBA 4 specification, extends AXI with additional signalling introducing system wide coherency. This system coherency allows multiple processors to share memory and enables technology like Arm's big.LITTLE processing. The ACE-Lite protocol enables one-way coherency, also known as I/O coherency; for example, a network interface that can read from the caches of a fully coherent ACE processor.
=== Advanced eXtensible Interface (AXI) ===
AXI, the third generation of AMBA interface defined in the AMBA 3 specification, is targeted at high performance, high clock frequency system designs and includes features that make it suitable for high speed sub-micrometer interconnect:
separate address/control and data phases
support for unaligned data transfers using byte strobes
burst based transactions with only start address issued
issuing of multiple outstanding addresses with out of order responses
easy addition of register stages to provide timing closure.
=== Advanced High-performance Bus (AHB) ===
AHB is a bus protocol introduced in Advanced Microcontroller Bus Architecture version 2 published by Arm Ltd company.
In addition to previous release, it has the following features:
large bus-widths (64/128/256/512/1024 bit).
A simple transaction on the AHB consists of an address phase and a subsequent data phase (without wait states: only two bus-cycles). Access to the target device is controlled through a MUX (non-tristate), thereby admitting bus-access to one bus-master at a time.
AHB-Lite is a subset of AHB formally defined in the AMBA 3 standard. This subset simplifies the design for a bus with a single master.
=== Advanced Peripheral Bus (APB) ===
APB is designed for low bandwidth control accesses, for example register interfaces on system peripherals. This bus has an address and data phase similar to AHB, but a much reduced, low complexity signal list (for example no bursts).
Furthermore, it is an interface designed for a low frequency system with a low bit width (32 bits).
== AMBA products ==
A family of synthesizable intellectual property (IP) cores AMBA Products is licensable from Arm Limited that implement a digital bus in an SoC for the efficient moving and storing of data using the AMBA protocol specifications. The AMBA family includes AMBA Network Interconnect (CoreLink NIC-400), Cache Coherent Interconnect (CoreLink CCI-500), SDRAM memory controllers (CoreLink DMC-400), DMA controllers (CoreLink DMA-230, DMA-330), level 2 cache controllers (L2C-310), etc.
A number of manufacturers utilize AMBA buses for non-ARM designs. As an example Infineon uses an AMBA bus for the ADM5120 SoC based on the MIPS architecture.
== Competitors ==
Wishbone from OpenCores – Free and open bus architecture (formerly from Silicore)
CoreConnect bus technology from IBM, used in IBM's embedded PowerPC, but also in many other SoC-like systems with the Xilinx MicroBlaze or similar cores
IPBus by IDT
Avalon – proprietary bus system by Altera for use in their Nios II SoCs
Open Core Protocol (OCP) from Accellera
HyperTransport (HT) from AMD (though this is an off-chip interface, not on-chip bus)
QuickPath Interconnect (QPI) by Intel (though this is an off-chip interface, not on-chip bus)
virtual share from PICC - free and open source
TileLink - Free and open bus architecture from CHIPS Alliance
== See also ==
Functional specification
Master/slave (technology)
Network on a chip, an alternative to bus-based architectures
== References ==
== External links ==
Arm Developer AMBA Homepage - from Arm
AMBA Specification home page - of ARM
AMBA of ARM
AMBA Documentation - from ARM
AMBA 2 Specification including AHB - from ARM
AMBA AXI and ACE Protocol Specification AXI3, AXI4, and AXI4-Lite, ACE and ACE-Lite - from ARM
AMBA APB Specification including APB4, APB3, APB2 - from ARM | Wikipedia/Advanced_Microcontroller_Bus_Architecture |
Diagnostic design specification is a document indicating how the diagnostics will be implemented on upcoming/new products that will be developed by the company. It describes the behavior of the diagnostics like how the test will execute, how the output messages are formatted, and how the final result is displayed (among others). This document is usually defined by the manufacturing team (more specifically, the manufacturing test engineers) and will be submitted to the software-diagnostic group for approvals.
In reality, not all the specifications can be delivered on time for the build-up of the first prototype boards so all the tests defined in the document usually are given in phases. Once both teams agree on the delivery timeline, the document is signed by both teams and put into some kind of document control. It is possible that the document may be changed from time to time like when other teams introduce new components or remove them.
== References ==
== See also ==
Functional specification
Specification (technical standard) | Wikipedia/Diagnostic_design_specification |
A design specification (or product design specification) is a document which details exactly what criteria a product or a process should comply with. If the product or its design are being created on behalf of a customer, the specification should reflect the requirements of the customer or client. A design specification could, for example, include required dimensions, environmental factors, ergonomic factors, aesthetic factors, maintenance requirement, etc. It may also give specific examples of how the design should be executed, helping others work properly (a guideline for what the person should do).
== Example of a design specification ==
An example design specification, which may be a physical product, software, the construction of a building, or another type of output. Columns and information may be adjustable based on the output format.
== Special requirements ==
Construction design specifications are referenced in US government procurement rules, where there is a requirement that an architect-engineer should specify using "the maximum practicable amount of recovered materials consistent with the performance requirements, availability, price reasonableness, and cost-effectiveness" in a construction design specification.
== See also ==
Data sheet (Spec sheet)
Design by contract
Software requirements specification
Specification
== References ==
== Other sources ==
Mohan, S., Dr. "Design Specifications", Dr. S. Mohan. N.p., n.d. Web. 27 Dec. 2015.
"What Are Specifications?" Specificationsdenver. N.p., n.d. Web. 27 Dec. 2015. | Wikipedia/Product_design_specification |
In a corporation, a stakeholder is a member of "groups without whose support the organization would cease to exist", as defined in the first usage of the word in a 1963 internal memorandum at the Stanford Research Institute. The theory was later developed and championed by R. Edward Freeman in the 1980s. Since then it has gained wide acceptance in business practice and in theorizing relating to strategic management, corporate governance, business purpose and corporate social responsibility (CSR). The definition of corporate responsibilities through a classification of stakeholders to consider has been criticized as creating a false dichotomy between the "shareholder model" and the "stakeholder model", or a false analogy of the obligations towards shareholders and other interested parties.
== Types ==
Any action taken by any organization or any group might affect those people who are linked with them in the private sector. For examples these are parents, children, customers, owners, employees, associates, partners, contractors, and suppliers, people that are related or located nearby. Broadly speaking there are three types of stakeholders:
Primary stakeholders are usually internal stakeholders, are those that engage in economic transactions with the business (for example stockholders, customers, suppliers, creditors, and employees).
Secondary stakeholders are usually external stakeholders, although they do not engage in direct economic exchange with the business – are affected by or can affect its actions (for example the general public, communities, activist groups, business support groups, and the media).
Excluded stakeholders are those such as children or the disinterested public, originally as they had no economic impact on business. Now as the concept takes an anthropocentric perspective, while some groups like the general public may be recognized as stakeholders others remain excluded. Such a perspective does not give plants, animals or even geology a voice as stakeholders, but only an instrumental value in relation to human groups or individuals.
A narrow mapping of a company's stakeholders might identify the following stakeholders:
Employees
Communities
Shareholders
Creditors
Investors
Government
Customers
Owners
Financiers
Managers
A broader mapping of a company's stakeholders may also include:
Suppliers
Distributors
Labor unions
Government regulatory agencies
Government legislative bodies
Government tax-collecting agencies
Industry trade groups
Professional associations
NGOs and other advocacy groups
Prospective employees
Prospective customers
Local communities
National communities
Public at Large (Global Community)
Competitors
Schools
Future generations
Analysts and Media
Research centers
== In corporate responsibility ==
In the field of corporate governance and corporate responsibility, a debate is ongoing about whether the firm or company should be managed primarily for stakeholders, stockholders (shareholders), customers, or others. Proponents in favor of stakeholders may base their arguments on the following four key assertions:
Value can best be created by trying to maximize joint outcomes. For example, according to this thinking, programs that satisfy both employees' needs and stockholders' wants are doubly valuable because they address two legitimate sets of stakeholders at the same time. There is evidence that the combined effects of such a policy are not only additive but even multiplicative. For instance, by simultaneously addressing customer wishes in addition to employee and stockholder interests, both of the latter two groups also benefit from increased sales.
Supporters also take issue with the preeminent role given to stockholders by many business thinkers, especially in the past. The argument is that debt holders, employees, and suppliers also make contributions and thus also take risks in creating a successful firm.
These normative arguments would matter little if stockholders (shareholders) had complete control in guiding the firm. However, many believe that due to certain kinds of board of directors structures, top managers like CEOs are mostly in control of the firm.
The greatest value of a company is its image and brand. By attempting to fulfill the needs and wants of many different people ranging from the local population and customers to their own employees and owners, companies can prevent damage to their image and brand, prevent losing large amounts of sales and disgruntled customers, and prevent costly legal expenses. While the stakeholder view has an increased cost, many firms have decided that the concept improves their image, increases sales, reduces the risks of liability for corporate negligence, and makes them less likely to be targeted by pressure groups, campaigning groups and NGOs.
A corporate stakeholder can affect or be affected by the actions of a business as a whole. Whereas shareholders are often the party with the most direct and obvious interest at stake in business decisions, they are one of various subsets of stakeholders, as customers and employees also have stakes in the outcome. In the most developed sense of stakeholders in terms of real corporate responsibility, the bearers of externalities are included in stakeholdership.
== In management ==
In the last decades of the 20th century, the word "stakeholder" became more commonly used to mean a person or organization that has a legitimate interest in a project or entity. In discussing the decision-making process for institutions—including large business corporations, government agencies, and non-profit organizations—the concept has been broadened to include everyone with an interest (or "stake") in what the entity does. This includes not only vendors, employees, and customers, but even members of a community where its offices or factory may affect the local economy or environment. In this context, a "stakeholder" includes not only the directors or trustees on its governing board (who are stakeholders in the traditional sense of the word) but also all persons who paid into the figurative stake and the persons to whom it may be "paid out" (in the sense of a "payoff" in game theory, meaning the outcome of the transaction). Therefore, in order to effectively engage with a community of stakeholders, the organisation's management needs to be aware of the stakeholders, understand their wants and expectations, understand their attitude (supportive, neutral or opposed), and be able to prioritize the members of the overall community to focus the organisation's scarce resources on the most significant stakeholders.
Example
For example, in the case of a professional landlord undertaking the refurbishment of some rented housing that is occupied while the work is being carried out, key stakeholders would be the residents, neighbors (for whom the work is a nuisance), and the tenancy-management team and housing-maintenance team employed by the landlord. Other stakeholders would be funders and the design-and-construction team.
The holders of each separate kind of interest in the entity's affairs are called a constituency, so there may be a constituency of stockholders, a constituency of adjoining property owners, a constituency of banks the entity owes money to, and so on. In that usage, "constituent" is a synonym for "stakeholder".
== Stakeholder theory ==
Post, Preston, Sachs (2002), use the following definition of the term "stakeholder":
"A person, group or organization that has interest or concern in an organization.
Stakeholders can affect or be affected by the organization's actions, objectives and policies. Some examples of key stakeholders are creditors, directors, employees, government (and its agencies), owners (shareholders), suppliers, unions, and the community from which the business draws its resources.
Not all stakeholders are equal. A company's customers are entitled to fair trading practices but they are not entitled to the same consideration as the company's employees. The stakeholders in a corporation are the individuals and constituencies that contribute, either voluntarily or involuntarily, to its wealth-creating capacity and activities, and that are therefore its potential beneficiaries and/or risk bearers." This definition differs from the older definition of the term stakeholder in Stakeholder theory (Freeman, 1983) that also includes competitors as stakeholders of a corporation. Robert Allen Phillips provides a moral foundation for stakeholder theory in Stakeholder Theory and Organizational Ethics. There he defends a "principle of stakeholder fairness" based on the work of John Rawls, as well as a distinction between normative and derivative legitimate stakeholders. Real stakeholders, labelled stakeholders: genuine stakeholders with a legitimate stake, the loyal partners who strive for mutual benefits. Stake owners own and deserve a stake in the firm. Stakeholder reciprocity could be an innovative criterion in the corporate governance debate as to who should be accorded representation on the board. Corporate social responsibility should imply a corporate stakeholder responsibility.
== Examples of a company's stakeholders ==
== See also ==
Stakeholder engagement
Stakeholder theory
Stakeholder (law)
UK company law
Strategy Markup Language, whose core elements include <Stakeholder>
Multistakeholder Governance Model
== Citations ==
== References ==
Freeman, R. Edward; Moutchnik, Alexander (2013). "Stakeholder management and CSR: questions and answers". UmweltWirtschaftsForum. 21 (1): 5–9. doi:10.1007/s00550-013-0266-3. S2CID 154210736.
Freeman, R.E. and Reed, D.L., 1983. Stockholders and stakeholders: A new perspective on corporate governance. California management review, 25(3), pp. 88–106.
Redefining the Corporation: An International Colloquy
Post, James (2002). Redefining the Corporation: Stakeholder Management and Organizational Wealth. Stanford University Press. ISBN 978-0-8047-4310-5.
Figge, F.; Schaltegger, S.: What is Stakeholder Value? Developing a Catchphrase into a Benchmarking Tool. Lüneburg/Geneva/Paris: University of Lüneburg/Pictet/ in association with United Nations Environment Program (UNEP), 2000 CSM Lüneburg (799 KB) | Wikipedia/Stakeholder_(corporate) |
Hard systems is a problem-solving approach in systems science. It can be contrasted with soft systems, for which systems thinking must handle many ill-defined, or not easily quantified elements.
Hard systems approaches such as systems analysis (structured methods), operations research and so on, assume that the problems associated with such systems are well-defined and likely to have a single, optimum solution, so a problem-solving approach will work well as technical factors tend to predominate.
== Developments in hard systems thinking ==
Hard systems began to emerge as a distinct philosophy in the 1950s.
== See also ==
Systems engineering
Systems analysis
Systems dynamics
== References == | Wikipedia/Hard_systems |
A human visual system model (HVS model) is used by image processing, video processing and computer vision experts to deal with biological and psychological processes that are not yet fully understood. Such a model is used to simplify the behaviors of what is a very complex system. As our knowledge of the true visual system improves, the model is updated.
Psychovisual study is the study of the psychology of vision.
The human visual system model can produce desired effects in perception and vision. Examples of using an HVS model include color television, lossy compression, and Cathode-ray tube (CRT) television.
Originally, it was thought that color television required too high a bandwidth for the then available technology. Then it was noticed that the color resolution of the HVS was much lower than the brightness resolution; this allowed color to be squeezed into the signal by chroma subsampling.
Another example is lossy image compression, like JPEG. Our HVS model says we cannot see high frequency detail, so in JPEG we can quantize these components without a perceptible loss of quality. Similar concepts are applied in audio compression, where sound frequencies inaudible to humans are band-stop filtered.
Several HVS features are derived from evolution when we needed to defend ourselves or hunt for food. We often see demonstrations of HVS features when we are looking at optical illusions.
== Block diagram of HVS ==
== Assumptions about the HVS ==
Low-pass filter characteristic (limited number of rods in human eye): see Mach bands
Lack of color resolution (fewer cones in human eye than rods)
Motion sensitivity
More sensitive in peripheral vision
Stronger than texture sensitivity, e.g. viewing a camouflaged animal
Texture stronger than disparity – 3D depth resolution does not need to be so accurate
Integral Face recognition (babies smile at faces)
Depth inverted face looks normal (facial features overrule depth information)
Upside down face with inverted mouth and eyes looks normal
== Examples of taking advantage of an HVS model ==
Flicker frequency of film and television using persistence of vision to fool viewer into seeing a continuous image
Interlaced television painting half images to give the impression of a higher flicker frequency
Color television (chrominance at half resolution of luminance corresponding to proportions of rods and cones in eye)
Image compression (difficult to see higher frequencies more harshly quantized)
Motion estimation (use luminance and ignore color)
Watermarking and Steganography
== See also ==
Psychoacoustics
Visual system
Visual perception
Depth perception
== References == | Wikipedia/Human_visual_system_model |
Open energy-system models are energy-system models that are open source. However, some of them may use third-party proprietary software as part of their workflows to input, process, or output data. Preferably, these models use open data, which facilitates open science.
Energy-system models are used to explore future energy systems and are often applied to questions involving energy and climate policy. The models themselves vary widely in terms of their type, design, programming, application, scope, level of detail, sophistication, and shortcomings. For many models, some form of mathematical optimization is used to inform the solution process.
Energy regulators and system operators in Europe and North America began adopting open energy-system models for planning purposes in the early‑2020s. Open models and open data are increasingly being used by government agencies to guide the develop of net‑zero public policy as well (with examples indicated throughout this article). Companies and engineering consultancies are likewise adopting open models for analysis (again see below).
== General considerations ==
=== Organization ===
The open energy modeling projects listed here fall exclusively within the bottom-up paradigm, in which a model is a relatively literal representation of the underlying system.
Several drivers favor the development of open models and open data. There is an increasing interest in making public policy energy models more transparent to improve their acceptance by policymakers and the public. There is also a desire to leverage the benefits that open data and open software development can bring, including reduced duplication of effort, better sharing of ideas and information, improved quality, and wider engagement and adoption. Model development is therefore usually a team effort and constituted as either an academic project, a commercial venture, or a genuinely inclusive community initiative.
This article does not cover projects which simply make their source code or spreadsheets available for public download, but which omit a recognized free and open-source software license. The absence of a license agreement creates a state of legal uncertainty whereby potential users cannot know which limitations the owner may want to enforce in the future.: 1 The projects listed here are deemed suitable for inclusion through having pending or published academic literature or by being reported in secondary sources.
A 2017 paper lists the benefits of open data and models and discusses the reasons that many projects nonetheless remain closed.: 211–213 The paper makes a number of recommendations for projects wishing to transition to a more open approach.: 214 The authors also conclude that, in terms of openness, energy research has lagged behind other fields, most notably physics, biotechnology, and medicine.: 213–214
=== Growth ===
Open energy-system modeling came of age in the 2010s. Just two projects were cited in a 2011 paper on the topic: OSeMOSYS and TEMOA.: 5861 Balmorel was also active at that time, having been made public in 2001. As of July 2022, 31 such undertakings are listed here (with an approximately equal number waiting to be added).
Chang et al (2021) survey modeling trends and find the open to closed division about even after reviewing 54 frameworks — although that interpretation is based on project count and not on uptake and use. A 2022 model comparison exercise in Germany reported eight from 40 modeling projects (20%) were open source, these projects also had active communities behind them.
=== Transparency, comprehensibility, and reproducibility ===
The use of open energy-system models and open energy data represents one attempt to improve the transparency, comprehensibility, and reproducibility of energy system models, particularly those used to aid public policy development.
A 2010 paper concerning energy efficiency modeling argues that "an open peer review process can greatly support model verification and validation, which are essential for model development".: 17 To further honor the process of peer review, researchers argue, in a 2012 paper, that it is essential to place both the source code and datasets under publicly accessible version control so that third-parties can run, verify, and scrutinize specific models. A 2016 paper contends that model-based energy scenario studies, seeking to influence decision-makers in government and industry, must become more comprehensible and more transparent. To these ends, the paper provides a checklist of transparency criteria that should be completed by modelers. The authors however state that they "consider open source approaches to be an extreme case of transparency that does not automatically facilitate the comprehensibility of studies for policy advice.": 4
A one-page opinion piece from 2017 advances the case for using open energy data and modeling to build public trust in policy analysis. The article also argues that scientific journals have a responsibility to require that data and code be submitted alongside text for peer review. And an academic commentary from 2020 argues that distributed development would facilitate a more diverse contributor base and thus improve model quality — a process supported by online platforms and enabled by open data and code.
=== State projects ===
State-sponsored open source projects in any domain are a relatively new phenomena.
As of 2017, the European Commission now supports several open source energy system modeling projects to aid the transition to a low-carbon energy system for Europe. The Dispa-SET project (below) is modeling the European electricity system and hosts its codebase on GitHub. The MEDEAS project, which will design and implement a new open source energy-economy model for Europe, held its kick-off meeting in February 2016.: 6 As of February 2017, the project had yet to publish any source code. The established OSeMOSYS project (below) is developing a multi-sector energy model for Europe with Commission funding to support stakeholder outreach. The flagship JRC-EU-TIMES model however remains closed source.
The United States NEMS national model is available but nonetheless difficult to use. NEMS does not classify as an open source project in the accepted sense.
A 2021 research call from the European Union Horizon Europe scientific research funding program expressly sought energy system models that are open source.
=== Surveys ===
A survey completed in 2021 investigated the degree to which open energy-system modeling frameworks support flexibility options, broken down by supply, demand, storage, sector coupled, and network response. Of the frameworks surveyed, none supported all types, which suggests that the soft coupling of complementary frameworks could provide more holistic assessments of flexibility. Even so, most candidates opt for perfect foresight and do not natively admit probabilistic actions or explicit behavioral responses.
== Electricity sector models ==
Open electricity sector models are confined to just the electricity sector. These models invariably have a temporal resolution of one hour or less. Some models concentrate on the engineering characteristics of the system, including a good representation of high-voltage transmission networks and AC power flow. Others models depict electricity spot markets and are known as dispatch models. While other models embed autonomous agents to capture, for instance, bidding decisions using techniques from bounded rationality. The ability to handle variable renewable energy, transmission systems, and grid storage are becoming important considerations.
=== AMIRIS ===
AMIRIS is the open Agent-based Market model for the Investigation of Renewable and Integrated energy Systems. The AMIRIS simulation framework was first developed by the German Aerospace Center (DLR) in 2008 and later released as an open source project in 2021.
AMIRIS enables researchers to address questions regarding future energy markets, their market design, and energy-related policy instruments.
In particular, AMIRIS is able to capture market effects that may arise from the integration of renewable energy sources and flexibility options by considering the strategies and behaviors of the various energy market actors present. For instance, those behaviors can be influenced by the prevailing political framework and by external uncertainties. AMIRIS may also uncover complex effects that may emerge from the inter‑dependencies of the energy market participants.
The embedded market clearing algorithm computes electricity prices based on the bids of prototyped market actors. These bids may not only reflect the marginal cost of electricity production but also the limited information available to the actors and related uncertainties. But also the bidding can be strategic as an attempt to game official support instruments or exploit market power opportunities.
Actors in AMIRIS are represented as agents that can be roughly divided into six classes: power plant operators, traders, market operators, policy providers, demand agents, and storage facility operators. In the model, power plant operators provide generation capacities to traders, but do not participate directly in markets. Instead, they supply traders who conduct the marketing and deploy bidding strategies on the operators behalf. Marketplaces serve as trading platforms and calculate market clearing. Policy providers define the regulatory framework which then may impact on the decisions of the other agents. Demand agents request energy directly at the electricity market. Finally, flexibility providers, such as storage operators, use forecasts to determine bidding patterns to match their particular objectives, for instance, projected profit maximization.
AMIRIS is based on the open Framework for distributed Agent-based Modelling of Energy systems or FAME.
AMIRIS can simulate large‑scale agent systems in acceptable timeframes. For instance, the simulation of one year at hourly resolution may take as little as one minute on a contemporary desktop computer. The researchers at DLR also have access to high-performance computing facilities.
=== Breakthrough Energy Model ===
The Breakthrough Energy Model is a production cost model with capacity expansion algorithms and heuristics, originally designed to explore the generation and transmission expansion needs to meet U.S. states' clean energy goals. The data management occurs within Python and the DCOPF optimization problem is created via Julia. The Breakthrough Energy Model is being developed by the Breakthrough Energy Sciences team.
The open data underlying the model builds upon the synthetic test cases developed by researchers at Texas A&M University.
The Breakthrough Energy Model initially explored the generation and transmission expansion necessary to meet clean energy goals in 2030 via the building of a Macro Grid. Ongoing work adds and expands modules to the model (e.g. electrification of buildings and transportation) to provide a framework for testing numerous scenario combinations. Development of and integration with other open-source data sets is in progress for modeling countries and regions beyond the United States.
The model was applied subsequently the 2021 Texas power crisis, in which winter power outages resulted in hundreds of deaths and billions of dollars in economic losses.: 1
=== DIETER ===
DIETER stands for Dispatch and Investment Evaluation Tool with Endogenous Renewables. DIETER is a dispatch and investment model. It was first used to study the role of power storage and other flexibility options in a future greenfield setting with high shares of renewable generation. DIETER is being developed at the German Institute for Economic Research (DIW), Berlin, Germany. The codebase and datasets for Germany can be downloaded from the project website. The basic model is fully described in a DIW working paper and a journal article. DIETER is written in GAMS and was developed using the CPLEX commercial solver.
DIETER is framed as a pure linear (no integer variables) cost minimization problem. In the initial formulation, the decision variables include the investment in and dispatch of generation, storage, and DSM capacities in the German wholesale and balancing electricity markets. Later model extensions include vehicle-to-grid interactions and prosumage of solar electricity.
The first study using DIETER examines the power storage requirements for renewables uptake ranging from 60% to 100%. Under the baseline scenario of 80% (the lower bound German government target for 2050), grid storage requirements remain moderate and other options on both the supply side and demand side offer flexibility at low cost. Nonetheless, storage plays an important role in the provision of reserves. Storage becomes more pronounced under higher shares of renewables, but strongly depends on the costs and availability of other flexibility options, particularly biomass availability.
=== Dispa-SET ===
Under development at the European Commission's Joint Research Centre (JRC), Petten, the Netherlands, Dispa-SET is a unit commitment and dispatch model intended primarily for Europe. It is written in Python (with Pyomo) and GAMS and uses Python for data processing. A valid GAMS license is required. The model is formulated as a mixed integer problem and JRC uses the proprietary CPLEX sover although open source libraries may also be deployed. Technical descriptions are available for versions 2.0 and 2.1. Dispa-SET is hosted on GitHub, together with a trial dataset, and third-party contributions are encouraged. The codebase has been tested on Windows, macOS, and Linux. Online documentation is available.
The SET in the project name refers to the European Strategic Energy Technology Plan (SET-Plan), which seeks to make Europe a leader in energy technologies that can fulfill future (2020 and 2050) energy and climate targets. Energy system modeling, in various forms, is central to this European Commission initiative.
The model power system is managed by a single operator with full knowledge of the economic and technical characteristics of the generation units, the loads at each node, and the heavily simplified transmission network. Demand is deemed fully inelastic. The system is subject to intra-period and inter-period unit commitment constraints (the latter covering nuclear and thermal generation for the most part) and operated under economic dispatch.: 4 Hourly data is used and the simulation horizon is normally one year. But to ensure the model remains tractable, two day rolling horizon optimization is employed. The model advances in steps of one day, optimizing the next 48 hours ahead but retaining results for just the first 24 hours.: 14–15
Two related publications describe the role and representation of flexibility measures within power systems facing ever greater shares of variable renewable energy (VRE). These flexibility measures comprise: dispatchable generation (with constraints on efficiency, ramp rate, part load, and up and down times), conventional storage (predominantly pumped-storage hydro), cross-border interconnectors, demand side management, renewables curtailment, last resort load shedding, and nascent power-to-X solutions (with X being gas, heat, or mobility). The modeler can set a target for renewables and place caps on CO2 and other pollutants. Planned extensions to the software include support for simplified AC power flow (transmission is currently treated as a transportation problem), new constraints (like cooling water supply), stochastic scenarios, and the inclusion of markets for ancillary services.
Dispa-SET has been or is being applied to case studies in Belgium, Bolivia, Greece, Ireland, and the Netherlands. A 2014 Belgium study investigates what if scenarios for different mixes of nuclear generation, combined cycle gas turbine (CCGT) plant, and VRE and finds that the CCGT plants are subject to more aggressive cycling as renewable generation penetrates.
A 2020 study investigated the collective impact of future climatic conditions on 34 European power systems, including potential variations in solar, wind, and hydro‑power output and electricity demand under various projected meteorological scenarios for the European continent.
Dispa-SET has been applied in Africa with soft linking to the LISFLOOD model to examine water‑energy nexus problems in the context of a changing climate.
=== EMLab-Generation ===
EMLab-Generation is an agent-based model covering two interconnected electricity markets – be they two adjoining countries or two groups of countries. The software is being developed at the Energy Modelling Lab, Delft University of Technology, Delft, the Netherlands. A factsheet is available. And software documentation is available. EMLab-Generation is written in Java.
EMLab-Generation simulates the actions of power companies investing in generation capacity and uses this to explore the long-term effects of various energy and climate protection policies. These policies may target renewable generation, CO2 emissions, security of supply, and/or energy affordability. The power companies are the main agents: they bid into power markets and they invest based on the net present value (NPV) of prospective power plant projects. They can adopt a variety of technologies, using scenarios from the 2011 IEA World Energy Outlook. The agent-based methodology enables different sets of assumptions to be tested, such as the heterogeneity of actors, the consequences of imperfect expectations, and the behavior of investors outside of ideal conditions.
EMLab-Generation offers a new way of modeling the effects of public policy on electricity markets. It can provide insights into actor and system behaviors over time – including such things as investment cycles, abatement cycles, delayed responses, and the effects of uncertainty and risk on investment decisions.
A 2014 study using EMLab-Generation investigates the effects of introducing floor and ceiling prices for CO2 under the EU ETS. And in particular, their influence on the dynamic investment pathway of two interlinked electricity markets (loosely Great Britain and Central Western Europe). The study finds a common, moderate CO2 auction reserve price results in a more continuous decarbonisation pathway and reduces CO2 price volatility. Adding a ceiling price can shield consumers from extreme price shocks. Such price restrictions should not lead to an overshoot of emissions targets in the long-run.
=== EMMA ===
EMMA is the European Electricity Market Model. It is a techno-economic model covering the integrated Northwestern European power system. EMMA is being developed by the energy economics consultancy Neon Neue Energieökonomik, Berlin, Germany. The source code and datasets can be downloaded from the project website. A manual is available. EMMA is written in GAMS and uses the CPLEX commercial solver.
EMMA models electricity dispatch and investment, minimizing the total cost with respect to investment, generation, and trades between market areas. In economic terms, EMMA classifies as a partial equilibrium model of the wholesale electricity market with a focus on the supply-side. EMMA identifies short-term or long-term optima (or equilibria) and estimates the corresponding capacity mix, hourly prices, dispatch, and cross-border trading. Technically, EMMA is a pure linear program (no integer variables) with about two million non-zero variables. As of 2016, the model covers Belgium, France, Germany, the Netherlands, and Poland and supports conventional generation, renewable generation, and cogeneration.
EMMA has been used to study the economic effects of the increasing penetration of variable renewable energy (VRE), specifically solar power and wind power, in the Northwestern European power system. A 2013 study finds that increasing VRE shares will depress prices and, as a consequence, the competitive large-scale deployment of renewable generation will be more difficult to accomplish than many anticipate. A 2015 study estimates the welfare-optimal market share for wind and solar power. For wind, this is 20%, three-fold more than at present.
An independent 2015 study reviews the EMMA model and comments on the high assumed specific costs for renewable investments.: 6
=== GENESYS ===
GENESYS stands for Genetic Optimisation of a European Energy Supply System. The software is being developed jointly by the Institute of Power Systems and Power Economics (IAEW) and the Institute for Power Electronics and Electrical Drives (ISEA), both of RWTH Aachen University, Aachen, Germany. The project maintains a website where potential users can request access to the codebase and the dataset for the 2050 base scenario only. Detailed descriptions of the software are available. GENESYS is written in C++ and uses Boost libraries, the MySQL relational database, the Qt 4 application framework, and optionally the CPLEX solver.
The GENESYS simulation tool is designed to optimize a future EUMENA (Europe, Middle East, and North Africa) power system and assumes a high share of renewable generation. It is able to find an economically optimal distribution of generator, storage, and transmission capacities within a 21 region EUMENA. It allows for the optimization of this energy system in combination with an evolutionary method. The optimization is based on a covariance matrix adaptation evolution strategy (CMA-ES), while the operation is simulated as a hierarchical set-up of system elements which balance the load between the various regions at minimum cost using the network simplex algorithm. GENESYS ships with a set of input time series and a set of parameters for the year 2050, which the user can modify.
A future EUMENA energy supply system with a high share of renewable energy sources (RES) will need a strongly interconnected energy transport grid and significant energy storage capacities. GENESYS was used to dimension the storage and transmission between the 21 different regions. Under the assumption of 100% self-supply, about 2500 GW of RES in total and a storage capacity of about 240000 GWh are needed, corresponding to 6% of the annual energy demand, and a HVDC transmission grid of 375000 GW·km. The combined cost estimate for generation, storage, and transmission, excluding distribution, is 6.87 ¢/kWh.
A 2016 study looked at the relationship between storage and transmission capacity under high shares of renewable energy sources (RES) in an EUMENA power system. It found that, up to a certain extent, transmission capacity and storage capacity can substitute for each other. For a transition to a fully renewable energy system by 2050, major structural changes are required. The results indicate the optimal allocation of photovoltaics and wind power, the resulting demand for storage capacities of different technologies (battery, pumped hydro, and hydrogen storage) and the capacity of the transmission grid.
=== NEMO ===
NEMO, the National Electricity Market Optimiser, is a chronological dispatch model for testing and optimizing different portfolios of conventional and renewable electricity generation technologies. It applies solely to the Australian National Electricity Market (NEM), which, despite its name, is limited to east and south Australia. NEMO has been in development at the Centre for Energy and Environmental Markets (CEEM), University of New South Wales (UNSW), Sydney, Australia since 2011. The project maintains a small website and runs an email list. NEMO is written in Python. NEMO itself is described in two publications.: sec 2 : sec 2 The data sources are also noted.: sec 3 Optimizations are carried out using a single-objective evaluation function, with penalties. The solution space of generator capacities is searched using the CMA-ES (covariance matrix adaptation evolution strategy) algorithm. The timestep is arbitrary but one hour is normally employed.
NEMO has been used to explore generation options for the year 2030 under a variety of renewable energy (RE) and abated fossil fuel technology scenarios. A 2012 study investigates the feasibility of a fully renewable system using concentrated solar power (CSP) with thermal storage, windfarms, photovoltaics, existing hydroelectricity, and biofuelled gas turbines. A number of potential systems, which also meet NEM reliability criteria, are identified. The principal challenge is servicing peak demand on winter evenings following overcast days and periods of low wind. A 2014 study investigates three scenarios using coal-fired thermal generation with carbon capture and storage (CCS) and gas-fired gas turbines with and without capture. These scenarios are compared to the 2012 analysis using fully renewable generation. The study finds that "only under a few, and seemingly unlikely, combinations of costs can any of the fossil fuel scenarios compete economically with 100% renewable electricity in a carbon constrained world".: 196 A 2016 study evaluates the incremental costs of increasing renewable energy shares under a range of greenhouse gas caps and carbon prices. The study finds that incremental costs increase linearly from zero to 80% RE and then escalate moderately. The study concludes that this cost escalation is not a sufficient reason to avoid renewables targets of 100%.
=== OnSSET ===
OnSSET is the OpeN Source Spatial Electrification Toolkit. OnSSET is being developed by the division of Energy Systems, KTH Royal Institute of Technology, Stockholm, Sweden. The software is used to examine areas not served by grid-based electricity and identify the technology options and investment requirements that will provide least-cost access to electricity services. OnSSET is designed to support the United Nations' SDG 7: the provision of affordable, reliable, sustainable, and modern energy for all. The toolkit is known as OnSSET and was released on 26 November 2016. OnSSET does not ship with data, but suitable datasets are available from energydata.info. The project maintains a website and runs a mailing list.
OnSSET can estimate, analyze, and visualize the most cost-effective electrification access options, be they conventional grid, mini-grid, or stand-alone. The toolkit supports a range of conventional and renewable energy technologies, including photovoltaics, wind turbines, and small hydro generation. As of 2017, bioenergy and hybrid technologies, such as wind-diesel, are being added.
OnSSET utilizes energy and geographic information, the latter may include settlement size and location, existing and planned transmission and generation infrastructure, economic activity, renewable energy resources, roading networks, and nighttime lighting needs. The GIS information can be supported using the proprietary ArcGIS package or an open source equivalent such as GRASS or QGIS. OnSSET has been applied to microgrids using a three‑tier analysis starting with settlement archetypes.
OnSSET has been used for case studies in Afghanistan, Bolivia, Cameroon, Ethiopia, Malawi, Nigeria, and Tanzania. OnSSET has also been applied in India, Kenya, and Zimbabwe. In addition, continental studies have been carried out for Sub-Saharan Africa and Latin America. A 4‑way GIS‑based study set in Nigeria reported that OnSSET offered the best set of capabilities.
OnSSET results have contributed to the IEA World Energy Outlook reports for 2014 and 2015, the World Bank Global Tracking Framework report in 2015, and the IEA Africa Energy Outlook report in 2019. OnSSET also forms part of the nascent GEP platform.
=== pandapower ===
pandapower is a power system analysis and optimization program being jointly developed by the Energy Management and Power System Operation research group, University of Kassel and the Department for Distribution System Operation, Fraunhofer Institute for Energy Economics and Energy System Technology (IEE), both of Kassel, Germany. The codebase is hosted on GitHub and is also available as a package. The project maintains a website, an emailing list, and online documentation. pandapower is written in Python. It uses the pandas library for data manipulation and analysis and the PYPOWER library to solve for power flow. Unlike some open source power system tools, pandapower does not depend on proprietary platforms like MATLAB.
pandapower supports the automated analysis and optimization of distribution and transmission networks. This allows a large of number of scenarios to be explored, based on different future grid configurations and technologies. pandapower offers a collection of power system elements, including: lines, 2-winding transformers, 3-winding transformers, and ward-equivalents. It also contains a switch model that allows the modeling of ideal bus-bus switches as well as bus-line/bus-trafo switches. The software supports topological searching. The network itself can be plotted, with or without geographical information, using the matplotlib and plotly libraries.
A 2016 publication evaluates the usefulness of the software by undertaking several case studies with major distribution system operators (DSO). These studies examine the integration of increasing levels of photovoltaics into existing distribution grids. The study concludes that being able to test a large number of detailed scenarios is essential for robust grid planning. Notwithstanding, issues of data availability and problem dimensionality will continue to present challenges.
A 2018 paper describes the package and its design and provides an example case study. The article explains how users work with an element-based model (EBM) which is converted internally to a bus-branch model (BBM) for computation. The package supports power system simulation, optimal power flow calculations (cost information is required), state estimation (should the system characterization lacks fidelity), and graph-based network analysis. The case study shows how a few tens of lines of scripting can interface with pandapower to advance the design of a system subject to diverse operating requirements. The associated code is hosted on GitHub as jupyter notebooks.
As of 2018, BNetzA, the German network regulator, is using pandapower for automated grid analysis. Energy research institutes in Germany are also following the development of pandapower.: 90
=== PowerMatcher ===
The PowerMatcher software implements a smart grid coordination mechanism which balances distributed energy resources (DER) and flexible loads through autonomous bidding. The project is managed by the Flexiblepower Alliance Network (FAN) in Amsterdam, the Netherlands. The project maintains a website and the source code is hosted on GitHub. As of June 2016, existing datasets are not available. PowerMatcher is written in Java.
Each device in the smart grid system – whether a washing machine, a wind generator, or an industrial turbine – expresses its willingness to consume or produce electricity in the form of a bid. These bids are then collected and used to determine an equilibrium price. The PowerMatcher software thereby allows high shares of renewable energy to be integrated into existing electricity systems and should also avoid any local overloading in possibly aging distribution networks.
=== Power TAC ===
Power TAC stands for Power Trading Agent Competition. Power TAC is an agent-based model simulating the performance of retail markets in an increasingly prosumer- and renewable-energy-influenced electricity landscape. The first version of the Power TAC project started in 2009, when the open source platform was released as an open-source multi-agent competitive gaming platform to simulate electricity retail market performance in smart grid scenarios. The inaugural annual tournament was held in Valencia, Spain in 2012.
Autonomous machine-learning trading agents, or 'brokers', compete directly with each other as profit-maximizing aggregators between wholesale markets and retail customers. Customer models represent households, small and large businesses, multi-residential buildings, wind parks, solar panel owners, electric vehicle owners, cold-storage warehouses, etc. Brokers aim at making profit through offering electricity tariffs to customers and trading electricity in the wholesale market, while carefully balancing supply and demand.
The competition is founded and orchestrated by Professors Wolfgang Ketter and John Collins and the platform software is developed collaboratively by researchers at the Rotterdam School of Management, Erasmus University Centre for Future Energy Business, the Institute for Energy Economics at the University of Cologne, and the Computer Science department at the University of Minnesota. The platform uses a variety of real-world data about weather, market prices and aggregate demand, and customer behavior. Broker agents are developed by research teams around the world and entered in annual tournaments. Data from those tournaments are publicly available and can be used to assess agent performance and interactions. The platform exploits competitive benchmarking to facilitate research into, among other topics, tariff design in retail electricity markets, bidding strategies in wholesale electricity markets, performance of markets as penetration of sustainable energy resources or electric vehicles is ramped up or down, effectiveness of machine learning approaches, and alternative policy approaches to market regulation. The software has contributed to research topics ranging from the use of electric vehicle fleets as virtual power plants to how an electricity customer decision support system (DSS) can be used to design effective demand response programs using methods such as dynamic pricing.
=== renpass ===
renpass is an acronym for Renewable Energy Pathways Simulation System. renpass is a simulation electricity model with high regional and temporal resolution, designed to capture existing systems and future systems with up to 100% renewable generation. The software is being developed by the Centre for Sustainable Energy Systems (CSES or ZNES), University of Flensburg, Germany. The project runs a website, from where the codebase can be download. renpass is written in R and links to a MySQL database. A PDF manual is available. renpass is also described in a PhD thesis. As of 2015, renpass is being extended as renpassG!S, based on oemof.
renpass is an electricity dispatch model which minimizes system costs for each time step (optimization) within the limits of a given infrastructure (simulation). Time steps are optionally 15 minutes or one hour. The method assumes perfect foresight. renpass supports the electricity systems found in Austria, Belgium, the Czech Republic, Denmark, Estonia, France, Finland, Germany, Latvia, Lithuania, Luxembourg, the Netherlands, Norway, Poland, Sweden, and Switzerland.
The optimization problem for each time step is to minimize the electricity supply cost using the existing power plant fleet for all regions. After this regional dispatch, the exchange between the regions is carried out and is restricted by the grid capacity. This latter problem is solved with a heuristic procedure rather than calculated deterministically. The input is the merit order, the marginal power plant, the excess energy (renewable energy that could be curtailed), and the excess demand (the demand that cannot be supplied) for each region. The exchange algorithm seeks the least cost for all regions, thus the target function is to minimize the total costs of all regions, given the existing grid infrastructure, storage, and generating capacities. The total cost is defined as the residual load multiplied by the price in each region, summed over all regions.
A 2012 study uses renpass to examine the feasibility of a 100% renewable electricity system for the Baltic Sea region (Denmark, Estonia, Finland, Germany, Latvia, Lithuania, Poland, and Sweden) in the year 2050. The base scenario presumes conservative renewable potentials and grid enhancements, a 20% drop in demand, a moderate uptake of storage options, and the deployment of biomass for flexible generation. The study finds that a 100% renewable electricity system is possible, albeit with occasional imports from abutting countries, and that biomass plays a key role in system stability. The costs for this transition are estimated at 50 €/MWh. A 2014 study uses renpass to model Germany and its neighbors. A 2014 thesis uses renpass to examine the benefits of both a new cable between Germany and Norway and new pumped storage capacity in Norway, given 100% renewable electricity systems in both countries. Another 2014 study uses renpass to examine the German Energiewende, the transition to a sustainable energy system for Germany. The study also argues that the public trust needed to underpin such a transition can only be built through the use of transparent open source energy models.
=== SciGRID ===
SciGRID, short for Scientific Grid, is an open source model of the German and European electricity transmission networks. The research project is managed by DLR Institute of Networked Energy Systems located in Oldenburg, Germany. The project maintains a website and an email newsletter. SciGRID is written in Python and uses a PostgreSQL database. The first release (v0.1) was made on 15 June 2015.
SciGRID aims to rectify the lack of open research data on the structure of electricity transmission networks within Europe. This lack of data frustrates attempts to build, characterise, and compare high resolution energy system models. SciGRID utilizes transmission network data available from the OpenStreetMap project, available under the Open Database License (ODbL), to automatically author transmission connections. SciGRID will not use data from closed sources. SciGRID can also mathematically decompose a given network into a simpler representation for use in energy models.
=== SIREN ===
SIREN stands for SEN Integrated Renewable Energy Network Toolkit. The project is run by Sustainable Energy Now, an NGO based in Perth, Australia. The project maintains a website. SIREN runs on Windows and the source code is hosted on SourceForge. The software is written in Python and uses the SAM model (System Advisor Model) from the US National Renewable Energy Laboratory to perform energy calculations. SIREN uses hourly datasets to model a given geographic region. Users can use the software to explore the location and scale of renewable energy sources to meet a specified electricity demand. SIREN utilizes a number of open or publicly available data sources: maps can be created from OpenStreetMap tiles and weather datasets can be created using NASA MERRA-2 satellite data.
A 2016 study using SIREN to analyze Western Australia's South-West Interconnected System (SWIS) finds that it can transition to 85% renewable energy (RE) for the same cost as new coal and gas. In addition, 11.1 million tonnes of CO2eq emissions would be avoided. The modeling assumes a carbon price of AUD $30/tCO2. Further scenarios examine the goal of 100% renewable generation.
=== SWITCH ===
SWITCH is a loose acronym for solar, wind, conventional and hydroelectric generation, and transmission. SWITCH is an optimal planning model for power systems with large shares of renewable energy. SWITCH is being developed by the Department of Electrical Engineering, University of Hawaiʻi at Mānoa, Hawaii, USA. The project runs a small website and hosts its codebase and datasets on GitHub. SWITCH is written in Pyomo, an optimization components library programmed in Python. It can use either the open source GLPK solver or the commercial CPLEX solver.
SWITCH is a power system model, focused on renewables integration. It can identify which generator and transmission projects to build in order to satisfy electricity demand at the lowest cost over a several-year period while also reducing CO2 emissions. SWITCH utilizes multi-stage stochastic linear optimization with the objective of minimizing the present value of the cost of power plants, transmission capacity, fuel usage, and an arbitrary per-tonne CO2 charge (to represent either a carbon tax or a certificate price), over the course of a multi-year investment period. It has two major sets of decision variables. First, at the start of each investment period, SWITCH selects how much generation capacity to build in each of several geographic load zones, how much power transfer capability to add between these zones, and whether to operate existing generation capacity during the investment period or to temporarily mothball it to avoid fixed operation and maintenance costs. Second, for a set of sample days within each investment period, SWITCH makes hourly decisions about how much power to generate from each dispatchable power plant, store at each pumped hydro facility, or transfer along each transmission interconnector. The system must also ensure enough generation and transmission capacity to provide a planning reserve margin of 15% above the load forecasts. For each sampled hour, SWITCH uses electricity demand and renewable power production based on actual measurements, so that the weather-driven correlations between these elements remain intact.
Following the optimization phase, SWITCH is used in a second phase to test the proposed investment plan against a more complete set of weather conditions and to add backstop generation capacity so that the planning reserve margin is always met. Finally, in a third phase, the costs are calculated by freezing the investment plan and operating the proposed power system over a full set of weather conditions.
A 2012 paper uses California from 2012 to 2027 as a case study for SWITCH. The study finds that there is no ceiling on the amount of wind and solar power that could be used and that these resources could potentially reduce emissions by 90% or more (relative to 1990 levels) without reducing reliability or severely raising costs. Furthermore, policies that encourage electricity customers to shift demand to times when renewable power is most abundant (for example, though the well-timed charging of electric vehicles) could achieve radical emission reductions at moderate cost.
SWITCH was used more recently to underpin consensus-based power system planning in Hawaii. The model is also being applied in Chile, Mexico, and elsewhere.
Major version 2.0 was released in late‑2018. An investigation that year favorably compared SWITCH with the proprietary General Electric MAPS model using Hawaii as a case study.
=== URBS ===
URBS, Latin for city, is a linear programming model for exploring capacity expansion and unit commitment problems and is particularly suited to distributed energy systems (DES). It is being developed by the Institute for Renewable and Sustainable Energy Systems, Technical University of Munich, Germany. The codebase is hosted on GitHub. URBS is written in Python and uses the Pyomo optimization packages.
URBS classes as an energy modeling framework and attempts to minimize the total discounted cost of the system. A particular model selects from a set of technologies to meet a predetermined electricity demand. It uses a time resolution of one hour and the spatial resolution is model-defined. The decision variables are the capacities for the production, storage, and transport of electricity and the time scheduling for their operation.: 11–14
The software has been used to explore cost-optimal extensions to the European transmission grid using projected wind and solar capacities for 2020. A 2012 study, using high spatial and technological resolutions, found variable renewable energy (VRE) additions cause lower revenues for conventional power plants and that grid extensions redistribute and alleviate this effect. The software has also been used to explore energy systems spanning Europe, the Middle East, and North Africa (EUMENA) and Indonesia, Malaysia, and Singapore.
== Energy system models ==
Open energy-system models capture some or all of the energy commodities found in an energy system. Typically models of the electricity sector are always included. Some models add the heat sector, which can be important for countries with significant district heating. Other models add gas networks. With the advent of emobility, other models still include aspects of the transport sector. Indeed, coupling these various sectors using power-to-X technologies is an emerging area of research.
=== AnyMOD.jl ===
AnyMOD.jl is a framework for planning macro‑energy systems at a high level of spatio-temporal detail. The framework covers the expansion and operation of short-term and seasonal storage, fossil and renewable generation, transmission infrastructure, and sector coupling technologies. It can be used to plan long‑term pathways under perfect foresight.
AnyMOD.jl is implemented in Julia and relies on the JuMP library for optimization and DataFrames.jl for data management. Models are formulated as linear optimization problems and can be solved with open-source libraries like HiGHS or commercial solvers like CPLEX. To increase accessibility and enable version-controlled development, specific models are fully defined using CSV files.
Compared to similar tools, AnyMOD.jl puts an emphasis on innovative methods to achieve high detail and capture intermittent renewables, while maintaining a comprehensive scope in terms of regions and sectors. These methods include varying the spatio-temporal resolution by energy carrier within the same model and a scaling algorithm to improve the properties of the underlying optimization problem. Methods from stochastic programming are now being implemented to better address the uncertainties associated with renewable generation.
As of 2022, most studies deploying the tool have focused on the German energy system in a European context, for instance investigating the trade‑offs between centralized and decentralized designs, the role of grid planning, and the potential of sufficiency measures. In addition, AnyMOD.jl has been used to support policy reports from the German Institute for Economic Research (DIW) on the European Green Deal and the coordination of the German Energiewende.
=== Backbone ===
Backbone is an energy system modeling framework that allows for a high level of detail and adaptability. It has been used to study city-level energy systems as well as multi-country energy systems. It was originally developed during 2015–2018 in an Academy of Finland‑funded project 'VaGe' by the Design and Operation of Energy Systems team at VTT. It has been further developed in a collaboration which includes VTT, UCD, and RUB.
The framework is agnostic about what is modeled, but still has capabilities to represent a large range of energy system characteristics — such as generation and transfer, reserves, unit commitment, heat diffusion in buildings, storages, multiple emissions and P2X, etc. It offers linear and mixed integer constraints for capturing things like unit start-ups and investment decisions. It allows the modeler to change the temporal resolution of the model between time steps. — and this enables, for example, to use a coarser time resolution further ahead in the time horizon of the model. The model can be solved as an investment model (single or multi-period, myopic, or full foresight) or as a rolling production cost unit commitment model to simulate operations.
Backbone's own wiki page has a tutorial for new users, example models, and user created mods. Open datasets include Northern European model for electricity, heat, and hydrogen and district heating and cooling model for the Finnish capital region.
=== Balmorel ===
Balmorel is a market-based energy system model from Denmark. Development was originally financed by the Danish Energy Research Program in 2001.: 23 The codebase was made public in March 2001. The Balmorel project maintains an extensive website, from where the codebase and datasets can be download as a zip file. Users are encouraged to register. Documentation is available from the same site. Balmorel is written in GAMS.
The original aim of the Balmorel project was to construct a partial equilibrium model of the electricity and CHP sectors in the Baltic Sea region, for the purposes of policy analysis. These ambitions and limitations have long since been superseded and Balmorel is no longer tied to its original geography and policy questions. Balmorel classes as a dispatch and investment model and uses a time resolution of one hour. It models electricity and heat supply and demand, and supports the intertemporal storage of both. Balmorel is structured as a pure linear program (no integer variables).
As of 2016, Balmorel has been the subject of some 22 publications. A 2008 study uses Balmorel to explore the Nordic energy system in 2050. The focus is on renewable energy supply and the deployment of hydrogen as the main transport fuel. Given certain assumptions about the future price of oil and carbon and the uptake of hydrogen, the model shows that it is economically optimal to cover, using renewable energy, more than 95% of the primary energy consumption for electricity and district heat and 65% of the transport. A 2010 study uses Balmorel to examine the integration of plug-in hybrid vehicles (PHEV) into a system comprising one quarter wind power and three quarters thermal generation. The study shows that PHEVs can reduce the CO2 emissions from the power system if actively integrated, whereas a hands-off approach – letting people charge their cars at will – is likely to result in an increase in emissions. A 2013 study uses Balmorel to examine cost-optimized wind power investments in the Nordic-Germany region. The study investigates the best placement of wind farms, taking into account wind conditions, distance to load, and the generation and transmission infrastructure already in place.
=== Calliope ===
Calliope is an energy system modeling framework, with a focus on flexibility, high spatial and temporal resolution, and the ability to execute different runs using the same base-case dataset. The project is being developed at the Department of Environmental Systems Science, ETH Zurich, Zürich, Switzerland. The project maintains a website, hosts the codebase at GitHub, operates an issues tracker, and runs two email lists. Calliope is written in Python and uses the Pyomo library. It can link to the open source GLPK solver and the commercial CPLEX solver. PDF documentation is available.
And a two‑page software review is available.
A Calliope model consists of a collection of structured text files, in YAML and CSV formats, that define the technologies, locations, and resource potentials. Calliope takes these files, constructs a pure linear optimization (no integer variables) problem, solves it, and reports the results in the form of pandas data structures for analysis. The framework contains five abstract base technologies – supply, demand, conversion, storage, transmission – from which new concrete technologies can be derived. The design of Calliope enforces the clear separation of framework (code) and model (data).
A 2015 study uses Calliope to compare the future roles of nuclear power and CSP in South Africa. It finds CSP could be competitive with nuclear by 2030 for baseload and more competitive when producing above baseload. CSP also offers less investment risk, less environmental risk, and other co-benefits. A second 2015 study compares a large number of cost-optimal future power systems for Great Britain. Three generation technologies are tested: renewables, nuclear power, and fossil fuels with and without carbon capture and storage (CCS). The scenarios are assessed on financial cost, emissions reductions, and energy security. Up to 60% of variable renewable capacity is possible with little increase in cost, while higher shares require large-scale storage, imports, and/or dispatchable renewables such as tidal range.
Calliope co‑developer Stefan Pfenninger discusses the role that energy system models can play in supporting real‑world decisions at a seminar held in mid‑2021. One study cited investigates the consequences of pursuing energy self‑sufficiency by duly adding increasingly restrictive internal constraints. Another at near optimal solutions for Italy. A 2023 video describes recent developments, many of which are designed to benefit users.
=== DESSTinEE ===
DESSTinEE stands for Demand for Energy Services, Supply and Transmission in EuropE. DESSTinEE is a model of the European energy system in 2050 with a focus on the electricity system. DESSTinEE is being developed primarily at the Imperial College Business School, Imperial College London (ICL), London, United Kingdom. The software can be downloaded from the project website. DESSTinEE is written in Excel/VBA and comprises a set of standalone spreadsheets. A flier is available.
DESSTinEE is designed to investigate assumptions about the technical requirements for energy transport – particularly electricity – and the scale of the economic challenge to develop the necessary infrastructure. Forty countries are considered in and around Europe and ten forms of primary and secondary energy are supported. The model uses a predictive simulation technique, rather than solving for either partial or general equilibrium. The model projects annual energy demands for each country to 2050, synthesizes hourly profiles for electricity demand in 2010 and 2050, and simulates the least-cost generation and transmission of electricity around the region.
A 2016 study using DESSTinEE (and a second model eLOAD) examines the evolution of electricity load curves in Germany and Britain from the present until 2050. In 2050, peak loads and ramp rates rise 20–60% and system utilization falls 15–20%, in part due to the substantial uptake of heat pumps and electric vehicles. These are significant changes.
=== Energy Transition Model ===
The Energy Transition Model (ETM) is an interactive web-based model using a holistic description of a country's energy system. It is being developed by Quintel Intelligence, Amsterdam, the Netherlands. The project maintains a project website, an interactive website, and a GitHub repository. ETM is written in Ruby (on Rails) and displays in a web browser. ETM consists of several software components as described in the documentation.
ETM is fully interactive. After selecting a region (France, Germany, the Netherlands, Poland, Spain, United Kingdom, EU-27, or Brazil) and a year (2020, 2030, 2040, or 2050), the user can set 300 sliders (or enter numerical values) to explore the following:
targets: set goals for the scenario and see if they can be achieved, targets comprise: CO2 reductions, renewables shares, total cost, and caps on imports
demands: expand or restrict energy demand in the future
costs: project the future costs of energy carriers and energy technologies, these costs do not include taxes or subsidies
supplies: select which technologies can be used to produce heat or electricity
ETM is based on an energy graph (digraph) where nodes (vertices) can convert from one type of energy to another, possibly with losses. The connections (directed edges) are the energy flows and are characterized by volume (in megajoules) and carrier type (such as coal, electricity, usable-heat, and so forth). Given a demand and other choices, ETM calculates the primary energy use, the total cost, and the resulting CO2 emissions. The model is demand driven, meaning that the digraph is traversed from useful demand (such as space heating, hot water usage, and car-kilometers) to primary demand (the extraction of gas, the import of coal, and so forth).
=== EnergyPATHWAYS ===
EnergyPATHWAYS is a bottom-up energy sector model used to explore the near-term implications of long-term deep decarbonization. The lead developer is energy and climate protection consultancy, Evolved Energy Research, San Francisco, USA. The code is hosted on GitHub. EnergyPATHWAYS is written in Python and links to the open source Cbc solver. Alternatively, the GLPK, or CPLEX solvers can be employed. EnergyPATHWAYS utilizes the PostgreSQL object-relational database management system (ORDBMS) to manage its data.
EnergyPATHWAYS is a comprehensive accounting framework used to construct economy-wide energy infrastructure scenarios. While portions of the model do use linear programming techniques, for instance, for electricity dispatch, the EnergyPATHWAYS model is not fundamentally an optimization model and embeds few decision dynamics. EnergyPATHWAYS offers detailed energy, cost, and emissions accounting for the energy flows from primary supply to final demand. The energy system representation is flexible, allowing for differing levels of detail and the nesting of cities, states, and countries. The model uses hourly least-cost electricity dispatch and supports power-to-gas, short-duration energy storage, long-duration energy storage, and demand response. Scenarios typically run to 2050.
A predecessor of the EnergyPATHWAYS software, named simply PATHWAYS, has been used to construct policy models. The California PATHWAYS model was used to inform Californian state climate targets for 2030. And the US PATHWAYS model contributed to the United Nations Deep Decarbonization Pathways Project (DDPP) assessments for the United States. As of 2016, the DDPP plans to employ EnergyPATHWAYS for future analysis.
=== ETEM ===
ETEM stands for Energy Technology Environment Model. The ETEM model offers a similar structure to OSeMOSYS but is aimed at urban planning. The software is being developed by the ORDECSYS company, Chêne-Bougeries, Switzerland, supported with European Union and national research grants. The project has two websites. The software can be downloaded from first of these websites (but as of July 2016, this looks out of date). A manual is available with the software. ETEM is written in MathProg. Presentations describing ETEM are available.
ETEM is a bottom-up model that identifies the optimal energy and technology options for a regional or city. The model finds an energy policy with minimal cost, while investing in new equipment (new technologies), developing production capacity (installed technologies), and/or proposing the feasible import/export of primary energy. ETEM typically casts forward 50 years, in two or five year steps, with time slices of four seasons using typically individual days or finer. The spatial resolution can be highly detailed. Electricity and heat are both supported, as are district heating networks, household energy systems, and grid storage, including the use of plug-in hybrid electric vehicles (PHEV). ETEM-SG, a development, supports demand response, an option which would be enabled by the development of smart grids.
The ETEM model has been applied to Luxembourg, the Geneva and Basel-Bern-Zurich cantons in Switzerland, and the Grenoble metropolitan and Midi-Pyrénées region in France. A 2005 study uses ETEM to study climate protection in the Swiss housing sector. The ETEM model was coupled with the GEMINI-E3 world computable general equilibrium model (CGEM) to complete the analysis. A 2012 study examines the design of smart grids. As distribution systems become more intelligent, so must the models needed to analysis them. ETEM is used to assess the potential of smart grid technologies using a case study, roughly calibrated on the Geneva canton, under three scenarios. These scenarios apply different constraints on CO2 emissions and electricity imports. A stochastic approach is used to deal with the uncertainty in future electricity prices and the uptake of electric vehicles.
=== ficus ===
ficus is a mixed integer optimization model for local energy systems. It is being developed at the Institute for Energy Economy and Application Technology, Technical University of Munich, Munich, Germany. The project maintains a website. The project is hosted on GitHub. ficus is written in Python and uses the Pyomo library. The user can choose between the open source GLPK solver or the commercial CPLEX solver.
Based on URBS, ficus was originally developed for optimizing the energy systems of factories and has now been extended to include local energy systems. ficus supports multiple energy commodities – goods that can be imported or exported, generated, stored, or consumed – including electricity and heat. It supports multiple-input and multiple-output energy conversion technologies with load-dependent efficiencies. The objective of the model is to supply the given demand at minimal cost. ficus uses exogenous cost time series for imported commodities as well as peak demand charges with a configurable timebase for each commodity in use.
=== GENeSYS-MOD ===
The Global Energy System Model (GENeSYS‑MOD) is a linear cost-minimizing optimization model being developed at Technische Universität Berlin, Germany. The project was originally based on the OSeMOSYS framework and the first version was released in 2017 using GAMS. The codebase was later translated into Julia. Both versions and a representative dataset are available on GitHub.
GENeSYS‑MOD couples the demand sectors covering electricity, buildings, industry, and transport and finds the cost-optimal investment into conventional and renewable energy generation, storage, and infrastructure. The research focus is on long-term system development and pathway analysis.
The model was first used to analyze decarbonization scenarios at the global level, broken down into ten regions. However, the framework is highly flexible, allowing for calculations at various levels of detail, from individual households to global aggregations, depending on the desired research question and availability of input data.
A 2019 study examined the low‑carbon transition of the European energy system and specifically the problem of stranded assets under a range of scenarios. It found that up to €200 billion in fossil-fueled capacities could be stranded by 2035 unless stronger policy signals are able to address short‑term planning biases. Another 2019 study evaluates China's energy system transformation, highlighting the need to reduce coal consumption by 60% by 2050 to meet global climate targets. Renewable energies, and in particular photovoltaics and onshore wind, emerge as cost-effective solutions, but overcoming local resistance and increasing stakeholder engagement remain crucial for success. A 2021 study investigates the European Green Deal goal of achieving 100% greenhouse gas reductions by 2050, examining the interplay of technological developments, policy imperatives, and societal attitudes. The study presents four future storylines that highlight the critical contribution of high rates of electrification combined with near‑term technology deployment to achieve the necessarily rapid decarbonization.
=== GenX ===
GenX is multi‑commodity sector capacity expansion model originally developed by researchers in the United States. The framework is written in Julia and deploys the JuMP library for building the underlying optimization problem. GenX through JuMP can utilize various open source (including CBC/CLP) and commercial optimization solvers (including CPLEX). In June 2021, the project launched as an active open source project and test suites are available to assist onboarding.
In parallel, the PowerGenome project is designed to provide GenX with a comprehensive current state dataset of the United States electricity system. That dataset can then be used as a springboard to develop future scenarios.
GenX has been used to explore long-term storage options in systems with high renewables shares, to explore the value of 'firm' low-carbon power generation options, and a variety of other applications. While North America remains a key focus, the software has been applied to problems in India, Italy, and Spain.
GenX was deployed in a 2021 case study with Louisville Gas and Electric and Kentucky Utilities that showed that stakeholder-driven modeling utilizing open‑source tools and public data can contribute productively to utility‑led analysis and planning.
A mid‑2022 study examined the natural gas crisis facing Europe, and particularly Germany, and concluded that there are several feasible paths (labeled "cases") to eliminate all imports of Russian natural gas by October 2022. Ongoing work seeks to examine the effect of extending the operating lives of Germany's three remaining nuclear reactors past 2022 and the effect of strong drought conditions on hydro generation and the system more generally.
=== oemof ===
oemof stands for Open Energy Modelling Framework. The project is managed by the Reiner Lemoine Institute, Berlin, Germany and the Center for Sustainable Energy Systems (CSES or ZNES) at the University of Flensburg and the Flensburg University of Applied Sciences, both Flensburg, Germany. The project runs two websites and a GitHub repository. oemof is written in Python and uses Pyomo and COIN-OR components for optimization. Energy systems can be represented using spreadsheets (CSV) which should simplify data preparation. Version 0.1.0 was released on 1 December 2016.
oemof classes as an energy modeling framework. It consists of a linear or mixed integer optimization problem formulation library (solph), an input data generation library (feedin-data), and other auxiliary libraries. The solph library is used to represent multi-regional and multi-sectoral (electricity, heat, gas, mobility) systems and can optimize for different targets, such as financial cost or CO2 emissions. Furthermore, it is possible to switch between dispatch and investment modes. In terms of scope, oemof can capture the European power system or alternatively it can describe a complex local power and heat sector scheme.
oemof has been applied in sub‑Saharan Africa. A masters project in 2020 compared oemof and OSeMOSYS.
=== OSeMOSYS ===
OSeMOSYS stands for Open Source Energy Modelling System. OSeMOSYS is intended for national and regional policy development and uses an intertemporal optimization framework. The model posits a single socially motivated operator/investor with perfect foresight. The OSeMOSYS project is a community endeavor, supported by the division of Energy Systems, KTH Royal Institute of Technology, Stockholm, Sweden. The project maintains a website providing background. The project also offers several active internet forums on Google Groups. OSeMOSYS was originally written in MathProg, a high-level mathematical programming language. It was subsequently reimplemented in GAMS and Python and all three codebases are now maintained. The project also provides a test model called UTOPIA. A manual is available.
OSeMOSYS provides a framework for the analysis of energy systems over the medium (10–15 years) and long term (50–100 years). OSeMOSYS uses pure linear optimization, with the option of mixed integer programming for the treatment of, for instance, discrete power plant capacity expansions. It covers most energy sectors, including heat, electricity, and transport. OSeMOSYS is driven by exogenously defined energy services demands. These are then met through a set of technologies which draw on a set of resources, both characterized by their potentials and costs. These resources are not limited to energy commodities and may include, for example, water and land-use. This enables OSeMOSYS to be applied in domains other than energy, such as water systems. Technical constraints, economic restrictions, and/or environmental targets may also be imposed to reflect policy considerations. OSeMOSYS is available in extended and compact MathProg formulations, either of which should give identical results. In its extended version, OSeMOSYS comprises a little more than 400 lines of code. OSeMOSYS has been used as a base for constructing reduced models of energy systems.
A key paper describing OSeMOSYS is available. A 2011 study uses OSeMOSYS to investigate the role of household investment decisions. A 2012 study extends OSeMOSYS to capture the salient features of a smart grid. The paper explains how to model variability in generation, flexible demand, and grid storage and how these impact on the stability of the grid. OSeMOSYS has been applied to village systems. A 2015 paper compares the merits of stand-alone, mini-grid, and grid electrification for rural areas in Timor-Leste under differing levels of access. In a 2016 study, OSeMOSYS is modified to take into account realistic consumer behavior. Another 2016 study uses OSeMOSYS to build a local multi-regional energy system model of the Lombardy region in Italy. One of the aims of the exercise was to encourage citizens to participate in the energy planning process. Preliminary results indicate that this was successful and that open modeling is needed to properly include both the technological dynamics and the non-technological issues. A 2017 paper covering Alberta, Canada factors in the risk of overrunning specified emissions targets because of technological uncertainty. Among other results, the paper finds that solar and wind technologies are built out seven and five years earlier respectively when emissions risks are included. Another 2017 paper analyses the electricity system in Cyprus and finds that, after European Union environmental regulations are applied post-2020, a switch from oil-fired to natural gas generation is indicated.
OSeMOSYS has been used to construct wide-area electricity models for Africa, comprising 45 countries and South America, comprising 13 countries. It has also been used to support United Nations' regional climate, land, energy, and water strategies (CLEWS) for the Sava river basin, central Europe, the Syr Darya river basin, eastern Europe,: 29 and Mauritius. Models have previously been built for the Baltic States, Bolivia, Nicaragua, Sweden, and Tanzania. A 2021 paper summarizes recent applications and also details various versions, forks, and local enhancements related to the OSeMOSYS codebase. An electricity sector analysis for Bangladesh completed in 2021 concluded that solar power is economically competitive under every investigated scenario. A 2022 study looked at the effects of a changing climate on the Ethiopian power system. OSeMOSYS has also been applied variously in Zimbabwe and Ecuador. Another 2022 study examined water usage, split by withdraws and consumption, for several low carbon energy strategies for Africa. Another study that year examined renewable energy in Egypt. And another the Dominican Republic. The Italian island of Pantelleria was used as a case study to compare battery and hydrogen storage and found that a hybrid system was least cost.
In 2016, work started on a browser-based interface to OSeMOSYS, known as the Model Management Infrastructure (MoManI). Led by the UN Department of Economic and Social Affairs (DESA), MoManI is being trialled in selected countries. The interface can be used to construct models, visualize results, and develop better scenarios. Atlantis is the name of a fictional country case-study for training purposes. A simplified GUI interface named clicSAND and utilizing Excel and Access was released in March 2021. A CLI workflow tool named otoole bundles several dedicated utilities, including one that can convert between OKI frictionless data and GNU MathProg data formats.: 3 In 2022, the project released starter kits for modeling selected countries in Africa, East Asia, and South America.
The OSeMBE reference model covering western and central Europe was announced on 27 April 2018. The model uses the MathProg implementation of OSeMOSYS but requires a small patch first. The model, funded as part of Horizon 2020 and falling under work package WP7 of the REEEM project, will be used to help stakeholders engage with a range of sustainable energy futures for Europe. The REEEM project runs from early-2016 until mid-2020.
A 2021 paper reviews the OSeMOSYS community, its composition, and its governance activities. And also describes the use of OSeMOSYS in education and for building analytical capacity within developing countries.
==== OSeMOSYS Global project ====
The OSeMOSYS community launched the OSeMOSYS Global project in 2022 to create a global model and associated workflows. As of late‑2022, OSeMOSYS Global is limited in scope to the electricity sector and the world system provided comprises 164 countries separated by 265 nodes.
=== PyPSA ===
PyPSA stands for Python for Power System Analysis. PyPSA is a free software toolbox for simulating and optimizing electric power systems and allied sectors. It supports conventional generation, variable wind and solar generation, electricity storage, coupling to the natural gas, hydrogen, heat, and transport sectors, and hybrid alternating and direct current networks. Moreover, PyPSA is designed to scale well. The project is managed by the Institute for Automation and Applied Informatics (IAI), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany, although the project itself exists independently under its own name and accounts. The project maintains a website and runs an email list. PyPSA itself is written in Python and uses the Pyomo library. The source code is hosted on GitHub and is also released periodically as a PyPI package.
The basic functionality of PyPSA is described in a 2018 paper. PyPSA bridges traditional steady-state power flow analysis software and full multi-period energy system models. It can be invoked using either non-linear power flow equations for system simulation or linearized approximations to enable the joint optimization of operations and investment across multiple periods. Generator ramping and multi-period up and down-times can be specified, DSM is supported, but demand remains price inelastic.
A 2018 study examines potential synergies between sector coupling and transmission reinforcement in a future European energy system constrained to reduce carbon emissions by 95%. The PyPSA-Eur-Sec-30 model captures the demand-side management potential of battery electric vehicles (BEV) as well as the role that power-to-gas, long-term thermal energy storage, and related technologies can play. Results indicate that BEVs can smooth the daily variations in solar power while the remaining technologies smooth the synoptic and seasonal variations in both demand and renewable supply. Substantial buildout of the electricity grid is required for a least-cost configuration. More generally, such a system is both feasible and affordable. The underlying datasets are available from Zenodo.
As of January 2018, PyPSA is used by more than a dozen research institutes and companies worldwide.: 2 Some research groups have independently extended the software, for instance to model integer transmission expansion.
In 2020, the PyPSA‑Eur‑Sec model for Europe was used to analyze several Paris Agreement Compatible Scenarios for Energy Infrastructure and determined that early action should pay off.
On 9 January 2019, the project released an interactive web-interfaced "toy" model, using the Cbc solver, to allow the public to experiment with different future costs and technologies. The site was relaunched on 5 November 2019 with some internal improvements, a new URL, and faster solver now completing in about 12 s. A newer version now uses the HiGHS solver.
During September 2021, PyPSA developers announced the PyPSA‑Server project to provide a web interface to a simplified version of their PyPSA‑Eur‑Sec sector‑coupled European model. Users need not install software and can define fresh scenarios "by difference" using a forms‑based webpage. Previously run scenarios are stored for future reference. The implementation as of October 2021 is essentially proof‑of‑concept.
In late‑2021, PyPSA‑Eur developers reported their investigation into integrated high-voltage electricity and hydrogen grid expansion options for Europe and the United Kingdom and the impact of the kind of trade‑offs that might stem from limited public acceptance of new infrastructure. Subsequent work added endogenous learning effects and identified steeper technology cost reductions than those anticipated by the European Commission. Work published in 2024 integrated PyPSA‑Eur with the global energy supply chain model TRACE and highlighted the need to coordinate infrastructure policies and import strategies.
A December 2021 study and ongoing work deployed a PyPSA‑PL model to assess policy options for Poland. Edinburgh University researchers published an independent power system model for Britain named PyPSA‑GB in 2024, together with assessments of official net‑zero Future Energy Scenarios (FES) from the UK National Grid.
Several PyPSA maintainers announced a new non‑profit startup in June 2023 to provide consulting services using PyPSA.
==== PyPSA meets Earth initiative ====
The PyPSA meets Earth initiative arose in October 2022 as a means of gathering together several historically disjoint PyPSA applications. One key strand is the PyPSA‑Africa project (previously PyPSA-meets-Africa), launched some months earlier to provide a single model and dataset spanning the African continent. A July 2022 webinar co‑hosted by CPEEL, Nigeria advanced this agenda. The first research paper, released in 2022, examines various pathways for Africa to be net zero by 2060 — with solar power and battery storage expected to be the predominant technologies.
Another key strand of the initiative is the PyPSA‑Earth project which seeks to create a global energy systems model at high spatial and temporal resolution. The project hopes to encourage large‑scale collaboration by providing software and processes that can capture the global energy system and thus also any subset of it. The codebase currently supports system integration studies that draw together electricity generation, storage, and transmission expansion. And a sector-coupled version of the framework is in development that will also offer a choice between myopic decision‑taking or perfect foresight.
=== REMix ===
REMix stands for "Renewable Energy Mix". It is an open source framework developed by the German Aerospace Center for setting up linear or mixed integer optimization models written in GAMS. A framework is understood as a collection of mutually compatible source codes required for a particular model, which can be combined in a modular manner. In this way, the same modeling concepts, along with the associated source code, can be reutilized to address various content focuses based on a common set of available model features.
REMix is developed for applications in energy system modeling studies. It is typically used to set up energy system optimization models, although potential applications beyond energy research are conceivable. In particular, these energy system optimization models are often characterized as bottom-up models in terms of explicitly modeling different technologies. In addition, these models are resolved on a spatial and a temporal dimension.
In practical terms, the framework allows for modeling competition between technologies that can serve the same purpose, such as power generation, while also providing insights into when and where a specific technology is required. Additionally, it can be applied to transportation problems, where the optimal exchange of a commodity between at least two distinct regions needs to be determined. Furthermore, it addresses storage problems, where the optimal balance between production and consumption at different points in time is calculated.
REMix offers several key features that make it a robust tool for energy system modeling. It is designed to handle large-scale models with high spatial and technological resolutions, making it suitable for complex analyses. The framework also incorporates path optimization, allowing for multi-year analyses and strategic planning over extended periods. Ongoing work deals with very large instances involving path optimization using the parallel solver PIPS-IPM++. A notable feature is its custom accounting capability, provided through the indicator module, which enables flexible definitions of what contributes to the objective functions. Additionally, REMix supports flexible modeling, offering multiple approaches to integrate and model technologies, allowing users to tailor the framework to their specific needs. Finally, it supports multi-criteria optimization, where, beyond cost minimization, additional factors such as ecological impacts or resilience indicators can be considered in the objective function, providing a more comprehensive approach to system optimization.
In the past, the model has been used to investigate a wide range of research questions. In addition to detailed analyses of the integration of renewable energies into the electricity system, for example, the role of hydrogen in the energy system of the future has also been examined.
For the purpose of validating the REMix model, German Aerospace Center has participated in various model comparisons.
=== TEMOA ===
TEMOA stands for Tools for Energy Model Optimization and Analysis. The software is being developed by the Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, North Carolina, USA. The project runs a website and a forum. The source code is hosted on GitHub. The model is programmed in Pyomo, an optimization components library written in Python. TEMOA can be used with any solver that Pyomo supports, including the open source GLPK solver. TEMOA uses version control to publicly archive source code and datasets and thereby enable third-parties to verify all published modeling work.
TEMOA classes as a modeling framework and is used to conduct analysis using a bottom-up, technology rich energy system model. The model objective is to minimize the system-wide cost of energy supply by deploying and utilizing energy technologies and commodities over time to meet a set of exogenously specified end-use demands. TEMOA is "strongly influenced by the well-documented MARKAL/TIMES model generators".: 4
TEMOA forms the basis of the Open Energy Outlook (OEO) research project spanning 2020–2022. The OEO project utilizes open source tools and open data to explore deep decarbonization policy options for the United States.
From mid‑2021, an interactive interface located on the main website allows registered users to manipulate scenario data locally, upload structured SQLite files, and then run these scenarios using the TEMOA software. The service also provides some limited data visualization and project management functionality.
== Specialist models ==
This section lists specialist modeling frameworks that cover particular aspects of an energy system in more detail than would normally be convenient or feasible with more general frameworks.
=== RAMP ===
RAMP is an open-source software suite for the stochastic simulation of user‑driven energy demand time series based on few simple inputs. For example, a minimal definition of a user type — say, a particular category of household — requires only information about which energy-consuming devices they own, when they tend to use them on any typical day, and for how long in total. The software then leverages stochasticity to make up for the absence of more detailed information and to include the unpredictability of human behavior.
The RAMP software can then generate synthetic data wherever metered data does not exist, such as when designing systems in remote areas or when looking forward to future electric-vehicle fleets. The limited data requirements also allow for a greater flexibility in scenario selection and development than similar but more data-intensive characterizations.
RAMP has been used in scientific research for a variety of use cases, including the generation of electricity demand profiles for remote or residential communities, domestic hot water usage, cooking practices, and electric mobility. Associated geographical scales can range from neighborhoods to continents.
RAMP has several dozen users worldwide. In the early‑2020s, the software became part of a multi-institution software development effort, supported by TU Delft, VITO, Reiner Lemoine Institute, University of Liège, Leibniz University Hannover, and Universidad Mayor de San Simón.
RAMP runs on Python and requires input in tabular form. Graphical user interfaces (GUI) are available, allowing the software to be run from web browsers.
=== venco.py ===
The venco.py model framework can be used to investigate interactions between the uptake of battery electric vehicles (BEV) and the electricity system at large. More specifically, BEVs can usefully contribute to short‑haul storage in power systems facing high shares of fluctuating renewable energy. But unlike dedicated grid storage, BEV contributions are highly dependent on the connection and charging choices that individual vehicle owners might make.
Venco.py has been applied to various scenarios in Germany in 2030 using a projected 9 million BEVs in service and an annual fleet power consumption of 27 TWh. Simulations show that owner decisions are indeed significant and that some system design variables have more influence than others. For instance, aggregate fleet capacity and the availability of fast charging facilities appear to strongly impact the likely system contribution. Further work is needed to assess the influence of more resolved weather and demand patterns. The mathematical formulation is available. Venco.py builds on an earlier spreadsheet prototype.
== Project statistics ==
Statistics for the 30 open energy modeling projects listed (given sufficient information is available) are as follows:
The GAMS language requires a proprietary environment and its significant cost effectively limits participation to those who can access an institutional copy.
== Programming components ==
Programming components, in this context, are coherent blocks of code or compiled libraries that can be relatively easily imported or linked to by higher‑level modeling frameworks in order to obtain some well‑defined functionality.
=== Technology modules ===
A number of technical component models are now also open source. While these component models do not constitute systems models aimed at public policy development (the focus of this page), they nonetheless warrant a mention. Technology modules can be linked or otherwise adapted into these broader initiatives.
Sandia photovoltaic array performance model
pvlib photovoltaics facility library
hplib heat pump facility library
windpowerlib wind turbine library
hydropowerlib hydroelectricity library
=== Auction models ===
A number of electricity auction models have been written in GAMS, AMPL, MathProg, and other languages. These include:
the EPOC nodal pricing model
Australian National Electricity Market examples using MathProg can be found at b:GLPK/Electricity markets
=== Open solvers ===
Many projects rely on a pure linear or mixed integer solver to perform classical optimization, constraint satisfaction, or some mix of the two. While there are several open source solver projects, the most commonly deployed solver is GLPK. GLPK has been adopted by Calliope, ETEM, ficus, OSeMOSYS, SWITCH, and TEMOA. Another alternative is the Clp solver. From mid‑2022, the HiGHS open source solver offers another option. HiGHS is used by the web‑based version of the PyPSA European multi‑sector model
Proprietary solvers outperform open source solvers by a considerable margin (perhaps ten-fold), so choosing an open solver will limit performance in terms of speed, memory consumption, and perhaps even tractability.
The flexible SMS++ optimization toolbox, written in C++17, is being developed specifically to meet the needs of energy system modeling.
== See also ==
General
Building energy simulation – the modeling of energy flows in buildings
Climate change mitigation scenarios
Energy modeling – the process of building computer models of energy systems
Energy system – the interpretation of the energy sector in system terms
Open Energy Modelling Initiative – a European-based energy modeling community
Open energy system databases – database projects which collect, clean, and republish energy-related datasets
Unit commitment problem in electrical power production
Software
List of free and open-source optimization solvers
Cbc (COIN-OR Branch and Cut) – an open source optimization solver
Clp (COIN-OR LP) – an open source linear optimization solver
Community Climate System Model – a mostly open source coupled global climate model
ESMF (Earth System Modeling Framework) – open source software for building climate, numerical weather prediction, and data assimilation applications
GHGProof – an open source land-use model
GLPK (GNU Linear Programming Kit) – an open source linear and mixed integer optimization solver
GridLAB-D – an open source simulation and analysis tool for smart grid energy technologies
GridSpice – an open source cloud-based simulation package for modelling smart grids
HiGHS – an open source optimization solver
People
Joe DeCarolis – energy system modeler and current head of the United States Energy Information Administration
== Notes ==
== References ==
== Further information ==
The following lists and databases cover energy system models to varying degrees of completeness and usually with a focus on open source:
Open energy models wiki maintained by the Open Energy Modelling Initiative
Open Energy Platform factsheets — structured summaries covering a range of open and closed energy system models
Global Power System Transformation Consortium database — filterable database of open models and related projects
Linux Foundation Energy inventory — allied projects with an emphasis on industrial rather than policy applications
== External links ==
Modeling efforts by region
Africa: reports and publications — broken down by region and country
Latin America: reports and publications — broken down by region and country
Oceania: reports and publications — broken down by region and country | Wikipedia/Open_energy_system_models |
Solar System models, especially mechanical models, called orreries, that illustrate the relative positions and motions of the planets and moons in the Solar System have been built for centuries. While they often showed relative sizes, these models were usually not built to scale. The enormous ratio of interplanetary distances to planetary diameters makes constructing a scale model of the Solar System a challenging task. As one example of the difficulty, the distance between the Earth and the Sun is almost 12,000 times the diameter of the Earth.
If the smaller planets are to be easily visible to the naked eye, large outdoor spaces are generally necessary, as is some means for highlighting objects that might otherwise not be noticed from a distance. The Boston Museum of Science had placed bronze models of the planets in major public buildings, all on similar stands with interpretive labels. For example, the model of Jupiter was located in the cavernous South Station waiting area. The properly-scaled, basket-ball-sized model is 1.3 miles (2.14 km) from the model Sun which is located at the museum, graphically illustrating the immense empty space in the Solar System.
The objects in such large models do not move. Traditional orreries often did move, and some used clockworks to display the relative speeds of objects accurately. These can be thought of as being correctly scaled in time, instead of distance.
== Permanent true scale models ==
Many towns and institutions have built outdoor scale models of the Solar System. Here is a table comparing these models with the actual system.
== Other models of the Solar System: historic, temporary, virtual, or dual-scale ==
Several sets of geocaching caches have been laid out as Solar System models.
== See also ==
Numerical model of the Solar System
Historical models of the Solar System
Infinite Corridor
== References ==
== External links ==
A list of websites related to Solar System models
The Otford Solar System
An accurate web-based scroll map of the Solar System scaled to the Moon being 1 pixel
An online scale model Archived 2020-01-05 at the Wayback Machine (does not work in some browsers)
An online 3D model
An article on the Solar System in Maine Archived 2013-10-10 at the Wayback Machine
An article about a temporary exhibit in Melbourne, Australia
A map with Solar System models in Germany
A tool to calculate the diameters and distances needed for an accurate scale model
To Scale: The Solar System - video of model built in desert with Earth as the size of a marble. | Wikipedia/Solar_System_model |
Systems theory is the transdisciplinary study of systems, i.e. cohesive groups of interrelated, interdependent components that can be natural or artificial. Every system has causal boundaries, is influenced by its context, defined by its structure, function and role, and expressed through its relations with other systems. A system is "more than the sum of its parts" when it expresses synergy or emergent behavior.
Changing one component of a system may affect other components or the whole system. It may be possible to predict these changes in patterns of behavior. For systems that learn and adapt, the growth and the degree of adaptation depend upon how well the system is engaged with its environment and other contexts influencing its organization. Some systems support other systems, maintaining the other system to prevent failure. The goals of systems theory are to model a system's dynamics, constraints, conditions, and relations; and to elucidate principles (such as purpose, measure, methods, tools) that can be discerned and applied to other systems at every level of nesting, and in a wide range of fields for achieving optimized equifinality.
General systems theory is about developing broadly applicable concepts and principles, as opposed to concepts and principles specific to one domain of knowledge. It distinguishes dynamic or active systems from static or passive systems. Active systems are activity structures or components that interact in behaviours and processes or interrelate through formal contextual boundary conditions (attractors). Passive systems are structures and components that are being processed. For example, a computer program is passive when it is a file stored on the hard drive and active when it runs in memory. The field is related to systems thinking, machine logic, and systems engineering.
== Overview ==
Systems theory is manifest in the work of practitioners in many disciplines, for example the works of physician Alexander Bogdanov, biologist Ludwig von Bertalanffy, linguist Béla H. Bánáthy, and sociologist Talcott Parsons; in the study of ecological systems by Howard T. Odum, Eugene Odum; in Fritjof Capra's study of organizational theory; in the study of management by Peter Senge; in interdisciplinary areas such as human resource development in the works of Richard A. Swanson; and in the works of educators Debora Hammond and Alfonso Montuori.
As a transdisciplinary, interdisciplinary, and multiperspectival endeavor, systems theory brings together principles and concepts from ontology, the philosophy of science, physics, computer science, biology, and engineering, as well as geography, sociology, political science, psychotherapy (especially family systems therapy), and economics.
Systems theory promotes dialogue between autonomous areas of study as well as within systems science itself. In this respect, with the possibility of misinterpretations, von Bertalanffy believed a general theory of systems "should be an important regulative device in science," to guard against superficial analogies that "are useless in science and harmful in their practical consequences."
Others remain closer to the direct systems concepts developed by the original systems theorists. For example, Ilya Prigogine, of the Center for Complex Quantum Systems at the University of Texas, has studied emergent properties, suggesting that they offer analogues for living systems. The distinction of autopoiesis as made by Humberto Maturana and Francisco Varela represent further developments in this field. Important names in contemporary systems science include Russell Ackoff, Ruzena Bajcsy, Béla H. Bánáthy, Gregory Bateson, Anthony Stafford Beer, Peter Checkland, Barbara Grosz, Brian Wilson, Robert L. Flood, Allenna Leonard, Radhika Nagpal, Fritjof Capra, Warren McCulloch, Kathleen Carley, Michael C. Jackson, Katia Sycara, and Edgar Morin among others.
With the modern foundations for a general theory of systems following World War I, Ervin László, in the preface for Bertalanffy's book, Perspectives on General System Theory, points out that the translation of "general system theory" from German into English has "wrought a certain amount of havoc":
It (General System Theory) was criticized as pseudoscience and said to be nothing more than an admonishment to attend to things in a holistic way. Such criticisms would have lost their point had it been recognized that von Bertalanffy's general system theory is a perspective or paradigm, and that such basic conceptual frameworks play a key role in the development of exact scientific theory. .. Allgemeine Systemtheorie is not directly consistent with an interpretation often put on 'general system theory,' to wit, that it is a (scientific) "theory of general systems." To criticize it as such is to shoot at straw men. Von Bertalanffy opened up something much broader and of much greater significance than a single theory (which, as we now know, can always be falsified and has usually an ephemeral existence): he created a new paradigm for the development of theories.
Theorie (or Lehre) "has a much broader meaning in German than the closest English words 'theory' and 'science'," just as Wissenschaft (or 'Science'). These ideas refer to an organized body of knowledge and "any systematically presented set of concepts, whether empirically, axiomatically, or philosophically" represented, while many associate Lehre with theory and science in the etymology of general systems, though it also does not translate from the German very well; its "closest equivalent" translates to 'teaching', but "sounds dogmatic and off the mark." An adequate overlap in meaning is found within the word "nomothetic", which can mean "having the capability to posit long-lasting sense." While the idea of a "general systems theory" might have lost many of its root meanings in the translation, by defining a new way of thinking about science and scientific paradigms, systems theory became a widespread term used for instance to describe the interdependence of relationships created in organizations.
A system in this frame of reference can contain regularly interacting or interrelating groups of activities. For example, in noting the influence in the evolution of "an individually oriented industrial psychology [into] a systems and developmentally oriented organizational psychology," some theorists recognize that organizations have complex social systems; separating the parts from the whole reduces the overall effectiveness of organizations. This difference, from conventional models that center on individuals, structures, departments and units, separates in part from the whole, instead of recognizing the interdependence between groups of individuals, structures and processes that enable an organization to function.
László explains that the new systems view of organized complexity went "one step beyond the Newtonian view of organized simplicity" which reduced the parts from the whole, or understood the whole without relation to the parts. The relationship between organisations and their environments can be seen as the foremost source of complexity and interdependence. In most cases, the whole has properties that cannot be known from analysis of the constituent elements in isolation.
Béla H. Bánáthy, who argued—along with the founders of the systems society—that "the benefit of humankind" is the purpose of science, has made significant and far-reaching contributions to the area of systems theory. For the Primer Group at the International Society for the System Sciences, Bánáthy defines a perspective that iterates this view:
The systems view is a world-view that is based on the discipline of SYSTEM INQUIRY. Central to systems inquiry is the concept of SYSTEM. In the most general sense, system means a configuration of parts connected and joined together by a web of relationships. The Primer Group defines system as a family of relationships among the members acting as a whole. Von Bertalanffy defined system as "elements in standing relationship."
== Applications ==
=== Art ===
=== Biology ===
Systems biology is a movement that draws on several trends in bioscience research. Proponents describe systems biology as a biology-based interdisciplinary study field that focuses on complex interactions in biological systems, claiming that it uses a new perspective (holism instead of reduction).
Particularly from the year 2000 onwards, the biosciences use the term widely and in a variety of contexts. An often stated ambition of systems biology is the modelling and discovery of emergent properties which represents properties of a system whose theoretical description requires the only possible useful techniques to fall under the remit of systems biology. It is thought that Ludwig von Bertalanffy may have created the term systems biology in 1928.
Subdisciplines of systems biology include:
Systems neuroscience
Systems pharmacology
==== Ecology ====
Systems ecology is an interdisciplinary field of ecology that takes a holistic approach to the study of ecological systems, especially ecosystems; it can be seen as an application of general systems theory to ecology.
Central to the systems ecology approach is the idea that an ecosystem is a complex system exhibiting emergent properties. Systems ecology focuses on interactions and transactions within and between biological and ecological systems, and is especially concerned with the way the functioning of ecosystems can be influenced by human interventions. It uses and extends concepts from thermodynamics and develops other macroscopic descriptions of complex systems.
=== Chemistry ===
Systems chemistry is the science of studying networks of interacting molecules, to create new functions from a set (or library) of molecules with different hierarchical levels and emergent properties. Systems chemistry is also related to the origin of life (abiogenesis).
=== Engineering ===
Systems engineering is an interdisciplinary approach and means for enabling the realisation and deployment of successful systems. It can be viewed as the application of engineering techniques to the engineering of systems, as well as the application of a systems approach to engineering efforts. Systems engineering integrates other disciplines and specialty groups into a team effort, forming a structured development process that proceeds from concept to production to operation and disposal. Systems engineering considers both the business and the technical needs of all customers, with the goal of providing a quality product that meets the user's needs.
==== User-centered design process ====
Systems thinking is a crucial part of user-centered design processes and is necessary to understand the whole impact of a new human computer interaction (HCI) information system. Overlooking this and developing software without insights input from the future users (mediated by user experience designers) is a serious design flaw that can lead to complete failure of information systems, increased stress and mental illness for users of information systems leading to increased costs and a huge waste of resources. It is currently surprisingly uncommon for organizations and governments to investigate the project management decisions leading to serious design flaws and lack of usability.
The Institute of Electrical and Electronics Engineers estimates that roughly 15% of the estimated $1 trillion used to develop information systems every year is completely wasted and the produced systems are discarded before implementation by entirely preventable mistakes. According to the CHAOS report published in 2018 by the Standish Group, a vast majority of information systems fail or partly fail according to their survey:
Pure success is the combination of high customer satisfaction with high return on value to the organization. Related figures for the year 2017 are: successful: 14%, challenged: 67%, failed 19%.
=== Mathematics ===
System dynamics is an approach to understanding the nonlinear behaviour of complex systems over time using stocks, flows, internal feedback loops, and time delays.
=== Social sciences and humanities ===
Systems theory in anthropology
Systems theory in archaeology
Systems theory in political science
==== Psychology ====
Systems psychology is a branch of psychology that studies human behaviour and experience in complex systems.
It received inspiration from systems theory and systems thinking, as well as the basics of theoretical work from Roger Barker, Gregory Bateson, Humberto Maturana and others. It makes an approach in psychology in which groups and individuals receive consideration as systems in homeostasis. Systems psychology "includes the domain of engineering psychology, but in addition seems more concerned with societal systems and with the study of motivational, affective, cognitive and group behavior that holds the name engineering psychology."
In systems psychology, characteristics of organizational behaviour (such as individual needs, rewards, expectations, and attributes of the people interacting with the systems) "considers this process in order to create an effective system."
=== Informatics ===
System theory has been applied in the field of neuroinformatics and connectionist cognitive science. Attempts are being made in neurocognition to merge connectionist cognitive neuroarchitectures with the approach of system theory and dynamical systems theory.
== History ==
=== Precursors ===
Systems thinking can date back to antiquity, whether considering the first systems of written communication with Sumerian cuneiform to Maya numerals, or the feats of engineering with the Egyptian pyramids. Differentiated from Western rationalist traditions of philosophy, C. West Churchman often identified with the I Ching as a systems approach sharing a frame of reference similar to pre-Socratic philosophy and Heraclitus.: 12–13 Ludwig von Bertalanffy traced systems concepts to the philosophy of Gottfried Leibniz and Nicholas of Cusa's coincidentia oppositorum. While modern systems can seem considerably more complicated, they may embed themselves in history.
Figures like James Joule and Sadi Carnot represent an important step to introduce the systems approach into the (rationalist) hard sciences of the 19th century, also known as the energy transformation. Then, the thermodynamics of this century, by Rudolf Clausius, Josiah Gibbs and others, established the system reference model as a formal scientific object.
Similar ideas are found in learning theories that developed from the same fundamental concepts, emphasising how understanding results from knowing concepts both in part and as a whole. In fact, Bertalanffy's organismic psychology paralleled the learning theory of Jean Piaget. Some consider interdisciplinary perspectives critical in breaking away from industrial age models and thinking, wherein history represents history and math represents math, while the arts and sciences specialization remain separate and many treat teaching as behaviorist conditioning.
The contemporary work of Peter Senge provides detailed discussion of the commonplace critique of educational systems grounded in conventional assumptions about learning, including the problems with fragmented knowledge and lack of holistic learning from the "machine-age thinking" that became a "model of school separated from daily life." In this way, some systems theorists attempt to provide alternatives to, and evolved ideation from orthodox theories which have grounds in classical assumptions, including individuals such as Max Weber and Émile Durkheim in sociology and Frederick Winslow Taylor in scientific management. The theorists sought holistic methods by developing systems concepts that could integrate with different areas.
Some may view the contradiction of reductionism in conventional theory (which has as its subject a single part) as simply an example of changing assumptions. The emphasis with systems theory shifts from parts to the organization of parts, recognizing interactions of the parts as not static and constant but dynamic processes. Some questioned the conventional closed systems with the development of open systems perspectives. The shift originated from absolute and universal authoritative principles and knowledge to relative and general conceptual and perceptual knowledge and still remains in the tradition of theorists that sought to provide means to organize human life. In other words, theorists rethought the preceding history of ideas; they did not lose them. Mechanistic thinking was particularly critiqued, especially the industrial-age mechanistic metaphor for the mind from interpretations of Newtonian mechanics by Enlightenment philosophers and later psychologists that laid the foundations of modern organizational theory and management by the late 19th century.
=== Founding and early development ===
Where assumptions in Western science from Plato and Aristotle to Isaac Newton's Principia (1687) have historically influenced all areas from the hard to social sciences (see, David Easton's seminal development of the "political system" as an analytical construct), the original systems theorists explored the implications of 20th-century advances in terms of systems.
Between 1929 and 1951, Robert Maynard Hutchins at the University of Chicago had undertaken efforts to encourage innovation and interdisciplinary research in the social sciences, aided by the Ford Foundation with the university's interdisciplinary Division of the Social Sciences established in 1931.: 5–9
Many early systems theorists aimed at finding a general systems theory that could explain all systems in all fields of science.
"General systems theory" (GST; German: allgemeine Systemlehre) was coined in the 1940s by Ludwig von Bertalanffy, who sought a new approach to the study of living systems. Bertalanffy developed the theory via lectures beginning in 1937 and then via publications beginning in 1946. According to Mike C. Jackson (2000), Bertalanffy promoted an embryonic form of GST as early as the 1920s and 1930s, but it was not until the early 1950s that it became more widely known in scientific circles.
Jackson also claimed that Bertalanffy's work was informed by Alexander Bogdanov's three-volume Tectology (1912–1917), providing the conceptual base for GST. A similar position is held by Richard Mattessich (1978) and Fritjof Capra (1996). Despite this, Bertalanffy never even mentioned Bogdanov in his works.
The systems view was based on several fundamental ideas. First, all phenomena can be viewed as a web of relationships among elements, or a system. Second, all systems, whether electrical, biological, or social, have common patterns, behaviors, and properties that the observer can analyze and use to develop greater insight into the behavior of complex phenomena and to move closer toward a unity of the sciences. System philosophy, methodology and application are complementary to this science.
Cognizant of advances in science that questioned classical assumptions in the organizational sciences, Bertalanffy's idea to develop a theory of systems began as early as the interwar period, publishing "An Outline for General Systems Theory" in the British Journal for the Philosophy of Science by 1950.
In 1954, von Bertalanffy, along with Anatol Rapoport, Ralph W. Gerard, and Kenneth Boulding, came together at the Center for Advanced Study in the Behavioral Sciences in Palo Alto to discuss the creation of a "society for the advancement of General Systems Theory." In December that year, a meeting of around 70 people was held in Berkeley to form a society for the exploration and development of GST. The Society for General Systems Research (renamed the International Society for Systems Science in 1988) was established in 1956 thereafter as an affiliate of the American Association for the Advancement of Science (AAAS), specifically catalyzing systems theory as an area of study. The field developed from the work of Bertalanffy, Rapoport, Gerard, and Boulding, as well as other theorists in the 1950s like William Ross Ashby, Margaret Mead, Gregory Bateson, and C. West Churchman, among others.
Bertalanffy's ideas were adopted by others, working in mathematics, psychology, biology, game theory, and social network analysis. Subjects that were studied included those of complexity, self-organization, connectionism and adaptive systems. In fields like cybernetics, researchers such as Ashby, Norbert Wiener, John von Neumann, and Heinz von Foerster examined complex systems mathematically; Von Neumann discovered cellular automata and self-reproducing systems, again with only pencil and paper. Aleksandr Lyapunov and Jules Henri Poincaré worked on the foundations of chaos theory without any computer at all. At the same time, Howard T. Odum, known as a radiation ecologist, recognized that the study of general systems required a language that could depict energetics, thermodynamics and kinetics at any system scale. To fulfill this role, Odum developed a general system, or universal language, based on the circuit language of electronics, known as the Energy Systems Language.
The Cold War affected the research project for systems theory in ways that sorely disappointed many of the seminal theorists. Some began to recognize that theories defined in association with systems theory had deviated from the initial general systems theory view. Economist Kenneth Boulding, an early researcher in systems theory, had concerns over the manipulation of systems concepts. Boulding concluded from the effects of the Cold War that abuses of power always prove consequential and that systems theory might address such issues.: 229–233 Since the end of the Cold War, a renewed interest in systems theory emerged, combined with efforts to strengthen an ethical view on the subject.
In sociology, systems thinking also began in the 20th century, including Talcott Parsons' action theory and Niklas Luhmann's social systems theory. According to Rudolf Stichweh (2011):: 2 Since its beginnings the social sciences were an important part of the establishment of systems theory... [T]he two most influential suggestions were the comprehensive sociological versions of systems theory which were proposed by Talcott Parsons since the 1950s and by Niklas Luhmann since the 1970s.Elements of systems thinking can also be seen in the work of James Clerk Maxwell, particularly control theory.
== General systems research and systems inquiry ==
Many early systems theorists aimed at finding a general systems theory that could explain all systems in all fields of science. Ludwig von Bertalanffy began developing his 'general systems theory' via lectures in 1937 and then via publications from 1946. The concept received extensive focus in his 1968 book, General System Theory: Foundations, Development, Applications.
There are many definitions of a general system, some properties that definitions include are: an overall goal of the system, parts of the system and relationships between these parts, and emergent properties of the interaction between the parts of the system that are not performed by any part on its own.: 58 Derek Hitchins defines a system in terms of entropy as a collection of parts and relationships between the parts where the parts of their interrelationships decrease entropy.: 58
Bertalanffy aimed to bring together under one heading the organismic science that he had observed in his work as a biologist. He wanted to use the word system for those principles that are common to systems in general. In General System Theory (1968), he wrote:: 32
[T]here exist models, principles, and laws that apply to generalized systems or their subclasses, irrespective of their particular kind, the nature of their component elements, and the relationships or "forces" between them. It seems legitimate to ask for a theory, not of systems of a more or less special kind, but of universal principles applying to systems in general.
In the preface to von Bertalanffy's Perspectives on General System Theory, Ervin László stated:
Thus when von Bertalanffy spoke of Allgemeine Systemtheorie it was consistent with his view that he was proposing a new perspective, a new way of doing science. It was not directly consistent with an interpretation often put on "general system theory", to wit, that it is a (scientific) "theory of general systems." To criticize it as such is to shoot at straw men. Von Bertalanffy opened up something much broader and of much greater significance than a single theory (which, as we now know, can always be falsified and has usually an ephemeral existence): he created a new paradigm for the development of theories.
Bertalanffy outlines systems inquiry into three major domains: philosophy, science, and technology. In his work with the Primer Group, Béla H. Bánáthy generalized the domains into four integratable domains of systemic inquiry:
philosophy: the ontology, epistemology, and axiology of systems
theory: a set of interrelated concepts and principles applying to all systems
methodology: the set of models, strategies, methods and tools that instrumentalize systems theory and philosophy
application: the application and interaction of the domains
These operate in a recursive relationship, he explained; integrating 'philosophy' and 'theory' as knowledge, and 'method' and 'application' as action; systems inquiry is thus knowledgeable action.
=== Properties of general systems ===
General systems may be split into a hierarchy of systems, where there is less interactions between the different systems than there is the components in the system. The alternative is heterarchy where all components within the system interact with one another.: 65 Sometimes an entire system will be represented inside another system as a part, sometimes referred to as a holon. These hierarchies of system are studied in hierarchy theory. The amount of interaction between parts of systems higher in the hierarchy and parts of the system lower in the hierarchy is reduced. If all the parts of a system are tightly coupled (interact with one another a lot) then the system cannot be decomposed into different systems. The amount of coupling between parts of a system may differ temporally, with some parts interacting more often than other, or for different processes in a system.: 293 Herbert A. Simon distinguished between decomposable, nearly decomposable and nondecomposable systems.: 72
Russell L. Ackoff distinguished general systems by how their goals and subgoals could change over time. He distinguished between goal-maintaining, goal-seeking, multi-goal and reflective (or goal-changing) systems.: 73
== System types and fields ==
=== Theoretical fields ===
Chaos theory
Complex system
Control theory
Dynamical systems theory
Earth system science
Ecological systems theory
Industrial ecology
Living systems theory
Sociotechnical system
Systemics
Telecoupling
Urban metabolism
World-systems theory
==== Cybernetics ====
Cybernetics is the study of the communication and control of regulatory feedback both in living and lifeless systems (organisms, organizations, machines), and in combinations of those. Its focus is how anything (digital, mechanical or biological) controls its behavior, processes information, reacts to information, and changes or can be changed to better accomplish those three primary tasks.
The terms systems theory and cybernetics have been widely used as synonyms. Some authors use the term cybernetic systems to denote a proper subset of the class of general systems, namely those systems that include feedback loops. However, Gordon Pask's differences of eternal interacting actor loops (that produce finite products) makes general systems a proper subset of cybernetics. In cybernetics, complex systems have been examined mathematically by such researchers as W. Ross Ashby, Norbert Wiener, John von Neumann, and Heinz von Foerster.
Threads of cybernetics began in the late 1800s that led toward the publishing of seminal works (such as Wiener's Cybernetics in 1948 and Bertalanffy's General System Theory in 1968). Cybernetics arose more from engineering fields and GST from biology. If anything, it appears that although the two probably mutually influenced each other, cybernetics had the greater influence. Bertalanffy specifically made the point of distinguishing between the areas in noting the influence of cybernetics:Systems theory is frequently identified with cybernetics and control theory. This again is incorrect. Cybernetics as the theory of control mechanisms in technology and nature is founded on the concepts of information and feedback, but as part of a general theory of systems.... [T]he model is of wide application but should not be identified with 'systems theory' in general ... [and] warning is necessary against its incautious expansion to fields for which its concepts are not made.: 17–23 Cybernetics, catastrophe theory, chaos theory and complexity theory have the common goal to explain complex systems that consist of a large number of mutually interacting and interrelated parts in terms of those interactions. Cellular automata, neural networks, artificial intelligence, and artificial life are related fields, but do not try to describe general (universal) complex (singular) systems. The best context to compare the different "C"-Theories about complex systems is historical, which emphasizes different tools and methodologies, from pure mathematics in the beginning to pure computer science today. Since the beginning of chaos theory, when Edward Lorenz accidentally discovered a strange attractor with his computer, computers have become an indispensable source of information. One could not imagine the study of complex systems without the use of computers today.
=== System types ===
Biological
Anatomical systems
Nervous
Sensory
Ecological systems
Living systems
Complex
Complex adaptive system
Conceptual
Coordinate
Deterministic (philosophy)
Digital ecosystem
Experimental
Writing
Coupled human–environment
Database
Deterministic (science)
Mathematical
Dynamical system
Formal system
Energy
Holarchical
Information
Measurement
Imperial
Metric
Multi-agent
Nonlinear
Operating
Planetary
Social
Cultural
Economic
Legal
Political
Star
==== Complex adaptive systems ====
Complex adaptive systems (CAS), coined by John H. Holland, Murray Gell-Mann, and others at the interdisciplinary Santa Fe Institute, are special cases of complex systems: they are complex in that they are diverse and composed of multiple, interconnected elements; they are adaptive in that they have the capacity to change and learn from experience.
In contrast to control systems, in which negative feedback dampens and reverses disequilibria, CAS are often subject to positive feedback, which magnifies and perpetuates changes, converting local irregularities into global features.
== See also ==
=== Organizations ===
List of systems sciences organizations
== References ==
== Further reading ==
Ashby, W. Ross. 1956. An Introduction to Cybernetics. Chapman & Hall.
—— 1960. Design for a Brain: The Origin of Adaptive Behavior (2nd ed.). Chapman & Hall.
Bateson, Gregory. 1972. Steps to an Ecology of Mind: Collected essays in Anthropology, Psychiatry, Evolution, and Epistemology. University of Chicago Press.
von Bertalanffy, Ludwig. 1968. General System Theory: Foundations, Development, Applications New York: George Braziller
Burks, Arthur. 1970. Essays on Cellular Automata. University of Illinois Press.
Cherry, Colin. 1957. On Human Communication: A Review, a Survey, and a Criticism. Cambridge: The MIT Press.
Churchman, C. West. 1971. The Design of Inquiring Systems: Basic Concepts of Systems and Organizations. New York: Basic Books.
Checkland, Peter. 1999. Systems Thinking, Systems Practice: Includes a 30-Year Retrospective. Wiley.
Gleick, James. 1997. Chaos: Making a New Science, Random House.
Haken, Hermann. 1983. Synergetics: An Introduction – 3rd Edition, Springer.
Holland, John H. 1992. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Cambridge: The MIT Press.
Luhmann, Niklas. 2013. Introduction to Systems Theory, Polity.
Macy, Joanna. 1991. Mutual Causality in Buddhism and General Systems Theory: The Dharma of Natural Systems. SUNY Press.
Maturana, Humberto, and Francisco Varela. 1980. Autopoiesis and Cognition: The Realization of the Living. Springer Science & Business Media.
Miller, James Grier. 1978. Living Systems. Mcgraw-Hill.
von Neumann, John. 1951 "The General and Logical Theory of Automata." pp. 1–41 in Cerebral Mechanisms in Behavior.
—— 1956. "Probabilistic Logics and the Synthesis of Reliable Organisms from Unreliable Components." Automata Studies 34: 43–98.
von Neumann, John, and Arthur Burks, eds. 1966. Theory of Self-Reproducing Automata. Illinois University Press.
Parsons, Talcott. 1951. The Social System. The Free Press.
Prigogine, Ilya. 1980. From Being to Becoming: Time and Complexity in the Physical Sciences. W H Freeman & Co.
Simon, Herbert A. 1962. "The Architecture of Complexity." Proceedings of the American Philosophical Society, 106.
—— 1996. The Sciences of the Artificial (3rd ed.), vol. 136. The MIT Press.
Shannon, Claude, and Warren Weaver. 1949. The Mathematical Theory of Communication. ISBN 0-252-72546-8.
Adapted from Shannon, Claude. 1948. "A Mathematical Theory of Communication." Bell System Technical Journal 27(3): 379–423. doi:10.1002/j.1538-7305.1948.tb01338.x.
Thom, René. 1972. Structural Stability and Morphogenesis: An Outline of a General Theory of Models. Reading, Massachusetts
Volk, Tyler. 1995. Metapatterns: Across Space, Time, and Mind. New York: Columbia University Press.
Weaver, Warren. 1948. "Science and Complexity." The American Scientist, pp. 536–544.
Wiener, Norbert. 1965. Cybernetics: Or the Control and Communication in the Animal and the Machine (2nd ed.). Cambridge: The MIT Press.
Wolfram, Stephen. 2002. A New Kind of Science. Wolfram Media.
Zadeh, Lofti. 1962. "From Circuit Theory to System Theory." Proceedings of the IRE 50(5): 856–865.
== External links ==
Systems Thinking at Wikiversity
Systems theory at Principia Cybernetica Web
Introduction to systems thinking – 55 slides
Organizations
International Society for the System Sciences
New England Complex Systems Institute
System Dynamics Society | Wikipedia/System_theory |
Systems science, also referred to as systems research or simply systems, is a transdisciplinary field that is concerned with understanding simple and complex systems in nature and society, which leads to the advancements of formal, natural, social, and applied attributions throughout engineering, technology, and science itself.
To systems scientists, the world can be understood as a system of systems. The field aims to develop transdisciplinary foundations that are applicable in a variety of areas, such as psychology, biology, medicine, communication, business, technology, computer science, engineering, and social sciences.
Themes commonly stressed in system science are (a) holistic view, (b) interaction between a system and its embedding environment, and (c) complex (often subtle) trajectories of dynamic behavior that sometimes are stable (and thus reinforcing), while at various 'boundary conditions' can become wildly unstable (and thus destructive). Concerns about Earth-scale biosphere/geosphere dynamics is an example of the nature of problems to which systems science seeks to contribute meaningful insights.
== Associated fields ==
The systems sciences are a broad array of fields. One way of conceiving of these is in three groups: fields that have developed systems ideas primarily through theory; those that have done so primarily through practical engagements with problem situations; and those that have applied ideas for other disciplines.
=== Theoretical fields ===
==== Chaos and dynamical systems ====
==== Complexity ====
==== Control theory ====
Affect control theory
Control engineering
Control systems
==== Cybernetics ====
Autopoiesis
Conversation Theory
Engineering Cybernetics
Perceptual Control Theory
Management Cybernetics
Second-Order Cybernetics
Cyber-Physical Systems
Artificial Intelligence
Synthetic Intelligence
==== Information theory ====
==== General systems theory ====
Systems theory in anthropology
Biochemical systems theory
Ecological systems theory
Developmental systems theory
General systems theory
Living systems theory
LTI system theory
Social systems
Sociotechnical systems theory
Mathematical system theory
World-systems theory
==== Hierarchy Theory ====
=== Practical fields ===
==== Critical systems thinking ====
==== Operations research and management science ====
==== Soft systems methodology ====
The soft systems methodology was developed in England by academics at the University of Lancaster Systems Department through a ten-year action research programme. The main contributor is Peter Checkland (born 18 December 1930, in Birmingham, UK), a British management scientist and emeritus professor of systems at Lancaster University.
==== Systems analysis ====
Systems analysis branch of systems science that analyzes systems, the interactions within those systems, or interaction with its environment, often prior to their automation as computer models. Systems analysis is closely associated with the RAND corporation.
==== Systemic design ====
Systemic design integrates methodologies from systems thinking with advanced design practices to address complex, multi-stakeholder situations.
==== Systems dynamics ====
System dynamics is an approach to understanding the behavior of complex systems over time. It offers "simulation technique for modeling business and social systems", which deals with internal feedback loops and time delays that affect the behavior of the entire system. What makes using system dynamics different from other approaches to studying complex systems is the use of feedback loops and stocks and flows.
==== Systems engineering ====
Systems engineering (SE) is an interdisciplinary field of engineering, that focuses on the development and organization of complex systems. It is the "art and science of creating whole solutions to complex problems", for example: signal processing systems, control systems and communication system, or other forms of high-level modelling and design in specific fields of engineering. Systems Science is foundational to the Embedded Software Development that is founded in the embedded requirements of Systems Engineering.
Aerospace systems
Biological systems engineering
Earth systems engineering and management
Electronic systems
Enterprise systems engineering
Software systems
Systems analysis
=== Applications in other disciplines ===
==== Earth system science ====
Climate systems
Systems geology
==== Systems biology ====
Computational systems biology
Synthetic biology
Systems immunology
Systems neuroscience
==== Systems chemistry ====
==== Systems ecology ====
Ecosystem ecology
Agroecology
==== Systems psychology ====
Ergonomics
Family systems theory
Systemic therapy
== See also ==
== References ==
== Further reading ==
B. A. Bayraktar (1979). Education in Systems Science. p. 369.
Kenneth D. Bailey, "Fifty Years of Systems Science:Further Reflections", Systems Research and Behavioral Science, 22, 2005, pp. 355–361. doi:10.1002/sres.711
Robert L. Flood, Ewart R Carson, Dealing with Complexity: An Introduction to the Theory and Application of Systems Science (2nd Edition), 1993.
George J. Klir, Facets of Systems Science (2nd Edition), Kluwer Academic/Plenum Publishers, 2001.
Ervin László, Systems Science and World Order: Selected Studies, 1983.
G. E. Mobus & M. C. Kalton, Principles of Systems Science, 2015, New York:Springer.
Anatol Rapoport (ed.), General Systems: Yearbook of the Society for the Advancement of General Systems Theory, Society for General Systems Research, Vol 1., 1956.
Li D. Xu, "The contributions of Systems Science to Information Systems Research", Systems Research and Behavioral Science, 17, 2000, pp. 105–116.
Graeme Donald Snooks, "A general theory of complex living systems: Exploring the demand side of dynamics", Complexity, vol. 13, no. 6, July/August 2008.
John N. Warfield, "A proposal for Systems Science", Systems Research and Behavioral Science, 20, 2003, pp. 507–520. doi:10.1002/sres.528
Michael C. Jackson, Critical Systems Thinking and the Management of Complexity, 2019, Wiley.
== External links ==
Principia Cybernetica Web
International Federation for Systems Research
Institute of System Science Knowledge (ISSK.org)
International Society for the System Sciences
American Society for Cybernetics
UK Systems Society
Cybernetics Society | Wikipedia/Systems_sciences |
A functional flow block diagram (FFBD) is a multi-tier, time-sequenced, step-by-step flow diagram of a system's functional flow. The term "functional" in this context is different from its use in functional programming or in mathematics, where pairing "functional" with "flow" would be ambiguous. Here, "functional flow" pertains to the sequencing of operations, with "flow" arrows expressing dependence on the success of prior operations. FFBDs may also express input and output data dependencies between functional blocks, as shown in figures below, but FFBDs primarily focus on sequencing.
The FFBD notation was developed in the 1950s, and is widely used in classical systems engineering. FFBDs are one of the classic business process modeling methodologies, along with flow charts, data flow diagrams, control flow diagrams, Gantt charts, PERT diagrams, and IDEF.
FFBDs are also referred to as functional flow diagrams, functional block diagrams, and functional flows.
== History ==
The first structured method for documenting process flow, the flow process chart, was introduced by Frank Gilbreth to members of American Society of Mechanical Engineers (ASME) in 1921 as the presentation “Process Charts—First Steps in Finding the One Best Way”. Gilbreth's tools quickly found their way into industrial engineering curricula.
In the early 1930s, an industrial engineer, Allan H. Mogensen began training business people in the use of some of the tools of industrial engineering at his Work Simplification Conferences in Lake Placid, New York. A 1944 graduate of Mogensen's class, Art Spinanger, took the tools back to Procter and Gamble where he developed their Deliberate Methods Change Program. Another 1944 graduate, Ben S. Graham, Director of Formcraft Engineering at Standard Register Industrial, adapted the flow process chart to information processing with his development of the multi-flow process chart to display multiple documents and their relationships. In 1947, ASME adopted a symbol set as the ASME Standard for Operation and Flow Process Charts, derived from Gilbreth's original work.
The modern Functional Flow Block Diagram was developed by TRW Incorporated, a defense-related business, in the 1950s. In the 1960s it was exploited by NASA to visualize the time sequence of events in space systems and flight missions. FFBDs became widely used in classical systems engineering to show the order of execution of system functions.
== Development of functional flow block diagrams ==
FFBDs can be developed in a series of levels. FFBDs show the same tasks identified through functional decomposition and display them in their logical, sequential relationship. For example, the entire flight mission of a spacecraft can be defined in a top level FFBD, as shown in Figure 2. Each block in the first level diagram can then be expanded to a series of functions, as shown in the second level diagram for "perform mission operations." Note that the diagram shows both input (transfer to operational orbit) and output (transfer to space transportation system orbit), thus initiating the interface identification and control process. Each block in the second level diagram can be progressively developed into a series of functions, as shown in the third level diagram on Figure 2.
These diagrams are used both to develop requirements and to identify profitable trade studies. For example, does the spacecraft antenna acquire the tracking and data relay satellite (TDRS) only when the payload data are to be transmitted, or does it track TDRS continually to allow for the reception of emergency commands or transmission of emergency data? The FFBD also incorporates alternate and contingency operations, which improve the probability of mission success. The flow diagram provides an understanding of total operation of the system, serves as a basis for development of operational and contingency procedures, and pinpoints areas where changes in operational procedures could simplify the overall system operation. In certain cases, alternate FFBDs may be used to represent various means of satisfying a particular function until data are acquired, which permits selection among the alternatives.
== Building blocks ==
=== Key attributes ===
An overview of the key FFBD attributes:
Function block: Each function on an FFBD should be separate and be represented by single box (solid line). Each function needs to stand for definite, finite, discrete action to be accomplished by system elements.
Function numbering: Each level should have a consistent number scheme and provide information concerning function origin. These numbers establish identification and relationships that will carry through all Functional Analysis and Allocation activities and facilitate traceability from lower to top levels.
Functional reference: Each diagram should contain a reference to other functional diagrams by using a functional title reference (box in brackets).
Flow connection: Lines connecting functions should only indicate function flow and not a lapse in time or intermediate activity.
Flow direction: Diagrams should be laid out so that the flow direction is generally from left to right. Arrows are often used to indicate functional flows.
Summing gate: A circle is used to denote a summing gate and is used when AND/OR is present. AND is used to indicate parallel functions and all conditions must be satisfied to proceed. OR is used to indicate that alternative paths can be satisfied to proceed.
GO and NO-GO path: “G” and “bar G” are used to denote “go” and “no-go” conditions. These symbols are placed adjacent to lines leaving a particular function to indicate alternative paths.
=== Function symbolism ===
A function shall be represented by a rectangle containing the title of the function (an action verb followed by a noun phrase) and its unique decimal delimited number. A horizontal line shall separate this number and the title, as shown in see Figure 3 above. The figure also depicts how to represent a reference function, which provides context within a specific FFBD. See Figure 9 for an example regarding use of a reference function.
=== Directed lines ===
A line with a single arrowhead shall depict functional flow from left to right, see Figure 4.
=== Logic symbols ===
The following basic logic symbols shall be used.
AND: A condition in which all preceding or succeeding paths are required. The symbol may contain a single input with multiple outputs or multiple inputs with a single output, but not multiple inputs and outputs combined (Figure 5). Read the figure as follows: F2 AND F3 may begin in parallel after completion of F1. Likewise, F4 may begin after completion of F2 AND F3.
Exclusive OR: A condition in which one of multiple preceding or succeeding paths is required, but not all. The symbol may contain a single input with multiple outputs or multiple inputs with single output, but not multiple inputs and outputs combined (Figure 6). Read the figure as follows: F2 OR F3 may begin after completion of F1. Likewise, F4 may begin after completion of either F2 OR F3.
Inclusive OR: A condition in which one, some, or all of the multiple preceding or succeeding paths are required. Figure 7 depicts Inclusive OR logic using a combination of the AND symbol (Figure 5) and the Exclusive OR symbol (Figure 6). Read Figure 7 as follows: F2 OR F3 (exclusively) may begin after completion of F1, OR (again exclusive) F2 AND F3 may begin after completion of F1. Likewise, F4 may begin after completion of either F2 OR F3 (exclusively), OR (again exclusive) F4 may begin after completion of both F2 AND F3
=== Contextual and administrative data ===
Each FFBD shall contain the following contextual and administrative data:
Date the diagram was created
Name of the engineer, organization, or working group that created the diagram
Unique decimal delimited number of the function being diagrammed
Unique function name of the function being diagrammed.
Figure 8 and Figure 9 present the data in an FFBD. Figure 9 is a decomposition of the function F2 contained in Figure 8 and illustrates the context between functions at different levels of the model.
== See also ==
Activity diagram
Block diagram
Business process mapping
Dataflow
Data and information visualization
DRAKON
Flow diagram
Flow process chart
Function model
Functional block diagram
IDEF0
N2 Chart
SADT
Signal flow
Signal-flow graph
== Notes ==
== Further reading ==
DAU (2001) Systems Engineering Fundamentals. Defense Acquisition University Press.
FAA (2007) System Engineering Manual. Federal Aviation Administration Washington. | Wikipedia/Functional_Flow_Block_Diagram |
The behavioral approach to systems theory and control theory was initiated in the late-1970s by J. C. Willems as a result of resolving inconsistencies present in classical approaches based on state-space, transfer function, and convolution representations. This approach is also motivated by the aim of obtaining a general framework for system analysis and control that respects the underlying physics.
The main object in the behavioral setting is the behavior – the set of all signals compatible with the system. An important feature of the behavioral approach is that it does not distinguish a priority between input and output variables. Apart from putting system theory and control on a rigorous basis, the behavioral approach unified the existing approaches and brought new results on controllability for nD systems, control via interconnection, and system identification.
== Dynamical system as a set of signals ==
In the behavioral setting, a dynamical system is a triple
Σ
=
(
T
,
W
,
B
)
{\displaystyle \Sigma =(\mathbb {T} ,\mathbb {W} ,{\mathcal {B}})}
where
T
⊆
R
{\displaystyle \mathbb {T} \subseteq \mathbb {R} }
is the "time set" – the time instances over which the system evolves,
W
{\displaystyle \mathbb {W} }
is the "signal space" – the set in which the variables whose time evolution is modeled take on their values, and
B
⊆
W
T
{\displaystyle {\mathcal {B}}\subseteq \mathbb {W} ^{\mathbb {T} }}
the "behavior" – the set of signals that are compatible with the laws of the system
(
W
T
{\displaystyle \mathbb {W} ^{\mathbb {T} }}
denotes the set of all signals, i.e., functions from
T
{\displaystyle \mathbb {T} }
into
W
{\displaystyle \mathbb {W} }
).
w
∈
B
{\displaystyle w\in {\mathcal {B}}}
means that
w
{\displaystyle w}
is a trajectory of the system, while
w
∉
B
{\displaystyle w\notin {\mathcal {B}}}
means that the laws of the system forbid the trajectory
w
{\displaystyle w}
to happen. Before the phenomenon is modeled, every signal in
W
T
{\displaystyle \mathbb {W} ^{\mathbb {T} }}
is deemed possible, while after modeling, only the outcomes in
B
{\displaystyle {\mathcal {B}}}
remain as possibilities.
Special cases:
T
=
R
{\displaystyle \mathbb {T} =\mathbb {R} }
– continuous-time systems
T
=
Z
{\displaystyle \mathbb {T} =\mathbb {Z} }
– discrete-time systems
W
=
R
q
{\displaystyle \mathbb {W} =\mathbb {R} ^{q}}
– most physical systems
W
{\displaystyle \mathbb {W} }
a finite set – discrete event systems
== Linear time-invariant differential systems ==
System properties are defined in terms of the behavior. The system
Σ
=
(
T
,
W
,
B
)
{\displaystyle \Sigma =(\mathbb {T} ,\mathbb {W} ,{\mathcal {B}})}
is said to be
"linear" if
W
{\displaystyle \mathbb {W} }
is a vector space and
B
{\displaystyle {\mathcal {B}}}
is a linear subspace of
W
T
{\displaystyle \mathbb {W} ^{\mathbb {T} }}
,
"time-invariant" if the time set consists of the real or natural numbers and
σ
t
B
⊆
B
{\displaystyle \sigma ^{t}{\mathcal {B}}\subseteq {\mathcal {B}}}
for all
t
∈
T
{\displaystyle t\in \mathbb {T} }
,
where
σ
t
{\displaystyle \sigma ^{t}}
denotes the
t
{\displaystyle t}
-shift, defined by
σ
t
(
f
)
(
t
′
)
:=
f
(
t
′
+
t
)
{\displaystyle \sigma ^{t}(f)(t'):=f(t'+t)}
.
In these definitions linearity articulates the superposition law, while time-invariance articulates that the time-shift of a legal trajectory is in its turn a legal trajectory.
A "linear time-invariant differential system" is a dynamical system
Σ
=
(
R
,
R
q
,
B
)
{\displaystyle \Sigma =(\mathbb {R} ,\mathbb {R} ^{q},{\mathcal {B}})}
whose behavior
B
{\displaystyle {\mathcal {B}}}
is the solution set of a system of constant coefficient linear ordinary differential equations
R
(
d
/
d
t
)
w
=
0
{\displaystyle R(d/dt)w=0}
, where
R
{\displaystyle R}
is a matrix of polynomials with real coefficients. The coefficients of
R
{\displaystyle R}
are the parameters of the model. In order to define the corresponding behavior, we need to specify when we consider a signal
w
:
R
→
R
q
{\displaystyle w:\mathbb {R} \rightarrow \mathbb {R} ^{q}}
to be a solution of
R
(
d
/
d
t
)
w
=
0
{\displaystyle R(d/dt)w=0}
. For ease of exposition, often infinite differentiable solutions are considered. There are other possibilities, as taking distributional solutions, or solutions in
L
l
o
c
a
l
(
R
,
R
q
)
{\displaystyle {\mathcal {L}}^{\rm {local}}(\mathbb {R} ,\mathbb {R} ^{q})}
, and with the ordinary differential equations interpreted in the sense of distributions. The behavior defined is
B
=
{
w
∈
C
∞
(
R
,
R
q
)
|
R
(
d
/
d
t
)
w
(
t
)
=
0
for all
t
∈
R
}
.
{\displaystyle {\mathcal {B}}=\{w\in {\mathcal {C}}^{\infty }(\mathbb {R} ,\mathbb {R} ^{q})~|~R(d/dt)w(t)=0{\text{ for all }}t\in \mathbb {R} \}.}
This particular way of representing the system is called "kernel representation" of the corresponding dynamical system. There are many other useful representations of the same behavior, including transfer function, state space, and convolution.
For accessible sources regarding the behavioral approach, see
.
== Observability of latent variables ==
A key question of the behavioral approach is whether a quantity w1 can be deduced given an observed quantity w2 and a model. If w1 can be deduced given w2 and the model, w2 is said to be observable. In terms of mathematical modeling, the to-be-deduced quantity or variable is often referred to as the latent variable and the observed variable is the manifest variable. Such a system is then called an observable (latent variable) system.
== References ==
== Additional sources ==
Paolo Rapisarda and Jan C.Willems, 2006. Recent Developments in Behavioral System Theory, July 24–28, 2006, MTNS 2006, Kyoto, Japan
J.C. Willems. Terminals and ports. IEEE Circuits and Systems Magazine Volume 10, issue 4, pages 8–16, December 2010
J.C. Willems and H.L. Trentelman. On quadratic differential forms. SIAM Journal on Control and Optimization Volume 36, pages 1702-1749, 1998
J.C. Willems. Paradigms and puzzles in the theory of dynamical systems. IEEE Transactions on Automatic Control Volume 36, pages 259-294, 1991
J.C. Willems. Models for dynamics. Dynamics Reported Volume 2, pages 171-269, 1989 | Wikipedia/Behavioral_modeling |
The viable system model (VSM) is a model of the organizational structure of any autonomous system capable of producing itself. It is an implementation of viable system theory. At the biological level, this model is correspondent to autopoiesis.
A viable system is any system organised in such a way as to meet the demands of surviving in the changing environment. One of the prime features of systems that survive is that they are adaptable. The VSM expresses a model for a viable system, which is an abstracted cybernetic (regulation theory) description that is claimed to be applicable to any organisation that is a viable system and capable of autonomy.
== Overview ==
The model was developed by operations research theorist and cybernetician Stafford Beer in his book Brain of the Firm (1972). Together with Beer's earlier works on cybernetics applied to management, this book effectively founded management cybernetics.
The first thing to note about the cybernetic theory of organizations encapsulated in the VSM is that viable systems are recursive; viable systems contain viable systems that can be modeled using an identical cybernetic description as the higher (and lower) level systems in the containment hierarchy (Beer expresses this property of viable systems as cybernetic isomorphism).
A development of this model has originated the theoretical proposal called viable systems approach.
== Components ==
Here we give a brief introduction to the cybernetic description of the organization encapsulated in a single level of the VSM.
A viable system is composed of five interacting subsystems which may be mapped onto aspects of organizational structure. In broad terms Systems 1–3 are concerned with the 'here and now' of the organization's operations, System 4 is concerned with the 'there and then' – strategical responses to the effects of external, environmental and future demands on the organization. System 5 is concerned with balancing the 'here and now' and the 'there and then' to give policy directives which maintain the organization as a viable entity.
System 1 in a viable system contains several primary activities. Each System 1 primary activity is itself a viable system due to the recursive nature of systems as described above. These are concerned with performing a function that implements at least part of the key transformation of the organization.
System 2 represents the information channels and bodies that allow the primary activities in System 1 to communicate between each other and which allow System 3 to monitor and co-ordinate the activities within System 1. Represents the scheduling function of shared resources to be used by System 1.
System 3 represents the structures and controls that are put into place to establish the rules, resources, rights and responsibilities of System 1 and to provide an interface with Systems 4/5. Represents the big picture view of the processes inside of System 1.
System 4 is made up of bodies that are responsible for looking outwards to the environment to monitor how the organization needs to adapt to remain viable.
System 5 is responsible for policy decisions within the organization as a whole to balance demands from different parts of the organization and steer the organization as a whole.
In addition to the subsystems that make up the first level of recursion, the environment is represented in the model. The presence of the environment in the model is necessary as the domain of action of the system and without it there is no way in the model to contextualize or ground the internal interactions of the organization.
Algedonic alerts (from the Greek αλγος, pain and ηδος, pleasure) are alarms and rewards that escalate through the levels of recursion when actual performance fails or exceeds capability, typically after a timeout.
The model is derived from the architecture of the brain and nervous system. Systems 3-2-1 are identified with the ancient brain or autonomic nervous system. System 4 embodies cognition and conversation. System 5, the higher brain functions, include introspection and decision making.
== Rules for the viable system ==
In "Heart of Enterprise" a companion volume to "Brain...", Beer applies Ashby's concept of (Requisite) Variety: the number of possible states of a system or of an element of the system. There are two aphorisms that permit observers to calculate Variety; four Principles of Organization; the Recursive System Theorem; three Axioms of Management and a Law of Cohesion. These rules ensure the Requisite Variety condition is satisfied, in effect that resources are matched to requirement.
=== Regulatory aphorisms ===
These aphorisms are:
It is not necessary to enter the black box to understand the nature of the function it performs.
It is not necessary to enter the black box to calculate the variety that it potentially may generate.
=== Principles of organization ===
(Principles are 'primary sources of particular outcome')
These principles are:
Managerial, operational and environmental varieties diffusing through an institutional system, tend to equate; they should be designed to do so with minimum damage to people and cost.
The four directional channels carrying information between the management unit, the operation, and the environment must each have a higher capacity to transmit a given amount of information relevant to variety selection in a given time than the originating subsystem has to generate it in that time.
Wherever the information carried on a channel capable of distinguishing a given variety crosses a boundary, it undergoes transduction (converting energy from one form to another); the variety of the transducer must be at least equivalent to the variety of the channel.
The operation of the first three principles must be cyclically maintained without delays.
=== Recursive system theorem ===
This theorem states:
In a recursive organizational structure any viable system contains, and is contained in, a viable system.
Society itself can be seen as a system of recursion. In this case, recursion refers to systems that are nested within other systems.
=== Axioms ===
(Axioms are statements 'worthy of belief')
These axioms are:
The sum of horizontal variety disposed by n operational elements (systems one) equals the sum of the vertical variety disposed by the six vertical components of corporate cohesion. (The six are from Environment, System Three*, the System Ones, System Two, System Three and Algedonic alerts.)
The variety disposed by System Three resulting from the operation of the First Axiom equals the variety disposed by System Four.
The variety disposed by System Five equals the residual variety generated by the operation of the Second Axiom.
=== The law of cohesion for multiple recursions of the viable system ===
This law ('something invariant in nature') states:
The System One variety accessible to System Three of recursion x equals the variety disposed by the sum of the metasystems of recursion y for every recursive pair.
== Measuring performance ==
In Brain of the Firm (p. 163) Beer describes a triple vector to characterize activity in a System 1. The components are:
Actuality: "What we are managing to do now, with existing resources, under existing constraints."
Capability: "This is what we could be doing (still right now) with existing resources, under existing constraints, if we really worked at it."
Potentiality: "This is what we ought to be doing by developing our resources and removing constraints, although still operating within the bounds of what is already known to be feasible."
Beer adds "It would help a lot to fix these definitions clearly in the mind." System 4's job is essentially to realize potential. He then defines
Productivity: is the ratio of actuality and capability;
Latency: is the ratio of capability and potentiality;
Performance: is the ratio of actuality and potentiality, and also the product of latency and productivity.
Consider the management of a process with cash earnings or savings for a company or government:
Potentially £100,000 but aiming to make £ 60,000. Actually sales, savings or taxes of £40,000 are realized.
So Potentiality = £100,000; Capability = £60,000; Actuality = £40,000.
Thus latency = 60/100 = 0.6; Productivity = 40/60 = 0.67; And performance = 0.6 × 0.67 = 0.4 (or actuality/potential 40/100).
These methods (also known as normalisations) can be similarly applied in general e.g. to hours worked in the performance of tasks or products in a production process of some kind.
When actuality deviates from capability, because someone did something well or something badly, an algedonic alert is sent to management. If corrective action, adoption of a good technique or correction of an error, is not taken in a timely manner the alert is escalated. Because the criteria are applied in an ordered hierarchy the management itself need not be, but the routine response functions must be ordered to reflect best known heuristic practice. These heuristics are constantly monitored for improvement by the organization's System 4s.
Pay structures reflect these constraints on performance when capability or potential is realized with, for example, productivity bonuses, stakeholder agreements and intellectual property rights.
== Metalanguage ==
In ascending the recursions of the viable system the context of each autonomous 5-4-3-2 metasystem enlarges and acquires more variety.
This defines a metalanguage stack of increasing capability to resolve undecidability in the autonomous lower levels. If someone near process level needs to innovate to achieve potential, or restore capability, help can be secured from management of higher variety.
An algedonic alert, sent when actuality deviates by some statistically significant amount from capability, makes this process automatic.
The notion of adding more variety or states to resolve ambiguity or undecidability (also known as the decision problem) is the subject of Chaitin's metamathematical conjecture algorithmic information theory and provides a potentially rigorous theoretical basis for a general management heuristic. If a process is not producing the agreed product more information, if applicable, will correct this, resolve ambiguity, conflict or undecidability.
In "Platform for Change" (Beer 1975) the thesis is developed via a collection of papers to learned bodies, including UK Police and Hospitals, to produce a visualization of the "Total System". Here a "Relevant ethic" evolves from "Experimental ethics" and the "Ethic with a busted gut" to produce a sustainable earth with reformed "old institutions" becoming "new institutions" driven by approval (eudemonic criteria "Questions of Metric" in Platform... pp 163– 179) from the "software milieu" while culture adopts the systems approach and "Homo faber" (man the maker) becomes "Homo Gubernator" (self-steering).
== Applying VSM ==
In applying the VSM variety measures are used to match people, machines and money to jobs that produce products or services. In a set of processes some jobs are done by one person. Some are done by many and often many processes are done by the same person. Throughout the working day a participant, in completing a task, may find the focus shifts between internal and external Systems 1–5 from moment to moment.
The choices, or decisions discriminated, and their cost (or effort) defines the variety and hence resources needed for the job. The processes (Systems 1) are operationally managed by System 3 by monitoring performance and assuring (System 2) the flow of product between System 1s and out to users.
System 3 is able to audit (via 3*) past performance so "bad times" for production can be compared to "good times". If things go wrong and levels of risk increase the System 3 asks for help or puts it to colleagues for a remedy. This is the pain of an algedonic alert, which can be automatic when performance fails to achieve capability targets. The autonomic 3–2–1 homeostatic loop's problem is absorbed for solution within the autonomy of its metasystem. Development (the System 4 role of research and marketing) is asked for recommendations.
If more resources are required System 5 has to make the decision on which is the best option from System 4. Escalation to higher management (up the metalinguistic levels of recursion) will be needed if the remedy requires more resources than the current level of capability or variety can sustain. The pleasure of an algedonic alert which are performance improving innovations can also be handled in this way.
In a small business all these functions might be done by one person or shared between the participants. In larger enterprises roles can differentiate and become more specialized emphasizing one or more aspects of the VSM. Local conditions, the environment and nature of the service or product, determines where warehousing, sales, advertising, promotion, dispatch, taxation, finance, salaries etc., fit into this picture. Not all enterprises charge for their transactions (e.g. some schools and medical services, policing) and voluntary staff may not be paid. Advertising or shipping might not be part of the business or they might be the principal activity. Whatever the circumstances, all enterprises are required to be useful to their users if they are to remain viable. For all participants the central question remains: "Do I do what I always do for this transaction or do I innovate?" It is embodied in the calls on System 4. The VSM describes the constraints: a knowledge of past performance and how it may be improved.
Beer dedicated Brain of the Firm to his colleagues past and present with the words "absolutum obsoletum" which he translated as "If it works it's out of date".
== See also ==
American Society for Cybernetics
Autonomous agency theory
Business model
Cybernetics Society
Dynamic governance
Project Cybersyn
Self-organization § Cybernetics
Viable system theory
== References ==
== Further reading ==
1959, Stafford Beer: Cybernetics and Management. The English Universities Press Ltd.
1972, Stafford Beer, Brain of the Firm; Allen Lane, The Penguin Press, London, Herder and Herder, USA. Translated into German, Italian, Swedish and French (The founding work)
1972, Stafford Beer, Managing modern complexity, in Landau, R., ed. 'Complexity', Architectural Design October 1972, pp. 629-632.
1974, Stafford Beer: Decision and Control. John Wiley & Sons, London and New York, ISBN 0-470-03210-3
1975, Stafford Beer, Platform for Change; John Wiley, London and New York. (Lectures, talks and papers)
1979, Stafford Beer, The Heart of Enterprise; John Wiley, London and New York. (Discussion of VSM applied)
1985, Stafford Beer, Diagnosing the System for Organizations; John Wiley, London and New York. Translated into Italian and Japanese. (Handbook of organizational structure, design and fault diagnosis)
1989, Ed. Espejo and Harnden The Viable System Model; John Wiley, London and New York.
2007, William F. Christopher Holistic Management; John Wiley, London and New York.
2008, Türke, Ralf-Eckhard: Governance – Systemic Foundation and Framework (Contributions to Management Science, Physica of Springer, September 2008).Link
2008, Patrick Hoverstadt: The Fractal Organization: Creating sustainable organizations with the Viable System Model Wiley
2008, José Pérez Ríos, Diseño y diagnóstico de organizaciones viables: un enfoque sistémico, Universidad de Valladolid ReadOnTime
2010, Golinelli Gaetano M, "Viable Systems Approach (VSA): Governing business dynamics", CEDAM, Padova.
2010, George Hobbs and Rens Scheepers, "Cybernetics and the Agility Question," Proceedings of IFIP 8.2/Organizations and Society in Information Systems (OASIS). Sprouts: Working Papers on Information Systems, 10(114).Link
2011, Eden Medina: Cybernetic Revolutionaries. Technology and Politics in Allende's Chile. The M.I.T. Press, Cambridge, Massachusetts, ISBN 978-0-262-01649-0
2019, Wolfgang Lassl: The Viability of Organizations Vol. 1. Decoding the "DNA" of Organizations, Springer Nature, ISBN 978-3-030-12013-9 (https://www.springer.com/us/book/9783030120139)
2019, Wolfgang Lassl: The Viability of Organizations Vol. 2. Diagnosing and Governing Organizations, Springer Nature, ISBN 978-3-030-16473-7 (https://www.springer.com/gp/book/9783030164720)
2020, Wolfgang Lassl: The Viability of Organizations Vol. 3. Designing and Changing Organizations, Springer Nature, ISBN 978-3-030-25854-2 https://www.springer.com/gp/book/9783030258535
== External links ==
Metaphorum: researching and developing VSM applications
ASVSA: Research Association on Viable Systems
The VSM on a memorial website of Stafford Beer
Video from Manchester Business School (1974) of Stafford Beer talking about VSM applied in Chile. Menu at bottom of page
VSM diagnosis and design for co-operatives and social economy enterprises
The Systems Perspective: Methods and Models for the Future by Allenna Leonard with Stafford Beer
Stafford Beer and the Humankind Future
To Change Ourselves: A Personal VSM Application by Allenna Leonard
Viable Software
Modelling Organisations Using the Viable Systems Model by Patrick Hoverstadt
VSM oriented Enterprise Architecture from Tetradian Consulting
The Viable System Model Livas short introductory videos on YouTube
Management Cybernetics Portal in Russia
The reasoning behind the Viable System Model
The Viable Systems Approach (Italian)
The Viable System Agent A Smalltalk implementation of the VSM.
The Viable System Agent A port of the Viable System Agent to the Ruby programming language.
The [1] Free Viable System Model Online Test.
=== Organizations ===
Metaphorum Society
Cybernetics and Society
SCiO – Systems & Cybernetics in Organisations (UK)
Cwarel Isaf Institute
Malik Management | Wikipedia/Viable_system_model |
Software and Systems Modeling (SoSyM) is a peer-reviewed scientific journal covering the development and application of software and systems modeling languages and techniques, including modeling foundations, semantics, analysis and synthesis techniques, model transformations, language definition and language engineering issues. It was established in 2002 and is published by Springer Science+Business Media. The editors-in-chief are Benoit Combemale (University of Rennes), Jeff Gray (University of Alabama), and Bernhard Rumpe (RWTH Aachen University). They are supported by the associate editors Marsha Chechik (University of Toronto), Martin Gogolla (University of Bremen), and Jean-Marc Jezequel (IRISA/INRIA and University of Rennes) and the assistant editors Stéphanie Challita (University of Rennes), Huseyin Ergin (Ball State University), and Martin Schindler (RWTH Aachen University). The members of the editorial board can be found on https://www.sosym.org/.
Robert France was co-founder and editor-in-chief of the journal from 1999 until 2015.
According to the Journal Citation Reports, the journal has a 2022 impact factor of 2.0.
The journal is widely abstracted and indexed, for example in ACM Digital Library, DBLP, EBSCO, INSPEC, ProQuest, and SCOPUS.
== References ==
== External links ==
Official website
Journal page at publisher's website | Wikipedia/Software_and_Systems_Modeling |
Modelling biological systems is a significant task of systems biology and mathematical biology. Computational systems biology aims to develop and use efficient algorithms, data structures, visualization and communication tools with the goal of computer modelling of biological systems. It involves the use of computer simulations of biological systems, including cellular subsystems (such as the networks of metabolites and enzymes which comprise metabolism, signal transduction pathways and gene regulatory networks), to both analyze and visualize the complex connections of these cellular processes.
An unexpected emergent property of a complex system may be a result of the interplay of the cause-and-effect among simpler, integrated parts (see biological organisation). Biological systems manifest many important examples of emergent properties in the complex interplay of components. Traditional study of biological systems requires reductive methods in which quantities of data are gathered by category, such as concentration over time in response to a certain stimulus. Computers are critical to analysis and modelling of these data. The goal is to create accurate real-time models of a system's response to environmental and internal stimuli, such as a model of a cancer cell in order to find weaknesses in its signalling pathways, or modelling of ion channel mutations to see effects on cardiomyocytes and in turn, the function of a beating heart.
== Standards ==
By far the most widely accepted standard format for storing and exchanging models in the field is the Systems Biology Markup Language (SBML). The SBML.org website includes a guide to many important software packages used in computational systems biology. A large number of models encoded in SBML can be retrieved from BioModels. Other markup languages with different emphases include BioPAX, CellML and MorpheusML.
== Particular tasks ==
=== Cellular model ===
Creating a cellular model has been a particularly challenging task of systems biology and mathematical biology. It involves the use of computer simulations of the many cellular subsystems such as the networks of metabolites, enzymes which comprise metabolism and transcription, translation, regulation and induction of gene regulatory networks.
The complex network of biochemical reaction/transport processes and their spatial organization make the development of a predictive model of a living cell a grand challenge for the 21st century, listed as such by the National Science Foundation (NSF) in 2006.
A whole cell computational model for the bacterium Mycoplasma genitalium, including all its 525 genes, gene products, and their interactions, was built by scientists from Stanford University and the J. Craig Venter Institute and published on 20 July 2012 in Cell.
A dynamic computer model of intracellular signaling was the basis for Merrimack Pharmaceuticals to discover the target for their cancer medicine MM-111.
Membrane computing is the task of modelling specifically a cell membrane.
=== Multi-cellular organism simulation ===
An open source simulation of C. elegans at the cellular level is being pursued by the OpenWorm community. So far the physics engine Gepetto has been built and models of the neural connectome and a muscle cell have been created in the NeuroML format.
=== Protein folding ===
Protein structure prediction is the prediction of the three-dimensional structure of a protein from its amino acid sequence—that is, the prediction of a protein's tertiary structure from its primary structure. It is one of the most important goals pursued by bioinformatics and theoretical chemistry. Protein structure prediction is of high importance in medicine (for example, in drug design) and biotechnology (for example, in the design of novel enzymes). Every two years, the performance of current methods is assessed in the CASP experiment.
=== Human biological systems ===
==== Brain model ====
The Blue Brain Project is an attempt to create a synthetic brain by reverse-engineering the mammalian brain down to the molecular level. The aim of this project, founded in May 2005 by the Brain and Mind Institute of the École Polytechnique in Lausanne, Switzerland, is to study the brain's architectural and functional principles. The project is headed by the Institute's director, Henry Markram. Using a Blue Gene supercomputer running Michael Hines's NEURON software, the simulation does not consist simply of an artificial neural network, but involves a partially biologically realistic model of neurons. It is hoped by its proponents that it will eventually shed light on the nature of consciousness.
There are a number of sub-projects, including the Cajal Blue Brain, coordinated by the Supercomputing and Visualization Center of Madrid (CeSViMa), and others run by universities and independent laboratories in the UK, U.S., and Israel. The Human Brain Project builds on the work of the Blue Brain Project. It is one of six pilot projects in the Future Emerging Technologies Research Program of the European Commission, competing for a billion euro funding.
==== Model of the immune system ====
The last decade has seen the emergence of a growing number of simulations of the immune system.
==== Virtual liver ====
The Virtual Liver project is a 43 million euro research program funded by the German Government, made up of seventy research group distributed across Germany. The goal is to produce a virtual liver, a dynamic mathematical model that represents human liver physiology, morphology and function.
=== Tree model ===
Electronic trees (e-trees) usually use L-systems to simulate growth. L-systems are very important in the field of complexity science and A-life.
A universally accepted system for describing changes in plant morphology at the cellular or modular level has yet to be devised.
The most widely implemented tree generating algorithms are described in the papers "Creation and Rendering of Realistic Trees" and Real-Time Tree Rendering.
=== Ecological models ===
Ecosystem models are mathematical representations of ecosystems. Typically they simplify complex foodwebs down to their major components or trophic levels, and quantify these as either numbers of organisms, biomass or the inventory/concentration of some pertinent chemical element (for instance, carbon or a nutrient species such as nitrogen or phosphorus).
=== Models in ecotoxicology ===
The purpose of models in ecotoxicology is the understanding, simulation and prediction of effects caused by toxicants in the environment. Most current models describe effects on one of many different levels of biological organization (e.g. organisms or populations). A challenge is the development of models that predict effects across biological scales. Ecotoxicology and models discusses some types of ecotoxicological models and provides links to many others.
=== Modelling of infectious disease ===
It is possible to model the progress of most infectious diseases mathematically to discover the likely outcome of an epidemic or to help manage them by vaccination. This field tries to find parameters for various infectious diseases and to use those parameters to make useful calculations about the effects of a mass vaccination programme.
== See also ==
Biological data visualization
Biosimulation
Gillespie algorithm
Molecular modelling software
Stochastic simulation
== Notes ==
== References ==
== Sources ==
Antmann, S. S.; Marsden, J. E.; Sirovich, L., eds. (2009). Mathematical Physiology (2nd ed.). New York, New York: Springer. ISBN 978-0-387-75846-6.
Barnes, D.J.; Chu, D. (2010), Introduction to Modelling for Biosciences, Springer Verlag
An Introduction to Infectious Disease Modelling by Emilia Vynnycky and Richard G White. An introductory book on infectious disease modelling and its applications.
== Further reading ==
== External links ==
The Center for Modeling Immunity to Enteric Pathogens (MIEP) | Wikipedia/Systems_biology_modeling |
Soft systems methodology (SSM) is an organised way of thinking applicable to problematic social situations and in the management of change by using action. It was developed in England by academics at the Lancaster Systems Department on the basis of a ten-year action research programme.
== Overview ==
The Soft Systems Methodology was developed primarily by Peter Checkland, through 10 years of research with his colleagues, such as Brian Wilson. The method was derived from numerous earlier systems engineering processes, primarily from the fact traditional 'hard' systems thinking was not able to account for larger organisational issues, with many complex relationships. SSM has a primary use in the analysis of these complex situations, where there are divergent views about the definition of the problem.
These complex situations are known as "soft problems". They are usually real world problems where the goals and purposes of the problem are problematic themselves. Examples of soft problems include: How to improve the delivery of health services? and How to manage homelessness with young people? Soft approaches take as tacit that people's view of the world will change all the time and their preferences of it will also change.
Depending on the current circumstances of a situation, trying to agree on the problem may be difficult as there might be multiple factors to take into consideration, such as all the different kinds of methods used to tackle these problems. Additionally, Peter Checkland had moved away from the idea of 'obvious' problems and started working with situations to make concepts of models to use them as a source of questions to help with the problem, soft systems methodologies then started emerging to be an organised learning system.
Purposeful activity models could be declared using worldviews, meaning they were never models of real-world action. Still, those relevant to disclosure and argument about real-world action led to them being called epistemological devices that could be used for discourse and debate. The distinction between the everyday world and systems thinking was to draw attention to the conscious use of systems language in developing intellectual devices which were used to structure debates or an exploration of the problem situation being addressed.
In its 'classic' form the methodology consists of seven steps, with initial appreciation of the problem situation leading to the modelling of several human activity systems that might be thought relevant to the problem situation. By getting all the relevant people who are the decision-makers in this situation to come together, sit down in discussion and exploration about the definition of the problem. Only then will the decision makers in said situation will more likely arrive at a mutual agreement which will settle any arguments or problems and help get to the solution over exactly what kind of changes could be either systemically desirable and feasible in the situation at hand.
Later explanations of the ideas give a more sophisticated view of this systemic method and give more attention to locating the methodology with respect to its philosophical underpinnings. It is the earlier classical view which is most widely used in practice (created by Peter Checkland). A common criticism of this earlier methodology is that it follows an approach that is too linear. Checkland himself agreed that the earlier methodology is 'rather bald'. Most advanced SSM analysts will agree, though, that the classical view is an easy way for inexperienced analysts to learn the SSM methodology.
SSM has been successfully used as a business analysis methodology in various fields. Real-world examples of SSM's wide range of applicability include research applying SSM in the sugar industry leading to improvements in business partner relationships, successful use as an approach in project management by directly involving stakeholders or aiding in business management by improving communication between stakeholders. It has proven to be a useful analysis approach to teaching and learning processes, as it does not require a specific problem to be identified as its starting point – which has led to "outside of the box" suggestions for improvement. SSM was even used by the UK government as part of the revaluation of their Structured Systems Analysis and Design Method (SSADM) system development methodology.
Even professional researchers who are to take the change for face value structure of thinking, show the same tendency to distort perceptions of the world rather than change the mental structure which we give our bearings with. Failure of classic systems in rich 'management' problem situations during the research programme led to examining the adequacy of the systems thinking.
The methodology has been described in several books and many academic articles.
SSM remains the most widely used and practical application of systems thinking, and other systems approaches such as critical systems thinking have incorporated many of its ideas.
== Representation evolution ==
SSM had a gradual development process of the methodology as a whole from 1972 to 1990. During this period of time, four different representations of SSM were designed, becoming more sophisticated and at the same time less structured and broader in scope.
=== Blocks and arrows (1972) ===
The first studies in the research programme were carried out in 1969, and the first account of what became SSM was published in a paper three-years later titled "Towards a systems-based methodology for real-world problem solving" (Checkland 1972). In this paper, soft systems methodology is presented as a sequence of stages with iteration back to previous stages.The sequence was as follows: analysis, root definition of relevant systems, conceptualisation, comparison and definition of changes, selection of change to implement, design of change and implementation and appraisal.
The overall aim to implement change instead of introducing or enhancing a system implies that the thinking was ongoing as a result of these early experiences, even if the straight arrows in the diagrams and the rectangular blocks in some of the models can now be misleading!
=== Seven stages (1981) ===
Soft systems methodology (SSM) is a powerful tool that is utilised to analyse very complex organisational and systemic problems, that do not have an obvious solution. The methodology incorporates seven steps to come up with a viable solution for the problem defined. The seven steps are;
Enter situation in which a problem situation(s) have been identified
Address the issue at hand
Formulate root definitions of relevant systems of purposeful activity
Build conceptual models of the systems named in the root definitions : This methodology comes into place from raising concerns/ capturing problems within an organisation and looking into ways how it can be solved. Defining the root definition also describes the root purpose of a system.
The comparison stage: The systems thinker is to compare the perceived conceptual models against an intuitive perception of a real-world situation or scenario. Checkland defines this stage as the comparison of Stage 4 with Stage 2, formally, "Comparison of 4 with 2". Parts of the problem situation analysed in Stage 2 are to be examined alongside the conceptual model(s) created in Stage 4, this helps to achieve a "complete" comparison.
Problems identified should be accompanied now by feasible and desirable changes that will distinctly help the problem situation based in the system given. Human activity systems and other aspects of the system should be considered so that soft systems thinking, and Mumford's needs can be achieved with the potential changes. These potential changes should not be acted on until step 7 but they should be feasible enough to act upon to improve the problem situation.
Take action to improve the problem situation
=== Two streams (1988) ===
The two-stream model of SSM recognizes the crucially important role of history in human affairs, and for a given group of people their history determines what will be noticed as significant and how it will be judged. This expression of SSM is presented as an approach embodying not only a logic-based stream of analysis (via activity models) but also a cultural and political stream which enable judgements to be made about the accommodations between conflicting interests which might be reachable by the people concerned and which would enable action to be taken.
This particular expression of SSM removes the dividing line between the world of the problem situation and the systems thinking world.
=== Four main activities (1990) ===
The four-activities model is iconic rather than descriptive and subsumes the cultural stream of analysis in the four activities. The seven stage model gave an approach which applies real world situations, both large and small and public and private sector. The four main activities were created as a way to capture the more flexible use of SSM and to include more of the cultural aspect of the workplace into the concept of SSM. The four activities are used to show that SSM does not have to be used rigidly; it's there to show real life and not be constrained. The four main activities should be seen as an individual concept rather than a descriptive which incorporates the cultural stream of analysis. The four activities are:
Finding out about a problem situation, including culturally/politically
Formulating some relevant purposeful activity models: Creating and drawing specific diagrammatic illustrations of activity processes that occur in an organisation, which shows the relevant processes that take place in a structured order, and depicts any problem situation visually by showing the flow of one action to another. An example of this would be a diagram of a Soft Systems Methodology method, which is a 'Conceptual Model', which is a representation of a systems' human actions, or an 'Architecture System Map', which is a visual representation of the implementation of sections of a software system.
Debating the situation, using the models, seeking from that debate both:
changes which would improve the situation and are regarded as both desirable and (culturally) feasible, and
the accommodations between conflicting interests which will enable action
Taking action in the situation to bring about improvement
== CATWOE ==
In 1975, David Smyth, a researcher in Checkland's department, observed that SSM was most successful when the root definition included certain elements. These elements, captured in the mnemonic CATWOE, identified the people, processes and environment that contribute to a situation, issue or problem that required analyzing.
This is used to prompt thinking about what the business is trying to achieve. In further detail, CATWOE helps explore a system by underlining the roots which involve turning the inputs into outputs. CATWOE helps businesses as it analyses a gap between current and useful systems. Business perspectives help the business analyst to consider the impact of any proposed solution on the people involved. This mainly involves stakeholders which allows them to test assumptions they have made as stakeholders will all have different opinions about certain problems and opportunities. CATWOE's method helps gain better and achievable results, as well as avoiding additional problems using six elements.
The six elements of CATWOE are:
Customers – Who are the beneficiaries of the highest level business process and how does the issue affect them?
Actors - The person or people directly involved in the transformation (T) part of CATWOE (Checkland & Scholes, 1999, p. 35). Implementation and involvement by the actors allows for the input to be transformed into an output (Checkland & Scholes, 1999, p. 35). Actors are also stakeholders as their actions can affect the transformation process and the system as a whole. As actors are directly involved, they also have a 'holon' by which they interpret the world outside (Checkland & Scholes, 1999, p. 19) and so how they view the situation would impact their work and success.
Transformation process – Change, in one word, is the centre of the transformation system; the process of the transformation is more important for the business solution system. This is because the change is what the industry 5.0 sustainability system intends. The purpose behind the transformation system where change is applied holds value. For example, when converting grapes into wine the purpose for Change is to supply to grape consumers more value of the grape (product), thus sustaining the product value systemically. What is the transformation that lies at the heart of the system - transforming grapes into wine, transforming unsold goods into sold goods, transforming a societal need into a societal need met? This means change, in one word, is the centre of the transformation system; the process of becoming is more important than the business solution system. This is because the change is what the industry 2.0 systemic sustainability system practice purpose solves. The purpose behind the transformation system where change is provides the change, thus the results. For example when converting grapes into wine the purpose for Change is to supply to members of the public interest or involvement in grapes more value of the product, thus sustaining the product value more systemically.
Weltanschauung (or Worldview) – What is the big picture and what are the wider impacts of the issue? "The word Weltanschauung is a German word that has no real English equivalent. It refers to "all the things that you take for granted" and is related to our values". But the closest translation would be "world view", which is the collective summary of the stakeholders belief that gives meaning to the root definition. Model of the human activity system as a whole.
Owner – Who owns the process or situation being investigated and what role will they play in the solution?
Environmental constraints – What are the constraints and limitations that will impact the solution and its success?
CATWOE can also be related to the holistic multi-benefit analysis due to the multiple perspectives that are taken into consideration. It further understands the perspectives and concerns of different stakeholders involved in the human activity systems adhering to the core values of soft systems thinking allowing multiple perspectives to be appreciated with good knowledge management
== Human activity system ==
A human activity system can be defined as "notional system (i.e. not existing in any tangible form) where human beings are undertaking some activities that achieve some purpose".
Within most systems there will be many human activity systems integrated within it to form the whole system. Human activity systems can be used in SSM to establish worldviews (Weltanschauung) for people involved in problematic situations. The assumption with all human activity systems is that all actors within them will act accordingly with their own worldviews.
== See also ==
Enterprise modelling
Hard systems
Holism
List of thought processes
Problem structuring methods
Rich picture
Structured systems analysis and design method
Systems theory
Systems philosophy
== References ==
== Further reading ==
=== Books ===
Avison, D., & Fitzgerald, G. (2006). Information Systems Development. methodologies, techniques & tools (4th ed.). McGraw-Hill Education.
Wilson, B. and van Haperen, K. (2015) Soft Systems Thinking, Methodology and the Management of Change (including the history of the systems engineering department at Lancaster University), London: Palgrave MacMillan. ISBN 978-1-137-43268-1.
Checkland, P.B. and J. Scholes (2001) Soft Systems Methodology in Action, in J. Rosenhead and J. Mingers (eds), Rational Analysis for a Problematic World Revisited. Chichester: Wiley
Checkland, P.B. & Poulter, J. (2006) Learning for Action: A short definitive account of Soft Systems Methodology and its use for Practitioners, teachers and Students, Wiley, Chichester. ISBN 0-470-02554-9
Checkland, P.B. Systems Thinking, Systems Practice, John Wiley & Sons Ltd. 1981, 1998. ISBN 0-471-98606-2
Checkland, P.B. and S. Holwell Information, Systems and Information Systems, John Wiley & Sons Ltd. 1998. ISBN 0-471-95820-4
Wilson, B. Systems: Concepts, Methodologies and Applications, John Wiley & Sons Ltd. 1984, 1990. ISBN 0-471-92716-3
Wilson, B. Soft Systems Methodology, John Wiley & Sons Ltd. 2001. ISBN 0-471-89489-3
=== Articles ===
Dale Couprie et al. (2007) Soft Systems Methodology Department of Computer Science, University of Calgary.
Mark P. Mobach, Jos J. van der Werf & F.J. Tromp (2000). The art of modelling in SSM, in papers ISSS meeting 2000.
Ian Bailey (2008) MODAF and Soft Systems. white paper.
Ivanov, K. (1991). Critical systems thinking and information technology. - In J. of Applied Systems Analysis, 18, 39-55. (ISSN 0308-9541). A review of soft systems methodology as related to critical systems thinking.
Michael Rada (2015-12-01) [1]. white paper, INDUSTRY 5.0 launch.
Michael Rada (2015-02-03) [2]. white paper, INDUSTRY 5.0 DEFINITION.
== External links ==
Peter Checkland homepage.
Models for Change Soft Systems Methodology . Business Process Transformation, 1996.
Soft systems methodology Action research and evaluation on line, 2007.
Checkland and Smyth's CATWOE and Soft Systems Methodology, Business Open Learning Archive 2007. | Wikipedia/Soft_system_modeling |
Intuitively, an algorithmically random sequence (or random sequence) is a sequence of binary digits that appears random to any algorithm running on a (prefix-free or not) universal Turing machine. The notion can be applied analogously to sequences on any finite alphabet (e.g. decimal digits). Random sequences are key objects of study in algorithmic information theory.
In measure-theoretic probability theory, introduced by Andrey Kolmogorov in 1933, there is no such thing as a random sequence. For example, consider flipping a fair coin infinitely many times. Any particular sequence, be it
0000
…
{\displaystyle 0000\dots }
or
011010
…
{\displaystyle 011010\dots }
, has equal probability of exactly zero. There is no way to state that one sequence is "more random" than another sequence, using the language of measure-theoretic probability. However, it is intuitively obvious that
011010
…
{\displaystyle 011010\dots }
looks more random than
0000
…
{\displaystyle 0000\dots }
. Algorithmic randomness theory formalizes this intuition.
As different types of algorithms are sometimes considered, ranging from algorithms with specific bounds on their running time to algorithms which may ask questions of an oracle machine, there are different notions of randomness. The most common of these is known as Martin-Löf randomness (K-randomness or 1-randomness), but stronger and weaker forms of randomness also exist. When the term "algorithmically random" is used to refer to a particular single (finite or infinite) sequence without clarification, it is usually taken to mean "incompressible" or, in the case the sequence is infinite and prefix algorithmically random (i.e., K-incompressible), "Martin-Löf–Chaitin random".
Since its inception, Martin-Löf randomness has been shown to admit many equivalent characterizations—in terms of compression, randomness tests, and gambling—that bear little outward resemblance to the original definition, but each of which satisfies our intuitive notion of properties that random sequences ought to have: random sequences should be incompressible, they should pass statistical tests for randomness, and it should be difficult to make money betting on them. The existence of these multiple definitions of Martin-Löf randomness, and the stability of these definitions under different models of computation, give evidence that Martin-Löf randomness is natural and not an accident of Martin-Löf's particular model.
It is important to disambiguate between algorithmic randomness and stochastic randomness. Unlike algorithmic randomness, which is defined for computable (and thus deterministic) processes, stochastic randomness is usually said to be a property of a sequence that is a priori known to be generated by (or is the outcome of) an independent identically distributed equiprobable stochastic process.
Because infinite sequences of binary digits can be identified with real numbers in the unit interval, random binary sequences are often called (algorithmically) random real numbers. Additionally, infinite binary sequences correspond to characteristic functions of sets of natural numbers; therefore those sequences might be seen as sets of natural numbers.
The class of all Martin-Löf random (binary) sequences is denoted by RAND or MLR.
== History ==
=== Richard von Mises ===
Richard von Mises formalized the notion of a test for randomness in order to define a random sequence as one that passed all tests for randomness. He defined a "collective" (kollektiv) to be an infinite binary string
x
1
:
∞
{\displaystyle x_{1:\infty }}
defined such that
There exists a limit
lim
n
1
n
∑
i
=
1
n
x
i
=
p
∈
(
0
,
1
)
{\displaystyle \lim _{n}{\frac {1}{n}}\sum _{i=1}^{n}x_{i}=p\in (0,1)}
.
For any "admissible" rule, such that it picks out an infinite subsequence
(
x
m
i
)
i
{\displaystyle (x_{m_{i}})_{i}}
from the string, we still have
lim
n
1
n
∑
i
=
1
n
x
m
i
=
p
{\displaystyle \lim _{n}{\frac {1}{n}}\sum _{i=1}^{n}x_{m_{i}}=p}
. He called this principle "impossibility of a gambling system".
To pick out a subsequence, first pick a binary function
ϕ
{\displaystyle \phi }
, such that given any binary string
x
1
:
k
{\displaystyle x_{1:k}}
, it outputs either 0 or 1. If it outputs 1, then we add
x
k
+
1
{\displaystyle x_{k+1}}
to the subsequence, else we continue. In this definition, some admissible rules might abstain forever on some sequences, and thus fail to pick out an infinite subsequence. We only consider those that do pick an infinite subsequence.
Stated in another way, each infinite binary string is a coin-flip game, and an admissible rule is a way for a gambler to decide when to place bets. A collective is a coin-flip game where there is no way for one gambler to do better than another over the long run. That is, there is no gambling system that works for the game.
The definition generalizes from binary alphabet to countable alphabet:
The frequency of each letter converges to a limit greater than zero.
For any "admissible" rule, such that it picks out an infinite subsequence
(
x
m
i
)
i
{\displaystyle (x_{m_{i}})_{i}}
from the string, the frequency of each letter in the subsequence still converges to the same limit.
Usually the admissible rules are defined to be rules computable by a Turing machine, and we require
p
=
1
/
2
{\displaystyle p=1/2}
. With this, we have the Mises–Wald–Church random sequences. This is not a restriction, since given a sequence with
p
=
1
/
2
{\displaystyle p=1/2}
, we can construct random sequences with any other computable
p
∈
(
0
,
1
)
{\displaystyle p\in (0,1)}
. (Here, "Church" refers to Alonzo Church, whose 1940 paper proposed using Turing-computable rules.)
However, this definition was found not to be strong enough.
Intuitively, the long-time average of a random sequence should oscillate on both sides of
p
{\displaystyle p}
, like how a random walk should cross the origin infinitely many times. However, Jean Ville showed that, even with countably many rules, there exists a binary sequence that tends towards
p
{\displaystyle p}
fraction of ones, but, for every finite prefix, the fraction of ones is less than
p
{\displaystyle p}
.
=== Per Martin-Löf ===
The Ville construction suggests that the Mises–Wald–Church sense of randomness is not good enough, because some random sequences do not satisfy some laws of randomness. For example, the Ville construction does not satisfy one of the laws of the iterated logarithm:
lim sup
n
→
∞
−
∑
k
=
1
n
(
x
k
−
1
/
2
)
2
n
log
log
n
≠
1
{\displaystyle \limsup _{n\to \infty }{\frac {-\sum _{k=1}^{n}(x_{k}-1/2)}{\sqrt {2n\log \log n}}}\neq 1}
Naively, one can fix this by requiring a sequence to satisfy all possible laws of randomness, where a "law of randomness" is a property that is satisfied by all sequences with probability 1. However, for each infinite sequence
y
1
:
∞
∈
2
N
{\displaystyle y_{1:\infty }\in 2^{\mathbb {N} }}
, we have a law of randomness that
x
1
:
∞
≠
y
1
:
∞
{\displaystyle x_{1:\infty }\neq y_{1:\infty }}
, leading to the conclusion that there are no random sequences.
(Per Martin-Löf, 1966) defined "Martin-Löf randomness" by only allowing laws of randomness that are Turing-computable. In other words, a sequence is random iff it passes all Turing-computable tests of randomness.
The thesis that the definition of Martin-Löf randomness "correctly" captures the intuitive notion of randomness has been called the Martin-Löf–Chaitin Thesis; it is somewhat similar to the Church–Turing thesis.
Church–Turing thesis. The mathematical concept of "computable by Turing machines" captures the intuitive notion of a function being "computable". Like how Turing-computability has many equivalent definitions, Martin-Löf randomness also has many equivalent definitions. See next section.
== Three equivalent definitions ==
Martin-Löf's original definition of a random sequence was in terms of constructive null covers; he defined a sequence to be random if it is not contained in any such cover. Gregory Chaitin, Leonid Levin and Claus-Peter Schnorr proved a characterization in terms of algorithmic complexity: a sequence is random if there is a uniform bound on the compressibility of its initial segments. Schnorr gave a third equivalent definition in terms of martingales. Li and Vitanyi's book An Introduction to Kolmogorov Complexity and Its Applications is the standard introduction to these ideas.
Algorithmic complexity (Chaitin 1969, Schnorr 1973, Levin 1973): Algorithmic complexity (also known as (prefix-free) Kolmogorov complexity or program-size complexity) can be thought of as a lower bound on the algorithmic compressibility of a finite sequence (of characters or binary digits). It assigns to each such sequence w a natural number K(w) that, intuitively, measures the minimum length of a computer program (written in some fixed programming language) that takes no input and will output w when run. The complexity is required to be prefix-free: The program (a sequence of 0 and 1) is followed by an infinite string of 0s, and the length of the program (assuming it halts) includes the number of zeroes to the right of the program that the universal Turing machine reads. The additional requirement is needed because we can choose a length such that the length codes information about the substring. Given a natural number c and a sequence w, we say that w is c-incompressible if
K
(
w
)
≥
|
w
|
−
c
{\displaystyle K(w)\geq |w|-c}
.
An infinite sequence S is Martin-Löf random if and only if there is a constant c such that all of S's finite prefixes are c-incompressible. More succinctly,
K
(
w
)
≥
|
w
|
−
O
(
1
)
{\displaystyle K(w)\geq |w|-O(1)}
.
Constructive null covers (Martin-Löf 1966): This is Martin-Löf's original definition. For a finite binary string w we let Cw denote the cylinder generated by w. This is the set of all infinite sequences beginning with w, which is a basic open set in Cantor space. The product measure μ(Cw) of the cylinder generated by w is defined to be 2−|w|. Every open subset of Cantor space is the union of a countable sequence of disjoint basic open sets, and the measure of an open set is the sum of the measures of any such sequence. An effective open set is an open set that is the union of the sequence of basic open sets determined by a recursively enumerable sequence of binary strings. A constructive null cover or effective measure 0 set is a recursively enumerable sequence
U
i
{\displaystyle U_{i}}
of effective open sets such that
U
i
+
1
⊆
U
i
{\displaystyle U_{i+1}\subseteq U_{i}}
and
μ
(
U
i
)
≤
2
−
i
{\displaystyle \mu (U_{i})\leq 2^{-i}}
for each natural number i. Every effective null cover determines a
G
δ
{\displaystyle G_{\delta }}
set of measure 0, namely the intersection of the sets
U
i
{\displaystyle U_{i}}
.
A sequence is defined to be Martin-Löf random if it is not contained in any
G
δ
{\displaystyle G_{\delta }}
set determined by a constructive null cover.
Constructive martingales (Schnorr 1971): A martingale is a function
d
:
{
0
,
1
}
∗
→
[
0
,
∞
)
{\displaystyle d:\{0,1\}^{*}\to [0,\infty )}
such that, for all finite strings w,
d
(
w
)
=
(
d
(
w
⌢
0
)
+
d
(
w
⌢
1
)
)
/
2
{\displaystyle d(w)=(d(w^{\smallfrown }0)+d(w^{\smallfrown }1))/2}
, where
a
⌢
b
{\displaystyle a^{\smallfrown }b}
is the concatenation of the strings a and b. This is called the "fairness condition": if a martingale is viewed as a betting strategy, then the above condition requires that the bettor plays against fair odds. A martingale d is said to succeed on a sequence S if
lim sup
n
→
∞
d
(
S
↾
n
)
=
∞
,
{\displaystyle \limsup _{n\to \infty }d(S\upharpoonright n)=\infty ,}
where
S
↾
n
{\displaystyle S\upharpoonright n}
is the first n bits of S. A martingale d is constructive (also known as weakly computable, lower semi-computable) if there exists a computable function
d
^
:
{
0
,
1
}
∗
×
N
→
Q
{\displaystyle {\widehat {d}}:\{0,1\}^{*}\times \mathbb {N} \to {\mathbb {Q} }}
such that, for all finite binary strings w
d
^
(
w
,
t
)
≤
d
^
(
w
,
t
+
1
)
<
d
(
w
)
,
{\displaystyle {\widehat {d}}(w,t)\leq {\widehat {d}}(w,t+1)<d(w),}
for all positive integers t,
lim
t
→
∞
d
^
(
w
,
t
)
=
d
(
w
)
.
{\displaystyle \lim _{t\to \infty }{\widehat {d}}(w,t)=d(w).}
A sequence is Martin-Löf random if and only if no constructive martingale succeeds on it.
== Interpretations of the definitions ==
The Kolmogorov complexity characterization conveys the intuition that a random sequence is incompressible: no prefix can be produced by a program much shorter than the prefix.
The null cover characterization conveys the intuition that a random real number should not have any property that is "uncommon". Each measure 0 set can be thought of as an uncommon property. It is not possible for a sequence to lie in no measure 0 sets, because each one-point set has measure 0. Martin-Löf's idea was to limit the definition to measure 0 sets that are effectively describable; the definition of an effective null cover determines a countable collection of effectively describable measure 0 sets and defines a sequence to be random if it does not lie in any of these particular measure 0 sets. Since the union of a countable collection of measure 0 sets has measure 0, this definition immediately leads to the theorem that there is a measure 1 set of random sequences. Note that if we identify the Cantor space of binary sequences with the interval [0,1] of real numbers, the measure on Cantor space agrees with Lebesgue measure.
An effective measure 0 set can be interpreted as a Turing machine that is able to tell, given an infinite binary string, whether the string looks random at levels of statistical significance. The set is the intersection of shrinking sets
U
1
⊃
U
2
⊃
U
3
⊃
⋯
{\displaystyle U_{1}\supset U_{2}\supset U_{3}\supset \cdots }
, and since each set
U
n
{\displaystyle U_{n}}
is specified by an enumerable sequence of prefixes, given any infinite binary string, if it is in
U
n
{\displaystyle U_{n}}
, then the Turing machine can decide in finite time that the string does fall inside
U
n
{\displaystyle U_{n}}
. Therefore, it can "reject the hypothesis that the string is random at significance level
2
−
n
{\displaystyle 2^{-n}}
". If the Turing machine can reject the hypothesis at all significance levels, then the string is not random. A random string is one that, for each Turing-computable test of randomness, manages to remain forever un-rejected at some significance level.
The martingale characterization conveys the intuition that no effective procedure should be able to make money betting against a random sequence. A martingale d is a betting strategy. d reads a finite string w and bets money on the next bit. It bets some fraction of its money that the next bit will be 0, and then remainder of its money that the next bit will be 1. d doubles the money it placed on the bit that actually occurred, and it loses the rest. d(w) is the amount of money it has after seeing the string w. Since the bet placed after seeing the string w can be calculated from the values d(w), d(w0), and d(w1), calculating the amount of money it has is equivalent to calculating the bet. The martingale characterization says that no betting strategy implementable by any computer (even in the weak sense of constructive strategies, which are not necessarily computable) can make money betting on a random sequence.
== Properties and examples of Martin-Löf random sequences ==
=== Universality ===
There is a universal constructive martingale d. This martingale is universal in the sense that, given any constructive martingale d, if d succeeds on a sequence, then d succeeds on that sequence as well. Thus, d succeeds on every sequence in RANDc (but, since d is constructive, it succeeds on no sequence in RAND). (Schnorr 1971)
There is a constructive null cover of RANDc. This means that all effective tests for randomness (that is, constructive null covers) are, in a sense, subsumed by this universal test for randomness, since any sequence that passes this single test for randomness will pass all tests for randomness. (Martin-Löf 1966) Intuitively, this universal test for randomness says "If the sequence has increasingly long prefixes that can be increasingly well-compressed on this universal Turing machine", then it is not random." -- see next section.
Construction sketch: Enumerate the effective null covers as
(
(
U
m
,
n
)
n
)
m
{\displaystyle ((U_{m,n})_{n})_{m}}
. The enumeration is also effective (enumerated by a modified universal Turing machine). Now we have a universal effective null cover by diagonalization:
(
∪
n
U
n
,
n
+
k
+
1
)
k
{\displaystyle (\cup _{n}U_{n,n+k+1})_{k}}
.
=== Passing randomness tests ===
If a sequence fails an algorithmic randomness test, then it is algorithmically compressible. Conversely, if it is algorithmically compressible, then it fails an algorithmic randomness test.
Construction sketch: Suppose the sequence fails a randomness test, then it can be compressed by lexicographically enumerating all sequences that fails the test, then code for the location of the sequence in the list of all such sequences. This is called "enumerative source encoding".
Conversely, if the sequence is compressible, then by the pigeonhole principle, only a vanishingly small fraction of sequences are like that, so we can define a new test for randomness by "has a compression by this universal Turing machine". Incidentally, this is the universal test for randomness.
For example, consider a binary sequence sampled IID from the Bernoulli distribution. After taking a large number
N
{\displaystyle N}
of samples, we should have about
M
≈
p
N
{\displaystyle M\approx pN}
ones. We can code for this sequence as "Generate all binary sequences with length
N
{\displaystyle N}
, and
M
{\displaystyle M}
ones. Of those, the
i
{\displaystyle i}
-th sequence in lexicographic order.".
By Stirling approximation,
log
2
(
N
p
N
)
≈
N
H
(
p
)
{\displaystyle \log _{2}{\binom {N}{pN}}\approx NH(p)}
where
H
{\displaystyle H}
is the binary entropy function. Thus, the number of bits in this description is:
2
(
1
+
ϵ
)
log
2
N
+
(
1
+
ϵ
)
N
H
(
p
)
+
O
(
1
)
{\displaystyle 2(1+\epsilon )\log _{2}N+(1+\epsilon )NH(p)+O(1)}
The first term is for prefix-coding the numbers
N
{\displaystyle N}
and
M
{\displaystyle M}
. The second term is for prefix-coding the number
i
{\displaystyle i}
. (Use Elias omega coding.) The third term is for prefix-coding the rest of the description.
When
N
{\displaystyle N}
is large, this description has just
∼
H
(
p
)
N
{\displaystyle \sim H(p)N}
bits, and so it is compressible, with compression ratio
∼
H
(
p
)
{\displaystyle \sim H(p)}
. In particular, the compression ratio is exactly one (incompressible) only when
p
=
1
/
2
{\displaystyle p=1/2}
. (Example 14.2.8 )
=== Impossibility of a gambling system ===
Consider a casino offering fair odds at a roulette table. The roulette table generates a sequence of random numbers. If this sequence is algorithmically random, then there is no lower semi-computable strategy to win, which in turn implies that there is no computable strategy to win. That is, for any gambling algorithm, the long-term log-payoff is zero (neither positive nor negative). Conversely, if this sequence is not algorithmically random, then there is a lower semi-computable strategy to win.
=== Examples ===
Chaitin's halting probability Ω is an example of a random sequence.
Every random sequence is not computable.
Every random sequence is normal, satisfies the law of large numbers, and satisfies all Turing-computable properties satisfied by an IID stream of uniformly random numbers. (Theorem 14.5.2 )
=== Relation to the arithmetic hierarchy ===
RANDc (the complement of RAND) is a measure 0 subset of the set of all infinite sequences. This is implied by the fact that each constructive null cover covers a measure 0 set, there are only countably many constructive null covers, and a countable union of measure 0 sets has measure 0. This implies that RAND is a measure 1 subset of the set of all infinite sequences.
The class RAND is a
Σ
2
0
{\displaystyle \Sigma _{2}^{0}}
subset of Cantor space, where
Σ
2
0
{\displaystyle \Sigma _{2}^{0}}
refers to the second level of the arithmetical hierarchy. This is because a sequence S is in RAND if and only if there is some open set in the universal effective null cover that does not contain S; this property can be seen to be definable by a
Σ
2
0
{\displaystyle \Sigma _{2}^{0}}
formula.
There is a random sequence which is
Δ
2
0
{\displaystyle \Delta _{2}^{0}}
, that is, computable relative to an oracle for the Halting problem. (Schnorr 1971) Chaitin's Ω is an example of such a sequence.
No random sequence is decidable, computably enumerable, or co-computably-enumerable. Since these correspond to the
Δ
1
0
{\displaystyle \Delta _{1}^{0}}
,
Σ
1
0
{\displaystyle \Sigma _{1}^{0}}
, and
Π
1
0
{\displaystyle \Pi _{1}^{0}}
levels of the arithmetical hierarchy, this means that
Δ
2
0
{\displaystyle \Delta _{2}^{0}}
is the lowest level in the arithmetical hierarchy where random sequences can be found.
Every sequence is Turing reducible to some random sequence. (Kučera 1985/1989, Gács 1986). Thus there are random sequences of arbitrarily high Turing degree.
== Relative randomness ==
As each of the equivalent definitions of a Martin-Löf random sequence is based on what is computable by some Turing machine, one can naturally ask what is computable by a Turing oracle machine. For a fixed oracle A, a sequence B which is not only random but in fact, satisfies the equivalent definitions for computability relative to A (e.g., no martingale which is constructive relative to the oracle A succeeds on B) is said to be random relative to A. Two sequences, while themselves random, may contain very similar information, and therefore neither will be random relative to the other. Any time there is a Turing reduction from one sequence to another, the second sequence cannot be random relative to the first, just as computable sequences are themselves nonrandom; in particular, this means that Chaitin's Ω is not random relative to the halting problem.
An important result relating to relative randomness is van Lambalgen's theorem, which states that if C is the sequence composed from A and B by interleaving the first bit of A, the first bit of B, the second bit of A, the second bit of B, and so on, then C is algorithmically random if and only if A is algorithmically random, and B is algorithmically random relative to A. A closely related consequence is that if A and B are both random themselves, then A is random relative to B if and only if B is random relative to A.
== Stronger than Martin-Löf randomness ==
Relative randomness gives us the first notion which is stronger than Martin-Löf randomness, which is randomness relative to some fixed oracle A. For any oracle, this is at least as strong, and for most oracles, it is strictly stronger, since there will be Martin-Löf random sequences which are not random relative to the oracle A. Important oracles often considered are the halting problem,
∅
′
{\displaystyle \emptyset '}
, and the nth jump oracle,
∅
(
n
)
{\displaystyle \emptyset ^{(n)}}
, as these oracles are able to answer specific questions which naturally arise. A sequence which is random relative to the oracle
∅
(
n
−
1
)
{\displaystyle \emptyset ^{(n-1)}}
is called n-random; a sequence is 1-random, therefore, if and only if it is Martin-Löf random. A sequence which is n-random for every n is called arithmetically random. The n-random sequences sometimes arise when considering more complicated properties. For example, there are only countably many
Δ
2
0
{\displaystyle \Delta _{2}^{0}}
sets, so one might think that these should be non-random. However, the halting probability Ω is
Δ
2
0
{\displaystyle \Delta _{2}^{0}}
and 1-random; it is only after 2-randomness is reached that it is impossible for a random set to be
Δ
2
0
{\displaystyle \Delta _{2}^{0}}
.
== Weaker than Martin-Löf randomness ==
Additionally, there are several notions of randomness which are weaker than Martin-Löf randomness. Some of these are weak 1-randomness, Schnorr randomness, computable randomness, partial computable randomness. Yongge Wang showed
that Schnorr randomness is different from computable randomness. Additionally, Kolmogorov–Loveland randomness is known to be no stronger than Martin-Löf randomness, but it is not known whether it is actually weaker.
At the opposite end of the randomness spectrum there is the notion of a K-trivial set. These sets are anti-random in that all initial segment is logarithmically compressible (i.e.,
K
(
w
)
≤
K
(
|
w
|
)
+
b
{\displaystyle K(w)\leq K(|w|)+b}
for each initial segment w), but they are not computable.
== See also ==
Random sequence
Gregory Chaitin
Stochastics
Monte Carlo method
K-trivial set
Universality probability
Statistical randomness
== References ==
== Further reading ==
Eagle, Antony (2021), "Chance versus Randomness", in Zalta, Edward N. (ed.), The Stanford Encyclopedia of Philosophy (Spring 2021 ed.), Metaphysics Research Lab, Stanford University, retrieved 2024-01-28
Downey, Rod; Hirschfeldt, Denis R.; Nies, André; Terwijn, Sebastiaan A. (2006). "Calibrating Randomness". The Bulletin of Symbolic Logic. 12 (3/4): 411–491. CiteSeerX 10.1.1.135.4162. doi:10.2178/bsl/1154698741. Archived from the original on 2016-02-02.
Gács, Péter (1986). "Every sequence is reducible to a random one" (PDF). Information and Control. 70 (2/3): 186–192. doi:10.1016/s0019-9958(86)80004-3.
Kučera, A. (1985). "Measure, Π01-classes and complete extensions of PA". Recursion Theory Week. Lecture Notes in Mathematics. Vol. 1141. Springer-Verlag. pp. 245–259. doi:10.1007/BFb0076224. ISBN 978-3-540-39596-6.
Kučera, A. (1989). "On the use of diagonally nonrecursive functions". Studies in Logic and the Foundations of Mathematics. Vol. 129. North-Holland. pp. 219–239.
Levin, L. (1973). "On the notion of a random sequence". Soviet Mathematics - Doklady. 14: 1413–1416.
Li, M.; Vitanyi, P. M. B. (1997). An Introduction to Kolmogorov Complexity and its Applications (Second ed.). Berlin: Springer-Verlag.
Martin-Löf, P. (1966). "The definition of random sequences". Information and Control. 9 (6): 602–619. doi:10.1016/s0019-9958(66)80018-9.
Nies, André (2009). Computability and randomness. Oxford Logic Guides. Vol. 51. Oxford: Oxford University Press. ISBN 978-0-19-923076-1. Zbl 1169.03034.
Schnorr, C. P. (1971). "A unified approach to the definition of a random sequence". Mathematical Systems Theory. 5 (3): 246–258. doi:10.1007/BF01694181. S2CID 8931514.
Schnorr, Claus P. (1973). "Process complexity and effective random tests". Journal of Computer and System Sciences. 7 (4): 376–388. doi:10.1016/s0022-0000(73)80030-3.
Chaitin, Gregory J. (1969). "On the Length of Programs for Computing Finite Binary Sequences: Statistical Considerations". Journal of the ACM. 16 (1): 145–159. doi:10.1145/321495.321506. S2CID 8209877.
Ville, J. (1939). Etude critique de la notion de collectif. Paris: Gauthier-Villars. | Wikipedia/Algorithmically_random_sequence |
Simplicity theory is a cognitive theory that seeks to explain the attractiveness of situations or events to human minds. It is based on work done by scientists like behavioural scientist Nick Chater, computer scientist Paul Vitanyi, psychologist Jacob Feldman, and artificial intelligence researchers Jean-Louis Dessalles and Jürgen Schmidhuber. It claims that interesting situations appear simpler than expected to the observer.
== Overview ==
Technically, simplicity corresponds in a drop in Kolmogorov complexity, which means that, for an observer, the shortest description
of the situation is shorter than anticipated. For instance, the description of a consecutive lottery draw, such as 22-23-24-25-26-27, is
significantly shorter than a typical one, such as 12-22-27-37-38-42. The former requires only one instantiation (choice of the first lottery number), whereas the latter requires six instantiations.
Simplicity theory makes several quantitative predictions concerning the way atypicality, distance, recency or prominence (places, individuals) influence interestingness.
== Formalization ==
The basic concept of simplicity theory is unexpectedness, defined as the difference between expected complexity and observed complexity:
U
=
C
exp
−
C
obs
.
{\displaystyle U=C_{\text{exp}}-C_{\text{obs}}.}
This definition extends the notion of randomness deficiency.
In most contexts,
C
exp
{\displaystyle C_{\text{exp}}}
corresponds to generation or causal complexity, which is the smallest description of all parameters
that must be set in the "world" for the situation to exist. In the lottery example, generation complexity is identical for a consecutive draw
and a typical draw (as long as no cheating is imagined) and amounts to six instantiations.
Simplicity theory avoids most criticisms addressed at Kolmogorov complexity by considering only descriptions that are available
to a given observer (instead of any imaginable description). This makes complexity, and thus unexpectedness, observer-dependent. For instance, the typical draw 12-22-27-37-38-42 will appear very simple, even simpler than the consecutive one, to
the person who played that combination.
== Connection with probability ==
Algorithmic probability is defined based on Kolmogorov complexity: complex objects are less probable than simple ones. The link between complexity and probability is reversed when probability measures surprise and unexpectedness: simple events appear less probable than complex ones.
Unexpectedness
U
{\displaystyle U}
is linked to subjective probability
P
{\displaystyle P}
as
P
=
2
−
U
.
{\displaystyle P=2^{-U}.}
The advantage of this formula is that subjective probability can be assessed without necessarily knowing the alternatives. Classical
approaches to (objective) probability consider sets of events, since fully instantiated individual events have virtually zero probability to have occurred and to occur again in the world. Subjective probability concerns individual events. Simplicity theory measures it based on randomness deficiency, or complexity drop. This notion of subjective probability does not refer to the event itself, but to what makes the event unique.
== References ==
== External links ==
A tutorial on Simplicity Theory
Juergen Schmidhuber's page on interest and low complexity | Wikipedia/Simplicity_theory |
The Standard CMMI Appraisal Method for Process Improvement (SCAMPI) is the official Software Engineering Institute (SEI) method to provide benchmark-quality ratings relative to Capability Maturity Model Integration (CMMI) models. SCAMPI appraisals are used to identify strengths and weaknesses of current processes, reveal development/acquisition risks, and determine capability and maturity level ratings. They are mostly used either as part of a process improvement program or for rating prospective suppliers. The method defines the appraisal process as consisting of preparation; on-site activities; preliminary observations, findings, and ratings; final reporting; and follow-on activities.
== Class A, B, and C Appraisals ==
The suite of documents associated with a particular version of the CMMI includes a requirements specification called the Appraisal Requirements for CMMI (ARC), which specifies three levels of formality for appraisals: Class A, B, and C. Formal (Class A) SCAMPIs are conducted by SEI-authorized Lead Appraisers who use the SCAMPI A Method Definition Document (MDD) to conduct the appraisals.
Class A, the most formal, is required to achieve a rating (Level 1 (lowest) to Level 5 (highest)), using the Staged Representation, for public record or for response to U.S. Department of Defense requirements.
== See also ==
Anti-pattern
Capability Maturity Model
Capability Maturity Model Integration (newer)
People Capability Maturity Model
ISO/IEC 29110: Software Life Cycle Profiles and Guidelines for Very Small Entities (VSEs)
== References == | Wikipedia/Standard_CMMI_Appraisal_Method_for_Process_Improvement |
The Mellon College of Science (MCS) is part of Carnegie Mellon University in Pittsburgh, Pennsylvania, US. The college is named for the Mellon family, founders of the Mellon Institute of Industrial Research, a predecessor of Carnegie Mellon University.
The college offers various bachelor's, master's, and doctoral degrees. It also awards the Dickson Prize in Science. Since January 2025, the Glen de Vries Dean of the Mellon College of Science is Barbara Shinn-Cunningham, an American bioengineer and neuroscientist. She succeeds Interim Dean Curtis A. Meyer, and the previous Dean Rebecca Doerge, who served in the role from 2016-2023.
== History ==
The Mellon College of Science was founded in 1967, when the Carnegie Institute of Technology merged with the Mellon Institute of Industrial Research to form Carnegie Mellon University. The scientific faculty and staff of both institutions became part of the new college, then named the Mellon College of Engineering and Science. As the college grew and scientific research advanced, the Carnegie Mellon College of Engineering was split off in 1970, and the Carnegie Mellon School of Computer Science split off in 1988.
== Facilities ==
The administration of MCS, as well as most of its biological sciences and chemistry faculty and research labs, and the college's library, are based in the Mellon Institute, which was constructed in 1937. The neoclassical building was added to the National Register of Historic Places in 1983, and was designated as a National Historic Chemical Landmark in 2013 by the American Chemical Society. The college's physics and mathematical sciences departments are based in Carnegie Mellon's main campus in Wean Hall, a Brutalist building constructed in 1971.
== Organization ==
The Mellon College of Science houses four academic departments: Chemistry, Biological Sciences, Physics, and Mathematical Sciences, each of which grants a variety of undergraduate and graduate degrees. In addition, the college also oversees or is affiliated with a number of interdisciplinary research centers, including the Pittsburgh Supercomputing Center.
== Notable people ==
Ada Yonath (Post-doctoral fellow, 1969; Honorary Doctorate in Science and Technology, 2018), 2009 Nobel Prize in Chemistry
Krzysztof Matyjaszewski (Professor), discoverer of atom transfer radical polymerization
Clarence Zener (Professor, 1968–1993), theoretical physicist, discoverer of Zener effect
John Pople (Professor, 1964–1993) 1998 Nobel Prize in Chemistry
Walter Kohn (Professor, Carnegie Institute of Technology, 1950–1960) 1998 Nobel Prize in Chemistry
Shafi Goldwasser (BS, 1979; Honorary Doctorate in Science and Technology, 2018) 2012 Turing Award
Clifford Shull (BS, Carnegie Institute of Technology, 1937) 1994 Nobel Prize in Physics
Paul Flory (Executive Director of Research, Mellon Institute of Industrial Research, 1957–1961) 1974 Nobel Prize in Chemistry
Otto Stern (Professor, Carnegie Institute of Technology, 1933–1945) 1943 Nobel Prize in Physics
Clinton Davisson (assistant professor, Carnegie Institute of Technology, 1911–1917) 1937 Nobel Prize in Physics
John Nash (BS, MS, Carnegie Institute of Technology, 1948) 1994 Nobel Memorial Prize in Economic Sciences, inspiration for A Beautiful Mind
John L. Hall (BS, MS, PhD, Carnegie Institute of Technology, 1956, 1958, 1961) 2005 Nobel Prize in Physics
Paul Lauterbur (research associate, Mellon Institute of Industrial Research, 1951–1953, 1955–1963) 2003 Nobel Prize in Physiology or Medicine
== References ==
Fenton, Edwin (2000). Carnegie Mellon 1900–2000: A Centennial History. Pittsburgh: Carnegie Mellon University Press. ISBN 0-88748-323-2.
Schaefer, Ludwig (1992). Evolution of a national research university, 1965–1990 : the Stever administration and the Cyert years at Carnegie Mellon (1st ed.). Carnegie Mellon University Press. ISBN 978-0887481178.
== External links ==
Official website | Wikipedia/Mellon_College_of_Science |
The Capability Maturity Model (CMM) is a development model created in 1986 after a study of data collected from organizations that contracted with the U.S. Department of Defense, who funded the research. The term "maturity" relates to the degree of formality and optimization of processes, from ad hoc practices, to formally defined steps, to managed result metrics, to active optimization of the processes.
The model's aim is to improve existing software development processes, but it can also be applied to other processes.
In 2006, the Software Engineering Institute at Carnegie Mellon University developed the Capability Maturity Model Integration, which has largely superseded the CMM and addresses some of its drawbacks.
== Overview ==
The Capability Maturity Model was originally developed as a tool for objectively assessing the ability of government contractors' processes to implement a contracted software project. The model is based on the process maturity framework first described in IEEE Software and, later, in the 1989 book Managing the Software Process by Watts Humphrey. It was later published as an article in 1993
and as a book by the same authors in 1994.
Though the model comes from the field of software development, it is also used as a model to aid in business processes generally, and has also been used extensively worldwide in government offices, commerce, and industry.
== History ==
=== Prior need for software processes ===
In the 1980s, the use of computers grew more widespread, more flexible and less costly. Organizations began to adopt computerized information systems, and the demand for software development grew significantly. Many processes for software development were in their infancy, with few standard or "best practice" approaches defined.
As a result, the growth was accompanied by growing pains: project failure was common, the field of computer science was still in its early years, and the ambitions for project scale and complexity exceeded the market capability to deliver adequate products within a planned budget. Individuals such as Edward Yourdon, Larry Constantine, Gerald Weinberg, Tom DeMarco, and David Parnas began to publish articles and books with research results in an attempt to professionalize the software-development processes.
In the 1980s, several US military projects involving software subcontractors ran over-budget and were completed far later than planned, if at all. In an effort to determine why this was occurring, the United States Air Force funded a study at the Software Engineering Institute (SEI).
=== Precursor ===
The first application of a staged maturity model to IT was not by CMU/SEI, but rather by Richard L. Nolan, who, in 1973 published the stages of growth model for IT organizations.
Watts Humphrey began developing his process maturity concepts during the later stages of his 27-year career at IBM.
=== Development at Software Engineering Institute ===
Active development of the model by the US Department of Defense Software Engineering Institute (SEI) began in 1986 when Humphrey joined the Software Engineering Institute located at Carnegie Mellon University in Pittsburgh, Pennsylvania after retiring from IBM. At the request of the U.S. Air Force he began formalizing his Process Maturity Framework to aid the U.S. Department of Defense in evaluating the capability of software contractors as part of awarding contracts.
The result of the Air Force study was a model for the military to use as an objective evaluation of software subcontractors' process capability maturity. Humphrey based this framework on the earlier Quality Management Maturity Grid developed by Philip B. Crosby in his book "Quality is Free". Humphrey's approach differed because of his unique insight that organizations mature their processes in stages based on solving process problems in a specific order. Humphrey based his approach on the staged evolution of a system of software development practices within an organization, rather than measuring the maturity of each separate development process independently. The CMMI has thus been used by different organizations as a general and powerful tool for understanding and then improving general business process performance.
Watts Humphrey's Capability Maturity Model (CMM) was published in 1988 and as a book in 1989, in Managing the Software Process.
Organizations were originally assessed using a process maturity questionnaire and a Software Capability Evaluation method devised by Humphrey and his colleagues at the Software Engineering Institute.
The full representation of the Capability Maturity Model as a set of defined process areas and practices at each of the five maturity levels was initiated in 1991, with Version 1.1 being published in July 1993.
The CMM was published as a book in 1994 by the same authors Mark C. Paulk, Charles V. Weber, Bill Curtis, and Mary Beth Chrissis.
=== Capability Maturity Model Integration ===
The CMMI model's application in software development has sometimes been problematic. Applying multiple models that are not integrated within and across an organization could be costly in training, appraisals, and improvement activities. The Capability Maturity Model Integration (CMMI) project was formed to sort out the problem of using multiple models for software development processes, thus the CMMI model has superseded the CMM model, though the CMM model continues to be a general theoretical process capability model used in the public domain.
In 2016, the responsibility for CMMI was transferred to the Information Systems Audit and Control Association (ISACA). ISACA subsequently released CMMI v2.0 in 2021. It was upgraded again to CMMI v3.0 in 2023. CMMI now places a greater emphasis on the process architecture which is typically realized as a process diagram. Copies of CMMI are available now only by subscription.
=== Adapted to other processes ===
The CMMI was originally intended as a tool to evaluate the ability of government contractors to perform a contracted software project. Though it comes from the area of software development, it can be, has been, and continues to be widely applied as a general model of the maturity of process (e.g., IT service management processes) in IS/IT (and other) organizations.
== Model topics ==
=== Maturity models ===
A maturity model can be viewed as a set of structured levels that describe how well the behaviors, practices and processes of an organization can reliably and sustainably produce required outcomes.
A maturity model can be used as a benchmark for comparison and as an aid to understanding - for example, for comparative assessment of different organizations where there is something in common that can be used as a basis for comparison. In the case of the CMM, for example, the basis for comparison would be the organizations' software development processes.
=== Structure ===
The model involves five aspects:
Maturity Levels: a 5-level process maturity continuum - where the uppermost (5th) level is a notional ideal state where processes would be systematically managed by a combination of process optimization and continuous process improvement.
Key Process Areas: a Key Process Area identifies a cluster of related activities that, when performed together, achieve a set of goals considered important.
Goals: the goals of a key process area summarize the states that must exist for that key process area to have been implemented in an effective and lasting way. The extent to which the goals have been accomplished is an indicator of how much capability the organization has established at that maturity level. The goals signify the scope, boundaries, and intent of each key process area.
Common Features: common features include practices that implement and institutionalize a key process area. There are five types of common features: commitment to perform, ability to perform, activities performed, measurement and analysis, and verifying implementation.
Key Practices: The key practices describe the elements of infrastructure and practice that contribute most effectively to the implementation and institutionalization of the area.
=== Levels ===
There are five levels defined along the continuum of the model and, according to the SEI: "Predictability, effectiveness, and control of an organization's software processes are believed to improve as the organization moves up these five levels. While not rigorous, the empirical evidence to date supports this belief".
Initial (chaotic, ad hoc, individual heroics) - the starting point for use of a new or undocumented repeat process.
Repeatable - the process is at least documented sufficiently such that repeating the same steps may be attempted.
Defined - the process is defined/confirmed as a standard business process
Capable - the process is quantitatively managed in accordance with agreed-upon metrics.
Efficient - process management includes deliberate process optimization/improvement.
Within each of these maturity levels are Key Process Areas which characterise that level, and for each such area there are five factors: goals, commitment, ability, measurement, and verification. These are not necessarily unique to CMMI, representing — as they do — the stages that organizations must go through on the way to becoming mature.
The model provides a theoretical continuum along which process maturity can be developed incrementally from one level to the next. Skipping levels is not allowed/feasible.
Level 1 - Initial
It is characteristic of processes at this level that they are (typically) undocumented and in a state of dynamic change, tending to be driven in an ad hoc, uncontrolled and reactive manner by users or events. This provides a chaotic or unstable environment for the processes. (Example - a surgeon performing a new operation a small number of times - the levels of negative outcome are not known).
Level 2 - Repeatable
It is characteristic of this level of maturity that some processes are repeatable, possibly with consistent results. Process discipline is unlikely to be rigorous, but where it exists it may help to ensure that existing processes are maintained during times of stress.
Level 3 - Defined
It is characteristic of processes at this level that there are sets of defined and documented standard processes established and subject to some degree of improvement over time. These standard processes are in place. The processes may not have been systematically or repeatedly used - sufficient for the users to become competent or the process to be validated in a range of situations. This could be considered a developmental stage - with use in a wider range of conditions and user competence development the process can develop to next level of maturity.
Level 4 - Managed (Capable)
It is characteristic of processes at this level that, using process metrics, effective achievement of the process objectives can be evidenced across a range of operational conditions. The suitability of the process in multiple environments has been tested and the process refined and adapted. Process users have experienced the process in multiple and varied conditions, and are able to demonstrate competence. The process maturity enables adaptions to particular projects without measurable losses of quality or deviations from specifications. Process Capability is established from this level. (Example - surgeon performing an operation hundreds of times with levels of negative outcome approaching zero).
Level 5 - Optimizing (Efficient)
It is a characteristic of processes at this level that the focus is on continually improving process performance through both incremental and innovative technological changes/improvements. At maturity level 5, processes are concerned with addressing statistical common causes of process variation and changing the process (for example, to shift the mean of the process performance) to improve process performance. This would be done at the same time as maintaining the likelihood of achieving the established quantitative process-improvement objectives.
Between 2008 and 2019, about 12% of appraisals given were at maturity levels 4 and 5.
=== Critique ===
The model was originally intended to evaluate the ability of government contractors to perform a software project. It has been used for and may be suited to that purpose, but critics pointed out that process maturity according to the CMM was not necessarily mandatory for successful software development.
=== Software process framework ===
The software process framework documented is intended to guide those wishing to assess an organization's or project's consistency with the Key Process Areas. For each maturity level there are five checklist types:
== See also ==
Capability Immaturity Model
Capability Maturity Model Integration
People Capability Maturity Model
Testing Maturity Model
== References ==
== External links ==
CMMI Institute
Architecture Maturity Models at The Open Group
ISACA | Wikipedia/Capability_maturity_model |
People Capability Maturity Model (short names: People CMM, PCMM, P-CMM) is a maturity framework that focuses on continuously improving the management and development of the human assets of an organization. It describes an evolutionary improvement path from ad hoc, inconsistently performed practices, to a mature, disciplined, and continuously improving development of the knowledge, skills, and motivation of the workforce that enhances strategic business performance.
Related to fields such as human resources, knowledge management, and organizational development, the People CMM guides organizations in improving their processes for managing and developing their workforces. The People CMM helps organizations characterize the maturity of their workforce practices establish a programme of continuous workforce development, set priorities for improvement actions, integrate workforce development with process improvement, and establish a culture of excellence. The term was promoted in 1995, published in book form in 2001, and a second edition was published in July 2009.
== Description ==
The People CMM consists of five maturity levels that establish successive foundations for continuously improving individual competencies, developing effective teams, motivating improved performance, and shaping the workforce the organization needs to accomplish its future business plans. Each maturity level is a well-defined evolutionary plateau that institutionalizes new capabilities for developing the organization's workforce. By following the maturity framework, an organization can avoid introducing workforce practices that its employees are unprepared to implement effectively.
== Structure ==
The People CMM document describes the practices that constitute each of its maturity levels and provides information on how to apply them to guide organizational improvements. It describes an organization's capability for developing its workforce at each maturity level. It also describes how the People CMM can be applied as a standard for assessing workforce practices and as a guide for planning and implementing improvement activities.
Version 2 of the People CMM has been designed to correct known issues in Version 1, which was released in 1995. It adds enhancements learned from five years of implementation experience and integrates the model better with CMMI and its IPPD extensions. The primary motivation for updating the People CMM was the error in Version 1 of placing team-building activities at Maturity Level 4. The authors made this placement based on substantial Feedback that it should not be placed at Maturity Level 3, as it had been in early review releases. Experience has indicated that many organizations initiate the formal development of workgroups while working toward Maturity Level 3. Thus, Version 2 of the People CMM initiates process-driven workgroup development at Maturity Level 3. This change is consistent with the placement of integrated teaming activities at Maturity Level 3 of the CMMI-IPPD.
== See also ==
Capability Immaturity Model (CIMM)
Capability Maturity Model (CMM)
Capability Maturity Model Integration (CMMI)
== References ==
== External links ==
Official website
Organisational maturity and functional performance
P-CMM Mobile App Android
P-CMM Mobile App Apple | Wikipedia/People_Capability_Maturity_Model |
Pittsburgh Life Sciences Greenhouse (PLSG) is an investment firm based in the South Side neighborhood of Pittsburgh, Pennsylvania that provides resources and tools to entrepreneurial life sciences enterprises in Pittsburgh and western Pennsylvania in order to advance research and patient care.
== History ==
Since PLSG began operations in 2002, it has assisted more than 435 life sciences companies and has affected more than 10,000 jobs in western Pennsylvania. PLSG has provided 34 companies with office or laboratory space, and 14 have been relocated to Pittsburgh from outside the region. PLSG has invested over $20 million in 77 companies, which has leveraged over $1.5 billion in additional capital to the region.
PLSG guides researchers, entrepreneurs and emerging companies through the challenges faced in early stages of company development. They provide support to companies developing product and service innovations in biotechnology tools, diagnostics/screening, healthcare IT, medical devices and therapeutics. PLSG also helps in the expansion of more mature life science companies, by supporting new product and market developments and connecting them to investors.
Pittsburgh Life Sciences Greenhouse grew out of an original plan known as BioVenture, developed by CMU and Pitt. The initiative received a major boost in 2001 when money from the state's settlement with the tobacco industry was pledged to create a life science greenhouse in Western Pennsylvania. In 2003, Pittsburgh Biomedical Corporation, a non-profit established in 1988 by the Pittsburgh Technology Council, consolidated with PLSG.
Today, PLSG exists as a partnership between the Commonwealth of Pennsylvania, University of Pittsburgh, Carnegie Mellon University, University of Pittsburgh Medical Center and the regional foundation of community. Their mission is to "create, nurture and help establish a globally dominant life sciences industry in western Pennsylvania."
== References ==
== External links ==
Pittsburgh Life Sciences Greenhouse
Video
WQED OnQ feature on the Pittsburgh Life Sciences Greenhouse | Wikipedia/Pittsburgh_Life_Sciences_Greenhouse |
Capability Immaturity Model (CIMM) in software engineering is a parody acronym, a semi-serious effort to provide a contrast to the Capability Maturity Model (CMM). The Capability Maturity Model is a five point scale of capability in an organization, ranging from random processes at level 1 to fully defined, managed and optimized processes at level 5. The ability of an organization to carry out its mission on time and within budget is claimed to improve as the CMM level increases.
The "Capability Im-Maturity Model" asserts that organizations can and do occupy levels below CMM level 1. An original article by Capt. Tom Schorsch USAF as part of a graduate project at the Air Force Institute of Technology provides the definitions for CIMM. He cites Anthony Finkelstein's ACM paper as an inspiration. The article describes situations that arise in dysfunctional organizations. Such situations are reportedly common in organizations of all kinds undertaking software development, i.e. they are really characterizations of the management of specific projects, since they can occur even in organizations with positive CMM levels.
Kik Piney, citing the original authors, later adapted the model to a somewhat satirical version that attracted a number of followers who felt that it was true to their experience.
== Levels ==
Finkelstein defined levels 0 (foolish), −1 (stupid) and −2 (lunatic). Schorsch changed the names and added level −3. Piney's structure, truer to the original, uses the terms incompetent, obstructive, antagonistic and psychotic.
=== 0: Negligent ===
The organization pays lip service, often with excessive fanfare, to implementing engineering processes, but lacks the will to carry through the necessary effort. Whereas CMM level 1 assumes eventual success in producing work, CIMM level 0 organizations generally fail to produce any product, or do so by abandoning regular procedures in favor of crash programs.
=== −1: Obstructive ===
Processes, however inappropriate and ineffective, are implemented with rigor and tend to obstruct work. Adherence to process is the measure of success in a level −1 organization. Any actual creation of viable product is incidental. The quality of any product is not assessed, presumably on the assumption that such assessment is unnecessary since if the proper process is followed, high quality is guaranteed. This is the most common level achieved by most organizations that pursue CMM ratings.
However, level −1 organizations believe fervently in following defined procedures, but lacking the will to measure the effectiveness of the procedures they rarely succeed at their basic task of creating work. This behavior is inherent in the CMMI evaluation process. Since many government agencies will only award contracts over a certain monetary value to organizations that can pass a CMMI-3 or higher SCAMPI appraisal, management may be willing to accept inefficiencies to win these lucrative contracts. Government contracting models in which organizations are paid not for the value of their products but by the number of hours spent building them reward organizations for performing non-value-added activities related to CMMI compliance. Thus, government contractors with CMMI ratings may be more profitable than non-CMMI rated companies regardless of the quality of the work they produce.
=== −2: Contemptuous ===
The organization's ineffectiveness has become apparent to the marketplace or the larger organization, which ignores or attempts to neutralize these unfavorable perceptions. Measurements are fudged to make the organization look good. Measures of activity (bugs fixed, lines of code written, hours worked) replace measures of productivity (% functions completed, test success rates). Volatility in specifications and schedules is recast as evidence of organizational "agility". Certifications on "best processes" are presented as evidence that the organization is performing optimally; poor results are blamed on factors outside the organization's control. The processes chosen typically omit or shortcut essential components of recognized methods (e.g. "6-week Six-Sigma" or "Lean CMM"), which are flexible and can cover both good and bad practices. The organization becomes committed to ineffective processes, leading to a feedback cycle of increasing disorganization.
=== −3: Undermining ===
Undermining organizations routinely work to downplay and sabotage the efforts of rival organizations, especially those successfully implementing processes common to CMM level 2 and higher. This behavior may involve competing for scarce resources, drawing those resources from more effective departments or organizations.
== See also ==
Anti-pattern
Capability Maturity Model
Capability Maturity Model Integration (CMMI is an evolution that reflected the need for greater process integration over the predecessor Capability Maturity Model – CMM)
ISO/IEC 29110: Software Life Cycle Profiles and Guidelines for Very Small Entities (VSEs)
People Capability Maturity Model
Standard CMMI Appraisal Method for Process Improvement SCAMPI Class A, B, C Appraisal
== References ==
== External links ==
The Capability Im-Maturity Model (original CrossTalk page as spidered from The Internet Archive) (CIMM) | Wikipedia/Capability_Immaturity_Model |
The Pittsburgh Science of Learning Center (aka LearnLab) is a Science of Learning Center funded by the National Science Foundation and managed by Carnegie Mellon University and the University of Pittsburgh. The PSLC is led by Kenneth Koedinger and Charles Perfetti, and includes many other notable scientists, including Vincent Aleven and David Klahr.
The PSLC theory wiki collects and organizes research results, including a list of instructional principles that are supported by learning science research. The wiki is open and freely editable.
Several notable tools, methods, and theories were developed at the PSLC, including DataShop, LearnSphere, the Knowledge-Learning-Instruction Framework, and the Baker Rodrigo Ocumpaugh Monitoring Protocol.
== References ==
== External links ==
Science of Learning Center
Learnlab's theory wiki | Wikipedia/Pittsburgh_Science_of_Learning_Center |
Meta-process modeling is a type of metamodeling used in software engineering and systems engineering for the analysis and construction of models applicable and useful to some predefined problems.
Meta-process modeling supports the effort of creating flexible process models. The purpose of process models is to document and communicate processes and to enhance the reuse of processes. Thus, processes can be better taught and executed. Results of using meta-process models are an increased productivity of process engineers and an improved quality of the models they produce.
== Overview ==
Meta-process modeling focuses on and supports the process of constructing process models. Its main concern is to improve process models and to make them evolve, which in turn, will support the development of systems. This is important due to the fact that "processes change with time and so do the process models underlying them. Thus, new processes and models may have to be built and existing ones improved". "The focus has been to increase the level of formality of process models in order to make possible their enactment in process-centred software environments".
A process meta-model is a meta model, "a description at the type level of a process model. A process model is, thus, an instantiation of a process meta-model. [..] A meta-model can be instantiated several times in order to define various process models. A process meta-model is at the meta-type level with respect to a process."
There exist standards for several domains:
Software engineering
Software Process Engineering Metamodel (SPEM) which is defined as a profile (UML) by the Object Management Group.
== Topics in metadata modeling ==
There are different techniques for constructing process models. "Construction techniques used in the information systems area have developed independently of those in software engineering. In information systems, construction techniques exploit the notion of a meta-model and the two principal techniques used are those of instantiation and assembly. In software engineering the main construction technique used today is language-based. However, early techniques in both, information systems and software engineering were based on the experience of process engineers and were, therefore, ad hoc in nature."
=== Ad hoc ===
"Traditional process models are expressions of the experiences of their developers. Since this experience is not formalised and is, consequently, not available as a fund of knowledge, it can be said that these process models are the result of an ad hoc construction technique. This has two major consequences: it is not possible to know how these process models were generated, and they become dependent on the domain of experience. If process models are to be domain independent and if they are to be rapidly generable and modifiable, then we need to go away from experience based process model construction. Clearly, generation and modifiability relate to the process management policy adopted (see Usage World). Instantiation and assembly, by promoting modularization, facilitate the capitalisation of good practice and the improvement of given process models."
=== Assembly ===
The assembly technique is based on the idea of a process repository from which process components can be selected. Rolland (1998) lists two selection strategies:
Promoting a global analysis of the project on hand based on contingency criteria (Example Van Slooten 1996)
Using the notion of descriptors as a means to describe process chunks. This eases the retrieval of components meeting the requirements of the user / matching with the situation at hand. (Example Plihon 1995 in NATURE and repository of scenario based approaches accessible on Internet in the CREWS project)
For the assembly technique to be successful, it is necessary that process models are modular. If the assembly technique is combined with the instantiation technique then the meta-model must itself be modular.
=== Instantiation ===
For reusing processes a meta-process model identifies "the common, generic features of process models and represents them in a system of concepts. Such a representation has the potential to 'generate' all process models that share these features. This potential is realised when a generation technique is defined whose application results in the desired process model."
Process models are then derived from the process meta-models through instantiation. Rolland associates a number of advantages with the instantiation approach:
The exploitation of the meta-model helps to define a wide range of process models.
It makes the activity of defining process models systematic and versatile.
It forces to look for and introduce, in the process meta-model, generic solutions to problems and this makes the derived process models inherit the solution characteristics.
"The instantiation technique has been used, for example, in NATURE, Rolland 1993, Rolland 1994, and Rolland 1996. The process engineer must define the instances of contexts and relationships that comprise the process model of interest."
=== Language ===
Rolland (1998) lists numerous languages for expressing process models used by the software engineering community:
E3
Various Prolog dialects for EPOS, Oikos, and PEACE
PS-Algol for PWI
as well as further computational paradigms:
Petri nets in EPOS and SPADE
Rule based paradigm in MERLIN
ALF
Marvel
EPOS
Triggers in ADELE and MVP-L.
Languages are typically related to process programs whereas instantiation techniques have been used to construct process scripts.
=== Tool support ===
The meta-modeling process is often supported through software tools, called CAME tools (Computer Aided Method Engineering) or MetaCASE tools (Meta-level Computer Assisted Software Engineering tools).
Often the instantiation technique "has been utilised to build the repository of Computer Aided Method Engineering environments".
Example tools for meta-process modeling are:
Maestro II
MetaEdit+
Mentor
== Example: "Multi-model view" ==
Colette Rolland (1999) provides an example of a meta-process model which utilizes the instantiation and assembly technique. In the paper the approach is called "Multi-model view" and was applied on the CREWS-L'Ecritoire method. The CREWS-L'Ecritoire method represents a methodical approach for Requirements Engineering, "the part of the IS development that involves investigating problems and requirements of the users community and developing a specification of the future system, the so-called conceptual schema.".
Besides the CREWS-L'Ecritoire approach, the multi-model view has served as a basis for representing:
(a) the three other requirements engineering approaches developed within the CREWS project, Real World Scenes approach, SAVRE approach for scenario exceptions discovery, and the scenario animation approach
(b) for integrating approaches one with the other and with the OOSE approach
Furthermore, the CREWS-L'Ecritoire utilizes process models and meta-process models in order to achieve flexibility for the situation at hand. The approach is based on the notion of a labelled graph of intentions and strategies called a map as well as its associated guidelines. Together, map (process model) and the guidelines form the method.
The main source of this explanation is the elaboration of Rolland.
=== Process model / map ===
The map is "a navigational structure which supports the dynamic selection of the intention to be achieved next and the appropriate strategy to achieve it"; it is "a process model in which a nondeterministic ordering of intentions and strategies has been included. It is a labelled directed graph with intentions as nodes and strategies as edges between intentions. The directed nature of the graph shows which intentions can follow which one."
The map of the CREWS-L'Ecritoire method looks as follow:
The map consists of goals / intentions (marked with ovals) which are connected by strategies (symbolized through arrows). An intention is a goal, an objective that the application engineer has in mind at a given point of time. A strategy is an approach, a manner to achieve an intention. The connection of two goals with a strategy is also called section.
A map "allows the application engineer to determine a path from Start intention to Stop intention. The map contains a finite number of paths, each of them prescribing a way to develop the product, i.e. each of them is a process model. Therefore the map is a multi-model. It embodies several process models, providing a multi-model view for modeling a class of processes. None of the finite set of models included in the map is recommended 'a priori'. Instead the approach suggests a dynamic construction of the actual path by navigating in the map. In this sense the approach is sensitive to the specific situations as they arise in the process. The next intention and strategy to achieve it are selected dynamically by the application engineer among the several possible ones offered by the map. Furthermore, the approach is meant to allow the dynamic adjunction of a path in the map, i.e. adding a new strategy or a new section in the actual course of the process. In such a case guidelines that make available all choices open to handle a given situation are of great convenience. The map is associated to such guidelines".
=== Guidelines ===
A guideline "helps in the operationalisation of the selected intention"; it is "a set of indications on how to proceed to achieve an objective or perform an activity." The description of the guidelines is based on the NATURE project's contextual approach and its corresponding enactment mechanism.
Three types of guidelines can be distinguished:
Intention Selection Guidelines (ISG) identify the set of intentions that can be achieved in the next step and selects the corresponding set of either IAGs (only one choice for an intention) or SSGs (several possible intentions).
Strategy Selection Guidelines (SSG) guide the selection of a strategy, thereby leading to the selection of the corresponding IAG.
Intention Achievement Guidelines (IAG) aim at supporting the application engineer in the achievement of an intention according to a strategy, are concerned with the tactics to implement these strategies, might offer several tactics, and thus may contain alternative operational ways to fulfil the intention.
In our case, the following guidelines – which correspond with the map displayed above – need to be defined:
Intention Selection Guidelines (ISG)
ISG-1 Progress from Elicit a goal
ISG-2 Progress from Conceptualize a Scenario
ISG-3 Progress from Write a scenario
ISG-4 Progress from Start
Strategy Selection Guidelines (SSG)
SSG-1 Progress to Elicit a goal
SSG-2 Progress to Conceptualize a Scenario
SSG-3 Progress to Write a scenario
SSG-4 Progress to Elicit a goal
SSG-5 Progress to Stop
Intention Achievement Guidelines (IAG)
IAG-1 Elicit a goal with case-based strategy
IAG-2 Elicit a goal with composition strategy
IAG-3 Elicit a goal with alternative strategy
IAG-4 Elicit a goal with refinement strategy
IAG-5 Elicit a goal with linguistic strategy
IAG-6 Elicit a goal with template-driven strategy
IAG-7 Write a scenario with template-driven strategy
IAG-8 Write a scenario in free prose
IAG-9 Conceptualize a Scenario with computer support strategy
IAG-10 Conceptualize a Scenario manually
IAG-11 Stop with completeness strategy
The following graph displays the details for the Intention Achievement Guideline 8 (IAG-8).
=== Meta-process map ===
In the multi-model view as presented in the paper of C. Rolland, the meta-process (the instance of the meta-process model) is "a process for the generation of a path from the map and its instantaneous enactment for the application at hand." While the meta-process model can be represented in many different ways, a map was chosen again as a means to do so. It is not to be mixed up with the map for the process model as presented above.
Colette Rolland describes the meta-model as follows:
(Meta-intentions are in bold, meta-strategies in italic – in green in the map.)
"The Start meta-intention starts the construction of a process by selecting a section in the method map which has map intention Start as source. The Choose Section meta-intention results in the selection of a method map section. The Enact Section meta-intention causes the execution of the method map section resulting from Choose Section. Finally, the Stop meta-intention stops the construction of the application process. This happens when the Enact Section meta-intention leads to the enactment of the method map section having Stop as the target.
As already explained in the previous sections, there are two ways in which a section of a method map can be selected, namely by selecting an intention or by selecting a strategy. Therefore, the meta-intention Choose Section has two meta-strategies associated with it, select intention and select strategy respectively. Once a method map section has been selected by Choose Section, the IAG to support its enactment must be retrieved; this is represented in [the graph] by associating the meta-strategy automated support with the meta-intention, Enact Section."
== Sample process ==
The sample process "Eliciting requirements of a Recycling Machine" is about a method for designing the requirements of recycling facilities. The recycling facilities are meant for customers of a supermarket. The adequate method is obtained through instantiation of the meta-process model on the process model.
The following table displays the stepwise trace of the process to elicit requirements for the recycling machine (from ):
== See also ==
== References == | Wikipedia/Meta-process_modeling |
Model-driven engineering (MDE) is a software development methodology that focuses on creating and exploiting domain models, which are conceptual models of all the topics related to a specific problem. Hence, it highlights and aims at abstract representations of the knowledge and activities that govern a particular application domain, rather than the computing (i.e. algorithmic) concepts.
MDE is a subfield of a software design approach referred as round-trip engineering. The scope of the MDE is much wider than that of the Model-Driven Architecture.
== Overview ==
The MDE approach is meant to increase productivity by maximizing compatibility between systems (via reuse of standardized models), simplifying the process of design (via models of recurring design patterns in the application domain), and promoting communication between individuals and teams working on the system (via a standardization of the terminology and the best practices used in the application domain). For instance, in model-driven development, technical artifacts such as source code, documentation, tests, and more are generated algorithmically from a domain model.
A modeling paradigm for MDE is considered effective if its models make sense from the point of view of a user that is familiar with the domain, and if they can serve as a basis for implementing systems. The models are developed through extensive communication among product managers, designers, developers and users of the application domain. As the models approach completion, they enable the development of software and systems.
Some of the better known MDE initiatives are:
The Object Management Group (OMG) initiative Model-Driven Architecture (MDA) which is leveraged by several of their standards such as Meta-Object Facility, XMI, CWM, CORBA, Unified Modeling Language (to be more precise, the OMG currently promotes the use of a subset of UML called fUML together with its action language, ALF, for model-driven architecture; a former approach relied on Executable UML and OCL, instead), and QVT.
The Eclipse "eco-system" of programming and modelling tools represented in general terms by the (Eclipse Modeling Framework). This framework allows the creation of tools implementing the MDA standards of the OMG; but, it is also possible to use it to implement other modeling-related tools.
== History ==
The first tools to support MDE were the Computer-Aided Software Engineering (CASE) tools developed in the 1980s. Companies like Integrated Development Environments (IDE – StP), Higher Order Software (now Hamilton Technologies, Inc., HTI), Cadre Technologies, Bachman Information Systems, and Logic Works (BP-Win and ER-Win) were pioneers in the field.
The US government got involved in the modeling definitions creating the IDEF specifications. With several variations of the modeling definitions (see Booch, Rumbaugh, Jacobson, Gane and Sarson, Harel, Shlaer and Mellor, and others) they were eventually joined creating the Unified Modeling Language (UML). Rational Rose, a product for UML implementation, was done by Rational Corporation (Booch) responding automation yield higher levels of abstraction in software development. This abstraction promotes simpler models with a greater focus on problem space. Combined with executable semantics this elevates the total level of automation possible. The Object Management Group (OMG) has developed a set of standards called Model-Driven Architecture (MDA), building a foundation for this advanced architecture-focused approach.
== Advantages ==
According to Douglas C. Schmidt, model-driven engineering technologies offer a promising approach to address the inability of third-generation languages to alleviate the complexity of platforms and express domain concepts effectively.
== Tools ==
Notable software tools for model-driven engineering include:
== See also ==
Application lifecycle management (ALM)
Business Process Model and Notation (BPMN)
Business-driven development (BDD)
Domain-driven design (DDD)
Domain-specific language (DSL)
Domain-specific modeling (DSM)
Domain-specific multimodeling
Language-oriented programming (LOP)
List of Unified Modeling Language tools
Model transformation (e.g. using QVT)
Model-based testing (MBT)
Modeling Maturity Level (MML)
Model-based systems engineering (MBSE)
Service-oriented modeling Framework (SOMF)
Software factory (SF)
Story-driven modeling (SDM)
Open API, open source specification for description of models and operations for HTTP interoperation and REST APIc
== References ==
== Further reading ==
David S. Frankel, Model Driven Architecture: Applying MDA to Enterprise Computing, John Wiley & Sons, ISBN 0-471-31920-1
Marco Brambilla, Jordi Cabot, Manuel Wimmer, Model Driven Software Engineering in Practice, foreword by Richard Soley (OMG Chairman), Morgan & Claypool, USA, 2012, Synthesis Lectures on Software Engineering #1. 182 pages. ISBN 9781608458820 (paperback), ISBN 9781608458837 (ebook). https://www.mdse-book.com
da Silva, Alberto Rodrigues (2015). "Model-Driven Engineering: A Survey Supported by a Unified Conceptual Model". Computer Languages, Systems & Structures. 43 (43): 139–155. doi:10.1016/j.cl.2015.06.001.
== External links ==
Model-Driven Architecture: Vision, Standards And Emerging Technologies at omg.org | Wikipedia/Model_Driven_Engineering |
The British Ministry of Defence Architecture Framework (MODAF) was an architecture framework which defined a standardised way of conducting enterprise architecture, originally developed by the UK Ministry of Defence. It has since been replaced with the NATO Architecture Framework.
Initially the purpose of MODAF was to provide rigour and structure to support the definition and integration of MOD equipment capability, particularly in support of network-enabled capability (NEC).
The MOD additionally used MODAF to underpin the use of the enterprise architecture approach to the capture of the information about the business to identify the processes and resources required to deliver the vision expressed in the strategy.
== Overview ==
MODAF was an internationally recognised enterprise architecture framework developed by the MOD to support Defence planning and change management activities. It does this by enabling the capture and presentation of information in a rigorous, coherent and comprehensive way that aids the understanding of complex issues, thereby providing managers with the key factors they should consider when making decisions about changes to the business. It is used extensively in Defence acquisition to support systems engineering, particularly in support of network-enabled capability (NEC), "which is about the coherent integration of sensors, decision-makers, weapon systems and support capabilities to achieve the desired effect".
With the publication of the MOD Information Strategy (MODIS) and its enterprise architecture (EA) sub-strategy, the MOD has recognised the utility of EA to support business improvement. MODAF is central to the use of EA in MOD.
MODAF was managed and maintained by staff working for the MOD's Chief Information Officer (CIO), as part of their role to provide information policy and standards. Additional support is provided by the MOD's System Engineering and Integration Group, as part of their role in developing the System of Systems Approach (SOSA), a common set of principles, rules, and standards to enable the delivery of better interoperability between systems.
The MOD works closely with its international allies to ensure coherence with their architecture frameworks to enable the sharing of information about capabilities fielded in coalition operations in-order to support interoperability. MODAF was developed from the US Department of Defense Architecture Framework (DoDAF) version 1.0, but has been extended and modified to meet MOD requirements by the addition of strategic, acquisition and service-oriented viewpoints and the provision of the M3.
MODAF version 1.0 was released in 2005, following development work by the MODAF Partners, a collaborative team of MOD staff and contractors from a number of industry partners and has been continuously improved since Version 1.0, the latest release, version 1.2.004, was released in May 2010
== History ==
MODAF was initially developed for MOD from two parallel work strands, an MOD-funded research programme undertaken by QinetiQ (formerly part of the Defence Evaluation Research Agency) and a separate DoDAF-based development by MODAF Partners, a consortium of Cornwell Management Consulting (now Serco) and PA Consulting Group with Model Futures providing the technical input, and extended by other key suppliers such as Logica and Vega through work for the MOD Integration Authority (as of April 2008 the System Engineering Integration Group (SEIG)). The draft version of MODAF combined the metamodel developed from the UK MOD funded QinetiQ research programme and the views developed by MODAF Partners. The meta-model was subsequently replaced with the M3 for the released version of MODAF.
== Framework ==
MODAF provides a set of templates (called "Views") that provide a standard notation for the capture of information about a business in order to identify ways to improve the business. Each MODAF View offers a different perspective on the business to support different stakeholder interests, presented in a format, usually graphical, that aids understanding of how a business operates.
The Views are grouped into seven Viewpoints (Note that this is a different use of the term 'viewpoint' from ISO/IEC/IEEE:42010 as a specification for a single view):
Strategic Viewpoint (StV) defines the desired business outcome, and what capabilities are required to achieve it;
Operational Viewpoint (OV) defines (in abstract rather than physical terms) the processes, information and entities needed to fulfil the capability requirements;
Service Orientated Viewpoint (SOV) describes the services, (i.e. units of work supplied by providers to consumers), required to support the processes described in the operational Views;
Systems Viewpoint (SV) describes the physical implementation of the Operational and Service Orientated Views and, thereby, define the solution;
Acquisition Viewpoint (AcV) describes the dependencies and timelines of the projects that will of deliver the solution;
Technical Viewpoint (TV) defines the standards that are to be applied to the solution;
All Viewpoint (AV) provides a description and glossary of the contents of the architecture.
The relationship between the data in the MODAF Views is defined in the MODAF Meta Model, known as the M3. The M3 provides a logical structure for the storage of the data in a database and subsequently provides the necessary coherence for the data to be shared with other MODAF architectures.
== Functionality of framework ==
In MOD, MODAF has primarily been used in acquisition domains, programmes and delivery teams to support the delivery of military capability, particularly NEC. A number of MODAF architectures directly support operations in Afghanistan. In addition, MODAF is widely used by its industry partners, such as BAE Systems, Thales, Lockheed Martin, Boeing and Serco. It is also used by other government departments and agencies, such as GCHQ, and external bodies such as the National Air Traffic Services (NATS). MODAF is used by the Swedish Armed Forces to support the development of military capability, and it has been adapted by NATO to form the core of the NATO Architecture Framework (NAF).
== Harmonisation ==
MODAF will continue in its current form for the foreseeable future. However, MOD is working closely with the United States Department of Defense, the Canadian Department of National Defence, the Australian Department of Defence, and the Swedish Armed Forces to develop the International Defence Enterprise Architecture Specification (IDEAS). Although the focus for IDEAS has been the ability to provide a mechanism to better enable the exchange of architecture information between Nations, the IDEAS Management Group are also actively considering how their architecture frameworks should converge, perhaps into a single unified architecture framework.
== Tools and tooling ==
The MOD is "agnostic" about which software tools should or should not be used to develop MODAF architecture descriptions. The key requirement is that they should correctly implement the M3 with downloads in Sparx Systems Enterprise Architect; HTML and XMI formats. to provide a coherent structure against which architecture information can be exchanged. A number of tools offer this functionality.
The MOD has been working with the Object Management Group (OMG) to develop the Unified Profile for DoDAF and MODAF (UPDM), an abstract UML profile that implements the MODAF Metamodel (M3), itself an abstract UML profile for UML modelling tools, as well as the DODAF metamodel (DM2) . It is based on the Unified Modelling Language (UML) and extends the Systems Modelling Language (SysML) UML profile.
== Terminology ==
An "architectural framework" or "architecture framework" is a specification of how to organise and present architectural models. An architectural framework consists of a standard set of views, which each have a specific purpose.
An "architectural description" is a contiguous, coherent model of an enterprise. An architectural description comprises "architectural products". MODAF is not an architectural description.
A "view" is a specification of a way to present an aspect of the enterprise. Views are defined with one or more purposes in mind - e.g., showing the logical topology of the enterprise, describing a process model, defining a data model, etc.
An "architectural product" is a model of a particular aspect of the enterprise. An architectural product conforms to a "view".
A "viewpoint" is a collection of "views." Viewpoints are usually categorized by domain - e.g., in MODAF there are seven viewpoints.
== Applications ==
Although originally developed by the UK Ministry of Defence, MODAF is the standard architecture framework for other organisations, such as:
GCHQ
Swedish Armed Forces
BAE Systems use MODAF on a number of internal programmes, most notably their TRAiDE environment
EADS use MODAF as part of the Modelling and Simulation process for NetCOS their Synthetic Environment
Thales Group use MODAF in their work for UK MOD
BAA Limited
In addition, revision 3 of the NATO Architecture Framework (NAF) is identical to MODAF at its core but extends the framework by adding views for Bandwidth Analysis, SOA and Standards configurations.
MODAF is also the basis for other frameworks such as TRAK, a domain-free framework, which is based on MODAF 1.2
== References ==
== External links ==
MOD Site for MODAF (note the standard is now WITHDRAWN)
modaf.com - the MODAF user site | Wikipedia/MODAF_Meta-Model |
A metamodel is a model of a model, and metamodeling is the process of generating such metamodels. Thus metamodeling or meta-modeling is the analysis, construction, and development of the frames, rules, constraints, models, and theories applicable and useful for modeling a predefined class of problems. As its name implies, this concept applies the notions of meta- and modeling in software engineering and systems engineering. Metamodels are of many types and have diverse applications.
== Overview ==
A metamodel/ surrogate model is a model of the model, i.e. a simplified model of an actual model of a circuit, system, or software-like entity. Metamodel can be a mathematical relation or algorithm representing input and output relations. A model is an abstraction of phenomena in the real world; a metamodel is yet another abstraction, highlighting the properties of the model itself. A model conforms to its metamodel in the way that a computer program conforms to the grammar of the programming language in which it is written. Various types of metamodels include polynomial equations, neural networks, Kriging, etc. "Metamodeling" is the construction of a collection of "concepts" (things, terms, etc.) within a certain domain. Metamodeling typically involves studying the output and input relationships and then fitting the right metamodels to represent that behavior.
Common uses for metamodels are:
As a schema for semantic data that needs to be exchanged or stored
As a language that supports a particular method or process
As a language to express additional semantics of existing information
As a mechanism to create tools that work with a broad class of models at run time
As a schema for modeling and automatically exploring sentences of a language with applications to automated test synthesis
As an approximation of a higher-fidelity model for use when reducing time, cost, or computational effort is necessary
Because of the "meta" character of metamodeling, both the praxis and theory of metamodels are of relevance to metascience, metaphilosophy, metatheories and systemics, and meta-consciousness. The concept can be useful in mathematics, and has practical applications in computer science and computer engineering/software engineering. The latter are the main focus of this article.
== Topics ==
=== Definition ===
In software engineering, the use of models is an alternative to more common code-based development techniques. A model always conforms to a unique metamodel. One of the currently most active branches of Model Driven Engineering is the approach named model-driven architecture proposed by OMG. This approach is embodied in the Meta Object Facility (MOF) specification.
Typical metamodelling specifications proposed by OMG are UML, SysML, SPEM or CWM. ISO has also published the standard metamodel ISO/IEC 24744. All the languages presented below could be defined as MOF metamodels.
=== Metadata modeling ===
Metadata modeling is a type of metamodeling used in software engineering and systems engineering for the analysis and construction of models applicable and useful to some predefined class of problems. (see also: data modeling).
=== Model transformations ===
One important move in model-driven engineering is the systematic use of model transformation languages. The OMG has proposed a standard for this called QVT for Queries/Views/Transformations. QVT is based on the meta-object facility (MOF). Among many other model transformation languages (MTLs), some examples of implementations of this standard are AndroMDA, VIATRA, Tefkat, MT, ManyDesigns Portofino.
=== Relationship to ontologies ===
Meta-models are closely related to ontologies. Both are often used to describe and analyze the relations between concepts:
Ontologies: express something meaningful within a specified universe or domain of discourse by utilizing grammar for using vocabulary. The grammar specifies what it means to be a well-formed statement, assertion, query, etc. (formal constraints) on how terms in the ontology’s controlled vocabulary can be used together.
Meta-modeling: can be considered as an explicit description (constructs and rules) of how a domain-specific model is built. In particular, this comprises a formalized specification of the domain-specific notations. Typically, metamodels are – and always should follow - a strict rule set. "A valid metamodel is an ontology, but not all ontologies are modeled explicitly as metamodels."
=== Types of metamodels ===
For software engineering, several types of models (and their corresponding modeling activities) can be distinguished:
Metadata modeling (MetaData model)
Meta-process modeling (MetaProcess model)
Executable meta-modeling (combining both of the above and much more, as in the general purpose tool Kermeta)
Model transformation language (see below)
Polynomial metamodels
Neural network metamodels
Kriging metamodels
Piecewise polynomial (spline) metamodels
Gradient-enhanced kriging (GEK)
=== Zoos of metamodels ===
A library of similar metamodels has been called a Zoo of metamodels.
There are several types of meta-model zoos. Some are expressed in ECore. Others are written in MOF 1.4 – XMI 1.2. The metamodels expressed in UML-XMI1.2 may be uploaded in Poseidon for UML, a UML CASE tool.
== See also ==
== References ==
== Further reading ==
Saraju Mohanty (2015). "Chapter 12 Metamodel-Based Fast AMS-SoC Design Methodologies". Nanoelectronic Mixed-Signal System Design. McGraw-Hill. ISBN 978-0071825719.
Booch, G., Rumbaugh, J., Jacobson, I. (1999), The Unified Modeling Language User Guide, Redwood City, CA: Addison Wesley Longman Publishing Co., Inc.
J. P. van Gigch, System Design Modeling and Metamodeling, Plenum Press, New York, 1991
Gopi Bulusu, hamara.in, 2004 Model Driven Transformation
P. C. Smolik, Mambo Metamodeling Environment, Doctoral Thesis, Brno University of Technology. 2006
Gonzalez-Perez, C. and B. Henderson-Sellers, 2008. Metamodelling for Software Engineering. Chichester (UK): Wiley. 210 p. ISBN 978-0-470-03036-3
M.A. Jeusfeld, M. Jarke, and J. Mylopoulos, 2009. Metamodeling for Method Engineering. Cambridge (USA): The MIT Press. 424 p. ISBN 978-0-262-10108-0, Open access via http://conceptbase.sourceforge.net/2021_Metamodeling_for_Method_Engineering.pdf
G. Caplat Modèles & Métamodèles, 2008 - ISBN 978-2-88074-749-7 (in French)
Fill, H.-G., Karagiannis, D., 2013. On the Conceptualisation of Modelling Methods Using the ADOxx Meta Modelling Platform, Enterprise Modelling and Information Systems Architectures, Vol. 8, Issue 1, 4-25. | Wikipedia/Meta_model |
Object process methodology (OPM) is a conceptual modeling language and methodology for capturing knowledge and designing systems, specified as ISO/PAS 19450. Based on a minimal universal ontology of stateful objects and processes that transform them, OPM can be used to formally specify the function, structure, and behavior of artificial and natural systems in a large variety of domains.
OPM was conceived and developed by Dov Dori. The ideas underlying OPM were published for the first time in 1995. Since then, OPM has evolved and developed.
In 2002, the first book on OPM was published, and on December 15, 2015, after six years of work by ISO TC184/SC5, ISO adopted OPM as ISO/PAS 19450. A second book on OPM was published in 2016.
Since 2019, OPM has become a foundation for a Professional Certificate program in Model-Based Systems Engineering - MBSE at EdX. Lectures are available as web videos on Youtube.
== Overview ==
Object process methodology (OPM) is a conceptual modeling language and methodology for capturing knowledge and designing systems. Based on a minimal universal ontology of stateful objects and processes that transform them, OPM can be used to formally specify the function, structure, and behavior of artificial and natural systems in a large variety of domains. Catering to human cognitive abilities, an OPM model represents the system under design or study bimodally in both graphics and text for improved representation, understanding, communication, and learning.
In OPM, an object is anything that does or does not exist. Objects are stateful—they may have states, such that at each point in time, the object is at one of its states or in transition between states. A process is a thing that transforms an object by creating or consuming it, or by changing its state.
OPM is bimodal; it is expressed both visually/graphically in object-process diagrams (OPD) and verbally/textually in Object-Process Language (OPL), a set of automatically generated sentences in a subset of English. A patented software package called OPCAT, for generating OPD and OPL, is freely available.
== History ==
The shift to the object-oriented (OO) paradigm for computer programming languages, which occurred in the 1980s and 1990s, was followed by the idea that programming should be preceded by object-oriented analysis and design of the programs, and, more generally, the systems those programs represent and serve. Thus, in the early 1990s, over 30 object-oriented analysis and design methods and notations flourished, leading to what was known as the "methods war".
Around that time, in 1991, Dov Dori, who then joined Technion – Israel Institute of Technology as faculty said in his 2016 book Model-Based Systems Engineering with OPM and SysML that he:
realized that just as the procedural approach to software was inadequate, so was the “pure” OO approach, which puts objects as the sole “first class” citizens, with “methods” (or “services”) being their second-class subordinate procedures.
Dori published the first paper on OPM in 1995.
In 1997, Unified Modeling Language (UML), by the Object Management Group (OMG), became the de facto standard for software design. UML 1.1 was submitted to the OMG in August 1997 and adopted by the OMG in November 1997.
The first book on OPM, Object-Process Methodology: a Holistic Systems Paradigm, was published in 2002, and OPM has since been applied in many domains.
In August 2014, the ISO adopted OPM as ISO/PAS 19450.
A second book on OPM, which also covers SysML, was published in 2016.
== Design ==
Object-Process Methodology (OPM) is a systems modeling paradigm that integrates two aspects inherent in any system: its structure and its behavior. Structure is represented via objects and structural relations among them, such as aggregation-participation (whole-part relation) and generalization-specialization ("is-a" relation). Behavior is represented by processes and how they transform objects: How they create or consume objects, or how they change the states of an object.: 2
OPM offers a way to model systems of almost any domain, be it artificial or natural.: x
=== Modeling ===
OPM consists of object process diagramׂs (OPD) and a corresponding set of sentences in a subset of English, called Object Process Language (OPL). OPL is generated automatically by OPCAT, a software tool that supports modeling in OPM.
Object process diagram (OPD)
OPD is the one and only kind of diagram of OPM. This uniqueness of diagram kind is a major contributor to OPM's simplicity, and it is in sharp contrast to UML, which has 14 kinds of diagrams, and to SysML, which has nine such kinds. An OPD graphically describes objects, processes and links among them. Links can be structural and procedural. Structural links connect objects to objects or processes to processes, expressing the static system aspect—how the system is structured. Procedural links connect objects to processes, expressing the dynamic system aspect—how the system changes over time. The entire system is represented by a set of hierarchically organized OPDs, such that the root OPD, called the systems diagram (SD), specifies the "bird's eye" view of the system, and lower-level OPDs specify the system in increasing levels of detail. All the OPDs in the system's OPD set are "aware" of each other, with each showing the system, or part of it, at some level of detail. The entire system is specified in its entirety by the union of the details (model facts) appearing in all the OPDs.
Object process language (OPL)
Each OPD construct (i.e., two or more things connected by one or more links) is translated to a sentence in OPL—a subset of natural English. The power of OPL lies in the fact that it is readable by humans but also interpretable by computers. These are the stages where the most important design decisions are made. The graphics-text bimodality of OPM makes it suitable to jointly model requirements by a team that involves both the customer or his domain expert on one hand, and the system architect, modelers, and designers on the other hand.: 3
OPM model animated simulation
OPM models are not just static graphical and textual representations of the system—they are also executable. A correct OPM model constructed in OPCAT can be simulated by animating it, visually expressing how the system behaves over time to achieve its function at all detail levels. An incorrect OPM model will not execute all the way through, and will indicate where and why it is stuck, effectively serving as a visual debugger.
=== Development ===
In his foreword to Dori's book Model-Based Systems Engineering with OPM and SysML, Edward F. Crawley said:
OPM semantics was originally geared towards systems engineering, as it can model information, hardware, people, and regulation. However, in recent years OPM started to serve also researchers in molecular biology, yielding new published findings related to the mRNA lifecycle. This is a clear indication of the universality of the object-and-process ontology.: vi
== Basics ==
OPM has two main parts: the language and the methodology. The language is bimodal—it is expressed in two complementary ways (modalities): the visual, graphical part—a set of one or more object-process diagrams (OPDs), and a corresponding textual part—a set of sentences in object-process language (OPL), which is a subset of English.
The top-level OPD is the system diagram (SD), which provides the context for the system's function. For man-made systems this function is expected to benefit a person or a group of people—the beneficiary. The function is the main process in SD, which also contains the objects involved in this process: the beneficiary, the operand (the object upon which the process operates), and possibly the attribute whose value the process changes.
OPM graphical elements are divided into entities, expressed as closed shapes, and relations, expressed as links that connect entities.
=== Entities ===
Entities are the building blocks of OPM. They include objects and processes, collectively called things, and object states.
Object
Associations among objects constitute the object structure of the system being modeled. In OPL text, the object name shall appear in bold face with capitalization of each word.
Object state
An object state is a particular situation classification of an object at some point during its lifetime. At every point in time, the object is in one of its states or in transition between two of its states—from its input state to its output state.
Process
A process is an expression of the pattern of transformation of objects in the system. A process does not exist in isolation; it is always associated with and occurs or happens to one or more objects. A process transforms objects by creating them, consuming them, or changing their state. Thus, processes complement objects by providing the dynamic, behavioral aspect of the system. In OPL text, the process name shall appear in bold face with capitalization of each word.
=== Links ===
Structural link
A structural links defines a structural relation. A structural relation shall specify an association that persists in the system for at least some interval of time.
Procedural link
A procedural link defines procedural relation. A procedural relation shall specify how the system operates to attain its function, designating time dependent or conditional triggering of processes, which transform objects.
Event and condition
The Event-Condition-Action paradigm provides the OPM operational semantics and flow of control. An event is a point in time at which an object is created (or appears to be created from the system's perspective) or an object enters a specified state. At runtime, this process triggering initiates evaluation of the process precondition. Thus, starting a process execution has two prerequisites: (1) a triggering event, and (2) satisfaction of a precondition.
Once the event triggers a process, the event ceases to exist.
== Syntax and semantics ==
=== Things ===
Objects and processes are symmetric in many regards and have much in common in terms of relations, such as aggregation, generalization, and characterization.
To apply OPM in a useful manner, the modeler has to make the essential distinction between objects and processes, as a prerequisite for successful system analysis and design. By default, a noun shall identify an object.
=== Thing generic attributes ===
OPM things have three generic attributes:
Perseverance
Essence
Affiliation
OPM thing generic attributes have the following default values:
The default value of the Affiliation generic attribute of a thing is systemic.
System essence shall be the primary essence of the system. Like thing essence, its values are informatical and physical. Information systems, in which the majority of things are informatical, shall be primarily informatical, while systems in which the majority of things are physical shall be primarily physical.
The default value of the Essence generic attribute of a thing in a primarily informatical [physical] system shall be informatical [physical].
=== Object states ===
Stateful and stateless objects
Dov Dori explains in Model-Based Systems Engineering with OPM and SysML that "An object state is a possible situation in which an object may exist. An object state has meaning only in the context of the object to which it belongs." A stateless object shall be an object that has no specification of states. A stateful object shall be an object for which a set of permissible states are specified. In a runtime model, at any point in time, any stateful object instance is at a particular permissible state or in transition between two states.
Attribute values
An attribute is an object that characterizes a thing. An attribute value is a specialization of state in the sense that a value is a state of an attribute: an object has an attribute, which is a different object, to which that value is assigned for some period of time during the existence of the object exhibiting that attribute.
Object state representation
A state is graphically defined by a labelled, rounded-corner rectangle placed inside the owning object. It can not live without an object. In OPL text, the state name shall appear in bold face without capitalization.
Initial, default, and final states
Initial, final, and default state representation
A state that is initial is graphically defined by a state representation with thick contour. A state that is final is graphically defined by a state representation with double contour. A state that is default is graphically defined by a state representation with an open arrow pointing diagonally from the left. The corresponding OPL sentences shall include explicit indicators for an initial, final or default state.
=== Links ===
==== Procedural links ====
A procedural link is one of three kinds:
Transforming link, which connects a transformer (an object that the process transforms) or its state with a process to model object transformation, namely generation, consumption, or state change of that object as a result of the process execution.
Enabling link, which connects an enabler (an object that enables the process occurrence but is not transformed by that process) or its state, to a process, which enables the occurrence of that process.
Control link, which is a procedural (transforming or enabling) link with a control modifier—the letter e (for event) or c (for condition), which adds semantics of a control element. The letter e signifies an event for triggering the linked process, while the letter c signifies a condition for execution of the linked process, or connection of two processes denoting invocation, or exception.
Procedural link uniqueness OPM principle
A process needs to transform at least one object. Hence, a process shall be connected via a transforming link to at least one object or object state. At any particular extent of abstraction, an object or any one of its states shall have exactly one role as a model element with respect to a process to which it links: the object may be a transformee or an enabler. Additionally, it can be a trigger for an event (if it has the control modifier e), or a conditioning object (if it has the control modifier c), or both.
State-specified procedural links
A state-specified procedural link is a detailed version of its procedural link counterpart in that rather than connecting a process to an object, it connects a process to a specific state of that object.
Transforming links
The three kinds of transforming links are:
Consumption link: Graphically, an arrow with a closed arrowhead pointing from the consumee to the consuming process defines the consumption link. By assumption, the consumed object disappears as soon as the process begins execution. The syntax of a consumption link OPL sentence is: Processing consumes Consumee.
Effect link: A transforming link specifying that the linked process affects the linked object, which is the affectee, i.e., the process causes some unspecified change in the state of the affectee. Graphically, a bidirectional arrow with two closed arrowheads, one pointing in each direction between the affecting process and the affected object, shall define the effect link. The syntax of an effect link OPL sentence is: Processing affects Affectee.
Result link: Graphically, an arrow with a closed arrowhead pointing from the creating process to the resultee shall define a result link. The syntax of a result link OPL sentence is: Processing yields Resultee.
Enabling links
An enabling link is a procedural link specifying an enabler for a process—an object that must be present for that process to occur, but the existence and state of that object after the process is complete are the same as just before the process began. The two kinds of enabling links are:
Agent and agent link: A human or a group of humans capable of intelligent decision-making, who enable a process by interacting with the system to enable or control the process throughout execution. Graphically, a line with a filled circle ("black lollipop") at the terminal end extending from the agent object to the process it enables defines an agent link. The syntax of an agent link OPL sentence is: Agent handles Processing.
Instrument and instrument link: An inanimate or otherwise non-decision-making enabler of a process that cannot start or take place without the existence and availability of the instrument.
State-specified transforming links
State-specified consumption link: A consumption link that originates from a particular state of the consumee, meaning that the consumee must be in that state for it to be consumed by the process to which it is linked. Graphically, an arrow with a closed arrowhead pointing from the particular object state to the process, which consumes the object, defines the state-specified consumption link.
State-specified result link: A result link that terminates at a specific state of the resultee, meaning that the resultee shall be in that resultant state upon its construction. Graphically, an arrow with a closed arrowhead pointing from the process to the particular object state defines the state-specified result link. The syntax OPL sentence is: Process yields qualified-state Object.
State-specified effect links:
Input and output effect links- An input link is the link from the object's input state to the transforming process, while the output link is the link from the transforming process to the object's output state.
Input-output-specified effect link: A pair of effect links, where the input link originates from a particular state of the affectee and the output link originates from that process and terminates at the output state of the same affectee. Graphically, a pair of arrows with a closed arrowhead from the input state of the affectee to the affecting process and a similar arrow from that process to the state of the affectee at process terminates defines the input-output-specified effect link. The syntax OPL sentence is: Process changes Object from input-state to output-state.
Input-specified effect link: A pair of effect links, where the input link originates from a particular state of the affectee and the output link originates from that process and terminates at the affectee without specifying a particular state. Graphically, a pair of arrows consisting of an arrow with a closed arrowhead from a particular state—the input state—of the affectee to the process, and a similar arrow from that process to the affectee but not to any one of its states defines the input-specified effect link. The syntax OPL sentence is: Process changes Object from input-state.
Output-specified effect link: A pair of effect links, where the input (source) link originates from an affectee, and the output link originates from the process and terminates at the output (destination, resultant) state of the same affectee. Graphically, a pair of arrows consisting of an arrow with a closed arrowhead from the affectee, but not from any one of its states, to the affecting process, and a similar arrow from that process to a particular state of that affectee— the output state— defines the output-specified effect link.
State-specified enabling links
Originate from a specific qualifying state and terminate at a process, meaning that the process may occur if and only if the object exists at the state from which the link originates.
State-specified agent link: Graphically, a line with a filled circle ("black lollipop") at the terminal end extending from the qualifying state of the agent object to the process it enables defines a state-specified agent link. The syntax OPL sentence is: Qualifying-state Agent handles Processing.
State-specified instrument link: An instrument link that originates from a specific qualifying state of the instrument. Graphically, a line with an empty circle ("white lollipop") at the terminal end extending from the qualifying state of the instrument object to the process it enables defines a state-specified instrument link. The syntax OPL sentence is: Processing requires qualifying-state instrument.
==== Event-Condition-Action control ====
Preprocess object set and process precondition
In order for an OPM process to start executing once it has been triggered, it needs a set of objects comprising one or more consumes, some possibly at specific states, and/or affects, collectively called the preprocess object set. At instance-level execution, each consume B in the pre-process object set of process P shall be consumed and stop to exist at the beginning of the lowest level sub-process of P which consumes B. Each affected (an object whose state changes) B in the preprocess object set of process P shall exit from its input state at the beginning of the lowest level sub-process of P.
Post-process object set and process post-condition
A set of objects, comprising one or more results, some possibly at given states, and/or affects, collectively called the post-process object set, shall result from executing a process and carrying out the transformations associated with its execution. Each resulted B in the post process object set of process P shall be created and start to exist at the end of the lowest level sub process of P which yields B. Each affected B in the post-process object set of process P shall enter its output state at the end of the lowest level sub-process of P.
==== Control links ====
An event link and a condition link express an event and a condition, respectively. Control links occur either between an object and a process or between two processes.
Event links
Triggering a process initiates an attempt to execute the process, but it does not guarantee success of this attempt. The triggering event forces an evaluation of the process' precondition for satisfaction, which, if and only if satisfied, allows process execution to proceed and the process becomes active. Regardless of whether the precondition is satisfied or not, the event will be lost. If the precondition is not satisfied, process execution will not occur until another event activates the process and a successful precondition evaluation allows the process to execute.
Basic transforming event links: A consumption event link is a link between an object and a process, which an instance of the object activates.
Consumption event link: Graphically, an arrow with a closed arrowhead pointing from the object to the process with the small letter e (for event). The syntax of a consumption event link OPL sentence is: Object triggers Process, which consumes Object.
Effect event link: Graphically, a bidirectional arrow with closed arrowheads at each end between the object and the process with a small letter e (for event). The syntax of an effect event link OPL sentence is: Object triggers Process, which affects Object.
Basic enabling event links:
Agent event link: An agent event link is an enabling link from an agent object to the process that it activates and enables. Graphically, a line with a filled circle ("black lollipop") at the terminal end extending from an agent object to the process it activates and enables with a small letter e (for event). The syntax of an agent event link OPL sentence is: Agent triggers and handles Process.
Instrument event link: Graphically, a line with an empty circle ("white lollipop') at the terminal end extending from the instrument object to the process it activates and enables with a small letter e (for event). The syntax of an instrument event link OPL sentence is: Instrument triggers Process, which requires Instrument.
State-specified transforming event links:
State-specified consumption event link: A state-specified consumption event link is a consumption link that originates from a specific state of an object and terminates at a process, which an instance of the object activates. Graphically, an arrow with a closed arrowhead pointing from the object state to the process with the small letter e (for event). The syntax of a state-specified consumption event link OPL sentence is: Specified-state Object triggers Process, which consumes Object.
Input-output-specified effect event link: An input-output-specified effect event link is an input-output-specified effect link with the additional meaning of activating the affecting process when the object enters the specified input state. Graphically, the input-output-specified effect link with a small letter e (for event). The syntax of an input-output specified effect event link OPL sentence is: Input-state Object triggers Process, which changes Object from input-state to output-state.
Input-specified effect event link: An input-specified effect event link is an input-specified effect link with the additional meaning of activating the affecting process when the object enters the specified input state. Graphically, the input-specified effect link with a small letter e (for event. The syntax of an input-specified effect event link OPL sentence is: Input-state Object triggers Process, which changes Object from input-state.
Output-specified effect event link: An output-specified effect event link is an output-specified effect link with the additional meaning of activating the affecting process when the object comes into existence. Graphically, the output-specified effect link with a small letter e (for event). The syntax of an output-specified effect event link OPL sentence is: Object in any state triggers Process, which changes Object to destination-state
State-specified agent event link:
State-specified agent event link: A state-specified agent event link is a state-specified agent link with the additional meaning of activating the process when the agent enters the specified state. Graphically, the state-specified agent link with a small letter e (for event). The syntax of a state-specified agent event link OPL sentence is: Qualifying-state Agent triggers and handles Processing".
State-specified instrument event link: A state-specified instrument event link is a state-specified instrument link with the additional meaning of activating the process when the instrument enters the specified state. Graphically, the state-specified instrument link with a small letter e (for event). The syntax of a state-specified instrument event link OPL sentence is: Qualifying-state Instrument triggers Processing, which requires qualifying-state Instrument."
Invocation links
Process invocation
Self-invocation link
Implicit invocation link: Implicit invocation occurs upon sub-process termination within the context of an in-zoomed process, at which time the sub-process invokes the one(s) immediately below it. Graphically, there is no link between the invoking and the invoked sub-processes; their relative heights within the in-zoom context of their ancestor process implies this semantics.
Condition links
A condition link is a procedural link between a source object or object state and a destination process that provides a bypass mechanism.
Condition consumption link: A condition consumption link is a condition link from an object to a process, meaning that if in run-time an object instance exists, then the process precondition is satisfied, the process executes and consumes the object instance. Graphically, an arrow with a closed arrowhead pointing from the object to the process with the small letter c (for condition) near the arrowhead shall denote a condition consumption link.
Condition effect link: However, if that object instance does not exist, then the process precondition evaluation fails and the control skips the process. Graphically, a bidirectional arrow with two closed arrowheads, one pointing in each direction between the affected object and the affecting process, with the small letter c (for condition) near the process end of the arrow.
Condition agent link: Graphically, a line with a filled circle ('black lollipop") at the terminal end extending from an agent object to the process it enables, with the small letter c (for condition) near the process end. The syntax of the condition agent link OPL sentence is: Agent handles Process if Agent exists, else Process is skipped.
Condition instrument link: Graphically, a line with an empty circle ("white lollipop") at the terminal end, extending from an instrument object to the process it enables, with the small letter c (for condition) near the process end, shall denote a condition instrument link. The syntax of the condition instrument link OPL sentence shall be: Process occurs if Instrument exists, else Process is skipped.
Condition state-specified consumption link: A condition state-specified consumption link is a condition consumption link that originates from a specified state of an object and terminates at a process, meaning that if an object instance exists in the specified state and the rest of the process precondition is satisfied, then the process executes and consumes the object instance. Graphically, an arrow with a closed arrowhead pointing from the object qualifying state to the process with the small letter c (for condition) near the arrowhead.
Condition input-output-specified effect link: A condition input-output-specified effect link is an input-output specified effect link with the additional meaning that if at run-time an object instance exists and it is in the process input state (and assuming that the rest of the process precondition is satisfied), then the process executes and affects the object instance. Graphically, the condition input-output-specified effect link with the small letter c (for condition) near the arrowhead of the input. The syntax of the condition input-output-specified effect link OPL sentence is: Process occurs if Object is input-state, in which case Process changes Object from input-state to output-state, otherwise Process is skipped.
Condition input-specified effect link: A condition input specified effect link is an input-specified effect link with the additional meaning that if at run-time an object instance exists in the specified input state and the rest of the process precondition is satisfied, then the process executes and affects the object instance by changing its state from its input state to an unspecified state. However, if that object instance does not exist at the input state, then the process precondition evaluation fails and the control skips the process. Graphically, the condition input-specified effect link with the small letter c (for condition) near the arrowhead of the input link. The syntax of a condition input-specified effect link OPL sentence is: Process occurs if Object is input state, in which case Process changes Object from input-state, otherwise Process is skipped.
Condition output-specified effect link: A condition output-specified effect link is an output-specified effect link with the additional meaning that if at run-time an object instance exists and the rest of the process precondition is satisfied, then the process executes and affects the object instance by changing its state to the specified output-state. However, if that object instance does not exist, then the process precondition evaluation fails and the control skips the process. Graphically, the condition output-specified effect link with the small letter c (for condition) near the arrowhead of the input link. The syntax of the condition output-specified effect OPL sentence is: Process occurs if Object exists, in which case Process changes Object to output-state, otherwise Process is skipped.
Condition state-specified agent link: The syntax of the condition state-specified agent link OPL sentence is: Agent handles Process if Agent is qualifying-state, else Process is skipped.
Condition state-specified instrument link
More information and examples can be found in Model-Based Systems Engineering with OPM and SysML, Chapter 13 "The Dynamic System Aspect".
==== Structural links ====
Structural links specify static, time-independent, long-lasting relations in the system. A structural link connects two or more objects or two or more processes, but not an object and a process, except in the case of an exhibition-characterization link.
Unidirectional tagged structural link
Has a user-defined semantics regarding the nature of the relation from one thing to the other. Graphically, an arrow with an open arrowhead. Along the tagged structural link, the modeler should record a meaningful tag in the form of a textual phrase that expresses the nature of the structural relation between the connected objects (or processes) and makes sense when placed in the OPL sentence whose syntax follows.
Unidirectional null-tagged structural link
A unidirectional tagged structural link with no tag. In this case, the default unidirectional tag is used. The modeler has the option of setting the default unidirectional tag for a specific system or a set of systems. If no default is defined, the default tag is "relates to".
Bidirectional tagged structural link
When the tags in both directions are meaningful and not just the inverse of each other, they may be recorded by two tags on either side of a single bidirectional tagged structural link. The syntax of the resulting tagged structural link is two separate tagged structural link OPL sentences, one for each direction. Graphically, a line with harpoon shaped arrowheads on opposite sides at both ends of the link's line shall.
Reciprocal tagged structural link
A bidirectional tagged structural link with one tag. In either case, reciprocity indicate that the tag of a bidirectional structural link has the same semantics for its forward and backward directions. When no tag appears, the default tag shall be "are related". The syntax of the reciprocal tagged structural link with only one tag shall be: Source-thing and destination thing are reciprocity-tag. The syntax of the reciprocal tagged structural link with no tag is: Source thing and Destination-thing are related.
Fundamental structural relations
The most prevalent structural relations among OPM things and are of particular significance for specifying and understanding systems. Each of the fundamental relations is elaborate or refine one OPM thing, the source thing, or refinee, into a collection of one or more OPM things, the destination thing or things, or refineables.
Aggregation-participation link
A refinee—the whole—aggregates one or more other refineables—the parts. Graphically, a black solid (filled in) triangle with its apex connecting by a line to the whole and the parts connecting by lines to the opposite horizontal base shall denote the aggregation-participation relation link.
Exhibition-characterization link
A thing exhibits, or is characterized by, another thing. The exhibition-characterization relation binds a refinee—the exhibitor—with one or more refineables, which shall identify features that characterize the exhibitor Graphically, a smaller black triangle inside a larger empty triangle with that larger triangle's apex connecting by a line to the exhibitor and the features connecting to the opposite (horizontal) base defines the exhibition-characterization relation link.
Generalization-specialization and inheritance
These are structural relations which provide for abstracting any number of objects or process classes into superclasses, and assigning attributes of superclasses to subordinate classes.
Generalization-specialization link
Inheritance through specialization
Specialization restriction through discriminating attribute: A subset of the possible values of an inherited attribute may restrict the specialization.
Classification-instantiation and system execution
Classification-instantiation link: A source thing, which is an object class or a process class connect to one or more destination things, which are valued instances of the source thing's pattern, i.e. the features specified by the pattern acquire explicit values. This relation provides the modeler with an explicit mechanism for expressing the relationship between a class and its instances created by the provision of feature values. Graphically, a small black circle inside an otherwise empty larger triangle with apex connecting by a line to the class thing and the instance things connecting by lines to the opposite base defines the classification-instantiation relation link. The syntax is: Instance-thing is an instance of Class-thing.
Instances of object class and process class
State-specified structural relations and links
State-specified characterization relation and link: An exhibition-characterization relation from a specialized object that exhibits a value for a discriminating attribute of that object, meaning that the specialized object shall have only that value. Graphically, the exhibition-characterization link triangular symbol, with its apex connecting to the specialized object and its opposite base connecting to the value, defines the state-specified characterization relation. The syntax is: Specialized-object exhibits value-name Attribute-Name.
State-specified tagged structural relations and links: A structural relation between a state of an object or value of an attribute and another object or its state or value, meaning that these two entities are associated with the tag expressing the semantics of the association. In case of a null tag (i.e., the tag is not specified), the corresponding default null tag is used. Three groups of state-specified tagged structural relations exist: (1) source state-specified tagged structural relation, (2) destination state-specified tagged structural relation, (3) source-and-destination state-specified tagged structural relation. Each of these groups includes the appropriate unidirectional, bidirectional, and reciprocal tagged structural relation, giving rise to seven kinds of state-specified tagged structural relation link and corresponding OPL sentences.
More information and examples can be found in Model-Based Systems Engineering with OPM and SysML, Chapter 3.3 "Adding structural links".
=== Relationship cardinalities ===
Object multiplicity in structural and procedural links
Object multiplicity shall refer to a requirement or constraint specification on the quantity or count of object instances associated with a link. Unless a multiplicity specification is present, each end of a link shall specify only one thing instance. The syntax of an OPL sentence that includes an object with multiplicity shall include the object multiplicity preceding the object name, with the object name appearing in its plural form. Multiplicity specifications may appear in the following cases:
to specify multiple source or destination object instances for a tagged structural link of any kind;
to specify a participant object with multiple instances in an aggregation-participation link, where a different participation specification may be attached to each one of the parts of the whole;
to specify an object with multiple instances in a procedural relation.
Object multiplicity expressions and constraints
Object multiplicity may include arithmetic expressions, which shall use the operator symbols "+", "–", "*", "/", "(", and ")" with their usual semantics and shall use the usual textual correspondence in the corresponding OPL sentences.
An integer or an arithmetic expression may constrain object multiplicity. Graphically, expression constraints shall appear after a semicolon separating them from the expression that they constrain and shall use the equality/inequality symbols "=", "<", ">", "<=", and ">=", the curly braces "{" and "}" for enclosing set members, and the membership operator "in" (element of, ∈), all with their usual semantics. The corresponding OPL sentence shall place the constraint phrase in bold letters after the object to which the constraint applies in the form ", where constraint".
Attribute value and multiplicity constraints
The expression of object multiplicity for structural and procedural links specifies integer values or parameter symbols that resolve to integer values. In contrast, the values associated with attributes of objects or processes may be integer or real values, or parameter symbols that resolve to integer or real values, as well as character strings and enumerated values. Graphically, a labelled, rounded-corner rectangle placed inside the attribute to which it belongs shall denote an attribute value with the value or value range (integers, real numbers, or string characters) corresponding to the label name. In OPL text, the attribute value shall appear in bold face without capitalization.
The syntax for an object with an attribute value OPL sentence shall be: Attribute of Object is value.
The syntax for an object with an attribute value range OPL sentence shall be: Attribute of Object range is value-range. A structural or a procedural link connecting with an attribute that has a real number value may specify a relationship constraint, which is distinct from an object multiplicity.
Graphically, an attribute value constraint is an annotation by a number, integer or real, or a symbol parameter, near the attribute end of the link and aligning with the link.
=== Logical operators: AND, XOR, and OR ===
Logical AND procedural links
The logical operators AND, XOR, and OR among procedural relations enable specification of elaborate process precondition and postcondition. Separate, non-touching links shall have the semantics of logical AND.
Here, unlocking the safe requires all three keys.
Logical XOR and OR procedural links
A link fan shall follow the semantics of either a XOR or an OR operator. The link fan end that is common to the links shall be the convergent link end. The link end that is not common to the links shall be the divergent link end.
The XOR operator shall mean that exactly one of the things in the span of the link fan exists, if the divergent link end has objects, or happens, if the divergent link end has processes. Graphically, a dashed arc across the links in the link fan with the arc focal point at the convergent end point of contact shall denote the XOR operator.
The OR operator shall mean that at least one of the two or more things in the span of the link fan exists, if the divergent link end has objects, or happens, if the divergent end has processes. Graphically, two concentric dashed arcs across the links with their focal point at the convergent end point of contact shall denote the OR operator.
State-specified XOR and OR link fans
Control-modified link fans
Link probabilities and probabilistic link fans
Execution path and path labels
A path label shall be a label along a procedural link, which, in the case that there is more than one option to follow upon process termination, prescribes that the link to follow will be the one having the same label as the one which we entered the process.
== Modeling principles and model comprehension ==
The definition of system purpose, scope, and function in terms of boundary, stakeholders and preconditions is the basis for determining whether other elements should appear in the model. This determines the scope of the system model.
OPM provides abstracting and refining mechanisms to manage the expression of model clarity and completeness.
Stakeholder and system's beneficiary identification
For man-made systems this function is expected to benefit a person or a group of people—the beneficiary. After the function of the system aligns with the functional value expectation of its main beneficiary, the modeler identifies and adds other principal stakeholders to the OPM model.
System diagram
The resulting top-level OPD is the system diagram (SD), which includes the stakeholder group, in particular the beneficiary group, and additional top-level environmental things, which provide the context for the system's operation. The SD should contain only the central and important things—those things indispensable for understanding the function and context of the system. The function is the main process in SD, which also contains the objects involved in this process: the beneficiary, the operand (the object upon which the process operates), and possibly the attribute of the operand whose value the process changes. SD should also contain an object representing the system that enables the function. The default name of this system is created by adding the word "System" to the name of the function. For example, if the function is Car Painting, the name of the system would be Car Painting System.
OPD tree
Clarity and completeness trade-off
Establishing an appropriate balance requires careful management of context during model development. However, the modeler may take advantage of the union of information provided by the entire OPD set of an OPM system model and have one OPD which is clear and unambiguous but not complete, and another that focuses on completeness for some smaller part of the system by adding more details.
Refinement-abstraction mechanisms
OPM shall provide abstracting and refining mechanisms to manage the expression of model clarity and completeness. These mechanisms shall enable presenting and viewing the system, and the things that comprise it, in various contexts that are interrelated by the objects, processes and relations that are common amongst them.
State expression and state suppression
The inverse of state suppression shall be state expression, i.e., refining the OPD by adding the information
concerning possible object states. The OPL corresponding to the OPD shall express only the states of the
objects that are depicted.
Unfolding and folding
It reveals a set of things that are hierarchically below the unfolded thing. The result is a hierarchy tree, the root of which is the unfolded thing. Linked to the root are the things that constitute the context of the unfolded thing. Conversely, folding is a mechanism for abstraction or composition, which applies to an unfolded hierarchical tree.
In-zooming and out-zooming
In-zooming is a kind of unfolding, which is applicable to aggregation-participation only and has additional semantics. For processes, in-zooming enables modeling the sub-processes, their temporal order, their interactions with objects, and passing of control to and from this context. For objects, in-zooming creates a distinct context that enables modeling the constituent objects spatial or logical order. Graphically, the timeline within the context of an in-zoomed process flows from the top of its process ellipse symbol to the ellipse bottom.
== Meta modeling ==
OPM model structure
Model of OPD Construct and Basic Construct
The model, as seen in the image of OPD metamodel, elaborates the OPD Construct concept. The purpose of this model is to distinguish Basic Construct from another possible OPD Construct. A Basic Construct is a specialization of OPD Construct, which consists of exactly two Things connected by exactly one Link. The non-basic constructs include, among others, those with link fans or more than two refinees.
A modeller could add a process to the model, by adding states disconnected and connected of Thing Set.
The purpose of the model thus includes the action of transforming a disconnected Thing Set to a connected Thing Set using the Link Set as an instrument of connection.
OPM model of Thing
OPM model of Thing, is a model for an OPM Thing, showing its specialization into Object and Process, as depicted in the image of model of thing below. A set of States characterize Object, which can be empty, in a Stateless Object, or non-empty in the case of a Stateful Object.
A Stateful Object with s States gives rise to a set of s stateless State-Specific Objects, one for each State.
A particular State-Specific Object refers to an object in a specific state. Modelling the concept of State-Specific Object as both an Object and a State enables simplifying the conceptual model by referring to an object and any one or its states by simply specifying Object.
OPM model of Thing generic properties
OPM model of Thing generic properties, depicts Thing and its Perseverance, Essence, and Affiliation generic properties modelled as attribute refinees of an exhibition-characterization link. Perseverance is the discriminating attribute between Object and Process.
In-zooming and out-zooming models
Both new-diagram in-zooming and new-diagram out-zooming create a new OPD context from an existing OPD context. New-diagram in-zooming starts with an OPD of relatively less details and adds elaboration or refinement as a descendant OPD that applies to a specific thing in the less detailed OPD.
== Versions ==
OPM
The current version of OPM is ISO/PAS 19450:2015 as specified in Automation Systems and Integration — Object-Process Methodology. The specification in Dori's 2016 book is a superset of ISO/PAS 19450:2015.
The previous version of OPM was specified in Dori's 2002 book.
OPCAT
The current OPCAT version is 4.1. It is available freely from Technion's Enterprise Systems Modeling Laboratory.
A previous OPCAT version, 3.1, with fewer capabilities, is also available from the same site. Both are coded in Java. The first OPCAT version, OPCAT 1.X, was written in Visual C++ in 1998.
In the beginning of 2016 a team of students under the management of Dori began working on the new generation of OPCAT which will be called OPCloud. As suggested by the name of the software, it will be a cloud-based application, and will enable users to create OPM models using a web-based application.
== Standardization ==
ISO—the International Organization for Standardization—is an independent, non-governmental international organization with a membership of 162 national standards bodies, which develops voluntary, consensus-based, market relevant International Standards that support innovation and provide solutions to global challenges. These standards provide world-class specifications for products, services and systems, to ensure quality, safety and efficiency.
=== ISO and OPM ===
In June 2008, Richard Martin approached Dov Dori after his presentation at the INCOSE International Symposium in Utrecht, the Netherlands, to inquire about the possibility of creating an International Standard for OPM. Martin, convener of ISO TC184/SC5/WG1 for automation systems interoperability architecture and modelling, had for some time been searching for methodologies offering more than static information and process modeling. He provided Dori with a simple example to model that could demonstrate both the modelling capability of OPM and its dynamic simulation opportunity.
In May 2010, Dori presented a brief overview of OPM and his demonstration model at the ISO Technical Committee 184/Sub-Committee 5 (TC184/SC5) plenary meeting, which then adopted a resolution to create an OPM Study Group for the purpose of examining the potential for OPM to enhance the standards created by SC5.
The OPM Study Group began its work in October 2010 and issued an interim report for the 2011 SC5 Plenary. The report included several uses of OPM to model existing SC5 standards and led to an initial motivation for the standardization of OPM with the realization that being text-based, ISO standards are prone to suffer from inconsistencies and incomplete information. This deficiency could be significantly reduced if the standards were model-based rather than text-based, and OPM offered a useful underlying modeling paradigm for this purpose.
A final OPM Study Group Report and a draft for a metamodel for model-based standards authoring document were delivered at the 2012 SC5 Plenary. As the OPM Study Group effort progressed, it became obvious that OPM could also serve as a solid and comprehensive basis for model-based systems engineering (MBSE) and for modeling both natural and man-made systems.
=== ISO 19450 Document ===
TC184/SC5/WG1 participants received the first draft of the OPM PAS in September 2011 with 16 pages, 2 annexes and a bibliography for a total of 25 pages. Most of the content simply identified sub-clause headings and space holder graphics. By the 2012 SC5 Plenary, the PAS draft included 10 full clauses describing OPM features and 6 annexes totaling 86 pages. One annex was an EBNF (Extended Backus-Naur Form, used to formally specify context free languages, enabling parsing of programming languages) specification for OPL and another detailed OPD graph grammar. To facilitate verification of the EBNF specification, David Shorter wrote a script to evaluate consistency and completeness of the EBNF statement set. Further effort to add meaningful examples and complete all of the identified sections resulted in a draft of 138 pages by the time of the 2013 SC5 Plenary. Subsequently, the working draft was registered with the SC5 Secretariat as a Committee Draft for initial circulation to SC5 members.
Because the SC5 resolution calling for the OPM specification indicated that the document was to be registered as a Publicly Available Specification (PAS), it would have only one acceptance ballot opportunity. In April 2014, the New Work Item Proposal and revised Committee Draft for ISO/PAS 19450 was delivered to SC5 for consideration. By now the Committee Draft was 98 pages plus front matter, four annexes and 30 bibliographic references, totaling 183 pages. In March 2015, ISO registered the result of balloting for ISO/PAS 19450 as 8 Approve, 1 Approve with comments, and 1 abstain.
ISO/PAS 19450 was formally published with a total of 162 pages by ISO on December 15, 2015, culminating a six-year effort to provide the standardization community with a formal specification for a new approach to modeling that binds together graphics and textual representations into a single paradigm suitable for automated simulation of model behavior.
== OPM vs. SysML and UML ==
OPM vs. SysML
SysML is defined as an extension of the Unified Modeling Language (UML) using UML's profile mechanism.
OPM vs. UML
The differences between OPM and UML are highly perceivable during the analysis and design stages. While UML is a multi-model, OPM supports a single unifying structure-behavior model. The crucial differences stem from the structure-oriented approach of UML, in which behavior is spread over thirteen diagram types, a fact that inevitably invokes the model multiplicity problem. First, using the OPM approach enables to view at main diagram (SD) the main process, objects and the connection between them. In addition, it is easy to understand what is the main system's benefit (presented at the SD). In OPM, it's also easier to understand the main three aspects of the system: behavior, structure and functionality (contrary to UML which describes these aspects with different types of diagrams). Database unfolding modeling contributes to the understanding of system and all details which is stored in the system. In addition, creating in-zooming enables simplifying the model. OPM requires extensive knowledge of systematic processes such as how the system saved the path and gets decisions.
== Generating SysML views from an OPM model ==
While both languages aim at the same purpose of providing a means for general-purpose systems engineering, these languages take different approaches in realizing this goal. SysML is a profile of UML (Unified Modeling Language).
The OPM-to SysML translation is one-to-many in the sense that a single OPM element (entity or link) usually translates to several SysML elements that belong in different SysML diagram types. For example, an OPM process, which is defined as an entity that transforms (generates, consumes, or changes the state of) an object, can be mapped to any subset of the following SysML entities:
Use case (in a use case diagram)
Action (in an activity diagram)
State transition trigger (in a state machine diagram).
As OPM and SysML are two distinct and differently designed languages, not all the constructs in one language have equivalent constructs in the other language.
The first type of diagram in UML that can be generated from an OPM diagram is the use case diagram which is intended for modeling the usage of a system. The main elements comprising the use case diagram are actors and use cases (the entities) along with the relationships (links) among them. Generation of a use case diagram from OPM is therefore based on environmental objects (the actors) and the processes (the use cases) linked to them. Figure 1 is an example of use case diagram generation of SD0. The figure shows the root OPM diagram (a), the corresponding OPL text (b), and the created use case diagram (c). Figure 2 shows a SD1 level of OPD from the same OPM model (a), and the generated use case diagram (b).
The second type of diagram is the block definition diagram (BDD) which defines features of blocks (like properties and operations) and relationships between blocks, such as associations and generalizations. Generating a BDD is based upon the systemic objects of the OPM model and their relationships—mainly structural relations to other model elements.
The third type of diagram is activity diagrams which are intended to specify flow. Key components included in the activity diagram are actions and routing flow elements. In our context, a separate Activity Diagram can be generated for each OPM process containing child subprocesses, i.e., a process which is in-zoomed in the OPM model. There are two kinds of user parameters that can be specified via the settings dialog. The first one deals with selection of the OPM processes: One option is to explicitly specify the required OPM processes by selection from a list. The alternative, which is the default option, is to start with the root OPD (SD) and go down the hierarchy. Here we reach the second parameter (that is independent of the first one), which is the required number of OPD levels (k) to go down the hierarchy. In order to give the user control over the level of abstraction, the diagrams are generated up to k levels down the hierarchy. Each level will result in the generation of an additional activity diagram, which is a child activity (subdiagram) contained in the enclosing higher-level activity. The default setting for this option is "all levels down" (i.e., "k = ∞").
== See also ==
Formal ontology
Process ontology
Ontology language
Upper ontology
== References ==
== External links ==
Object-Process Methodology and Its Application to the Visual Semantic Web, presentation by Dov Dori, 2003.
Some Features of the Technical Language of Navya-Nyāya
Formalizing the Conceptual Modeling Thought Process to Benefit Engineers and Scientists., presentation by Dov Dori, 2015.
Formalizing the Conceptual Modeling Thought Process to Benefit Engineers and Scientists
US Patent US7099809B2 on conversion of OPD to and from text formats | Wikipedia/Object_Process_Methodology |
An XML transformation language is a programming language designed specifically to transform an input XML document into an output document which satisfies some specific goal.
There are two special cases of transformation:
XML to XML: the output document is an XML document.
XML to Data: the output document is a byte stream.
== XML to XML ==
As XML to XML transformation outputs an XML document, XML to XML transformation chains form XML pipelines.
== XML to Data ==
The XML (EXtensible Markup Language) to Data transformation contains some important cases. The most notable one is XML to HTML (HyperText Markup Language), as an HTML document is not an XML document.
== SGML origins ==
The earliest transformation languages predate the advent of XML as an SGML profile, and thus accept input in arbitrary SGML rather than specifically XML. These include the SGML-to-SGML link process definition (LPD) format defined as part of the SGML standard itself; in SGML (but not XML), the LPD file can be referenced from the document itself by a LINKTYPE declaration, similarly to the DOCTYPE declaration used for a DTD. Other such transformation languages, addressing some of the deficiencies of LPDs, include Document Style Semantics and Specification Language (DSSSL) and OmniMark. Newer transformation languages tend to target XML specifically, and thus only accept XML, not arbitrary SGML.
== Existing languages ==
XSLT: XSLT is the best known XML transformation language. The XSLT 1.0 W3C recommendation was published in 1999 together with XPath 1.0, and it has been widely implemented since then. XSLT 2.0 has become a W3C recommendation since January 2007 and implementations of the specification like Saxon 8 are already available.
XQuery: XQuery is a full functional language, despite having "query" in the name. It is a de facto standard used by Microsoft, Oracle, DB2, MarkLogic, etc., is the foundation for the XRX web programming model, and has a W3C recommendation for versions 1.0. XQuery is not written in XML itself like XSLT is, so its syntax is much lighter. The language is based on XPath 2.0. XQuery programs cannot have side-effects, just like XSLT and provides almost the same capabilities (for instance: declaring variables and functions, iterating over sequences, using W3C schema types), even though the program syntax are quite different. XQuery is logic driven, using FOR, WHERE and function composition (e.g. fn:concat("<html>", generate-body(), "</html>")). In contrast, XSLT is data-driven (push processing model) where certain conditions of the input document trigger the execution of templates rather than the code executing in the order in which it is written.
XProc: XProc is an XML Pipeline language. The XProc 1.0 W3C Recommendation was published in May 2010.
XML document transform: Is a Microsoft standard for performing simple transforms on XML documents. Primarily for creating IIS Web.config files (Config Transforms), other implementations allow it to be used for generic config files as build time (Slow Cheetah) or from the command line (CTT).
STX: STX (Streaming Transformations for XML) is inspired by XSLT but has been designed to allow a one-pass transformation process that never prevents streaming. Implementations are available in Java (Joost) and Perl (XML::STX).
XML Script: XML Script is an imperative scripting language inspired by Perl that uses the XML syntax. XML Script supports XPath and its proprietary DSLPath for selecting nodes from the input tree.
FXT: FXT is a functional XML transformation tool, implemented in Standard ML.
XDuce: XDuce is a typed language with a lightweight syntax, compared to XSLT. It is written in ML.
CDuce: CDuce extends XDuce to a general-purpose functional programming language, see CDuce homepage.
XACT: XACT is a Java-based system for programming XML transformations. Notable features include XML templates as immutable values and a static analysis to ensure type safety using XML Schema types (XACT home page).
XFun: XFun is a functional language X-Fun for defining transformations between XML data trees, while providing shredding instructions. X-Fun can be understood as an extension of Frisch's XStream language with output shredding, while pattern matching is replaced by tree navigation with XPath expressions. ([1])
XStream: XStream is a simple functional transformation language for XML documents based on CAML. XML transformations written in XStream are evaluated in streaming: when possible, parts of the output are computed and produced while the input document is still being parsed. Some transformations can thus be applied to huge XML documents which would not even fit in memory. The XStream compiler is distributed under the terms of the CeCILL free software license.
Xtatic: Xtatic applies methods from XDuce to C#, see Xtatic homepage.
HaXml: HaXml is a library and collection of tools to write XML transformations in Haskell. Also see this paper about HaXml published in 1999 and this IBM developerWorks article. See also the more recent HXML and Haskell XML Toolbox (HXT), which is based on the ideas of HaXml and HXML but takes a more general approach to XML processing.
XMLambda: XMLambda (XMλ) is described in a 1999 paper by Erik Meijer and Mark Shields. No implementation is available. See XMLambda home page.
FleXML: FleXML is an XML processing language first implemented by Kristofer Rose. Its approach is to add actions to an XML DTD specifying processing instructions for any subset of the DTD's rules.
Scala: Scala is a general-purpose functional and object-oriented language with specific support for XML transformation in the form of XML pattern matching, literals, and expressions, along with standard XML libraries.
LINQ to XML: LINQ to XML is a .NET 3.5 syntax and programming API available in C#, VB and some other .NET languages. LINQ is primarily designed as a query language, but it also supports XML transforms.
== See also ==
Filter (software)
Filter (Unix)
Web template
Web template system
Template engine (web)
Tritium (programming language)
== References == | Wikipedia/XML_transformation_language |
A model transformation language in systems and software engineering is a language intended specifically for model transformation.
== Overview ==
The notion of model transformation is central to model-driven development. A model transformation, which is essentially a program which operates on models, can be written in a general-purpose programming language, such as Java. However, special-purpose model transformation languages can offer advantages, such as syntax that makes it easy to refer to model elements. For writing bidirectional model transformations, which maintain consistency between two or more models, a specialist bidirectional model transformation language is particularly important, because it can help avoid the duplication that would result from writing each direction of the transformation separately.
Currently, most model transformation languages are being developed in academia. The OMG has standardised a family of model transformation languages called QVT, but the field is still immature.
There are ongoing debates regarding the benefits of specialised model transformation languages, compared to the use of general-purpose programming languages (GPLs) such as Java. While GPLs have advantages in terms of more widely-available practitioner knowledge and tool support, the specialised transformation languages do provide more declarative facilities and more powerful specialised features to support model transformations.
== Available transformation languages ==
ATL : a transformation language developed by the INRIA
Beanbag (see [1]) : an operation-based language for establishing consistency over data incrementally
GReAT : a transformation language available in the GME
Epsilon family (see [2]) : a model management platform that provides transformation languages for model-to-model, model-to-text, update-in-place, migration and model merging transformations.
F-Alloy [3]: a DSL reusing part of the Alloy syntax and allowing the concise specification of efficiently computable model transformations.
Henshin (see [4]) : a model transformation language for EMF, based on graph transformation concepts, providing state space exploration capabilities
JTL : a bidirectional model transformation language specifically designed to support non-bijective transformations and change propagation (see [5]).
Kermeta : a general purpose modeling and programming language, also able to perform transformations
Lx family (see [6]) : a set of low-level transformation languages
M2M is the Eclipse implementation of the OMG QVT standard
Mia-TL : a transformation language developed by Mia-Software
MOF Model to Text Transformation Language: the OMG has defined a standard for expressing M2T transformations
MOLA (see [7]) : a graphical high-level transformation language built in upon Lx.
MT : a transformation language developed at King's College, London (UK) (based on Converge PL)
QVT : the OMG has defined a standard for expressing M2M transformations, called MOF/QVT or in short QVT.
SiTra [8] : a pragmatic transformation approach based on using a standard programming language, e.g. Java, C#
Stratego/XT : a transformation language based on rewriting with programmable strategies
Tefkat : a transformation language and a model transformation engine
Tom : a language based on rewriting calculus, with pattern-matching and strategies
UML-RSDS [9] : a model transformation and MDD approach using UML and OCL
VIATRA : a framework for transformation-based verification and validation environment
YAMTL (see [10]): An internal DSL for model transformation within JVM languages (Java, Groovy, Xtend, Kotlin), featuring key characteristics such as runtime performance, reuse of transformation logic, incremental execution, and independence from IDEs.
== See also ==
Data transformation
Domain-specific language (DSL)
Filter (software)
Model-driven engineering (MDE)
Model-driven architecture (MDA)
Template processor
Transformation language
Graph Transformation
Web template
XSLT - a standard language
== References ==
== Further reading ==
The MDA Journal: Model Driven Architecture Straight From The Masters
Model Driven Architecture: Applying MDA to Enterprise Computing, David S. Frankel, John Wiley & Sons, ISBN 0-471-31920-1
OMG MDA Guide MDA Guide Version 1.0.1
Model-Driven Architecture: Vision, Standards And Emerging Technologies at omg.org
An Introduction to Model Driven Architecture at ibm.com
From Object Composition to Model Transformation with the MDA at omg.org
Mens, T, and Van Gorp, P: A Taxonomy of Model Transformation, Electronic Notes in Theoretical Computer Science, Volume 152, 27 March 2006, Pages 125-142
Czarnecki, K, and Helsen, S : Classification of Model Transformation Approaches. In: Proceedings of the OOPSLA'03 Workshop on the Generative Techniques in the Context Of Model-Driven Architecture, Anaheim, California, USA. Webpublished.
Gronmo, R, and Oldevik, J : An Empirical Study of the UML Model Transformation Tool (UMT). [11] | Wikipedia/Model_transformation_language |
In computer graphics, turtle graphics are vector graphics using a relative cursor (the "turtle") upon a Cartesian plane (x and y axis). Turtle graphics is a key feature of the Logo programming language. It is also a simple and didactic way of dealing with moving frames.
== Overview ==
The turtle has three attributes: a location, an orientation (or direction), and a pen. The pen, too, has attributes: color, width, and on/off state (also called down and up).
The turtle moves with commands that are relative to its own position, such as "move forward 10 spaces" and "turn left 90 degrees". The pen carried by the turtle can also be controlled, by enabling it, setting its color, or setting its width. A student could understand (and predict and reason about) the turtle's motion by imagining what they would do if they were the turtle. Seymour Papert called this "body syntonic" reasoning.
A full turtle graphics system requires control flow, procedures, and recursion: many turtle drawing programs fall short. From these building blocks one can build more complex shapes like squares, triangles, circles and other composite figures. The idea of turtle graphics, for example is useful in a Lindenmayer system for generating fractals.
Turtle geometry is also sometimes used in graphics environments as an alternative to a strictly coordinate-addressed graphics system.
== History ==
Turtle graphics are often associated with the Logo programming language. Seymour Papert added support for turtle graphics to Logo in the late 1960s to support his version of the turtle robot, a simple robot controlled from the user's workstation that is designed to carry out the drawing functions assigned to it using a small retractable pen set into or attached to the robot's body. Turtle geometry works somewhat differently from (x,y) addressed Cartesian geometry, being primarily vector-based (i.e. relative direction and distance from a starting point) in comparison to coordinate-addressed systems such as bitmaps or raster graphics. As a practical matter, the use of turtle geometry instead of a more traditional model mimics the actual movement logic of the turtle robot. The turtle is traditionally and most often represented pictorially either as a triangle or a turtle icon (though it can be represented by any icon).
import turtle
tina = turtle.Turtle()
tina.shape('turtle')
x = 1
tina.speed(10000)
colors = ["red", "orange", "yellow", "green", "blue", "purple"]
for i in range(100):
for i in colors:
tina.forward(x*.3)
tina.left(60)
tina.color(i)
tina.right(30.5)
x = x+1
Today, the Python programming language's standard library includes a Turtle graphics module. Like its Logo predecessor, the Python implementation of turtle allows programmers to control one or more turtles in a two-dimensional space. Since the standard Python syntax, control flow, and data structures can be used alongside the turtle module, turtle has become a popular way for programmers learning Python to familiarize themselves with the basics of the language.
== Extension to three dimensions ==
The ideas behind turtle graphics can be extended to include three-dimensional space. This is achieved by using one of several different coordinate models. A common setup is cartesian-rotational as with the original 2D turtle: an additional "up" vector (normal vector) is defined to choose the plane the turtle's 2D "forward" vector rotates in; the "up" vector itself also rotates around the "forward" vector. In effect, the turtle has two different heading angles, one within the plane and the other determining the plane's angle. Usually changing the plane's angle does not move the turtle, in line with the traditional setup.
Verhoeff 2010 implements the two vector approach; a roll command is used to rotate the "up" vector around the "forward" vector. The article proceeds to develop an algebraic theory to prove geometric properties from syntactic properties of the underlying turtle programs. One of the insights is that a dive command is really a shorthand of a turn-roll-turn sequence.
Cheloniidae Turtle Graphics is a 3D turtle library for Java. It has a bank command (same as roll) and a pitch command (same as dive) in the "Rotational Cartesian Turtle". Other coordinate models, including non-Euclidean geometry, are allowed but not included.
== See also ==
Moving frame
KTurtle
L-system
UCBLogo
NetLogo
FMSLogo
MSWLogo
Joy (programming language)
== References ==
== Further reading ==
Papert, Seymour (1993). Mindstorms: Children, Computers, and Powerful Ideas (2nd ed.). New York: Basic Books. ISBN 0-465-04674-6. OCLC 794964988.
Papert, Seymour (1993). The Children's Machine: Rethinking School in the Age of the Computer. New York: Basic Books. ISBN 0-465-01830-0. OCLC 248428992. | Wikipedia/Turtle_graphics |
A vector graphic editor is a computer program that enables its users to create, compose and edit images with the use of mathematical and geometrical commands rather than individual pixels. This software is used in creating high-definition vector graphic images that can be scaled indefinitely without losing their quality. The output is saved in vector graphic formats, such as EPS, ODG, or SVG.
== Vector editors versus bitmap editors ==
Vector editors are often contrasted with bitmap editors, and their capabilities complement each other. Vector editors are often better for page layout, typography, logos, sharp-edged artistic illustrations (e.g. cartoons, clip art, complex geometric patterns), technical illustrations, diagramming and flowcharting. Bitmap editors are more suitable for retouching, photo processing, photorealistic illustrations, collage, and illustrations drawn by hand with a pen tablet. Recent versions of bitmap editors such as GIMP and Adobe Photoshop support vector tools (e.g. editable paths), and vector editors have adopted raster effects that were once limited to bitmap editors (e.g. blurring).
== Specialized features ==
Some vector editors support animation, while others (e.g. Synfig Studio) are specifically geared towards producing animated graphics. Generally, vector graphics are more suitable for animation, though there are raster-based animation tools as well.
3D computer graphics software such as Maya, Blender or Autodesk 3ds Max can also be thought of as an extension of the traditional 2D vector editors, as they share some common concepts and tools.
== See also ==
Vector graphics
Comparison of vector graphics editors
Raster graphics editor
Image editing
Graphics
MetaPost
== References ==
== External links ==
Bitmap and Vector Graphics Explained
Free SVG Editor Online
Edit SVG Images Online
App Vector Graphics | Wikipedia/Vector_graphics_editor |
Off-model is a term used in the animation and visual arts industries to describe art that does not match the style, design, or proportions that have been previously established for a given project (i.e. any work that is not on-model). Any kind of visual art can be off-model, so long as it defies the conventions of an established design. Consequently, art can be made off-model accidentally, due to skill or time constraints that may limit an artist's ability to accurately replicate a style. However, off-model can also be an intentional choice on the part of an animator. John Kricfalusi has argued that off-model animation allows originality and can help a scene come to life, as strictly sticking to poses and expressions as dictated in model sheets can be too restricting.
Off-model art is often associated with 2D animation, such as cartoons and anime. For much of the history of 2D animation, individual frames have been hand-drawn in sequence. This task may be outsourced to multiple individuals or studios, increasing the chances for the miscommunication of character, environment, or item design. Animation studios attempt to limit this issue by distributing model sheets amongst animators, which may include poses or expressions for artist reference.
Off-model work may also be the product of artists or cartoonists intending to parody another franchise but not wishing to incur a lawsuit or commit copyright infringement by drawing someone else's trademarked characters.
== See also ==
Uncanny valley
== References == | Wikipedia/Off-model |
An image file format is a file format for a digital image. There are many formats that can be used, such as JPEG, PNG, and GIF. Most formats up until 2022 were for storing 2D images, not 3D ones. The data stored in an image file format may be compressed or uncompressed. If the data is compressed, it may be done so using lossy compression or lossless compression. For graphic design applications, vector formats are often used. Some image file formats support transparency.
Raster formats are for 2D images. A 3D image can be represented within a 2D format, as in a stereogram or autostereogram, but this 3D image will not be a true light field, and thereby may cause the vergence-accommodation conflict.
Image files are composed of digital data in one of these formats so that the data can be displayed on a digital (computer) display or printed out using a printer. A common method for displaying digital image information has historically been rasterization.
== Image file sizes ==
The size of raster image files is positively correlated with the number of pixels in the image and the color depth (bits per pixel). Images can be compressed in various ways, however. A compression algorithm stores either an exact representation or an approximation of the original image in a smaller number of bytes that can be expanded back to its uncompressed form with a corresponding decompression algorithm. Images with the same number of pixels and color depth can have very different compressed file sizes. Considering exactly the same compression, number of pixels, and color depth for two images, different graphical complexity of the original images may also result in very different file sizes after compression due to the nature of compression algorithms. With some compression formats, images that are less complex may result in smaller compressed file sizes. This characteristic sometimes results in a smaller file size for some lossless formats than lossy formats. For example, graphically simple images (i.e., images with large continuous regions like line art or animation sequences) may be losslessly compressed into a GIF or PNG format and result in a smaller file size than a lossy JPEG format.
For example, a 640 × 480 pixel image with 24-bit color would occupy almost a megabyte of space:
640 × 480 × 24 = 7,372,800 bits = 921,600 bytes = 900 KiB
With vector images, the file size increases only with the addition of more vectors.
== Image file compression ==
There are two types of image file compression algorithms: lossless and lossy.
Lossless compression algorithms reduce file size while preserving a perfect copy of the original uncompressed image. Lossless compression generally, but not always, results in larger files than lossy compression. Lossless compression should be used to avoid accumulating stages of re-compression when editing images.
Lossy compression algorithms preserve a representation of the original uncompressed image that may appear to be a perfect copy, but is not a perfect copy. Often lossy compression is able to achieve smaller file sizes than lossless compression. Most lossy compression algorithms allow for variable compression that trades image quality for file size.
== Major graphic file formats ==
Including proprietary types, there are hundreds of image file types. The PNG, JPEG, and GIF formats are most often used to display images on the Internet. Some of these graphic formats are listed and briefly described below, separated into the two main families of graphics: raster and vector. Raster images are further divided into formats primarily aimed at (web) delivery (i.e., supporting relatively strong compression) versus formats primarily aimed at authoring or interchange (uncompressed or only relatively weak compression).
In addition to straight image formats, Metafile formats are portable formats that can include both raster and vector information. Examples are application-independent formats such as WMF and EMF. The metafile format is an intermediate format. Most applications open metafiles and then save them in their own native format. Page description language refers to formats used to describe the layout of a printed page containing text, objects, and images. Examples are PostScript, PDF, and PCL.
=== Raster formats (2D) ===
==== Delivery formats ====
===== JPEG =====
JPEG (Joint Photographic Experts Group) is a lossy compression method; JPEG-compressed images are usually stored in the JFIF (JPEG File Interchange Format) or the Exif (Exchangeable Image File Format) file format. The JPEG filename extension is JPG or JPEG. Nearly every digital camera can save images in the JPEG format, which supports eight-bit grayscale images and 24-bit color images (eight bits each for red, green, and blue). JPEG applies lossy compression to images, which can result in a significant reduction of the file size. Applications can determine the degree of compression to apply, and the amount of compression affects the visual quality of the result. When not too great, the compression does not noticeably affect or detract from the image's quality, but JPEG files suffer generational degradation when repeatedly edited and saved. (JPEG also provides lossless image storage, but the lossless version is not widely supported.)
===== GIF =====
The GIF (Graphics Interchange Format) is in normal use limited to an 8-bit palette, or 256 colors (while 24-bit color depth is technically possible). GIF is most suitable for storing graphics with few colors, such as simple diagrams, shapes, logos, and cartoon-style images, as it uses LZW lossless compression, which is more effective when large areas have a single color and less effective for photographic or dithered images. Due to GIF's simplicity and age, it achieved almost universal software support. Due to its animation capabilities, it is still widely used to provide image animation effects, despite its low compression ratio compared to modern video formats.
===== PNG =====
The PNG (Portable Network Graphics) file format was created as a free, open-source alternative to GIF. The PNG file format supports 8-bit (256 colors) paletted images (with optional transparency for all palette colors) and 24-bit truecolor (16 million colors) or 48-bit truecolor with and without an alpha channel – while GIF supports only 8-bit palettes with a single transparent color.
Compared to JPEG, PNG excels when the image has large, uniformly colored areas. Even for photographs – where JPEG is often the choice for final distribution since its lossy compression typically yields smaller file sizes – PNG is still well-suited to storing images during the editing process because of its lossless compression.
PNG provides a patent-free replacement for GIF (though GIF is itself now patent-free) and can also replace many common uses of TIFF. Indexed-color, grayscale, and truecolor images are supported, plus an optional alpha channel. The Adam7 interlacing allows an early preview, even when only a small percentage of the image data has been transmitted—useful in online viewing applications like web browsers. PNG can store gamma and chromaticity data, as well as ICC profiles, for accurate color matching on heterogeneous platforms.
Animated formats derived from PNG are MNG and APNG, which is backwards compatible with PNG and supported by most browsers.
===== JPEG 2000 =====
JPEG 2000 is a compression standard enabling both lossless and lossy storage. The compression methods used are different from the ones in standard JFIF/JPEG; they improve quality and compression ratios, but also require more computational power to process. JPEG 2000 also adds features that are missing in JPEG. It is not nearly as common as JPEG but it is used currently in professional movie editing and distribution (some digital cinemas, for example, use JPEG 2000 for individual movie frames).
===== WebP =====
WebP is an open image format released in 2010 that uses both lossless and lossy compression. It was designed by Google to reduce image file size to speed up web page loading: its principal purpose is to supersede JPEG as the primary format for photographs on the web. WebP is based on VP8's intra-frame coding and uses a container based on RIFF.
In 2011, Google added an "Extended File Format" allowing WebP support for animation, ICC profile, XMP and Exif metadata, and tiling.
The support for animation allowed for converting older animated GIFs to animated WebP.
The WebP container (i.e., RIFF container for WebP) allows feature support over and above the basic use case of WebP (i.e., a file containing a single image encoded as a VP8 key frame). The WebP container provides additional support for:
Lossless compression – An image can be losslessly compressed, using the WebP Lossless Format.
Metadata – An image may have metadata stored in EXIF or XMP formats.
Transparency – An image may have transparency, i.e., an alpha channel.
Color Profile – An image may have an embedded ICC profile as described by the International Color Consortium.
Animation – An image may have multiple frames with pauses between them, making it an animation.
===== HDR raster formats =====
Most typical raster formats cannot store HDR data (32 bit floating point values per pixel component), which is why some relatively old or complex formats are still predominant here, and worth mentioning separately. Newer alternatives are showing up, though. RGBE is the format for HDR images originating from Radiance and also supported by Adobe Photoshop. JPEG-HDR is a file format from Dolby Labs similar to RGBE encoding, standardized as JPEG XT Part 2.
JPEG XT Part 7 includes support for encoding floating point HDR images in the base 8-bit JPEG file using enhancement layers encoded with four profiles (A-D); Profile A is based on the RGBE format and Profile B on the XDepth format from Trellis Management.
===== HEIF =====
The High Efficiency Image File Format (HEIF) is an image container format that was standardized by MPEG on the basis of the ISO base media file format. While HEIF can be used with any image compression format, the HEIF standard specifies the storage of HEVC intra-coded images and HEVC-coded image sequences taking advantage of inter-picture prediction.
===== AVIF =====
AVIF is an image container, that is used to store AV1 encoded images. It was created by Alliance for open media (AOMedia) and is completely open source and royalty-free. It supports encoding images in 8, 10 and 12-bit depth.
===== JPEG XL =====
JPEG XL is a royalty-free raster-graphics file format that supports both lossy and lossless compression. It supports reversible recompression of existing JPEG files, as well as high-precision HDR (up to 32-bit floating point values per pixel component). It is designed to be usable for both delivery and authoring use cases.
==== Authoring / Interchange formats ====
===== TIFF =====
The TIFF (Tag Image File Format) format is a flexible format usually using either the TIFF or TIF filename extension. The tag structure was designed to be easily extendible, and many vendors have introduced proprietary special-purpose tags – with the result that no one reader handles every flavor of TIFF file. TIFFs can be lossy or lossless, depending on the technique chosen for storing the pixel data. Some offer relatively good lossless compression for bi-level (black&white) images. Some digital cameras can save images in TIFF format, using the LZW compression algorithm for lossless storage. TIFF image format is not widely supported by web browsers, but it remains widely accepted as a photograph file standard in the printing business. TIFF can handle device-specific color spaces, such as the CMYK defined by a particular set of printing press inks. OCR (Optical Character Recognition) software packages commonly generate some form of TIFF image (often monochromatic) for scanned text pages.
===== BMP =====
The BMP file format (Windows bitmap) is a raster-based, device-independent file type designed in the early days of computer graphics. It handles graphic files within the Microsoft Windows OS. Typically, BMP files are uncompressed and therefore large and lossless; their advantage is their simple structure and wide acceptance in Windows programs.
===== PPM, PGM, PBM, and PNM =====
Netpbm format is a family including the portable pixmap file format (PPM), the portable graymap file format (PGM), and the portable bitmap file format (PBM). These are either pure ASCII files or raw binary files with an ASCII header that provide very basic functionality and serve as a lowest common denominator for converting pixmap, graymap, or bitmap files between different platforms. Several applications refer to them collectively as PNM ("Portable aNy Map").
===== Container formats of raster graphics editors =====
These image formats contain various images, layers and objects, out of which the final image is to be composed
AFPhoto (Affinity Photo Document)
CD5 (Chasys Draw Image)
CLIP (Clip Studio Paint)
CPT (Corel Photo Paint)
KRA (Krita)
MDP (Medibang and FireAlpaca)
PDN (Paint Dot Net)
PLD (PhotoLine Document)
PSD (Adobe PhotoShop Document)
PSP (Corel Paint Shop Pro)
SAI (Paint Tool SAI)
XCF (eXperimental Computing Facility format)—native GIMP format
==== Other raster formats ====
BPG (Better Portable Graphics)—an image format from 2014. Its purpose is to replace JPEG when quality or file size is an issue. To that end, it features a high data compression ratio, based on a subset of the HEVC video compression standard, including lossless compression. In addition, it supports various meta data (such as EXIF).
DEEP—IFF-style format used by TVPaint
DRW (Drawn File)
ECW (Enhanced Compression Wavelet)
FITS (Flexible Image Transport System)
FLIF (Free Lossless Image Format)—a discontinued lossless image format which claims to outperform PNG, lossless WebP, lossless BPG and lossless JPEG 2000 in terms of compression ratio. It uses the MANIAC (Meta-Adaptive Near-zero Integer Arithmetic Coding) entropy encoding algorithm, a variant of the CABAC (context-adaptive binary arithmetic coding) entropy encoding algorithm.
ICO—container for one or more icons (subsets of BMP and/or PNG)
ILBM—IFF-style format for up to 32 bit in planar representation, plus optional 64 bit extensions
IMG (ERDAS IMAGINE Image)
IMG (Graphics Environment Manager (GEM) image file)—planar, run-length encoded
JPEG XR—JPEG standard based on Microsoft HD Photo
Nrrd (Nearly raw raster data)
PAM (Portable Arbitrary Map)—late addition to the Netpbm family
PCX (PiCture eXchange)—obsolete
PGF (Progressive Graphics File)
SGI (Silicon Graphics Image)—native raster graphics file format for Silicon Graphics workstations
SID (multiresolution seamless image database, MrSID)
Sun Raster—obsolete
TGA (TARGA)—obsolete
VICAR file format—NASA/JPL image transport format
XISF (Extensible Image Serialization Format)
=== Vector formats ===
As opposed to the raster image formats above (where the data describes the characteristics of each individual pixel), vector image formats contain a geometric description which can be rendered smoothly at any desired display size.
At some point, all vector graphics must be rasterized in order to be displayed on digital monitors. Vector images may also be displayed with analog CRT technology such as that used in some electronic test equipment, medical monitors, radar displays, laser shows and early video games. Plotters are printers that use vector data rather than pixel data to draw graphics.
==== CGM ====
CGM (Computer Graphics Metafile) is a file format for 2D vector graphics, raster graphics, and text, and is defined by ISO/IEC 8632. All graphical elements can be specified in a textual source file that can be compiled into a binary file or one of two text representations. CGM provides a means of graphics data interchange for computer representation of 2D graphical information independent from any particular application, system, platform, or device.
It has been adopted to some extent in the areas of technical illustration and professional design, but has largely been superseded by formats such as SVG and DXF.
==== Gerber format (RS-274X) ====
The Gerber format (aka Extended Gerber, RS-274X) is a 2D bi-level image description format developed by Ucamco. It is the de facto standard format for printed circuit board or PCB software.
==== SVG ====
SVG (Scalable Vector Graphics) is an open standard created and developed by the World Wide Web Consortium to address the need (and attempts of several corporations) for a versatile, scriptable and all-purpose vector format for the web and otherwise. The SVG format does not have a compression scheme of its own, but due to the textual nature of XML, an SVG graphic can be compressed using a program such as gzip. Because of its scripting potential, SVG is a key component in web applications: interactive web pages that look and act like applications.
==== Other 2D vector formats ====
AFDesign (Affinity Designer document)
AI (Adobe Illustrator Artwork)— proprietary file format developed by Adobe Systems
CDR—proprietary format for CorelDRAW vector graphics editor
!DRAW—a native vector graphic format (in several backward compatible versions) for the RISC-OS computer system begun by Acorn in the mid-1980s and still present on that platform today
DrawingML—used in Office Open XML documents
GEM—metafiles interpreted and written by the Graphics Environment Manager VDI subsystem
GLE (Graphics Layout Engine)—graphics scripting language
HP-GL (Hewlett-Packard Graphics Language)—introduced on Hewlett-Packard plotters, but generalized into a printer language
HVIF (Haiku Vector Icon Format)
Lottie—format for vector graphics animation
MathML (Mathematical Markup Language)—an application of XML for describing mathematical notations
NAPLPS (North American Presentation Layer Protocol Syntax)
ODG (OpenDocument Graphics)
PGML (Precision Graphics Markup Language)—a W3C submission that was not adopted as a recommendation
PSTricks and PGF/TikZ are languages for creating graphics in TeX documents
QCC—used by Quilt Manager (by Quilt EZ) for designing quilts
ReGIS (Remote Graphic Instruction Set)—used by DEC computer terminals
Remote imaging protocol—system for sending vector graphics over low-bandwidth links
TinyVG—binary, simpler alternative to SVG
VML (Vector Markup Language)—obsolete XML-based format
Xar—format used in vector applications from Xara
XPS (XML Paper Specification)—page description language and a fixed-document format
==== 3D vector formats ====
AMF – Additive Manufacturing File Format
Asymptote – A language that lifts TeX to 3D.
.blend – Blender
COLLADA
DGN
.dwf
.dwg
.dxf
eDrawings
.flt – OpenFlight
FVRML – and FX3D, function-based extensions of VRML and X3D
glTF - 3D asset delivery format (.glb binary version)
HSF
IGES
JT
.MA (Maya ASCII format)
.MB (Maya Binary format)
.OBJ Wavefront
OpenGEX – Open Game Engine Exchange
PLY
POV-Ray scene description language
PRC
STEP
SKP
STL – A stereolithography format
U3D – Universal 3D file format
VRML – Virtual Reality Modeling Language
XAML
XGL
XVL
xVRML
X3D
3DF
.3DM
.3ds – Autodesk 3D Studio
3DXML
X3D – Vector format used in 3D applications from Xara
=== Compound formats ===
These are formats containing both pixel and vector data, possible other data, e.g. the interactive features of PDF.
EPS (Encapsulated PostScript)
MODCA (Mixed Object:Document Content Architecture)
PDF (Portable Document Format)
PostScript, a page description language with strong graphics capabilities
PICT (Classic Macintosh QuickDraw file)
WMF / EMF (Windows Metafile / Enhanced Metafile)
SWF (Shockwave Flash)
XAML User interface language using vector graphics for images.
=== Stereo formats ===
MPO The Multi Picture Object (.mpo) format consists of multiple JPEG images (Camera & Imaging Products Association) (CIPA).
PNS The PNG Stereo (.pns) format consists of a side-by-side image based on PNG (Portable Network Graphics).
JPS The JPEG Stereo (.jps) format consists of a side-by-side image format based on JPEG.
== See also ==
Display resolution
Display aspect ratio
List of common display resolutions
Display resolution standards
== References == | Wikipedia/Graphics_file_format |
In visual arts, a model sheet, also known as a character board, character sheet, character study or simply a study, is a document used to help standardize the appearance, poses, and gestures of a character in arts such as animation, comics, and video games.
Model sheets are required when multiple artists are involved in the production of an animated film, game, or comic to help maintain continuity in characters from scene to scene. In animation, one animator may only do one shot out of the several hundred that are required to complete an animated feature film. A character not drawn according to the production's standardized model is referred to as off-model.
Model sheets are also used for references in 3D modeling to guide proper proportions of models.
== Purposes ==
Model sheets have also been used in the past to maintain graphic continuity over the years for long lasting cartoon productions of short or short features such as the Looney Tunes or Merrie Melodies series.
Model sheets are drawings of posed cartoon or comic strip characters that are created to provide a reference template for several artists who collaborate in the production of a lengthy or multiple-edition work of art such as a comic book, animated film or television series. Model sheets usually depict the character's head and body as they appear at various angles (a process known as "model rotation"), includes sketches of the character's hands and feet, and shows several basic facial expressions.
Model sheets ensure that, despite the efforts of several or many artists, their work exhibits unity, as if one artist created the drawings (that is, they are "on model"). They show the character's structure, proportions, attire, and body language. Often, several sheets are required to depict a character's subtler emotional and physical attitudes.
Depending on the whim of animation direction, deviations from the model may be permitted in the course of final animation; this "tightness" of model is a major distinguishing factor in overall animation style, as it constitutes a tradeoff between expressiveness and smoothness/consistency. As such, the usage of models varies widely between studios and projects.
Model sheets can also be used in the construction of costumes or sculpted figurines.
== Specific annotations ==
Model sheets also provide notes that present specific information about how to develop particular features of the character, such as his or her head shape, hair length and style, size and position of the eyes and the mouth.
== Examples ==
Some model sheets are specific to particular completed or ongoing projects, whereas others are more general and inclusive of a studio's entire collection of characters. Animation studios besides Disney and fans also post model sheets on their Internet Web sites. Larry's Toon Institute provides a generic model sheet for the purpose of introducing the concept of model sheets.
== Copyright and fair use ==
Model sheets are not typically in the public domain, but are copyrighted material owned by the animation studio which created it.
Although model sheets originally are intended for artists who work for the studios that own the characters for which these templates are developed, other artists, such as those who create fan art, profit from them by adapting their characters to their own uses.
== See also ==
Character animation
Glossary of comics terminology
== References ==
== Further reading ==
Bancroft, Tom (2006). Creating characters with personality. New York: Watson-Guptill. pp. 15, 54–57. ISBN 0823023494. OCLC 61821740.
Chapman, Robyn (2012). Drawing comics lab: characters, panels, storytelling, publishing, and professional practices. Beverly: Quarry Books. p. 23. ISBN 9781592538126. OCLC 781679591.
McCloud, Scott (2006). Making comics: storytelling secrets of comics, manga and graphic novels. New York: Harper. pp. 74–77. ISBN 9780060780944. OCLC 71225478.
White, Tony (2006). Animation from pencils to pixels: classical techniques for digital animators. Amsterdam; Burlington, MA: Focal Press. p. 38. ISBN 0240806700. OCLC 70229895.
== External links ==
Media related to Model sheets at Wikimedia Commons | Wikipedia/Model_sheet |
Animation is a filmmaking technique whereby still images are manipulated to create moving images. In traditional animation, images are drawn or painted by hand on transparent celluloid sheets to be photographed and exhibited on film. Animation has been recognized as an artistic medium, specifically within the entertainment industry. Many animations are either traditional animations or computer animations made with computer-generated imagery (CGI). Stop motion animation, in particular claymation, has continued to exist alongside these other forms.
Animation is contrasted with live action, although the two do not exist in isolation. Many moviemakers have produced films that are a hybrid of the two. As CGI increasingly approximates photographic imagery, filmmakers can easily composite 3D animations into their film rather than using practical effects for showy visual effects (VFX).
== General overview ==
Computer animation can be very detailed 3D animation, while 2D computer animation (which may have the look of traditional animation) can be used for stylistic reasons, low bandwidth, or faster real-time renderings. Other common animation methods apply a stop motion technique to two- and three-dimensional objects like paper cutouts, puppets, or clay figures.
An animated cartoon, or simply a cartoon, is an animated film, usually short, that features an exaggerated visual style. This style is often inspired by comic strips, gag cartoons, and other non-animated art forms. Cartoons frequently include anthropomorphic animals, superheroes, or the adventures of human protagonists. The action often revolves around exaggerated physical humor, particularly in predator/prey dynamics (e.g. cats and mices, coyotes and birds), where violent pratfalls such as falls, collisions, and explosions occur, often in ways that would be lethal in the real life.
During the 1980s, the term "cartoon" was shortened to toon, referring to characters in animated productions, or more specifically, cartoonishly-drawn characters. This term gained popularity first in 1988 with the live-action/animated hybrid film Who Framed Roger Rabbit, which introduced ToonTown, a world inhabited by various animated cartoon characters. In 1990, Tiny Toon Adventures embraced the classic cartoon spirit, introducing a new generation of cartoon characters. Then, in 1993, Animaniacs followed, featuring the rubber-hose-styled Warner siblings, Yakko Warner, Wakko Warner, and Dot Warner, who are trapped in the 1930s, eventually escaped and found themselves in the Warner Bros. water tower in the 1990s.
The illusion of animation—as in motion pictures in general—has traditionally been attributed to the persistence of vision and later to the phi phenomenon and beta movement, but the exact neurological causes are still uncertain. The illusion of motion caused by a rapid succession of images that minimally differ from each other, with unnoticeable interruptions, is a stroboscopic effect. While animators traditionally used to draw each part of the movements and changes of figures on transparent cels that could be moved over a separate background, computer animation is usually based on programming paths between key frames to maneuver digitally created figures throughout a digitally created environment.
Analog mechanical animation media that rely on the rapid display of sequential images include the phenakistiscope, zoetrope, flip book, praxinoscope, and film. Television and video are popular electronic animation media that originally were analog and now operate digitally. For display on computers, technology such as the animated GIF and Flash animation were developed.
In addition to short films, feature films, television series, animated GIFs, and other media dedicated to the display of moving images, animation is also prevalent in video games, motion graphics, user interfaces, and visual effects.
The physical movement of image parts through simple mechanics—for instance, moving images in magic lantern shows—can also be considered animation. The mechanical manipulation of three-dimensional puppets and objects to emulate living beings has a very long history in automata. Electronic automata were popularized by Disney as animatronics.
== Etymology ==
The word animation comes to the Latin word animātiō, meaning 'bestowing of life'. The earlier meaning of the English word is 'liveliness' and has been in use much longer than the meaning of 'moving image medium'.
== History ==
=== Before cinematography ===
Long before modern animation began, audiences around the world were captivated by the magic of moving characters. For centuries, master artists and craftsmen have brought puppets, automatons, shadow puppets, and fantastical lanterns to life, inspiring the imagination through physically manipulated wonders.
In 1833, the stroboscopic disc (better known as the phenakistiscope) introduced the principle of modern animation, which would also be applied in the zoetrope (introduced in 1866), the flip book (1868), the praxinoscope (1877) and film.
=== Silent era ===
When cinematography eventually broke through in the 1890s, the wonder of the realistic details in the new medium was seen as its biggest accomplishment. It took years before animation found its way to the cinemas. The successful short The Haunted Hotel (1907) by J. Stuart Blackton popularized stop motion and reportedly inspired Émile Cohl to create Fantasmagorie (1908), regarded as the oldest known example of a complete traditional (hand-drawn) animation on standard cinematographic film. Other great artistic and very influential short films were created by Ladislas Starevich with his puppet animations since 1910 and by Winsor McCay with detailed hand-drawn animation in films such as Little Nemo (1911) and Gertie the Dinosaur (1914).
During the 1910s, the production of animated "cartoons" became an industry in the US. Successful producer John Randolph Bray and animator Earl Hurd, patented the cel animation process that dominated the animation industry for the rest of the century. Felix the Cat, who debuted in 1919, became the first fully realized anthropomorphic animal character in the history of American animation.
=== American golden age ===
In 1928, Steamboat Willie, featuring Mickey Mouse and Minnie Mouse, popularized film-with-synchronized-sound and put Walt Disney's studio at the forefront of the animation industry. Although Disney Animation's actual output relative to total global animation output has always been very small, the studio has overwhelmingly dominated the "aesthetic norms" of animation ever since.
The enormous success of Mickey Mouse is seen as the start of the golden age of American animation that would last until the 1960s. The United States dominated the world market of animation with a plethora of cel-animated theatrical shorts. Several studios would introduce characters that would become very popular and would have long-lasting careers, including Walt Disney Productions' Goofy (1932) and Donald Duck (1934), Fleischer Studios/Paramount Cartoon Studios' Out of the Inkwell' Koko the Clown (1918), Bimbo and Betty Boop (1930), Popeye (1933) and Casper the Friendly Ghost (1945), Warner Bros. Cartoon Studios' Looney Tunes' Porky Pig (1935), Daffy Duck (1937), Elmer Fudd (1937–1940), Bugs Bunny (1938–1940), Tweety (1942), Sylvester the Cat (1945), Wile E. Coyote and the Road Runner (1949), MGM cartoon studio's Tom and Jerry (1940) and Droopy, Universal Cartoon Studios' Woody Woodpecker (1940), Terrytoons/20th Century Fox's Mighty Mouse (1942), and United Artists' Pink Panther (1963).
=== Features before CGI ===
In 1917, Italian-Argentine director Quirino Cristiani made the first feature-length film El Apóstol (now lost), which became a critical and commercial success. It was followed by Cristiani's Sin dejar rastros in 1918, but one day after its premiere, the film was confiscated by the government.
After working on it for three years, Lotte Reiniger released the German feature-length silhouette animation Die Abenteuer des Prinzen Achmed in 1926, the oldest extant animated feature.
In 1937, Walt Disney Studios premiered their first animated feature Snow White and the Seven Dwarfs, still one of the highest-grossing traditional animation features as of May 2020. The Fleischer studios followed this example in 1939 with Gulliver's Travels with some success. Partly due to foreign markets being cut off by the Second World War, Disney's next features Pinocchio, Fantasia (both 1940), Fleischer Studios' second animated feature Mr. Bug Goes to Town (1941–1942) and Disney's feature films Cinderella (1950), Alice in Wonderland (1951) and Lady and the Tramp (1955) failed at the box office. For several decades, Disney was the only American studio to regularly produce animated features, until Ralph Bakshi became the first to release more than a handful of features. Sullivan-Bluth Studios began to regularly produce animated features starting with An American Tail in 1986.
Although relatively few titles became as successful as Disney's features, other countries developed their own animation industries that produced both short and feature theatrical animations in a wide variety of styles, relatively often including stop motion and cutout animation techniques. Soviet Soyuzmultfilm animation studio, founded in 1936, produced 20 films (including shorts) per year on average and reached 1,582 titles in 2018. China, Czechoslovakia / Czech Republic, Italy, France, and Belgium were other countries that more than occasionally released feature films.
=== Television ===
Animation became very popular on television since the 1950s, when television sets started to become common in most developed countries. Cartoons were mainly programmed for children, on convenient time slots, and especially US youth spent many hours watching Saturday-morning cartoons. Many classic cartoons found a new life on the small screen and by the end of the 1950s, the production of new animated cartoons started to shift from theatrical releases to TV series. Hanna-Barbera Productions was especially prolific and had huge hit series, such as The Flintstones (1960–1966) (the first prime time animated series), Scooby-Doo (since 1969) and Belgian co-production The Smurfs (1981–1989). The constraints of American television programming and the demand for an enormous quantity resulted in cheaper and quicker limited animation methods and much more formulaic scripts. Quality dwindled until more daring animation surfaced in the late 1980s and in the early 1990s with hit series, the first cartoon of The Simpsons (1987), which later developed into its own show (in 1989) and SpongeBob SquarePants (since 1999) as part of a "renaissance" of American animation.
While US animated series also spawned successes internationally, many other countries produced their own child-oriented programming, relatively often preferring stop motion and puppetry over cel animation. Japanese anime TV series became very successful internationally since the 1960s, and European producers looking for affordable cel animators relatively often started co-productions with Japanese studios, resulting in hit series such as Barbapapa (The Netherlands/Japan/France 1973–1977), Wickie und die starken Männer/小さなバイキング ビッケ (Vicky the Viking) (Austria/Germany/Japan 1974), Maya the Honey Bee (Japan/Germany 1975) and The Jungle Book (Italy/Japan 1989).
=== Switch from cels to computers ===
Computer animation was gradually developed since the 1940s. 3D wireframe animation started popping up in the mainstream in the 1970s, with an early (short) appearance in the sci-fi thriller Futureworld (1976).
The Rescuers Down Under was the first feature film to be completely created digitally without a camera. It was produced using the Computer Animation Production System (CAPS), developed by Pixar in collaboration with The Walt Disney Company in the late 1980s, in a style similar to traditional cel animation.
The so-called 3D style, more often associated with computer animation, became the dominant technique following the success of Pixar's Toy Story (1995), the first computer-animated feature in this style.
Most of the cel animation studios switched to producing mostly computer-animated films around the 1990s, as it proved cheaper and more profitable. Not only the very popular 3D animation style was generated with computers, but also most of the films and series with a more traditional hand-crafted appearance, in which the charming characteristics of cel animation could be emulated with software, while new digital tools helped developing new styles and effects.
== Economic status ==
In 2010, the animation market was estimated to be worth circa US$80 billion. By 2021, the value had increased to an estimated US$370 billion. Animated feature-length films returned the highest gross margins (around 52%) of all film genres between 2004 and 2013. Animation as an art and industry continues to thrive as of the early 2020s.
== Education, propaganda and commercials ==
The clarity of animation makes it a powerful tool for instruction, while its total malleability also allows exaggeration that can be employed to convey strong emotions and to thwart reality. It has therefore been widely used for other purposes than mere entertainment.
During World War II, animation was widely exploited for propaganda. Many American studios, including Warner Bros. and Disney, lent their talents and their cartoon characters to convey to the public certain war values. These efforts extended to other countries well into the Cold War era, particularly as it pertained to "combatting" communism. For example, the English 1954 adaptation of George Orwell's Animal Farm (the nation's first feature-length animated film) is speculated to have had its production funded by the CIA.
Animation has been very popular in television commercials, both due to its graphic appeal, and the humour it can provide. Some animated characters in commercials have survived for decades, such as Snap, Crackle and Pop in advertisements for Kellogg's cereals. Tex Avery was the producer of the first Raid "Kills Bugs Dead" commercials in 1966, which were very successful for the company.
== Other media, merchandise and theme parks ==
Apart from their success in movie theaters and television series, many cartoon characters would also prove lucrative when licensed for all kinds of merchandise and for other media.
Animation has traditionally been very closely related to comic books. While many comic book characters found their way to the screen (which is often the case in Japan, where many manga are adapted into anime), original animated characters also commonly appear in comic books and magazines. Somewhat similarly, characters and plots for video games (an interactive form of animation that became its own medium) have been derived from films and vice versa.
Some of the original content produced for the screen can be used and marketed in other media. Stories and images can easily be adapted into children's books and other printed media. Songs and music have appeared on records and as streaming media.
While very many animation companies commercially exploit their creations outside moving image media, The Walt Disney Company is the best known and most extreme example. Since first being licensed for a children's writing tablet in 1929, their Mickey Mouse mascot has been depicted on an enormous amount of products, as have many other Disney characters. This may have influenced some pejorative use of Mickey's name, but licensed Disney products sell well, and the so-called Disneyana has many avid collectors, and even a dedicated Disneyana Fan Club (since 1984).
Disneyland opened in 1955 and features many attractions that were based on Disney's cartoon characters. Its enormous success spawned several other Disney theme parks and resorts. Disney's earnings from the theme parks have relatively often been higher than those from their movies.
== Awards ==
As with any other form of media, animation has instituted awards for excellence in the field. Many are part of general or regional film award programs, like the China's Golden Rooster Award for Best Animation (since 1981). Awards programs dedicated to animation, with many categories, include ASIFA-Hollywood's Annie Awards, the Emile Awards in Europe and the Anima Mundi awards in Brazil.
=== Academy Awards ===
Apart from Academy Awards for Best Animated Short Film (since 1932) and Best Animated Feature (since 2002), animated movies have been nominated and rewarded in other categories, relatively often for Best Original Song and Best Original Score.
Beauty and the Beast was the first animated film nominated for Best Picture, in 1991. Up (2009) and Toy Story 3 (2010) also received Best Picture nominations, after the academy expanded the number of nominees from five to ten.
== Production ==
The creation of non-trivial animation works (i.e., longer than a few seconds) has developed as a form of filmmaking, with certain unique aspects. Traits common to both live-action and animated feature films are labor intensity and high production costs.
The most important difference is that once a film is in the production phase, the marginal cost of one more shot is higher for animated films than live-action films. It is relatively easy for a director to ask for one more take during principal photography of a live-action film, but every take on an animated film must be manually rendered by animators (although the task of rendering slightly different takes has been made less tedious by modern computer animation). It is pointless for a studio to pay the salaries of dozens of animators to spend weeks creating a visually dazzling five-minute scene if that scene fails to effectively advance the plot of the film. Thus, animation studios starting with Disney began the practice in the 1930s of maintaining story departments where storyboard artists develop every single scene through storyboards, then handing the film over to the animators only after the production team is satisfied that all the scenes make sense as a whole. While live-action films are now also storyboarded, they enjoy more latitude to depart from storyboards (i.e., real-time improvisation).
Another problem unique to animation is the requirement to maintain a film's consistency from start to finish, even as films have grown longer and teams have grown larger. Animators, like all artists, necessarily have individual styles, but must subordinate their individuality in a consistent way to whatever style is employed on a particular film. Since the early 1980s, teams of about 500 to 600 people, of whom 50 to 70 are animators, typically have created feature-length animated films. It is relatively easy for two or three artists to match their styles; synchronizing those of dozens of artists is more difficult.
This problem is usually solved by having a separate group of visual development artists develop an overall look and palette for each film before the animation begins. While animators must "sacrifice their personal drawing styles so that the work of many hands appears to be that of one", visual development artists are allowed to "create new worlds, new characters, and new entertainment possibilities in their own individualistic graphic styles". Character designers on the visual development team draw model sheets to show how each character should look like with different facial expressions, posed in different positions, and viewed from different angles. On traditionally animated projects, maquettes were often sculpted to further help the animators see how characters would look from different angles.
Unlike live-action films, animated films were traditionally developed beyond the synopsis stage through the storyboard format; the storyboard artists would then receive credit for writing the film. The traditional approach worked for several decades because prior to the 1960s, no one except Disney was attempting to regularly produce feature-length animated films. All other animation studios, with occasional exceptions, were producing short films only a few minutes in length. For short films, it was enough for the storyboard artists to work up a few visual gags and then string them together to form a crude plot.
In 1960, Hanna-Barbera pioneered the longer animated sitcom format for television with The Flintstones. Hanna-Barbera and the other early television animation studios soon discovered that storyboarding was far too inefficient to fill up a half-hour episode on the extremely tight budgets typical of television. During the 1960s, these studios experimented with a more efficient method for developing story material: a screenwriter is hired to draft a written screenplay which is approved and handed over to the storyboard artists for storyboarding. This method creates significant tension between screenwriters and storyboard artists, in that some artists feel that people who cannot draw should not be writing for animation, while some writers feel that artists do not understand how to write. Despite that tension, it has become and remains the dominant method by which animation studios develop both feature-length films and television shows.
Ironically, the Disney studio was relatively slow to adopt this method. The first Disney feature animated film to have a complete screenplay written and approved before storyboarding was One Hundred and One Dalmatians (1961). However, 101 Dalmatians was "a short-lived experiment"; the next Disney film to follow this method was The Great Mouse Detective (1986).
Finally, another key difference is that actors traditionally record vocal tracks for animated films in separate individual sessions. Actors usually schedule sessions in the recording studio around their live-action work. In live-action filmmaking, it is very common for actors to drop out of projects due to scheduling conflicts, while in animation, recording actors separately makes it possible to "get a lot more stars into one movie than" would be possible if those actors needed to be physically present on the same set at the same time.
== Techniques ==
=== Traditional ===
Traditional animation (also called cel animation or hand-drawn animation) is the process that was used for most animated films of the 20th century. The individual frames of a traditionally animated film are photographs of drawings, first drawn on paper. To create the illusion of movement, each drawing differs slightly from the one before it. The animators' drawings are traced or photocopied onto transparent acetate sheets called cels, which are filled in with paints in assigned colors or tones on the side opposite the line drawings. The completed character cels are photographed one-by-one against a painted background by a rostrum camera onto motion picture film.
The traditional cel animation process became obsolete by the beginning of the 21st century. In modern traditionally animated films, animators' drawings and the backgrounds are either scanned into or drawn directly into a computer system. Various software programs are used to color the drawings and simulate camera movement and effects. The final animated piece is output to one of several delivery media, including traditional 35 mm film and newer media with digital video. The "look" of traditional cel animation is still preserved, and the character animators' work has remained essentially the same over the past 90 years. Some animation producers have used the term "tradigital" (a play on the words "traditional" and "digital") to describe cel animation that uses significant computer technology.
Examples of traditionally animated feature films include Pinocchio (United States, 1940), Animal Farm (United Kingdom, 1954), Lucky and Zorba (Italy, 1998), and The Illusionist (British-French, 2010). Traditionally animated films produced with the aid of computer technology include The Lion King (US, 1994), Anastasia (US, 1997), The Prince of Egypt (US, 1998), Akira (Japan, 1988), Spirited Away (Japan, 2001), The Triplets of Belleville (France, 2003), and The Secret of Kells (Irish-French-Belgian, 2009).
==== Full ====
Full animation is the process of producing high-quality traditionally animated films that regularly use detailed drawings and plausible movement, having a smooth animation. Fully animated films can be made in a variety of styles, from more realistically animated works like those produced by the Walt Disney studio (The Little Mermaid, Beauty and the Beast, Aladdin, The Lion King) to the more 'cartoon' styles of the Warner Bros. animation studio. Many of the Disney animated features are examples of full animation, as are non-Disney works, The Secret of NIMH (US, 1982), The Iron Giant (US, 1999), and Nocturna (Spain, 2007). Fully animated films are often animated on "twos", sometimes on "ones", which means that 12 to 24 drawings are required for a single second of film.
==== Limited ====
Limited animation involves the use of less detailed or more stylized drawings and methods of movement usually a choppy or "skippy" movement animation. Limited animation uses fewer drawings per second, thereby limiting the fluidity of the animation. This is a more economic technique. Pioneered by the artists at the American studio United Productions of America, limited animation can be used as a method of stylized artistic expression, as in Gerald McBoing-Boing (US, 1951), Yellow Submarine (UK, 1968), and certain anime produced in Japan. Its primary use, however, has been in producing cost-effective animated content for media for television (the work of Hanna-Barbera, Filmation, and other TV animation studios) and later the Internet (web cartoons).
==== Rotoscoping ====
Rotoscoping is a technique patented by Max Fleischer in 1917 where animators trace live-action movement, frame by frame. The source film can be directly copied from actors' outlines into animated drawings, as in The Lord of the Rings (US, 1978), or used in a stylized and expressive manner, as in Waking Life (US, 2001) and A Scanner Darkly (US, 2006). Some other examples are Fire and Ice (US, 1983), Heavy Metal (1981), and Aku no Hana (Japan, 2013).
==== Live-action blending ====
Live-action/animation is a technique combining hand-drawn characters into live action shots or live-action actors into animated shots. One of the earlier uses was in Koko the Clown when Koko was drawn over live-action footage. Walt Disney and Ub Iwerks created a series of Alice Comedies (1923–1927), in which a live-action girl enters an animated world. Other examples include Allegro Non Troppo (Italy, 1976), Who Framed Roger Rabbit (US, 1988), Volere volare (Italy 1991), Space Jam (US, 1996) and Osmosis Jones (US, 2001).
=== Stop motion ===
Stop motion is used to describe animation created by physically manipulating real-world objects and photographing them one frame of film at a time to create the illusion of movement. There are many different types of stop-motion animation, usually named after the materials used to create the animation. Computer software is widely available to create this type of animation; traditional stop-motion animation is usually less expensive but more time-consuming to produce than current computer animation.
Stop motion
Typically involves stop-motion puppet figures interacting in a constructed environment, in contrast to real-world interaction in model animation. The puppets generally have an armature inside of them to keep them still and steady to constrain their motion to particular joints. Examples include The Tale of the Fox (France, 1937), The Nightmare Before Christmas (US, 1993), Corpse Bride (US, 2005), Coraline (US, 2009), the films of Jiří Trnka and the adult animated sketch-comedy television series Robot Chicken (US, 2005–present).
Puppetoons
Created using techniques developed by George Pal, are puppet-animated films that typically use a different version of a puppet for different frames, rather than manipulating one existing puppet.
Clay animation or Plasticine animation
(Often called claymation, which, however, is a trademarked name). It uses figures made of clay or a similar malleable material to create stop-motion animation. The figures may have an armature or wire frame inside, similar to the related puppet animation (below), that can be manipulated to pose the figures. Alternatively, the figures may be made entirely of clay, in the films of Bruce Bickford, where clay creatures morph into a variety of different shapes. Examples of clay-animated works include The Gumby Show (US, 1957–1967), Mio Mao (Italy, 1974–2005), Morph shorts (UK, 1977–2000), Wallace & Gromit shorts (UK, as of 1989), Jan Švankmajer's Dimensions of Dialogue (Czechoslovakia, 1982), The Trap Door (UK, 1984). Films include Wallace & Gromit: The Curse of the Were-Rabbit, Chicken Run and The Adventures of Mark Twain.
Strata-cut animation
Most commonly a form of clay animation in which a long bread-like "loaf" of clay, internally packed tight and loaded with varying imagery, is sliced into thin sheets, with the animation camera taking a frame of the end of the loaf for each cut, eventually revealing the movement of the internal images within.
Cutout animation
A type of stop-motion animation produced by moving two-dimensional pieces of material paper or cloth. Examples include Terry Gilliam's animated sequences from Monty Python's Flying Circus (UK, 1969–1974); Fantastic Planet (France/Czechoslovakia, 1973); Tale of Tales (Russia, 1979), Matt Stone and Trey Parker the first cutout animation South Park (1992), the pilot episode of the adult television sitcom series (and sometimes in episodes) of South Park (US, 1997) and the music video Live for the moment, from Verona Riots band (produced by Alberto Serrano and Nívola Uyá, Spain 2014).
Silhouette animation
A variant of cutout animation in which the characters are backlit and only visible as silhouettes. Examples include The Adventures of Prince Achmed (Weimar Republic, 1926) and Princes et Princesses (France, 2000).
Model animation
Stop-motion animation created to interact with and exist as a part of a live-action world. Intercutting, matte effects and split screens are often employed to blend stop-motion characters or objects with live actors and settings. Examples include the work of Ray Harryhausen, as seen in films, Jason and the Argonauts (1963), and the work of Willis H. O'Brien on films, King Kong (1933).
Go motion
A variant of model animation that uses various techniques to create motion blur between frames of film, which is not present in traditional stop motion. The technique was invented by Industrial Light & Magic and Phil Tippett to create special effect scenes for the film Star Wars: Episode V – The Empire Strikes Back (1980). Another example is the dragon named "Vermithrax" from the 1981 film Dragonslayer.
Object animation
The use of regular inanimate objects in stop-motion animation, as opposed to specially created items.
Graphic animation
Uses non-drawn flat visual graphic material (photographs, newspaper clippings, magazines, etc.), which are sometimes manipulated frame by frame to create movement. At other times, the graphics remain stationary, while the stop-motion camera is moved to create on-screen action.
Brickfilm
A subgenre of object animation involving using Lego or other similar brick toys to make an animation. These have had a recent boost in popularity with the advent of video sharing sites, YouTube and the availability of cheap cameras and animation software.
Pixilation
Involves the use of live humans as stop-motion characters. This allows for a number of surreal effects, including disappearances and reappearances, allowing people to appear to slide across the ground, and other effects. Examples of pixilation include The Secret Adventures of Tom Thumb and Angry Kid shorts, and the Academy Award-winning Neighbours by Norman McLaren.
=== Computer ===
Computer animation encompasses a variety of techniques, the unifying factor being that the animation is created digitally on a computer. 2D animation techniques tend to focus on image manipulation while 3D techniques usually build virtual worlds in which characters and objects move and interact. 3D animation can create images that seem real to the viewer.
==== 2D ====
2D animation figures are created or edited on the computer using 2D bitmap graphics and 2D vector graphics. This includes automated computerized versions of traditional animation techniques, interpolated morphing, onion skinning and interpolated rotoscoping. 2D animation has many applications, including After Effects Animation, analog computer animation, Flash animation, and PowerPoint animation. Cinemagraphs are still photographs in the form of an animated GIF file of which part is animated.
Final line advection animation is a technique used in 2D animation, to give artists and animators more influence and control over the final product as everything is done within the same department. Speaking about using this approach in Paperman, John Kahrs said that "Our animators can change things, actually erase away the CG underlayer if they want, and change the profile of the arm."
When working with game animations, skeletal 2D animations are commonly created using tools like Spine, DragonBones, Blender COA Tools, Rive, and the built-in Unity editor. The primary benefit of this approach is the ability to reuse images, which reduces the amount of graphics stored in RAM. This principle of maximizing resource efficiency means that by reusing existing elements, you can enhance the visual appeal of animations without needing to create additional graphics.
==== 3D ====
3D animation is digitally modeled and manipulated by an animator. The 3D model maker usually starts by creating a 3D polygon mesh for the animator to manipulate. A mesh typically includes many vertices that are connected by edges and faces, which give the visual appearance of form to a 3D object or 3D environment. Sometimes, the mesh is given an internal digital skeletal structure called an armature that can be used to control the mesh by weighting the vertices. This process is called rigging and can be used in conjunction with key frames to create movement.
Other techniques can be applied, mathematical functions (e.g., gravity, particle simulations), simulated fur or hair, and effects, fire and water simulations. These techniques fall under the category of 3D dynamics.
===== Terms =====
Cel shading is used to mimic traditional animation using computer software. The shading looks stark, with less blending of colors. Examples include Skyland (2007, France), The Iron Giant (1999, U.S.), Futurama (1999, U.S.) Appleseed Ex Machina (2007, Japan), The Legend of Zelda: The Wind Waker (2002, Japan), The Legend of Zelda: Breath of the Wild (2017, Japan)
Machinima – Films created by screen capturing in video games and virtual worlds. The term originated from the software introduction in the 1980s demoscene, as well as the 1990s recordings of the first-person shooter video game Quake.
Motion capture is used when live-action actors wear special suits that allow computers to copy their movements into CG characters. Examples include Polar Express (2004, US), Beowulf (2007, US), A Christmas Carol (2009, US), The Adventures of Tintin (2011, US) kochadiiyan (2014, India)
Computer animation is used primarily for animation that attempts to resemble real life while having a stylized cartoonish appearance, using advanced rendering that mimics in detail skin, plants, water, fire, clouds, etc. Examples include Up (2009, US), How to Train Your Dragon (2010, US)
Physically based animation is animation using computer simulations.
=== Mechanical ===
Animatronics is the use of mechatronics to create machines that seem animate rather than robotic.
Audio-Animatronics is a form of robotics animation, combined with 3-D animation, created by Walt Disney Imagineering for shows and attractions at Disney theme parks move and make noise (generally a recorded speech or song). They are fixed to whatever supports them. They can sit and stand, and they cannot walk. An Audio-Animatron is different from an android-type robot in that it uses prerecorded movements and sounds, rather than responding to external stimuli. In 2009, Disney created an interactive version of the technology called Autonomatronics.
Linear Animation Generator is a form of animation by using static picture frames installed in a tunnel or a shaft. The animation illusion is created by putting the viewer in a linear motion, parallel to the installed picture frames.
Chuckimation is a type of animation created by the makers of the television series Action League Now! in which characters/props are thrown, or chucked from off camera or wiggled around to simulate talking by unseen hands.
The magic lantern used mechanical slides to project moving images. Christiaan Huygens was thought to have invented the magic lantern in the mid-1600s.
=== Other ===
Musical fountain: a hydrautechnical show that includes water and lights, nowadays often combined with lasers and high-definition projections on mist screens.
Drawn-on-film animation: a technique where footage is produced by creating the images directly on film stock; for example, by Norman McLaren, Len Lye and Stan Brakhage.
Paint-on-glass animation: a technique for making animated films by manipulating slow drying oil paints on sheets of glass, for example by Aleksandr Petrov.
Erasure animation: a technique using traditional 2D media, photographed over time as the artist manipulates the image. For example, William Kentridge is famous for his charcoal erasure films, and Piotr Dumała for his auteur technique of animating scratches on plaster.
Pinscreen animation: makes use of a screen filled with movable pins that can be moved in or out by pressing an object onto the screen. The screen is lit from the side so that the pins cast shadows. The technique has been used to create animated films with a range of textural effects difficult to achieve with traditional cel animation.
Sand animation: sand is moved around on a back- or front-lighted piece of glass to create each frame for an animated film. This creates an interesting effect when animated because of the light contrast.
Flip book: a flip book (sometimes, especially in British English, called a flick book) is a book with a series of pictures that vary gradually from one page to the next, so that when the pages are turned rapidly, the pictures appear to animate by simulating motion or some other change. Flip books are often illustrated books for children, they also are geared towards adults and employ a series of photographs rather than drawings. Flip books are not always separate books, they appear as an added feature in ordinary books or magazines, often in the page corners. Software packages and websites are also available that convert digital video files into custom-made flip books.
Character animation
Multi-sketch animation
Special effects animation
2.5D Animation: A mix of 2D and 3D animation elements that emphasize the illusion of depth utilizing the pseudo-3D effect. During the 1970s, the term "2.5D" started to gain recognition. But its background comes from anime and manga during the 1920s where theatrical stage productions were popular. Stage adaptations of well-liked anime series featured live performances by voice actors called 2.5D.
== See also ==
== References ==
=== Citations ===
=== Sources ===
==== Journal articles ====
==== Books ====
==== Online sources ====
== External links ==
The making of an 8-minute cartoon short
"Animando", a 12-minute film demonstrating 10 different animation techniques (and teaching how to use them) (archived 1 October 2009). | Wikipedia/Graphic_animation |
Vector graphics are a form of computer graphics in which visual images are created directly from geometric shapes defined on a Cartesian plane, such as points, lines, curves and polygons. The associated mechanisms may include vector display and printing hardware, vector data models and file formats, as well as the software based on these data models (especially graphic design software, computer-aided design, and geographic information systems). Vector graphics are an alternative to raster or bitmap graphics, with each having advantages and disadvantages in specific situations.
While vector hardware has largely disappeared in favor of raster-based monitors and printers, vector data and software continue to be widely used, especially when a high degree of geometric precision is required, and when complex information can be decomposed into simple geometric primitives. Thus, it is the preferred model for domains such as engineering, architecture, surveying, 3D rendering, and typography, but is entirely inappropriate for applications such as photography and remote sensing, where raster is more effective and efficient. Some application domains, such as geographic information systems (GIS) and graphic design, use both vector and raster graphics at times, depending on purpose.
Vector graphics are based on the mathematics of analytic or coordinate geometry, and is not related to other mathematical uses of the term vector. This can lead to some confusion in disciplines in which both meanings are used.
== Data model ==
The logical data model of vector graphics is based on the mathematics of coordinate geometry, in which shapes are defined as a set of points in a two- or three-dimensional cartesian coordinate system, as p = (x, y) or p = (x, y, z). Because almost all shapes consist of an infinite number of points, the vector model defines a limited set of geometric primitives that can be specified using a finite sample of salient points called vertices. For example, a square can be unambiguously defined by the locations of three of its four corners, from which the software can interpolate the connecting boundary lines and the interior space. Because it is a regular shape, a square could also be defined by the location of one corner, a size (width=height), and a rotation angle.
The fundamental geometric primitives are:
A single point.
A line segment, defined by two end points, allowing for a simple linear interpolation of the intervening line.
A polygonal chain or polyline, a connected set of line segments, defined by an ordered list of points.
A polygon, representing a region of space, defined by its boundary, a polyline with coincident starting and ending vertices.
A variety of more complex shapes may be supported:
Parametric curves, in which polylines or polygons are augmented with parameters to define a non-linear interpolation between vertices, including circular arcs, cubic splines, Catmull–Rom splines, Bézier curves and bezigons.
Standard parametric shapes in two or three dimensions, such as circles, ellipses, squares, superellipses, spheres, tetrahedrons, superellipsoids, etc.
Irregular three-dimensional surfaces and solids, are usually defined as a connected set of polygons (e.g., a polygon mesh) or as parametric surfaces (e.g., NURBS).
Fractals, often defined as an iterated function system.
In many vector datasets, each shape can be combined with a set of properties. The most common are visual characteristics, such as color, line weight, or dash pattern. In systems in which shapes represent real-world features, such as GIS and BIM, a variety of attributes of each represented feature can be stored, such as name, age, size, and so on.
In some Vector data, especially in GIS, information about topological relationships between objects may be represented in the data model, such as tracking the connections between road segments in a transport network.
If a dataset stored in one vector file format is converted to another file format that supports all the primitive objects used in that particular image, then the conversion can be lossless.
== Vector display hardware ==
Vector-based devices, such as the vector CRT and the pen plotter, directly control a drawing mechanism to produce geometric shapes. Since vector display devices can define a line by dealing with just two points (that is, the coordinates of each end of the line), the device can reduce the total amount of data it must deal with by organizing the image in terms of pairs of points.
Vector graphic displays were first used in 1958 by the US SAGE air defense system. Vector graphics systems were retired from the U.S. en route air traffic control in 1999. Vector graphics were also used on the TX-2 at the Massachusetts Institute of Technology Lincoln Laboratory by computer graphics pioneer Ivan Sutherland to run his program Sketchpad in 1963.
Subsequent vector graphics systems, most of which iterated through dynamically modifiable stored lists of drawing instructions, include the IBM 2250, Imlac PDS-1, and DEC GT40. There was a video game console that used vector graphics called Vectrex as well as various arcade games like Asteroids, Space Wars, Tempest and many cinematronics titles such as Rip Off, and Tail Gunner using vector monitors. Storage scope displays, such as the Tektronix 4014, could display vector images but not modify them without first erasing the display. However, these were never as widely used as the raster-based scanning displays used for television, and had largely disappeared by the mid-1980s except for specialized applications.
Plotters used in technical drawing still draw vectors directly to paper by moving a pen as directed through the two-dimensional space of the paper. However, as with monitors, these have largely been replaced by the wide-format printer that prints a raster image (which may be rendered from vector data).
== Software ==
Because this model is useful in a variety of application domains, many different software programs have been created for drawing, manipulating, and visualizing vector graphics. While these are all based on the same basic vector data model, they can interpret and structure shapes very differently, using very different file formats.
Graphic design and illustration, using a vector graphics editor or graphic art software such as Adobe Illustrator. See Comparison of vector graphics editors for capabilities.
Geographic information systems (GIS), which can represent a geographic feature by a combination of a vector shape and a set of attributes. GIS includes vector editing, mapping, and vector spatial analysis capabilities.
Computer-aided design (CAD), used in engineering, architecture, and surveying. Building information modeling (BIM) models add attributes to each shape, similar to a GIS.
3D computer graphics software, including computer animation.
== File formats ==
Vector graphics are commonly found today in the SVG, WMF, EPS, PDF, CDR or AI types of graphic file formats, and are intrinsically different from the more common raster graphics file formats such as JPEG, PNG, APNG, GIF, WebP, BMP and MPEG4.
The World Wide Web Consortium (W3C) standard for vector graphics is Scalable Vector Graphics (SVG). The standard is complex and has been relatively slow to be established at least in part owing to commercial interests. Many web browsers now have some support for rendering SVG data but full implementations of the standard are still comparatively rare.
In recent years, SVG has become a significant format that is completely independent of the resolution of the rendering device, typically a printer or display monitor. SVG files are essentially printable text that describes both straight and curved paths, as well as other attributes. Wikipedia prefers SVG for images such as simple maps, line illustrations, coats of arms, and flags, which generally are not like photographs or other continuous-tone images. Rendering SVG requires conversion to a raster format at a resolution appropriate for the current task. SVG is also a format for animated graphics.
There is also a version of SVG for mobile phones called SVGT (SVG Tiny version). These images can count links and also exploit anti-aliasing. They can also be displayed as wallpaper.
CAD software uses its own vector data formats, usually proprietary formats created by software vendors, such as Autodesk's DWG and public exchange formats such as DXF. Hundreds of distinct vector file formats have been created for GIS data over its history, including proprietary formats like the Esri file geodatabase, proprietary but public formats like the Shapefile and the original KML, open source formats like GeoJSON, and formats created by standards bodies like Simple Features and GML from the Open Geospatial Consortium.
=== Conversion ===
==== To raster ====
Modern displays and printers are raster devices; vector formats have to be converted to a raster format (bitmaps – pixel arrays) before they can be rendered (displayed or printed). The size of the bitmap/raster-format file generated by the conversion will depend on the resolution required, but the size of the vector file generating the bitmap/raster file will always remain the same. Thus, it is easy to convert from a vector file to a range of bitmap/raster file formats but it is much more difficult to go in the opposite direction, especially if subsequent editing of the vector picture is required. It might be an advantage to save an image created from a vector source file as a bitmap/raster format, because different systems have different (and incompatible) vector formats, and some might not support vector graphics at all. However, once a file is converted from the vector format, it is likely to be bigger, and it loses the advantage of scalability without loss of resolution. It will also no longer be possible to edit individual parts of the image as discrete objects. The file size of a vector graphic image depends on the number of graphic elements it contains; it is a list of descriptions.
==== From raster ====
=== Printing ===
Vector art is ideal for printing since the art is made from a series of mathematical curves; it will print very crisply even when resized. For instance, one can print a vector logo on a small sheet of copy paper, and then enlarge the same vector logo to billboard size and keep the same crisp quality. A low-resolution raster graphic would blur or pixelate excessively if it were enlarged from business card size to billboard size. (The precise resolution of a raster graphic necessary for high-quality results depends on the viewing distance; e.g., a billboard may still appear to be of high quality even at low resolution if the viewing distance is great enough.)
If we regard typographic characters as images, then the same considerations that we have made for graphics apply even to the composition of written text for printing (typesetting). Older character sets were stored as bitmaps. Therefore, to achieve maximum print quality they had to be used at a given resolution only; these font formats are said to be non-scalable. High-quality typography is nowadays based on character drawings (fonts) which are typically stored as vector graphics, and as such are scalable to any size. Examples of these vector formats for characters are Postscript fonts and TrueType fonts.
== Operation ==
Advantages of this style of drawing over raster graphics:
Because vector graphics consist of coordinates with lines/curves between them, the size of the representation does not depend on the dimensions of the object. This minimal amount of information translates to a much smaller file size compared to large raster images which are defined pixel by pixel. This said, a vector graphic with a small file size is often said to lack detail compared with a real-world photo.
Correspondingly, one can infinitely zoom in on e.g., a circle arc, and it remains smooth. On the other hand, a polygon representing a curve will reveal being not really curved.
On zooming in, lines and curves need not get wider proportionally. Often the width is either not increased or less than proportional. On the other hand, irregular curves represented by simple geometric shapes may be made proportionally wider when zooming in, to keep them looking smooth and not like these geometric shapes.
The parameters of objects are stored and can be later modified. This means that moving, scaling, rotating, filling, etc. does not degrade the quality of a drawing. Moreover, it is usual to specify the dimensions in device-independent units, which results in the best possible rasterization on raster devices.
From a 3-D perspective, rendering shadows is also much more realistic with vector graphics, as shadows can be abstracted into the rays of light from which they are formed. This allows for photorealistic images and renderings.
For example, consider a circle of radius r. The main pieces of information a program needs in order to draw this circle are
An indication that what is to be drawn is a circle
the radius r
the location of the center point of the circle
stroke line style and color (possibly transparent)
fill style and color (possibly transparent)
Vector formats are not always appropriate in graphics work and also have numerous disadvantages. For example, devices such as cameras and scanners produce essentially continuous-tone raster graphics that are impractical to convert into vectors, and so for this type of work, an image editor will operate on the pixels rather than on drawing objects defined by mathematical expressions. Comprehensive graphics tools will combine images from vector and raster sources, and may provide editing tools for both, since some parts of an image could come from a camera source, and others could have been drawn using vector tools.
Some authors have criticized the term vector graphics as being confusing. In particular, vector graphics does not simply refer to graphics described by Euclidean vectors. Some authors have proposed to use object-oriented graphics instead. However this term can also be confusing as it can be read as any kind of graphics implemented using object-oriented programming.
== Vector operations ==
Vector graphics editors typically allow translation, rotation, mirroring, stretching, skewing, affine transformations, changing of z-order (loosely, what's in front of what) and combination of primitives into more complex objects. More sophisticated transformations include set operations on closed shapes (union, difference, intersection, etc.). In SVG, the composition operations are based on alpha composition.
Vector graphics are ideal for simple or composite drawings that need to be device-independent, or do not need to achieve photo-realism. For example, the PostScript and PDF page description languages use a vector graphics model.
== Vector image repositories ==
Many stock photo websites provide vectorized versions of hosted images, while specific repositories specialize in vector images given their growing popularity among graphic designers.
== See also ==
== Notes ==
== References ==
Barr, Alan H. (July 1984). "Global and local deformations of solid primitives" (PDF). Proceedings of the 11th annual conference on Computer graphics and interactive techniques. Vol. 18. pp. 21–30. CiteSeerX 10.1.1.67.6046. doi:10.1145/800031.808573. ISBN 0897911385. S2CID 16162806. Retrieved July 31, 2020.
Gharachorloo, Nader; Gupta, Satish; Sproull, Robert F.; Sutherland, Ivan E. (July 1989). "A characterization of ten rasterization techniques" (PDF). Proceedings of the 16th annual conference on Computer graphics and interactive techniques. Vol. 23. pp. 355–368. CiteSeerX 10.1.1.105.461. doi:10.1145/74333.74370. ISBN 0201504340. S2CID 8253227. Retrieved July 28, 2020.
Murray, Stephen (2002). "Graphic Devices". In Roger R. Flynn (ed.). Computer Sciences, Vol 2: Software and Hardware, Macmillan Reference USA. Gale eBooks. Retrieved August 3, 2020.
== External links ==
Media related to Vector graphics at Wikimedia Commons | Wikipedia/Object-oriented_graphics |
In computer graphics, graphics software refers to a program or collection of programs that enable a person to manipulate images or models visually on a computer.
Computer graphics can be classified into two distinct categories: raster graphics and vector graphics, with further 2D and 3D variants. Many graphics programs focus exclusively on either vector or raster graphics, but there are a few that operate on both. It is simple to convert from vector graphics to raster graphics, but going the other way is harder. Some software attempts to do this.
In addition to static graphics, there are animation and video editing software. Different types of software are often designed to edit different types of graphics such as video, photos, and vector-based drawings. The exact sources of graphics may vary for different tasks, but most can read and write files.
Most graphics programs have the ability to import and export one or more graphics file formats, including those formats written for a particular computer graphics program. Such programs include, but are not limited to: GIMP, Adobe Photoshop, CorelDRAW, Microsoft Publisher, Picasa, etc.
The use of a swatch is a palette of active colours that are selected and rearranged by the preference of the user. A swatch may be used in a program or be part of the universal palette on an operating system. It is used to change the colour of a text or image and in video editing. Vector graphics animation can be described as a series of mathematical transformations that are applied in sequence to one or more shapes in a scene. Raster graphics animation works in a similar fashion to film-based animation, where a series of still images produces the illusion of continuous movement.
== History ==
SuperPaint was one of the earliest graphics software applications, first conceptualized in 1972 and achieving its first stable image in 1973
Fauve Matisse (later Macromedia xRes) was a pioneering program of the early 1990s, notably introducing layers in customer software.
Currently Adobe Photoshop is one of the most used and best-known graphics programs in the Americas, having created more custom hardware solutions in the early 1990s, but was initially subject to various litigation. GIMP is a popular open-source alternative to Adobe Photoshop.
== See also ==
== References == | Wikipedia/Graphics_editor |
In recent years, 3D printing has developed significantly and can now perform crucial roles in many applications, with the most common applications being manufacturing, medicine, architecture, custom art and design, and can vary from fully functional to purely aesthetic applications.
3D printing processes are finally catching up to their full potential, and are currently being used in manufacturing and medical industries, as well as by sociocultural sectors which facilitate 3D printing for commercial purposes. There has been a lot of hype in the last decade when referring to the possibilities we can achieve by adopting 3D printing as one of the main manufacturing technologies. Utilizing this technology would replace traditional methods that can be costly and time consuming. There have been case studies outlining how the customization abilities of 3D printing through modifiable files have been beneficial for cost and time effectiveness in a healthcare applications.
There are different types of 3D printing such as fused filament fabrication (FFF), stereolithography (SLA), selective laser sintering (SLS), polyjet printing, multi-jet fusion (MJF), direct metal laser sintering (DMLS), and electron beam melting (EBM).
For a long time, the issue with 3D printing was that it has demanded very high entry costs, which does not allow profitable implementation to mass-manufacturers when compared to standard processes. However, recent market trends spotted have found that this is finally changing. As the market for 3D printing has shown some of the quickest growth within the manufacturing industry in recent years. The applications of 3D printing are vast due to the ability to print complex pieces with a use of a wide range of materials. Materials can range from plastic and polymers as thermoplastic filaments, to resins, and even stem cells.
== Manufacturing applications ==
Three-dimensional printing makes it as cheap to create single items as it is to produce thousands and thus undermines economies of scale. It may have as profound an impact on the world as the coming of the factory did (...) Just as nobody could have predicted the impact of the steam engine in 1750—or the printing press in 1450, or the transistor in 1950—it is impossible to foresee the long-term impact of 3D printing. But the technology is coming, and it is likely to disrupt every field it touches.
AM technologies found applications starting in the 1980s in product development, data visualization, rapid prototyping, and specialized manufacturing. Their expansion into production (job production, mass production, and distributed manufacturing) has been under development in the decades since. Industrial production roles within the metalworking industries achieved significant scale for the first time in the early 2010s. Since the start of the 21st century there has been a large growth in the sales of AM machines, and their price has dropped substantially. According to Wohlers Associates, a consultancy, the market for 3D printers and services was worth $2.2 billion worldwide in 2012, up 29% from 2011. McKinsey predicts that additive manufacturing could have an economic impact of $550 billion annually by 2025. There are many applications for AM technologies, including architecture, construction (AEC), industrial design, automotive, aerospace, military, engineering, dental and medical industries, biotech (human tissue replacement), fashion, footwear, jewelry, eyewear, education, geographic information systems, food, and many other fields.
Additive manufacturing's earliest applications have been on the toolroom end of the manufacturing spectrum. For example, rapid prototyping was one of the earliest additive variants, and its mission was to reduce the lead time and cost of developing prototypes of new parts and devices, which was earlier only done with subtractive toolroom methods such as CNC milling and turning, and precision grinding, far more accurate than 3D printing with accuracy down to 0.00005" and creating better quality parts faster, but sometimes too expensive for low accuracy prototype parts. With technological advances in additive manufacturing, however, and the dissemination of those advances into the business world, additive methods are moving ever further into the production end of manufacturing in creative and sometimes unexpected ways. Parts that were formerly the sole province of subtractive methods can now in some cases be made more profitably via additive ones. In addition, new developments in RepRap technology allow the same device to perform both additive and subtractive manufacturing by swapping magnetic-mounted tool heads.
=== Cloud-based additive manufacturing ===
Additive manufacturing in combination with cloud computing technologies allows decentralized and geographically independent distributed production. Cloud-based additive manufacturing refers to a service-oriented networked manufacturing model in which service consumers are able to build parts through Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), Hardware-as-a-Service (HaaS), and Software-as-a-Service (SaaS). Distributed manufacturing as such is carried out by some enterprises; there are also services like 3D Hubs that put people needing 3D printing in contact with owners of printers.
Some companies offer online 3D printing services to both commercial and private customers, working from 3D designs uploaded to the company website. 3D-printed designs are either shipped to the customer or picked up from the service provider.
There are many open source websites that have downloadable STL files which are able to be modified or printed as is. Files ranging from functional tools to aesthetic figurines are available to the general public. Open source files can be beneficial for the user as the printed object can be more cost effective than commercial counterparts.
=== Mass customization ===
Companies have created services where consumers can customize objects using simplified web based customization software, and order the resulting items as 3D printed unique objects. This now allows consumers to create things like custom cases for their mobile phones or scans of their brains. Nokia has released the 3D designs for its case so that owners can customize their own case and have it 3D printed.
=== Rapid manufacturing ===
Advances in RP technology have introduced materials that are appropriate for final manufacture, which has in turn introduced the possibility of directly manufacturing finished components. One advantage of 3D printing for rapid manufacturing lies in the relatively quick and inexpensive production of small numbers of parts.
Rapid manufacturing is a new method of manufacturing and many of its processes remain unproven. 3D printing is now entering the field of rapid manufacturing and was identified as a "next level" technology by many experts in a 2009 report. One of the most promising processes looks to be the adaptation of selective laser sintering (SLS), or direct metal laser sintering (DMLS) some of the better-established rapid prototyping methods. As of 2006, however, these techniques were still very much in their infancy, with many obstacles to be overcome before RM could be considered a realistic manufacturing method.
There have been patent lawsuits concerning 3-D printing for manufacturing.
=== Rapid prototyping ===
Industrial 3D printers have existed since the early 1980s and have been used extensively for rapid prototyping and research purposes. These are generally larger machines that use proprietary powdered metals, casting media (e.g. sand), plastics, paper or cartridges, and are used for rapid prototyping by universities and commercial companies.
=== Research ===
3D printing can be particularly useful in research labs due to its ability to make specialized, bespoke geometries. In 2012 a proof of principle project at the University of Glasgow, UK, showed that it is possible to use 3D printing techniques to assist in the production of chemical compounds. They first printed chemical reaction vessels, then used the printer to deposit reactants into them. They have produced new compounds to verify the validity of the process, but have not pursued anything with a particular application.
Usually, the FDM process is used to print hollow reaction vessels or microreactors. If the 3D print is performed within an inert gas atmosphere, the reaction vessels can be filled with highly reactive substances during the print. The 3D printed objects are air- and watertight for several weeks. By the print of reaction vessels in the geometry of common cuvettes or measurement tubes, routine analytical measurements such as UV/VIS-, IR- and NMR-spectroscopy can be performed directly in the 3D printed vessel.
In addition, 3D printing has been used in research labs as alternative method to manufacture components for use in experiments, such as magnetic shielding and vacuum components with demonstrated performance comparable to traditionally produced parts.
=== Food ===
Additive manufacturing of food is being developed by squeezing out food, layer by layer, into three-dimensional objects. A large variety of foods are appropriate candidates, such as chocolate and candy, and flat foods such as crackers, pasta, and pizza. NASA has considered the versatility of the concept, awarding a contract to the Systems and Materials Research Consultancy to study the feasibility of printing food in space. NASA is also looking into the technology in order to create 3D printed food to limit food waste and to make food that are designed to fit an astronaut's dietary needs. A food-tech startup Novameat from Barcelona 3D-printed a steak from peas, rice, seaweed, and some other ingredients that were laid down criss-cross, imitating the intracellular proteins. One of the problems with food printing is the nature of the texture of a food. For example, foods that are not strong enough to be filed are not appropriate for 3D printing.
=== Agile tooling ===
Agile tooling is the process of using modular means to design tooling that is produced by additive manufacturing or 3D printing methods to enable quick prototyping and responses to tooling and fixture needs. Agile tooling uses a cost-effective and high-quality method to quickly respond to customer and market needs. It can be used in hydro-forming, stamping, injection molding and other manufacturing processes.
== Medical applications ==
Surgical uses of 3D printing-centric therapies have a history beginning in the mid-1990s with anatomical modeling for bony reconstructive surgery planning. By practicing on a tactile model before surgery, surgeons were more prepared and patients received better care. Patient-matched implants were a natural extension of this work, leading to truly personalized implants that fit one unique individual. Virtual planning of surgery and guidance using 3D printed, personalized instruments have been applied to many areas of surgery including total joint replacement and craniomaxillofacial reconstruction with great success. Further study of the use of models for planning heart and solid organ surgery has led to increased use in these areas. Hospital-based 3D printing is now of great interest and many institutions are pursuing adding this specialty within individual radiology departments. The technology is being used to create unique, patient-matched devices for rare illnesses. One example of this is the bioresorbable trachial splint to treat newborns with tracheobronchomalacia developed at the University of Michigan. Several devices manufacturers have also begun using 3D printing for patient-matched surgical guides (polymers). The use of additive manufacturing for serialized production of orthopedic implants (metals) is also increasing due to the ability to efficiently create porous surface structures that facilitate osseointegration. Printed casts for broken bones can be custom-fitted and open, letting the wearer scratch any itches, wash and ventilate the damaged area. They can also be recycled.
Fused filament fabrication (FFF) has been used to create microstructures with a three-dimensional internal geometry. Sacrificial structures or additional support materials are not needed. Structure using polylactic acid (PLA) can have fully controllable porosity in the range 20%–60%. Such scaffolds could serve as biomedical templates for cell culturing, or biodegradable implants for tissue engineering.
3D printing has been used to print patient-specific implant and device for medical use. Successful operations include a titanium pelvis implanted into a British patient, titanium lower jaw transplanted to a Dutch patient, and a plastic tracheal splint for an American infant. The hearing aid and dental industries are expected to be the biggest areas of future development using custom 3D printing technology. In March 2014, surgeons in Swansea used 3D printed parts to rebuild the face of a motorcyclist who had been seriously injured in a road accident. Research is also being conducted on methods to bio-print replacements for lost tissue due to arthritis and cancer .
3D printing technology can now be used to make exact replicas of organs. The printer uses images from patients' MRI or CT scan images as a template and lays down layers of rubber or plastic. These models can be used to plan difficult operations, as was the case in May 2018, when surgeons used a 3D printed replica of a kidney to practice a kidney transplant on a three-year-old boy.
Thermal degradation during 3D printing of resorbable polymers, same as in surgical sutures, has been studied, and parameters can be adjusted to minimize the degradation during processing. Soft pliable scaffold structures for cell cultures can be printed.
In 3D printing, computer-simulated microstructures are commonly used to fabricate objects with spatially varying properties. This is achieved by dividing the volume of the desired object into smaller subcells using computer aided simulation tools and then filling these cells with appropriate microstructures during fabrication. Several different candidate structures with similar behaviours are checked against each other and the object is fabricated when an optimal set of structures are found. Advanced topology optimization methods are used to ensure the compatibility of structures in adjacent cells. This flexible approach to 3D fabrication is widely used across various disciplines from biomedical sciences where they are used to create complex bone structures and human tissue to robotics where they are used in the creation of soft robots with movable parts. 3D printing also finds its uses more and more in design and fabrication of laboratory apparatuses.
3D printing technology can also be used to produce personal protective equipment, also known as PPE, is worn by medical and laboratory professionals to protect themselves from infection when they are treating patients. Examples of PPE include face masks, face shields, connectors, gowns, and goggles. The most popular forms of 3D printed PPE are face masks, face shields, and connectors.
Nowadays, Additive Manufacturing is also employed in the field of pharmaceutical sciences to create 3D printed medication. Different techniques of 3D printing (e.g. FDM, SLS, Inkjet Printing etc) are utilized according to their respective advantages and drawbacks for various applications regarding drug delivery.
=== Bio-printing ===
In 2006, researchers at Cornell University published some of the pioneer work in 3D printing for tissue fabrication, successfully printing hydrogel bio-inks. The work at Cornell was expanded using specialized bioprinters produced by Seraph Robotics, Inc., a university spin-out, which helped to catalyze a global interest in biomedical 3D printing research.
3D printing has been considered as a method of implanting stem cells capable of generating new tissues and organs in living humans. With their ability to transform into any other kind of cell in the human body, stem cells offer huge potential in 3D bioprinting. Professor Leroy Cronin of Glasgow University proposed in a 2012 TED Talk that it was possible to use chemical inks to print medicine. In 2015 the FDA approved Spritam ®, a 3D printed drug also known as levetiracetam. Currently, there are three methods of 3D printing that have been explored for the production of drug making: laser based writing systems, printing-based inkjet systems, and nozzle based systems.
As of 2012, 3D bio-printing technology has been studied by biotechnology firms and academia for possible use in tissue engineering applications in which organs and body parts are built using inkjet techniques. In this process, layers of living cells are deposited onto a gel medium or sugar matrix and slowly built up to form three-dimensional structures including vascular systems. The first production system for 3D tissue printing was delivered in 2009, based on NovoGen bioprinting technology. Several terms have been used to refer to this field of research: organ printing, bio-printing, body part printing, and computer-aided tissue engineering, among others. The possibility of using 3D tissue printing to create soft tissue architectures for reconstructive surgery is also being explored.
In 2013, Chinese scientists began printing ears, livers and kidneys, with living tissue. Researchers in China have been able to successfully print human organs using specialized 3D bioprinters that use living cells instead of plastic . Researchers at Hangzhou Dianzi University designed the "3D bioprinter" dubbed the "Regenovo". Xu Mingen, Regenovo's developer, said that it can produce a miniature sample of liver tissue or ear cartilage in less than an hour, predicting that fully functional printed organs might take 10 to 20 years to develop.
=== Medical devices ===
On October 24, 2014, a five-year-old girl born without fully formed fingers on her left hand became the first child in the UK to have a prosthetic hand made with 3D printing technology. Her hand was designed by US-based e-NABLE, an open source design organisation which uses a network of volunteers to design and make prosthetics mainly for children. The prosthetic hand was based on a plaster cast made by her parents. A boy named Alex was also born with a missing arm from just above the elbow. The team was able to use 3D printing to upload an e-NABLE Myoelectric arm that runs off of servos and batteries that are actuated by the electromyography muscle. With the use of 3D printers, e-NABLE has so far distributed thousands of plastic hands to children. Another example is Open Bionics, a company that makes fully functional bionic arms through 3D printing technology. 3D printing allows Open Bionics to create personalized designs for their clients, as there can be different colours, textures, patterns, and even "Hero Arms" that emulate superheroes like Ironman or characters from Star Wars.
Printed prosthetics have been used in rehabilitation of crippled animals. In 2013, a 3D printed foot let a crippled duckling walk again. 3D printed hermit crab shells let hermit crabs inhabit a new style home. A prosthetic beak was another tool developed by the use of 3D printing to help aid a bald eagle named Beauty, whose beak was severely mutilated from a shot in the face. Since 2014, commercially available titanium knee implants made with 3D printer for dogs have been used to restore the animals' mobility. Over 10,000 dogs in Europe and the United States have been treated after only one year.
In February 2015, FDA approved the marketing of a surgical bolt which facilitates less-invasive foot surgery and eliminates the need to drill through bone. The 3D printed titanium device, 'FastForward Bone Tether Plate' is approved to use in correction surgery to treat bunion. In October 2015, the group of Professor Andreas Herrmann at the University of Groningen has developed the first 3D printable resins with antimicrobial properties. Employing stereolithography, quaternary ammonium groups are incorporated into dental appliances that kill bacteria on contact. This type of material can be further applied in medical devices and implants.
3D Printing has been especially beneficial for the creation of patient specific prosthetics for large or invasive surgeries. In a case study published in 2020 about the benefits of 3D printing for hip prostheses, three patients with acetabular defects needed revisions of total hip arthroplasty (THA). 3D printing was utilized to produce prostheses that were specific to each of the three patients and their complex bone defect, which resulted in better post procedure recovery and prognosis of the individual.
In a case study about the applications of 3D printing in occupational therapy, the aspect of customization and quick fabrication at a low cost is utilized in different tools such as customized scissor handles and bottle openers for someone with hand motor complications. Beverage holders, writing guides, grip strengtheners, and other occupational therapy items were designed, printed, and compared with commercially available counterparts in a cost analysis. It found that the 3D printed items were on average 10.5 times more cost effective than commercial alternatives.
3D printing for medical devices can range from human prosthetics applications, to animal prostheses, to medical machine tools: On June 6, 2011, the company Xilloc Medical together with researchers at the University of Hasselt, in Belgium had successfully printed a new jawbone for an 83-year-old Dutch woman from the province of Limburg. 3D printing has been used to produce prosthetic beaks for eagles, a Brazilian goose named Victoria, and a Costa Rican toucan called Grecia. In March 2020, the Isinnova company in Italy printed 100 respirator valves in 24 hours for a hospital that lacked them in the midst of the coronavirus outbreak. It's clear that 3D printing technology is beneficial in many areas of healthcare.
=== Pharmaceutical Formulations ===
In May 2015 the first formulation manufactured by 3D printing was produced. In August 2015 the FDA approved the first 3D printed tablet. Binder-jetting into a powder bed of the drug allows very porous tablets to be produced, which enables high drug doses in a single formulation that rapidly dissolves and is easily absorbed. This has been demonstrated for Spritam, a reformulation of levetiracetam for the treatment of epilepsy.
Additive Manufacturing has been increasingly utilized by scientists in the pharmaceutical field. However, after the first FDA approval of a 3D printed formulation, scientific interest for 3D applications in drug delivery grew even bigger. Research groups around the world are studying different ways of incorporating drugs within a 3D printed formulation, for example by incorporating poorly water-soluble drugs in self-emulsifying systems or emulsion gels. 3D printing technology allows scientists to develop formulations with a personalized approach, i.e. dosage forms tailored specifically to an individual patient. Moreover, according to the advantages of the diverse utilized techniques, formulations with various properties can be achieved. These may contain multiple drugs in a single dosage form, multi-compartmental designs, drug delivery systems with distinct release characteristics, etc. During the earlier years, researchers have mainly focused on the Fused Deposition Modelling (FDM) technique. Nowadays, other printing techniques such as Selective Laser Sintering (SLS), Stereolithography (SLA) and Semi-solid extrusion (SSE) are also gaining traction and are being used for pharmaceutical applications.
== Industrial applications ==
=== Apparel ===
3D printing has entered the world of clothing with fashion designers experimenting with 3D-printed bikinis, shoes, and dresses. In commercial production, Nike used 3D printing to prototype and manufacture the 2012 Vapor Laser Talon football shoe for players of American football, and New Balance is 3D manufacturing custom-fit shoes for athletes.
3D printing has come to the point where companies are printing consumer grade eyewear with on-demand custom fit and styling (although they cannot print the lenses). On-demand customization of glasses is possible with rapid prototyping.
However, comments have been made in academic circles as to the potential limitation of the human acceptance of such mass customized apparel items due to the potential reduction of brand value communication.
In the world of high fashion courtiers such as Karl Lagerfeld designing for Chanel, Iris van Herpen and Noa Raviv working with technology from Stratasys, have employed and featured 3d printing in their collections. Selections from their lines and other working with 3d printing were showcased at
the 2016 Metropolitan Museum of Art Anna Wintour Costume Center, exhibition "Manus X Machina".
Vanessa Friedman, fashion director and chief fashion critic at The New York Times, says 3D printing will have a significant value for fashion companies down the road, especially if it transforms into a print-it-yourself tool for shoppers. "There's real sense that this is not going to happen anytime soon," she says, "but it will happen, and it will create dramatic change in how we think both about intellectual property and how things are in the supply chain". She adds: "Certainly some of the fabrications that brands can use will be dramatically changed by technology."
During the COVID-19 pandemic, the Ukrainian-American undergraduate Karina Popovich founded Markers for COVID-19 which used 3D printing to create face shields, face masks and other items of personal protective equipment.
=== Industrial art and jewelry ===
3D printing is used to manufacture moulds for making jewelry, and even the jewelry itself. 3D printing is becoming popular in the customisable gifts industry, with products such as personalized models of art and dolls, in many shapes: in metal or plastic, or as consumable art, such as 3D printed chocolate.
=== Transportation Industries ===
In cars, trucks, and aircraft, additive manufacturing is beginning to transform both unibody and fuselage design and production, and powertrain design and production. For example, General Electric uses high-end 3D printers to build parts for turbines. Many of these systems are used for rapid prototyping before mass production methods are employed.
In early 2014, Swedish supercar manufacturer Koenigsegg announced the One:1, a supercar that utilizes many components that were 3D printed. In the limited run of vehicles Koenigsegg produces, the One:1 has side-mirror internals, air ducts, titanium exhaust components, and complete turbocharger assemblies that were 3D printed as part of the manufacturing process.
Urbee is the name of the first car in the world car mounted using the technology 3D printing (its bodywork and car windows were "printed"). Created in 2010 through the partnership between the US engineering group Kor Ecologic and the company Stratasys (manufacturer of printers Stratasys 3D), it is a hybrid vehicle with futuristic look.
In 2014, Local Motors debuted Strati, a functioning vehicle that was entirely 3D Printed using ABS plastic and carbon fiber, except the powertrain. In 2015, the company produced another iteration known as the LM3D Swim that was 80 percent 3D-printed. In 2016, the company has used 3D printing in the creation of automotive parts, such ones used in Olli, a self-driving vehicle developed by the company.
In May 2015 Airbus announced that its new Airbus A350 XWB included over 1000 components manufactured by 3D printing.
3D printing is also being utilized by air forces to print spare parts for planes. In 2015, a Royal Air Force Eurofighter Typhoon fighter jet flew with printed parts. The United States Air Force has begun to work with 3D printers, and the Israeli Air Force has also purchased a 3D printer to print spare parts.
In 2017, GE Aviation revealed that it had used design for additive manufacturing to create a helicopter engine with 16 parts instead of 900, weighing 40% lighter and being 60% cheaper. This also led to a simplified supply chain with less support from outer suppliers, as many of the parts could be produced in-house.
=== Construction, home development ===
The use of 3D printing to produce scale models within architecture and construction has steadily increased in popularity as the cost of 3D printers has reduced. This has enabled faster turn around of such scale models and allowed a steady increase in the speed of production and the complexity of the objects being produced.
Construction 3D printing, the application of 3D printing to fabricate construction components or entire buildings has been in development since the mid-1990s, development of new technologies has steadily gained pace since 2012 and the sub-sector of 3D printing is beginning to mature.
=== Firearms ===
In 2012, the US-based group Defense Distributed disclosed plans to "design a working plastic gun that could be downloaded and reproduced by anybody with a 3D printer." Defense Distributed has also designed a 3D printable AR-15 type rifle lower receiver (capable of lasting more than 650 rounds) and a 30-round M16 magazine. The AR-15 has multiple receivers (both an upper and lower receiver), but the legally controlled part is the one that is serialized (the lower, in the AR-15's case). Soon after Defense Distributed succeeded in designing the first working blueprint to produce a plastic gun with a 3D printer in May 2013, the United States Department of State demanded that they remove the instructions from their website. After Defense Distributed released their plans, questions were raised regarding the effects that 3D printing and widespread consumer-level CNC machining may have on gun control effectiveness.
In 2014, a man from Japan became the first person in the world to be imprisoned for making 3D printed firearms. Yoshitomo Imura posted videos and blueprints of the gun online and was sentenced to jail for two years. Police found at least two guns in his household that were capable of firing bullets.
=== Computers and robots ===
3D printing can also be used to make laptops and other computers and cases. For example, Novena and VIA OpenBook standard laptop cases. I.e. a Novena motherboard can be bought and be used in a printed VIA OpenBook case.
Open-source robots are built using 3D printers. Double Robotics grant access to their technology (an open SDK). On the other hand, 3&DBot is an Arduino 3D printer-robot with wheels and ODOI is a 3D printed humanoid robot.
=== Soft sensors and actuators ===
3D printing has found its place in soft sensors and actuators manufacturing inspired by 4D printing concept. The majority of the conventional soft sensors and actuators are fabricated using multistep low yield processes entailing manual fabrication, post-processing/assembly, and lengthy iterations with less flexibility in customization and reproducibility of final products. 3D printing has been a game changer in these fields with introducing the custom geometrical, functional, and control properties to avoid the tedious and time-consuming aspects of the earlier fabrication processes.
== Sociocultural applications ==
In 2005, a rapidly expanding hobbyist and home-use market was established with the inauguration of the open-source RepRap and Fab@Home projects. Virtually all home-use 3D printers released to-date have their technical roots in the ongoing RepRap Project and associated open-source software initiatives. In distributed manufacturing, one study has found that 3D printing could become a mass market product enabling consumers to save money associated with purchasing common household objects. For example, instead of going to a store to buy an object made in a factory by injection molding (such as a measuring cup or a funnel), a person might instead print it at home from a downloaded 3D model.
=== Art and jewellery ===
In 2005, academic journals began to report on the possible artistic applications of 3D printing technology, being used by artists such as Martin John Callanan at The Bartlett school of architecture. By 2007 the mass media followed with an article in the Wall Street Journal and Time magazine, listing a printed design among their 100 most influential designs of the year. During the 2011 London Design Festival, an installation, curated by Murray Moss and focused on 3D Printing, was held in the Victoria and Albert Museum (the V&A). The installation was called Industrial Revolution 2.0: How the Material World will Newly Materialize.
At the 3DPrintshow in London, which took place in November 2013 and 2014, the art sections had works made with 3D printed plastic and metal. Several artists such as Joshua Harker, Davide Prete, Sophie Kahn, Helena Lukasova, Foteini Setaki showed how 3D printing can modify aesthetic and art processes. In 2015, engineers and designers at MIT's Mediated Matter Group and Glass Lab created an additive 3D printer that prints with glass, called G3DP. The results can be structural as well as artistic. Transparent glass vessels printed on it are part of some museum collections.
The use of 3D scanning technologies allows the replication of real objects without the use of moulding techniques that in many cases can be more expensive, more difficult, or too invasive to be performed, particularly for precious artwork or delicate cultural heritage artifacts where direct contact with the moulding substances could harm the original object's surface.
A collection of 3D-printable adapters for popular toy construction systems called the Free Universal Construction Kit (2012) is in the collection of the Museum of Modern Art in New York. The work, by Golan Levin and Shawn Sims, was also included in Pirouette: Turning Points in Design, the museum's 2025 exhibition featuring "widely recognized design icons [...] highlighting pivotal moments in design history."
=== 3D selfies ===
A 3D photo booth such as the Fantasitron located at Madurodam, the miniature park, generates 3D selfie models from 2D pictures of customers. These selfies are often printed by dedicated 3D printing companies such as Shapeways. These models are also known as 3D portraits, 3D figurines or mini-me figurines.
=== Communication ===
Employing additive layer technology offered by 3D printing, Terahertz devices which act as waveguides, couplers and bends have been created. The complex shape of these devices could not be achieved using conventional fabrication techniques. Commercially available professional grade printer EDEN 260V was used to create structures with minimum feature size of 100 μm. The printed structures were later DC sputter coated with gold (or any other metal) to create a Terahertz Plasmonic Device.
In 2016 artist/scientist Janine Carr Created the first 3d printed vocal percussion (beatbox) as a waveform, with the ability to play the soundwave by laser, along with four vocalised emotions these were also playable by laser.
=== Domestic use ===
Some early consumer examples of 3d printing include the 64DD released in 1999 in Japan. As of 2012, domestic 3D printing was mainly practiced by hobbyists and enthusiasts. However, little was used for practical household applications, for example, ornamental objects. Some practical examples include a working clock and gears printed for home woodworking machines among other purposes. Web sites associated with home 3D printing tended to include backscratchers, coat hooks, door knobs, etc.
As of 2023 consumer 3D printing has become increasingly common, an estimated 85% of 3D printers sold now are of the personal/desktop markets. Now more than ever its increasingly common to see 3D printing utilized by at home DIY/maker communities as 3D printers have become significantly more affordable for consumer audiences in recent years.
The open source Fab@Home project has developed printers for general use. They have been used in research environments to produce chemical compounds with 3D printing technology, including new ones, initially without immediate application as proof of principle. The printer can print with anything that can be dispensed from a syringe as liquid or paste. The developers of the chemical application envisage both industrial and domestic use for this technology, including enabling users in remote locations to be able to produce their own medicine or household chemicals.
3D printing is now working its way into households, and more and more children are being introduced to the concept of 3D printing at earlier ages. The prospects of 3D printing are growing, and as more people have access to this new innovation, new uses in households will emerge.
The OpenReflex SLR film camera was developed for 3D printing as an open-source student project.
=== Education and research ===
3D printing, and open source 3D printers in particular, are the latest technology making inroads into the classroom. 3D printing allows students to create prototypes of items without the use of expensive tooling required in subtractive methods. Students design and produce actual models they can hold. The classroom environment allows students to learn and employ new applications for 3D printing. RepRaps, for example, have already been used for an educational mobile robotics platform.
Some authors have claimed that 3D printers offer an unprecedented "revolution" in STEM education. The evidence for such claims comes from both the low cost ability for rapid prototyping in the classroom by students, but also the fabrication of low-cost high-quality scientific equipment from open hardware designs forming open-source labs. Engineering and design principles are explored as well as architectural planning. Students recreate duplicates of museum items such as fossils and historical artifacts for study in the classroom without possibly damaging sensitive collections. Other students interested in graphic designing can construct models with complex working parts easily. 3D printing gives students a new perspective with topographic maps. Science students can study cross-sections of internal organs of the human body and other biological specimens. And chemistry students can explore 3D models of molecules and the relationship within chemical compounds. The true representation of exactly scaled bond length and bond angles in 3D printed molecular models can be used in organic chemistry lecture courses to explain molecular geometry and reactivity.
According to a recent paper by Kostakis et al., 3D printing and design can electrify various literacies and creative capacities of children in accordance with the spirit of the interconnected, information-based world.
Future applications for 3D printing might include creating open-source scientific equipment.
=== Environmental use ===
In Bahrain, large-scale 3D printing using a sandstone-like material has been used to create unique coral-shaped structures, which encourage coral polyps to colonize and regenerate damaged reefs. These structures have a much more natural shape than other structures used to create artificial reefs, and, unlike concrete, are neither acid nor alkaline with neutral pH.
=== Cultural heritage ===
In the last several years 3D printing has been intensively used by in the cultural heritage field for preservation, restoration and dissemination purposes. Many Europeans and North American Museums have purchased 3D printers and actively recreate missing pieces of their relics.
Scan the World is the largest archive of 3D printable objects of cultural significance from across the globe. Each object, originating from 3D scan data provided by their community, is optimised for 3D printing and free to download on MyMiniFactory. Through working alongside museums, such as The Victoria and Albert Museum and private collectors, the initiative serves as a platform for democratizing the art object.
The Metropolitan Museum of Art and the British Museum have started using their 3D printers to create museum souvenirs that are available in the museum shops. Other museums, like the National Museum of Military History and Varna Historical Museum, have gone further and sell through the online platform Threeding digital models of their artifacts, created using Artec 3D scanners, in 3D printing friendly file format, which everyone can 3D print at home.
=== Specialty materials ===
Consumer grade 3D printing has resulted in new materials that have been developed specifically for 3D printers. For example, filament materials have been developed to imitate wood in its appearance as well as its texture. Furthermore, new technologies, such as infusing carbon fiber into printable plastics, allowing for a stronger, lighter material. In addition to new structural materials that have been developed due to 3D printing, new technologies have allowed for patterns to be applied directly to 3D printed parts. Iron oxide-free Portland cement powder has been used to create architectural structures up to 9 feet in height.
== See also ==
3D printing processes
3D printing
Construction 3D printing
Health and safety hazards of 3D printing
== References ==
=== Sources ===
Vincent; Earls, Alan R. (February 2011). "Origins: A 3D Vision Spawns Stratasys, Inc". Today's Machining World. 7 (1): 24–25. Archived from the original on March 10, 2012. | Wikipedia/Applications_of_3D_printing |
A video display controller (VDC), also called a display engine or display interface, is an integrated circuit which is the main component in a video-signal generator, a device responsible for the production of a TV video signal in a computing or game system. Some VDCs also generate an audio signal, but that is not their main function.
VDCs were used in the home computers of the 1980s and also in some early video picture systems.
The VDC is the main component of the video signal generator logic, responsible for generating the timing of video signals such as the horizontal and vertical synchronization signals and the blanking interval signal. Sometimes other supporting chips were necessary to build a complete system, such as RAM to hold pixel data, ROM to hold character fonts, or some discrete logic such as shift registers.
Most often the VDC chip is completely integrated in the logic of the main computer system, (its video RAM appears in the memory map of the main CPU), but sometimes it functions as a coprocessor that can manipulate the video RAM contents independently.
== Video display controller vs. graphics processing unit ==
The difference between a display controller, a graphics accelerator, and a video compression/decompression IC is huge, but, since all of this logic is usually found on the chip of a graphics processing unit and is usually not available separately to the end-customer, there is often much confusion about these very different functional blocks.
GPUs with hardware acceleration became popular during the 1990s, including the S3 ViRGE, the Matrox Mystique, and the Voodoo Graphics; though earlier examples such as the NEC μPD7220 had already existed for some time. VDCs often had special hardware for the creation of "sprites", a function that in more modern VDP chips is done with the "Bit Blitter" using the "Bit blit" function.
One example of a typical video display processor is the "VDP2 32-bit background and scroll plane video display processor" of the Sega Saturn.
Another example is the Lisa (AGA) chip that was used for the improved graphics of the later generation Amiga computers.
That said, it is not completely clear when a "video chip" is a "video display controller" and when it is a "video display processor". For example, the TMS9918 is sometimes called a "video display controller" and sometimes a "video display processor". In general however a "video display processor" has some power to "process" the contents of the video RAM (filling an area of RAM for example), while a "video display controller" only controls the timing of the video synchronization signals and the access to the video RAM.
The graphics processing unit (GPU) goes one step further than the VDP and normally also supports 3D functionality. This is the kind of chip that is used in modern personal computers.
== Types ==
Video display controllers can be divided in several different types, listed here from simplest to most complex;
Video shifters, or "video shift register based systems" (there is no generally agreed upon name for these types of devices), are the most simple type of video controllers. They are directly or indirectly responsible for the video timing signals, but they normally do not access the video RAM directly. They get the video data from the main CPU, a byte at a time, and convert it to a serial bitstream, hence the technical name "video shifter". This serial data stream is then used together with the synchronization signals to output a video signal. The main CPU needs to do the bulk of the work. Normally these chips only support a very low resolution raster graphics mode.
A CRTC, or cathode-ray tube controller, generates the video timings and reads video data from RAM attached to the CRTC to output it via an external character generator ROM (for text modes) or directly to the video output shift register (for high resolution graphics modes). Because the actual capabilities of the video generator depend to a large degree on the external logic, video generator based on a CRTC chip can have a wide range of capabilities, from simple text-mode only systems to high-resolution systems supporting a wide range of colours. Sprites, however, are normally not supported by these systems.
Video interface controllers are much more complex than CRT controllers, and the external circuitry that is needed with a CRTC is embedded in the video controller chip. Sprites are often supported, as are (RAM based) character generators and video RAM dedicated to colour attributes and pallette registers (colour lookup tables) for the high-resolution or text modes.
Video coprocessors have their own internal CPU dedicated to reading (and writing) their own video RAM (which may be shared with the CPU), and converting the contents of this video RAM to a video signal. The main CPU can give commands to the coprocessor, for example to change the video modes or to manipulate the video RAM contents. The video coprocessor also controls the (most often RAM-based) character generator, the colour attribute RAM, palette registers, and the sprite logic (as long as these exist of course).
== List of example VDCs ==
Examples of video display controllers are:
Video shifters
The RCA CDP1861 was a very simple chip, built in CMOS technology (which was unusual for the mid-1970s) to complement the RCA 1802 microprocessor, it was mainly used in the COSMAC VIP. It could only support a very low resolution monochrome graphic mode.
The Television Interface Adaptor (TIA) is the custom video chip that is the heart of the Atari 2600 games console, a primitive chip that relied on the 6502 microprocessor to do most of the work, also was used to generate the audio.
CRT Controllers
The Intel 8275 CRT controller was used in the Convergent Technologies AWS / Burroughs B20, along with some S-100 bus systems.
The Motorola 6845 (MC6845) is a video address generator first introduced by Motorola and used for the Amstrad CPC, and the BBC Micro. It was also used for almost all the early video adapters for the PC, such as the MDA, CGA and EGA adapters. The MDA and CGA use an actual Motorola chip, while the EGA has a custom IBM chipset of five LSI chips; one of those chips includes IBM's reimplementation of the CRTC, which operates like an MC6845 but differs in a few register addresses and functions so it is not 100% compatible. In all later VGA compatible adapters the function of the 6845 is still reproduced inside the video chip, so in a sense all current IBM PC compatible PCs still incorporate the logic of the 6845 CRTC.
Video interface controllers
The Signetics 2636 and 2637 are video controllers best known for their use in the Interton VC 4000 and Emerson Arcadia 2001 respectively.
The MC6847 is a video display generator (VDG) first introduced by Motorola and used in the TRS-80 Color Computer, Dragon 32/64, Laser 200 and Acorn Atom among others.
The MOS Technology 6560 (NTSC) and 6561 (PAL) are known as the video interface controller (VIC) and used in the VIC-20.
The MOS Technology 6567/8562/8564 (NTSC versions) and 6569/8565/8566 (PAL) were known as the VIC-II and were used in the Commodore 64.
The MOS Technology 8563/8568 was used in the Commodore 128 (8563) and Commodore 128D (8568) to create an 80 column text display, as well as several high resolution graphics modes. The Commodore 128 models included a VIC-II to support Commodore 64 compatible video modes.
The MOS Technology 7360 text editing device (TED) was used in the Commodore Plus/4, Commodore 16 and Commodore 116 computers and had an integrated audio capability.
The Philips semiconductors SCC66470 was a VSC (Video- and Systems Controller) used in conjunction with their 68070-Microcontroller e.g. in CD-i systems.
Video coprocessors
The ANTIC (Alpha-Numeric Television Interface Circuit) was an early video system chip used in Atari 8-bit computers. It could read a "Display list" with its own built in CPU and use this data to generate a complex video signal.
The TMS9918 is known as the Video Display Processor (VDP) and was first designed for the Texas Instruments TI-99/4, but was later also used in systems like the MSX (MSX-1), ColecoVision, Memotech MTX series, and for the Sega SG-1000 and SC-3000. The Master System uses an enhanced VDP based on the TMS9918, and the Sega 315-5313 (Yamaha YM7101) VDP used in the Sega Genesis and some arcade machines is a further advancement of the Master System VDP with the original (inferior) TMS9918 modes removed.
The NEC μPD7220. Used in some high-end graphics boards for the IBM PC in the mid 80s, notably in products from Number Nine Visual Technology.
The RP2C02 (NTSC) or RP2C07 (PAL) was a video coprocessor designed by Ricoh for Nintendo's use in the Famicom and Nintendo Entertainment System. It was connected to 2048 bytes of dedicated video RAM, and had a dedicated address bus that allowed additional RAM or ROM to be accessed from the game cartridge. A scrollable playfield of 256×240 pixels was supported, along with a display list of 64 OBJs (sprites), of which 8 could be displayed per scanline.
The Yamaha V9938 is an improved version of the TMS9918, and was mainly used in the MSX2.
The Yamaha V9958 is the Video Display Processor (VDP) mainly used in the MSX2+ and MSX turboR computers.
The VLSI VS21S010D-L is a 128kB SPI/parallel SRAM with an integrated video display controller with variable-bit-depth pixels and a block-move blitter.
The Thomson EF936x series of Graphic Display Processor (GDP), which offers a draw rate of 1 million pixels per second and resolutions up to 1024×512.
== Alternatives to a VDC chip ==
Note that many early home computers did not use a VDP chip, but built the whole video display controller from a lot of discrete logic chips, (examples are the Apple II, PET, and TRS-80). Because these methods are very flexible, video display generators could be very capable (or extremely primitive, depending on the quality of the design), but also needed a lot of components.
Many early systems used some form of an early programmable logic array to create a video system; examples include the ZX Spectrum and ZX81 systems and Elektronika BK-0010, but there were many others. Early implementations were often very primitive, but later implementations sometimes resulted in fairly advanced video systems, like the one in the SAM Coupé. On the lower end, as in the ZX81, the hardware would only perform electrical functions and the timing and level of the video stream was provided by the microprocessor. As the video data rate was high relative to the processor speed, the computer could only perform actual non-display computations during the retrace period between display frames. This limited performance to at most 25% of overall available CPU cycles.
These systems could thus build a very capable system with relatively few components, but the low transistor count of early programmable logic meant that the capabilities of early PLA-based systems were often less impressive than those using the video interface controllers or video coprocessors that were available at the same time. Later PLA solutions, such as those using CPLDs or FPGAs, could result in much more advanced video systems, surpassing those built using off-the-shelf components.
An often-used hybrid solution was to use a video interface controller (often the Motorola 6845) as a basis and expand its capabilities with programmable logic or an ASIC. An example of such a hybrid solution is the original VGA card, that used a 6845 in combination with an ASIC. That is why all current VGA based video systems still use the hardware registers that were provided by the 6845.
== Modern solutions ==
With the advancements made in semiconductor device fabrication, more and more functionality is implemented as integrated circuits, often licensable as semiconductor intellectual property core (SIP core). Display controller System In Package (SiP) blocks can be found on the die of GPUs, APUs and SoCs.
They support a variety of interfaces: VGA, DVI, HDMI, DisplayPort, VHDCI, DMS-59 and more. The PHY includes LVDS, Embedded DisplayPort, TMDS and Flat Panel Display Link, OpenLDI and CML. A modern computer monitor may has built-in LCD controller or OLED controller.
For example, a VGA-signal, which is created by GPU is being transported over a VGA-cable to the monitor built-in controller. Both ends of the cable end in a VGA connector. Laptops and other mobile computers use different interfaces between the display controller and the display. A display controller usually supports multiple computer display standards.
KMS driver is an example of a device driver for display controllers and AMD Eyefinity is a special brand of display controller with multi-monitor support.
RandR (resize and rotate) is a method to configure screen resolution and refresh rate on each individual outputs separately and at the same time configure the settings of the windowing system accordingly.
An example for this dichotomy is offered by ARM Holdings: they offer SIP core for 3D rendering acceleration and for display controller independently. The former has marketing names such as Mali-200 or Mali-T880 while the latter is available as Mali-DP500, Mali-DP550 and Mali-DP650.
== History ==
In 1982, NEC released the NEC μPD7220, one of the most widely used video display controllers in 1980s personal computers. It was used in the NEC PC-9801, APC III, IBM PC compatibles, DEC Rainbow, Tulip System-1, and Epson QX-10. Intel licensed the design and called it the 82720 graphics display controller.
Previously, graphic cards were also called graphic adapters, and the chips used on these ISA/EISA cards consisted solely of a display controller, as this was the only functionality required to connect a computer to a display. Later cards included ICs to perform calculations related to 2D rendering in parallel with the CPU; these cards were referred to as graphics accelerator cards. Similarly, ICs for 3D rendering eventually followed. Such cards were available with VLB, PCI, and AGP interfaces; modern cards typically use the PCI Express bus, as they require much greater bandwidth then the ISA bus can deliver.
== See also ==
List of home computers by video hardware
List of color palettes
== References ==
== External links ==
Embedded Linux Conference 2013 – Anatomy of an Embedded KMS driver on YouTube KMS driver is a device driver for display controllers | Wikipedia/Graphics_chip |
Cartoon physics or animation physics are terms for a jocular system of laws of physics (and biology) that supersedes the normal laws, used in animation for humorous effect.
Many of the most famous American animated films, particularly those from Warner Bros. and Metro-Goldwyn-Mayer studios, indirectly developed a relatively consistent set of such "laws" which have become de rigueur in comic animation. They usually involve things behaving in accordance with how they appear to the cartoon characters, or what the characters expect, rather than how they objectively are. In one common example, when a cartoon character runs off a cliff, gravity has no effect until the character notices there's nothing under their feet.
In words attributed to Art Babbitt, an animator with the Walt Disney Studios, "Animation follows the laws of physics—unless it is funnier otherwise."
== Examples ==
Specific reference to cartoon physics extends back at least to June 1980, when an article "O'Donnell's Laws of Cartoon Motion" appeared in Esquire. A version printed in V.18 No. 7 p. 12, 1994 by the Institute of Electrical and Electronics Engineers in its journal helped spread the word among the technical crowd, which has expanded and refined the idea.
O'Donnell's examples include:
Any body suspended in space will remain suspended in space until made aware of its situation. A character steps off a cliff but remains in midair until looking down, then the familiar principle of 16 feet per second squared takes over.
A body passing through solid matter will leave a perforation conforming to its perimeter called the silhouette of passage.
The time required for an object to fall 20 stories is greater than or equal to the time it takes for whoever knocked it off the ledge to spiral down 20 flights to attempt to capture it unbroken. Such an object is inevitably priceless; the attempt to capture it, inevitably unsuccessful.
All principles of gravity are negated by fear.
Psychic forces are sufficient in most bodies for a shock to propel them directly away from the ground. A spooky noise or an adversary's signature sound will introduce motion upward, usually to the cradle of a chandelier, a treetop or the crest of a flagpole.
The feet of a running character or the wheels of a speeding auto need never touch the ground, ergo fleeing turns to flight.
As speed increases, objects can be in several places at once.
Certain bodies can pass through a solid wall painted to resemble tunnel entrances; others cannot. ... Whoever paints an entrance on a wall's surface to trick an opponent will be unable to pursue him into this theoretical space. The painter is flattened against the wall when he attempts to follow into the painting. This is ultimately a problem of art, not science.
Any violent rearrangement of feline matter is impermanent. Cartoon cats can be sliced, splayed, accordion-pleated, spindled or disassembled, but they cannot be destroyed. After a few moments of blinking self-pity, they reinflate, elongate, snap back or solidify.
== History of the idea ==
The idea that cartoons behave differently from the real world, but not randomly, is virtually as old as animation. Walt Disney, for example, spoke of the plausible impossible in 1956 on an episode of the Disneyland television program.
Warner Brothers' Looney Tunes and Merrie Melodies series had numerous examples of their own cartoon physics (such as in the Wile E. Coyote and the Road Runner cartoons) or even acknowledged they ignore real world physics. In High Diving Hare (1948), when Yosemite Sam cuts through a high diving board Bugs Bunny is standing on, the ladder and platform that Sam is on falls, leaving the cut plank suspended in mid-air. Bugs turns to the camera and cracks: "I know this defies the law of gravity, but, you see, I never studied law!"
After being seen on the big screen, cartoon physics was soon taken down to the small screen through many shows from Hanna-Barbera, where Yogi Bear and Boo Boo and the rest of the anthropomorphic animals used it many times.
The animated television series Tiny Toon Adventures had an episode dedicated to it "Toon Physics", in which Orson Whales teaches how it differs from actual science.
More recently, it has been explicitly described by some cartoon characters, including Bugs Bunny, Daffy Duck, Tom, Jerry, and Roger Rabbit. who say that cartoon characters are allowed to bend or break natural laws for the purposes of comedy. Doing this is extremely tricky, so, the cartoon characters have a natural sense of comedic timing, giving them inherently funny properties. In Who Framed Roger Rabbit, for example, Roger is unable to escape handcuffs for most of a sequence, doing so only to use both hands to hold the table still while Eddie Valiant attempts to saw the cuffs off. When Eddie asks, exasperated, "Do you mean to tell me you could've taken your hand out of that cuff at any time?!" Roger responds: "Not at any time! Only when it was funny!" Several aspects of cartoon physics were discussed in the film's dialogue, and the concept was a minor plot theme.
In 1993, Stephen R. Gould, then a financial training consultant, wrote in New Scientist, said that "... these seemingly nonsensical phenomena can be described by logical laws similar to those in our world. Nonsensical events are by no means limited to the Looniverse. Laws that govern our own Universe often seem contrary to common sense." This theme is also described by Alan Cholodenko in his article, "The Nutty Universe of Animation".
In a Garfield animated short entitled "Secrets of the Animated Cartoon", the characters Orson and Wade give demonstrations of different laws of the cartoons and show humorous examples of them.
In 2012 O'Donnell's Laws of Cartoon Motion were used as the basis for a presentation and exhibition by Andy Holden at Kingston University in Great Britain. Titled 'Laws of Motion in a Cartoon Landscape', it explored ideas of cartoon physics in relation to art and the end of art history. This was later made into a film with the artist as an animated cartoon character and shown at Glasgow International Festival in 2016, Tate Britain in 2017, and Future Generation Art Prize at Venice Biennale in 2017.
== Non-exclusivity ==
Cartoon physics is not limited to physics. For example, when a character recovers impossibly fast from a serious injury, the laws of biology rather than physics are being altered.
It is also not limited to cartoons; in live-action, the physics-defying stunts would fall under the umbrella of slapstick. Live-action shows and movies can also be subject to the laws of cartoon physics, explaining why, for example, The Three Stooges did not go blind from all the eye-poking, and the burglars in the Home Alone series survive life-threatening booby traps. In the live-action Pete's Dragon (1977), the titular dragon Elliot, while invisible, bursts through a wooden wall, leaving a dragon-shaped "silhouette of passage". The Ernest P. Worrell film series often made note of the title character's cartoon-like traits, with Ernest himself remarking in Ernest Rides Again that he would be dead "if I wasn't this close to being an actual cartoon."
In a review of one of the Home Alone films, film critic Roger Ebert noted that in the case of live-action productions, cartoon physics are not as effective at producing a comic effect, as the effects seem more realistic:
Most of the live-action attempts to duplicate animation have failed, because when flesh-and-blood figures hit the pavement, we can almost hear the bones crunch, and it isn't funny.
Printed cartoons have their own family of cartoon physics "laws" and conventions. Additionally, some video games utilize these elements during their cutscenes. For example, in the game Sonic Unleashed, titular character Sonic the Hedgehog is seen making effective use of hammerspace to stash a Chaos Emerald.
The concept can be used as a metaphor outside video. In an editorial for the New York Times in 2003 titled Don't Look Down, for example, economist Paul Krugman wrote while describing a gap between revenue and spending, "The crisis won't come immediately. For a few years, America will still be able to borrow freely, simply because lenders assume that things will somehow work out.... But at a certain point we'll have a Wile E. Coyote moment. For those not familiar with the Road Runner cartoons, Mr. Coyote had a habit of running off cliffs and taking several steps on thin air before noticing that there was nothing underneath his feet. Only then would he plunge. What will that plunge look like?"
== See also ==
12 basic principles of animation
Acme Corporation
Slapstick
Toon (role-playing game)
'Pataphysics
== Notes ==
== External links ==
The Laws of Cartoon Motion adapted from An Elementary Education: An Easy Alternative to Actual Learning by Mark O'Donnell (ISBN 978-0-394-54430-4).
Laws of Cartoon Thermodynamics from Roger Ebert's website.
Acceleration Due to Gravity: Super Mario Brothers - a physicist's determination of the value of g used in Super Mario Bros.
=== Other ===
Kent Pitman's Theory of RelativeTV (Soap Opera Physics) | Wikipedia/Cartoon_physics |
Barrier-grid animation or picket-fence animation is an animation effect created by moving a striped transparent overlay across an interlaced image. The barrier-grid technique originated in the late 1890s, overlapping with the development of parallax stereography (Relièphographie) for 3D autostereograms. The technique has also been used for color-changing pictures, but to a much lesser extent.
The development of barrier-grid technologies can also be regarded as a step towards lenticular printing, although the technique has remained after the invention of lenticular technologies as a relatively cheap and simple way to produce animated images in print.
== Concept ==
The barrier-grid technique uses a grid of barriers to control images reaching the viewer's eyes. The grid consists of a series of vertical or horizontal strips that can be either opaque or transparent. Typically, the barriers (opaque strips) alternate with transparent regions.
=== Animation ===
In barrier-grid animation, several images are cut into strips and interleaved. The barrier grid allows the strips from one of the interleaved images to be seen at a time. Movement of the grid relative to the interleaved image causes the viewer to see each of the images in succession.
=== Stereography ===
In parallax stereography, a barrier grid is placed in front of an image or a screen, with the distance between the grid and the image chosen such that the strips of image visible to each eye do not overlap. The images presented to each eye are slightly different, and are constructed such that the brain can combine them to create the illusion of depth via stereopsis. This allows viewing of 3D images without the need for specialized glasses, making it a practical and convenient solution for a wide range of applications.
== History ==
Using screens for photographic printing was suggested by William Fox Talbot as "photographic screens or veils" in an 1852 patent. This resulted in several halftone processes in the next decades. For color photography the use of colored line sheets had been suggested by Louis Arthur Ducos du Hauron in 1869. Several halftone printing and color photography processes, including the 1895 Joly colour screen with >0.1 mm RGB lines, inspired the use of line screens for autostereoscopic images.
=== Motograph ===
W. Symons received British Patent No. 5,759 on March 14, 1896 for a technique that was used about two years later for the oldest known publication that used a line-sheet to create the illusion of motion in pictures.
The Motograph Moving Picture Book was published in London at the start of 1898 by Bliss, Sands & Co. It came with a "transparency" with black stripes to add the illusion of motion to the pictures in the book (13 in the original black and white edition and 23 in the later color edition). The illustrations were credited to "F.J. Vernay, Yorick, &c.".
The pictures feature different hatching patterns, causing moiré type effects when the striped transparency is moved across it. It creates a vibrant type of motion illusion with revolving wheels, billowing smoke, ripples in water, etc.
The expanded "new edition" of the book had a cover design "specially drawn for the book" by famous French painter Henri de Toulouse-Lautrec, depicting a woman viewing pictures with the transparency (accompanied by a girl, a man and three different pets).
=== Auguste Berthier's autostereograms ===
In May 1896 Auguste Berthier published an article about the history of stereoscopic images in French scientific magazine Le Cosmos, which included his method of creating an autostereogram. Alternating strips from the left and right image of a traditional stereoscopic negative had to be recomposed as an interlaced image, preferably during the printing of the image on paper. A glass plate with opaque lines had to be fixed in front of the interlaced print with a few millimeters in between, so the lines on the screen formed a parallax barrier: from the right distance and angle each eye could only see the photographic strips shot from the corresponding angle. The article was illustrated with a diagram of the principle, an image of the two parts of a stereoscopic photograph divided into exaggerated wide bands, and the same strips recomposed as an interlaced image. Berthier's idea was hardly noticed. After Frederic Ives' similar autostereograms were presented at the French Academy of Sciences in 1904, Berthier reminded the institute about his autostereograms that he in the meantime had also managed to create in color.
=== Frederic Ives' parallax stereogram & changeable picture ===
On December 5, 1901 American inventor Frederic Eugene Ives presented his "parallax stereogram" at the Franklin Institute of the State of Pennsylvania. He claimed that he first had the idea 16 years earlier while working with the line screen in a study of "the dioptrics of half-tone screen photography". At the time he didn't think it was important enough to spend his time on. In 1901 Ives realized that he could easily adapt his Kromolinoskop color photo camera to create the stereogram and thought it would be an interesting scientific novelty worthy of presentation at the Franklin Institute. The "parallax stereogram" was a photo shot through two apertures behind the lens with a "transparent-line screen, consisting of opaque lines with clear spaces between them" in front of the sensitive plate, slightly separated from it. The line screen had 200 parallel lines per inch (79/cm) and was contact-printed from an original factory halftone screen. The technique received U.S. patent 725,567 on April 14, 1903 (application filed on September 25, 1902).
On October 11, 1904 Ives received U.S. patent 771,824 (application filed on October 27, 1903) for a "Changeable sign, picture, &c.". This was basically the same technique but with interlaced different images instead of a stereoscopic image. Shifting from one angle to the other, by passing the image or by a vibration of the image, the image would change from one to the other.
=== Eugène Estanave's animated autostereograms ===
In 1904 Léon Gaumont came across Ives' pictures at the World's Fair in St. Louis and had them presented at the French Academy of Sciences in October and the Société Française de Physique in November. Gaumont gave two parallax stereograms to the Conservatoire national des arts et métiers in 1905 and two others became part of the collection of the Société française de photographie.
French mathematician Eugène Estanave was encouraged by Gaumont to investigate the parallax stereogram and started working with the technique late in 1905. On January 24, 1906, Estanave filed for French patent 371.487 for a stereophotography device and stereoscopy using line sheets. It included his "changing" pictures that applied the principle of Ives' "Changeable sign" to animated photography, for instance the portrait of a woman with eyes open or closed depending on the viewing angle. On February 3, 1910 he requested an addition to his patent to include animated stereoscopic photography. This system used line sheets with vertical and horizontal lines, and combined four images: two stereoscopic pairs of two different moments. August 1, 1908 Estanave was granted French patent N° 392871 for an autostereoscopic photographic plate. This plate was exposed and developed to create a positive stereoscopic image, avoiding the trouble of aligning the interlaced photograph with a line screen. In the same year he was awarded a special prize at the French Academy of Sciences by Gabriel Lippmann for an x-ray stereogram. In 1911 Estanave discovered another variation: the Joly colour screen (with lines in three colors) could be adapted to create color photographs with the hues shifting when the viewing angle was changed. Fifteen examples of Estanave's stereograms are known to have survived. He seems not have commercialized any of his methods. Others marketed very similar animated portraits, usually with plastic line sheets, with some success in the 1910s and 1920s.
=== Magic moving picture cards ===
Magic moving pictures were composed of images containing black vertical and regularly interlaced stripes, alternating between two or three phases of a depicted motion or between distinctly different pictures. A little transparent sheet with regular vertical black stripes was glued beneath a window in a cardboard envelope holding the picture card. The card was pulled out and pushed back in to produce the illusion of change or motion.
The technique was patented in the United States on August 28, 1906, by Alexander S. Spiegel as a nameless "display device" (application date November 29, 1905). Spiegel patented several improvements, the last in 1911. Initially the pictures were drawings; later photographs were used. The postcards were marketed under Spiegel's patent as Magic moving pictures by G. Felsenthal & Co and as Magic moving picture card by the Franklin Postcard Company, both from Chicago. The latter produced a card in 1912 which enabled the viewer to choose among the portraits of three presidential candidates during that year's U.S. presidential election.
Similar cards have been published in Japan around 1920 as Cinematograph by SK and in France around 1940 as Mon cinema chez moi.
=== Ombro-Cinéma ===
Ombro-Cinéma toys operated on rotating scrolls of paper with sequences of images printed as interlaced two-frame animations: thin regularly-spaced vertical stripes of one frame of the animation were alternated with stripes of the next frame, alternately hidden by regularly-spaced black vertical stripes on a transparent viewing pane. In some versions the stripes on the viewing pane were disguised as a picket fence. Ombro-Cinema toys had of a wooden or cardboard chassis with a rack and hand-crank for cycling the image scroll across the viewing pane. In some versions a wind-up clockwork mechanism transferred the scroll while operating a music box.
The Ombro-Cinéma toy was produced by Saussine Ed. in Paris and patented in 1921 and six months later received a gold medal at the 19th Concours Lépine. Saussine had previously published versions with regular non-animated silhouettes on the scroll, as Ombres Chinoises (Chinese Shadows), patented in 1897. Both toys were named after and inspired by the Ombres Chinoises shadow play that had been very popular in France since 1772. Some of the animated Ombro-Cinéma toys are found with the same oriental design and Ombres Chinoises or Theatre Ombres title, but most had a design with Charlie Chaplin's Tramp character and Charles Prince's Rigadin (also interpreted as Fatty Arbuckle) on the proscenium and box. The Ombro-Cinéma received a gold medal.
At least fourteen different "films" with twelve images each were available, ten in black and white and four in color. The strips varied in length from circa 2.5 meters to more than 4 meter.
Series in black and white:
Film N° 1. Scènes des rues (Street scenes)
Film N° 2. Aventure de Marius (Marius' adventure)
Film N° 3. La fête de mon pays (The celebration of my country)
Film N° 4. Tous aux sports (All about sports)
Film N° 5. Poursuivants et poursuivis (Chasing and being chased)
Film N° 6. Carnaval de Nice (Carnival in Nice)
Film N° 7. Au cirque (At the circus)
Film N° 8. Voyages de John Sellery (The travels of John Sellery)
Film N° 9. Voyages de Gulliver (Gulliver's Travels)
Film N° 10. Scènes exotiques (Exotic scenes)
Series in color:
Film N° 20. Le petit Poucet (Little Thumb)
Film N° 21. Le Chat botté (Puss in boots)
Film N° 22. Le petit Chaperon rouge (Little Red Riding Hood)
Film N° 23. Au jardin d'Acclimentation (At the Acclimatization Garden?)
French circular disc versions inside children's picture books were produced in the 1940s as Album télévision and Livre de Télévision.
A French version from around 1950 was named Ciné Enfantin.
Similar wind-up musical toy "televisions" have been produced until late in the 20th century.
=== Maurice Bonnet's relièphographie ===
Many inventors tried to expand the number of images that could be used in line screen technology, which was mostly limited to how wide the lines could be without making the image too dark. French inventor Maurice Bonnet (1907-1994) made several patented improvements with his Relièphographie system of the same name. He used a camera with a horizontal row of eleven lenses (French patent N° 774145, June 5, 1934), followed by one with 33 lenses in 1937 (French patent n°833891, July 2, 1937). The darkness of the images is remedied with a lightbox frame. While the pictures of Estanave could only be seen from the right viewpoint, the multiple images that formed the Relièphographs ensured a clear 3D image when viewed from different angles. Bonnet was the only creator of autostereograms with line sheet technology who managed to successfully market his technique, for which he founded his "La Relièphographie" company in 1937. About 13 extant Relièphographie pictures by Bonnet are known, including three advertisements, two portraits and eight medical subjects. With their 30x40 cm format these are the largest preserved line sheet autostereograms. Despite the success Bonnet abandoned line sheet technology after he developed a lenticular sheet around 1940.
=== Autostereoscopic cinema ===
Russian inventor Semyon Ivanov invented an autostereoscopic cinema system in 1935. In December 1940 the 180-seat theater Moscow Stereokino was built especially for autostereoscopic movies with a rear projection screen (14×19 feet) that used 50 kilometres of fine copper wire as a barrier grid on a metal framework weighing six tons. It opened to public on February 4, 1941, premiering stereoscopic concert film Kontsert (Zemlya molodosti) by Aleksandr Andrievskiy. In 1947 Ivanov helped make the first autostereoscopic feature film Robinzon Kruzo. Moscow Stereokino showed autostereoscopic movies for 18 years and four more stereokino theatres were built in Russia. Ivanov would later work on lenticular techniques. Sergei Eisenstein wrote in 1947, relating to Ivanov's work: “To doubt that stereoscopic cinema has its tomorrows, is as naïve as doubting whether there will be tomorrows at all.”
François Savoye's first Cyclostéréoscope system with a rotating grid was shown to audiences around 1945–46 at the Paris Luna Park. An improved version was shown in the Clichy Palace in Paris in 1953. The size and weight of the required installation and the limited viewing zone to view barrier-grid movies were problems that probably made most barrier-grid cinema systems financially unviable.
=== Kinegram ===
Visual artist Gianni A. Sarcone has produced animations that he calls kinegrams since 1997. He describes his animations as "optic kinetic media" that "artfully combine the visual effects of moiré patterns with the zoetrope animation technique". Sarcone also created rotating animations that use a transparent disc with radial lines that has to be spun around its center to animate the picture.
=== Scanimation ===
"Scanimation", incorporating sliding striped acetate sheets into book pages or folding cards to produce barrier-grid animations of six phases or more at each page, was produced by Rufus Butler Seder starting in 2007. The first book Gallop! was followed by Swing!, Kick, Waddle!, Santa and licensed scanimation books of Star Wars, The Wizard of Oz and Peanuts.
== See also ==
Gas sculpture
Lenticular printing
History of animation
PHSCologram
Praxinoscope
Zoetrope
Zoopraxiscope
== References ==
== External links ==
Kinegram information
Video | Wikipedia/Barrier-grid_animation_and_stereography |
Graphic art software is a subclass of application software used for graphic design, multimedia development, stylized image development, technical illustration, general image editing, or simply to access graphic files. Art software uses either raster graphics or vector graphics reading and editing methods to create, edit, and view art.
Many artists and other creative professionals today use personal computers rather than traditional media. Using graphic art software may be more efficient than rendering using traditional media by needing less eye–hand coordination and less mental imaging skill, and using the computer's quicker (sometimes more accurate) automated rendering functions to create images. However, advanced level computer styles, effects and editing methods may need a steeper learning curve of computer technical skills than what was needed to learn traditional hand rendering and mental imaging skills. The potential of the software to enhance or hinder creativity may depend on the intuitiveness of the user interface.
== Specialized software ==
Most art software includes common functions, creation tools, editing tools, filters, and automated rendering modes. Many, however, are designed to enhance a specialized skill or technique. Specialized software packages may be discontinued for various reasons such as lack of appreciation for the result, lack of expertise and training for the product, or simply not worth the time and money investment, but most likely due to obsolescence compared to newer methods or integration as a feature of newer more complete software packages.
=== Graphic design software ===
Graphic design professionals favor general image editing software and page layout software commonly referred to as desktop publishing software. Graphic designers that are also image developers or multimedia developers may use a combination of page layout software with the following:
=== Multimedia development software ===
Multimedia development professionals favor software with audio, motion and interactivity such as software for creating and editing hypermedia, electronic presentations (more specifically slide presentations), computer simulations and games.
=== Stylized image development software ===
Image development professionals may use general graphic editors or may prefer more specialized software for rending or capturing images with style. Although images can be created from scratch with most art software, specialized software applications or advanced features of generalized applications are used for more accurate visual effects. These visual effects include:
==== Traditional medium effects ====
Vector editors are ideal for solid crisp lines seen in line art, poster, woodcut ink effects, and mosaic effects.
Some generalized image editors, such as Adobe Photoshop are used for digital painting (representing real brush and canvas textures such as watercolor or burlap canvas) or handicraft textures such as mosaic or stained glass. However, unlike Adobe Photoshop, which was originally designed for photo editing, software such as Corel Painter and Photo-Paint were originally designed for rendering with digital painting effects and continue to evolve with more emphasis on hand-rendering styles that don't appear computer generated.
==== Photorealistic effects ====
Unlike traditional medium effects, photorealistic effects create the illusion of a photographed image. Specialized software may contain 3D modeling and ray tracing features to make images appear photographed. Some 3D software is for general 3D object modeling, whereas other 3D software is more specialized, such as Poser for characters or Bryce for scenery. Software such as Adobe Photoshop may be used to create 3D effects from 2D (flat) images instead of 3D models. AddDepth is a discontinued software for extruding 2D shapes into 3D images with the option of beveled effects. MetaCreations Detailer and Painter 3D are discontinued software applications specifically for painting texture maps on 3D Models.
==== Hyperrealistic effects ====
Specialized software may be used to combine traditional medium effects and photorealistic effects. 3-D modeling software may be exclusively for, include features for, or include the option of 3rd party plugins for rendering 3-D models with 2-D effects (e.g. cartoons, illustrations) for hyperrealistic effects. Other 2-D image editing software may be used to trace photographs or rotoscope animations from the film. This allows artists to rapidly apply unique styles to what would be purely photorealistic images from computer generated imagery from 3-D models or photographs. Some styles of hyperrealism may need motion visual effects (e.g., geometrically accurate rotation, accurate kinetics, simulated organic growth, lifelike motion constraints) to notice the realism of the imagery. Software may be used to bridge the gap between the imagination and the laws of physics.
=== Technical graphic software ===
Technical professionals and technical illustrators may use technical graphic software that might allow for stylized effects with more emphasis on clarity and accuracy and little or no emphasis on creative expression and aesthetics. For this reason, the results are seldom referred to as "art." For designing or technical illustration of synthetic physical objects, the software is usually referred to as CAD or CADD, Computer-Aided Design and Drafting. This software allows more precise handling of measurements and mathematical calculations, some of which simulate physics to conduct virtual testing of the models. Aside from physical objects, technical graphic software may include software for visualizing concepts, manually representing scientific data, visualizing algorithms, visual instructions, and navigation aids in the form of information graphics. Specialized software for concept maps may be used for both technical purposes and non-technical conceptualizing, which may or may not be considered technical illustration.
=== Education aspects ===
Ellen Mazur Thomson's exploration of technology's influence on design education in "The Literature of Graphic Design" aligns with the broader context of graphic software design. The discussion about technology's impact on design education directly correlates with the use and integration of graphic software in teaching design principles.
The insights from Thomson's book highlight how technology, specifically graphic design software, plays a pivotal role in modern design education. This software is often discussed in the context of graphic art and design and forms the practical foundation of many design courses. Thomson's exploration likely emphasizes how graphic software tools empower students to create, experiment, and visualize their design ideas in a digital format. It ties into the discussion by showcasing how these software applications are not just tools for creating art but essential components in the education and practice of modern graphic design. Understanding technology's role in design education, as discussed in the book, provides valuable context to appreciate the significance and integration of graphic software design in teaching and learning graphic design principles and techniques.
=== Specialized graphic format handling ===
This may include software for handling specialized graphic file formats such as Fontographer software, which is dedicated to creating and editing computer fonts. Some general image editing software has unique image file handling features as well. Vector graphic editors handle vector graphic files and are able to load PostScript files natively. Some tools enable professional photographers to use nondestructive image processing for editing digital photography without permanently changing or duplicating the original, using the raw image format. Other special handling software includes software for capturing images such as 2D scanning software, 3D scanning software and screen-capturing, or software for specialized graphic format processing such as raster image processing and file format conversion. Some tools may reduce the file size of graphics for web performance optimization while maintaining the image quality as best as possible.
== Software lists ==
List of raster graphics editors
List of vector graphics editors
Comparison of computer-aided design software
List of digital art software
List of information graphics software
List of concept- and mind-mapping software (description and list)
List of 2D graphics software
3D computer graphics software (description and list)
Presentation software (description and list)
Desktop publishing (description and list)
List of media players (viewing access only)
Comparison of media players
== See also ==
Computer art
Computer generated imagery
Computer graphics
Digital artist
Graphics programs
Raster graphics editor
Vector graphics editor
== References ==
2. The Literature of Graphic Design - JSTOR, www.jstor.org/stable/27948508. Accessed 15 Dec. 2023. | Wikipedia/Graphic_art_software |
Model animation is a form of stop motion animation designed to merge with live-action footage to create the illusion of a real-world fantasy sequence.
== Techniques ==
Many types of models have been created and developed, and the choice mainly depends on the budget of the film:
Clay Models: Unlike most clay figures used for animation, clay models have an inner metal skeleton designed to allow them realistic movements and expressions.
Built-Up Models: These types of models are more expensive and detailed than clay models. They are made by building up pieces of foam on a metal skeleton to create a body, and then either brushing on several layers of liquid latex or casting soft rubbery skins and attaching then to the padded armature.
"Cast" Models: These types of models are the most expensive used in the industry and are longer-lasting than the other types. They start as clay sculptures onto which two- (or more) part molds are made in order to reproduce all the details. Then the mold parts are assembled with an armature inside, and they are filled with a liquid material (foam latex, silicone rubber, urethane foam, etc.) that then forms a soft rubbery "flesh" over the skeleton.
== Works ==
Model animation was pioneered by Willis O'Brien, and it was first used in The Lost World (1925). His work also includes
King Kong (1933)
The Son of Kong (1933)
Mighty Joe Young (1949)
The Black Scorpion (1957)
The Giant Behemoth (1958)
Picking up the model animation baton from O'Brien, and refining the process further, introducing color and smoother animation, was his protégé, Ray Harryhausen. Assisting O'Brien in Mighty Joe Young in 1949, Harryhausen went on to do model animation (and other special visual effects) on a series of feature-length films, such as:
The Beast From 20,000 Fathoms (1953)
It Came from Beneath the Sea (1955)
Earth vs. the Flying Saucers (1956)
The Animal World (Opening Dinosaur sequence, with O'Brien, 1956)
20 Million Miles To Earth (1957)
The 7th Voyage of Sinbad (1958)
The Three Worlds of Gulliver (1960)
Mysterious Island (1961)
Jason and the Argonauts (1963)
First Men in the Moon (1964)
One Million Years B.C. (1967)
The Valley of Gwangi (1969)
The Golden Voyage of Sinbad (1974)
Sinbad and the Eye of the Tiger (1977)
Clash of the Titans (with Jim Danforth, 1981)
The third generation of model animators featured such notables as Jim Danforth, David Allen, and Phil Tippett.
== References ==
=== Works cited ===
Harryhausen, Ray; Dalton, Tony (2008). A Century of Model Animation: From Méliès to Aardman. Aurum Press. ISBN 978-0-8230-9980-1.
Priebe, Ken A. (2006). The Art of Stop-Motion Animation. Thompson Course Technology. ISBN 1-59863-244-2.
== See also ==
List of stop-motion films
Go motion | Wikipedia/Model_animation |
Transparency in computer graphics is possible in a number of file formats. The term "transparency" is used in various ways by different people, but at its simplest there is "full transparency" i.e. something that is completely invisible. Only part of a graphic should be fully transparent, or there would be nothing to see. More complex is "partial transparency" or "translucency" where the effect is achieved that a graphic is partially transparent in the same way as colored glass. Since ultimately a printed page or computer or television screen can only be one color at a point, partial transparency is always simulated at some level by mixing colors. There are many different ways to mix colors, so in some cases transparency is ambiguous.
In addition, transparency is often an "extra" for a graphics format, and some graphics programs will ignore the transparency.
Raster file formats that support transparency include GIF, PNG, WebP, BMP, TIFF, TGA and JPEG 2000, through either a transparent color or an alpha channel.
Most vector formats implicitly support transparency because they simply avoid putting any objects at a given point. This includes EPS and WMF. For vector graphics this may not strictly be seen as transparency, but it requires much of the same careful programming as transparency in raster formats.
More complex vector formats may allow transparency combinations between the elements within the graphic, as well as that above. This includes SVG and PDF.
A suitable raster graphics editor shows transparency by a special pattern, e.g. a checkerboard pattern.
== Transparent pixels ==
One color entry in a single GIF or PNG image's palette can be defined as "transparent" rather than an actual color. This means that when the decoder encounters a pixel with this value, it is rendered in the background color of the part of the screen where the image is placed, also if this varies pixel-by-pixel as in the case of a background image.
Applications include:
an image that is not rectangular can be filled to the required rectangle using transparent surroundings; the image can even have holes (e.g. be ring-shaped)
in a run of text, a special symbol for which an image is used because it is not available in the character set, can be given a transparent background, resulting in a matching background.
The transparent color should be chosen carefully, to avoid items that just happen to be the same color vanishing.
Even this limited form of transparency has patchy implementation, though most popular web browsers are capable of displaying transparent GIF images. This support often does not extend to printing, especially to printing devices (such as PostScript) which do not include support for transparency in the device or driver. Outside the world of web browsers, support is fairly hit-or-miss for transparent GIF files.
=== Edge limitations of transparent pixels ===
The edges of characters and other images with transparent background should not have shades of gray: these are normally used for intermediate colors between the color of the letter/image and that of the background, typically shades of gray being intermediate between a black letter and a white background. However, with, for example, a red background the intermediate colors would be dark red. Gray edge pixels would give an ugly and unclear result. For a variable background color there are no suitable fixed intermediate colors.
== Partial transparency by alpha channels ==
Some image formats, such as PNG and TIFF, also allow partial transparency through an alpha channel, which solves the edge limitation problem. Instead of each pixel either being transparent or not transparent, it can be set to 254 levels of partially transparent, allowing some of the background image to show through the foreground image.
A major use of partial transparency is to produce "soft edges" in graphics so that they blend into their background. See also monochrome or with shades of gray and anti-aliasing. Partial transparency can also be used to make an image less prominent, such as a watermark or other logo; or to render something see-through, such as a ghostly apparition in a video game. Animating the alpha channel in an image-editing program can allow smooth transitions between different images.
The process of combining a partially transparent color with its background ("compositing") is often ill-defined and the results may not be exactly the same in all cases. For example, where color correction is in use, should the colors be composited before or after color correction?
== Transparency by clipping path ==
An alternative approach to full transparency is to use a clipping path. A clipping path is simply a shape or outline, that is used in conjunction with the other graphics. Everything inside the path is visible, and everything outside the path is invisible. The path is inherently vector, but can potentially be used to mask both vector and bitmap data. The main usage of clipping paths is in PostScript files.
== Compositing calculations ==
While some transparency specifications are vague, others may give mathematical details of how two colors are to be composited. This gives a fairly simple example of how compositing calculations can work, can produce the expected results, and can also produce surprises.
In this example, two grayscale colors are to be composited. Grayscale values are considered to be numbers between 0.0 (white) and 1.0 (black). To emphasize: this is only one possible rule for transparency. If working with transparency, check the rules in use for your situation.
The color at a point, where color G1 and G2 are to be combined, is ( G1 + G2 ) / 2.
Some consequences of this are:
Where the colors are equal, the result is the same color because ( G1 + G1 ) /2 = G1.
Where one color (G1) is white (0.0), the result is G2 / 2. This will always be less than any nonzero value of G2, so the result is whiter than G2. (This is easily reversed for the case where G2 is white).
Where one color (G1) is black (1.0), the result is ( G2 + 1 ) / 2. This will always be more than G2, so the result is blacker than G2.
The formula is commutative since ( G1 + G2 ) / 2 = ( G2 + G1 ) / 2. This means it does not matter which order two graphics are mixed i.e. which of the two is on the top and which is on the bottom.
The formula is not associative since
( ( G1 + G2 ) / 2 + G3 ) / 2 = G1 / 4 + G2 / 4 + G3 / 2
( G1 + ( G2 + G3 ) / 2 ) / 2 = G1 / 2 + G2 / 4 + G3 / 4
This is important as it means that when combining three or more objects with this rule for transparency, the final color depends very much on the order of doing the calculations.
Although the formula is simple, it may not be ideal. Human perception of brightness is not linear - we do not necessarily consider that a gray value of 0.5 is halfway between black and white. Such details may not matter when transparency is used only to soften edges, but in more complex designs this may be significant. Most people working seriously with transparency will need to see the results and may fiddle with the colors or (where possible) the algorithm to arrive at the results they need.
This formula can easily be generalized to RGB color or CMYK color by applying the formula to each channel separately. For example, final red = ( R1 + R2 ) / 2. But it cannot be applied to all color models. For example, Lab color would produce results that were surprising.
An alternative model is that at every point in each element to be combined for transparency there is an associated color and opacity between 0 and 1. For each color channel, you might work with this model: if a channel with intensity G2 and opacity T2 overlays a channel with intensity G1 and opacity T1 the result will be a channel with intensity equal to (1 - T2) * G1 + G2, and opacity 1 - (1 - T2) * (1 - T1). Each channel must be multiplied by corresponding alpha value before composition (so called premultiplied alpha). The SVG file specification uses this type of blending, and this is one of the models that can be used in PDF.
Alpha channels may be implemented in this way, where the alpha channel provides an opacity level to be applied equally to all other channels. To work with the above formula, the opacity needs to be scaled to the range 0 to 1, whatever its external representation (often 0 to 255 if using 8 bit samples such as "RGBA").
== Transparency in PDF ==
Starting with version 1.4 of the PDF standard (Adobe Acrobat version 5), transparency (including translucency) is supported. Transparency in PDF files allows creators to achieve various effects, including adding shadows to objects, making objects semi-transparent and having objects blend into each other or into text. PDF supports many different blend modes, not just the most common averaging method, and the rules for compositing many overlapping objects allow choices (such as whether a group of objects are blended before being blended with the background, or whether each object in turn is blended into the background).
PDF transparency is a very complex model, its original specification by Adobe being over 100 pages long. A key source of complication is that blending objects with different color spaces can be tricky and error-prone as well as cause compatibility issues. Transparency in PDF was designed not to cause errors in PDF viewers that did not understand it – they would simply display all elements as fully opaque. However, this was a two-edged sword as users with older viewers, PDF printers, etc. could see or print something completely different from the original design.
The fact that the PDF transparency model is so complicated means that it is not well supported. This means that RIPs and printers often have problems printing PDFs with transparency. The solution to this is either to rasterize the image or to apply vector transparency flattening to the PDF. However vector transparency flattening is extremely complex and only supported by a few specialist packages.
== Transparency in PostScript ==
The PostScript language has limited support for full (not partial) transparency, depending on the PostScript level. Partial transparency is available with the pdfmark extension, available on many PostScript implementations.
=== Level 1 ===
Level 1 PostScript offers transparency via two methods:
A one-bit (monochrome) image can be treated as a mask. In this case the 1-bits can be painted any single color, while the 0-bits are not painted at all. This technique cannot be generalised to more than one color, or to vector shapes.
Clipping paths can be defined. These restrict what part of all subsequent graphics can be seen. This can be used for any kind of graphic, however in level 1, the maximum number of nodes in a path was often limited to 1500, so complex paths (e.g. cutting around the hair in a photograph of a person's head) often failed.
=== Level 2 ===
Level 2 PostScript adds no specific transparency features. However, by the use of patterns, arbitrary graphics can be painted through masks defined by any vector or text operations. This is, however, complex to implement. In addition, this too often reached implementation limits, and few if any application programs ever offered this technique.
=== Level 3 ===
Level 3 PostScript adds further transparency option for any raster image. A transparent color, or range of colors, can be applied; or a separate 1-bit mask can be used to provide an alpha channel.
=== Encapsulated PostScript ===
EPS files contain PostScript, which may be level 1, 2 or 3 and make use of the features above. A more subtle issue arises with the previews for EPS files that are typically used to show the view of the EPS file on screen. There are viable techniques for setting transparency in the preview. For example, a TIFF preview might use a TIFF alpha channel. However, many applications do not use this transparency information and will therefore show the preview as a rectangle. A semi-proprietary technique pioneered in Photoshop and adopted by a number of pre-press applications is to store a clipping path in a standard location of the EPS, and use that for display.
In addition, few of the programs that generate EPS previews will generate transparency information in the preview.
Some programs have sought to get around this by treating all white in the preview as transparent, but this too is problematic in the cases where some whites are not transparent.
More recently, applications have been appearing that ignore the preview altogether; they therefore get information on which parts of the preview to paint by interpreting the PostScript.
== See also ==
3D computer graphics
Transparent color in palettes
Image masks
Alpha channel
Magic pink
Video overlay
Genlock
Bitblit
== References == | Wikipedia/Transparency_(graphic) |
Object–role modeling (ORM) is used to model the semantics of a universe of discourse. ORM is often used for data modeling and software engineering.
An object–role model uses graphical symbols that are based on first order predicate logic and set theory to enable the modeler to create an unambiguous definition of an arbitrary universe of discourse. Attribute free, the predicates of an ORM Model lend themselves to the analysis and design of graph database models in as much as ORM was originally conceived to benefit relational database design.
The term "object–role model" was coined in the 1970s and ORM based tools have been used for more than 30 years – principally for data modeling. More recently ORM has been used to model business rules, XML-Schemas, data warehouses, requirements engineering and web forms.
== History ==
The roots of ORM can be traced to research into semantic modeling for information systems in Europe during the 1970s. There were many pioneers and this short summary does not by any means mention them all. An early contribution came in 1973 when Michael Senko wrote about "data structuring" in the IBM Systems Journal. In 1974 Jean-Raymond Abrial contributed an article about "Data Semantics". In June 1975, Eckhard Falkenberg's doctoral thesis was published and in 1976 one of Falkenberg's papers mentions the term "object–role model".
G.M. Nijssen made fundamental contributions by introducing the "circle-box" notation for object types and roles, and by formulating the first version of the conceptual schema design procedure. Robert Meersman extended the approach by adding subtyping, and introducing the first truly conceptual query language.
Object role modeling also evolved from the Natural language Information Analysis Method, a methodology that was initially developed by the academic researcher, G.M. Nijssen in the Netherlands (Europe) in the mid-1970s and his research team at the Control Data Corporation Research Laboratory in Belgium, and later at the University of Queensland, Australia in the 1980s. The acronym NIAM originally stood for "Nijssen's Information Analysis Methodology", and later generalised to "Natural language Information Analysis Methodology" and Binary Relationship Modeling since G. M. Nijssen was only one of many people involved in the development of the method.
In 1989, Terry Halpin completed his PhD thesis on ORM, providing the first full formalization of the approach and incorporating several extensions.
Also in 1989, Terry Halpin and G.M. Nijssen co-authored the book "Conceptual Schema and Relational Database Design" and several joint papers, providing the first formalization of object–role modeling.
A graphical NIAM design tool which included the ability to generate database-creation scripts for Oracle, DB2 and DBQ was developed in the early 1990s in Paris. It was originally named Genesys and was marketed successfully in France and later Canada. It could also handle ER diagram design. It was ported to SCO Unix, SunOs, DEC 3151's and Windows 3.0 platforms, and was later migrated to succeeding Microsoft operating systems, utilising XVT for cross operating system graphical portability. The tool was renamed OORIANE and is currently being used for large data warehouse and SOA projects.
Also evolving from NIAM is "Fully Communication Oriented Information Modeling" FCO-IM (1992). It distinguishes itself from traditional ORM in that it takes a strict communication-oriented perspective. Rather than attempting to model the domain and its essential concepts, it models the communication in this domain (universe of discourse). Another important difference is that it does this on instance level, deriving type level and object/fact level during analysis.
Another recent development is the use of ORM in combination with standardised relation types with associated roles and a standard machine-readable dictionary and taxonomy of concepts as are provided in the Gellish English dictionary. Standardisation of relation types (fact types), roles and concepts enables increased possibilities for model integration and model reuse.
== Concepts ==
=== Facts ===
Object–role models are based on elementary facts, and expressed in diagrams that can be verbalised into natural language. A fact is a proposition such as "John Smith was hired on 5 January 1995" or "Mary Jones was hired on 3 March 2010".
With ORM, propositions such as these, are abstracted into "fact types" for example "Person was hired on Date" and the individual propositions are regarded as sample data. The difference between a "fact" and an "elementary fact" is that an elementary fact cannot be simplified without loss of meaning. This "fact-based" approach facilitates modeling, transforming, and querying information from any domain.
=== Attribute-free ===
ORM is attribute-free: unlike models in the entity–relationship (ER) and Unified Modeling Language (UML) methods, ORM treats all elementary facts as relationships and so treats decisions for grouping facts into structures (e.g. attribute-based entity types, classes, relation schemes, XML schemas) as implementation concerns irrelevant to semantics. By avoiding attributes, ORM improves semantic stability and enables verbalization into natural language.
=== Fact-based modeling ===
Fact-based modeling includes procedures for mapping facts to attribute-based structures, such as those of ER or UML.
Fact-based textual representations are based on formal subsets of native languages. ORM proponents argue that ORM models are easier to understand by people without a technical education. For example, proponents argue that object–role models are easier to understand than declarative languages such as Object Constraint Language (OCL) and other graphical languages such as UML class models. Fact-based graphical notations are more expressive than those of ER and UML. An object–role model can be automatically mapped to relational and deductive databases (such as datalog).
=== ORM 2 graphical notation ===
ORM2 is the latest generation of object–role modeling. The main objectives for the ORM 2 graphical notation are:
More compact display of ORM models without compromising clarity
Improved internationalization (e.g. avoid English language symbols)
Simplified drawing rules to facilitate creation of a graphical editor
Extended use of views for selectively displaying/suppressing detail
Support for new features (e.g. role path delineation, closure aspects, modalities)
=== Design procedure ===
System development typically involves several stages such as: feasibility study; requirements analysis; conceptual design of data and operations; logical design; external design; prototyping; internal design and implementation; testing and validation; and maintenance. The seven steps of the conceptual schema design procedure are:
Transform familiar information examples into elementary facts, and apply quality checks
Draw the fact types, and apply a population check
Check for entity types that should be combined, and note any arithmetic derivations
Add uniqueness constraints, and check arity of fact types
Add mandatory role constraints, and check for logical derivations
Add value, set comparison and subtyping constraints
Add other constraints and perform final checks
ORM's conceptual schema design procedure (CSDP) focuses on the analysis and design of data.
== See also ==
Concept map
Conceptual schema
Enhanced entity–relationship model (EER)
Information flow diagram
Ontology double articulation
Ontology engineering
Relational algebra
Three-schema approach
== References ==
== Further reading ==
Halpin, Terry (1989), Conceptual Schema and Relational Database Design, Sydney: Prentice Hall, ISBN 978-0-13-167263-5
Rossi, Matti; Siau, Keng (April 2001), Information Modeling in the New Millennium, IGI Global, ISBN 978-1-878289-77-3
Halpin, Terry; Evans, Ken; Hallock, Pat; Maclean, Bill (September 2003), Database Modeling with Microsoft Visio for Enterprise Architects, Morgan Kaufmann, ISBN 978-1-55860-919-8
Halpin, Terry; Morgan, Tony (March 2008), Information Modeling and Relational Databases: From Conceptual Analysis to Logical Design (2nd ed.), Morgan Kaufmann, ISBN 978-0-12-373568-3
== External links == | Wikipedia/Object–Role_Modeling |
The XQuery and XPath Data Model (XDM) is the data model shared by the XPath 2.0, XSLT 2.0, XQuery, and XForms programming languages. It is defined in a W3C recommendation. Originally, it was based on the XPath 1.0 data model which in turn is based on the XML Information Set.
The XDM consists of flat sequences of zero or more items which can be typed or untyped, and are either atomic values or XML nodes (of seven kinds: document, element, attribute, text, namespace, processing instruction, and comment). Instances of the XDM can optionally be XML schema-validated.
== References ==
== External links ==
IBM: XQuery and XPath data model | Wikipedia/XQuery_and_XPath_Data_Model |
Integrated Data Store (IDS) was an early network database management system largely used by industry, known for its high performance. IDS became the basis for the CODASYL Data Base Task Group standards.
IDS was designed in the 1960s at the computer division of General Electric (which later became Honeywell Information Systems) by Charles Bachman, who received the Turing Award from the Association for Computing Machinery for its creation, in 1973.
The software was released in 1964 for the GE 235 computer.
By 1965, a network version for the customer Weyerhaeuser Lumber was in operation.
IDS/II, introduced in 1975, was a chargeable program product. At this time the original version was labeled IDS/I.
It was not easy to use or implement applications with IDS, because it was designed to maximize performance using the hardware available at that time. However, that weakness was equally its strength because skilful implementations of IDS-type databases, such as British Telecom's huge CSS project (an IDMS database servicing more than 10 billion transactions per year), show levels of performance on terabyte-sized databases that are unmatchable by all relational database implementations. Charles Bachman's innovative design work continues to find state-of-the-art application with major commercial operations.
Later, BF Goodrich Chemical Co., rewrote the entire system to make it more usable, calling the result integrated data management system (IDMS).
== See also ==
Navigational Database
== References ==
== External links ==
Bachman, Charles W. (October–December 2009). "The Origin of the Integrated Data Store (IDS): The First Direct-Access DBMS". IEEE Annals of the History of Computing. 31 (4): 42–54. doi:10.1109/MAHC.2009.110. S2CID 12615473. Retrieved Jan 8, 2022. | Wikipedia/Integrated_Data_Store |
In computer science, domain relational calculus (DRC) is a calculus that was introduced by Michel Lacroix and Alain Pirotte as a declarative database query language for the relational data model.
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{\displaystyle \{\langle X_{1},X_{2},....,X_{n}\rangle \mid p(\langle X_{1},X_{2},....,X_{n}\rangle )\}}
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denotes a DRC formula. The result of the query is the set of tuples X1 to Xn that make the DRC formula true.
This language uses the same operators as tuple calculus,
the logical connectives ∧ (and), ∨ (or) and ¬ (not). The existential quantifier (∃) and the universal quantifier (∀) can be used to bind the variables.
Its computational expressiveness is equivalent to that of relational algebra.
== Examples ==
Let (A, B, C) mean (Rank, Name, ID) in the Enterprise relation
and let (D, E, F) mean (Name, DeptName, ID) in the Department relation
All captains of the starship USS Enterprise:
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{\displaystyle \left\{\ {\left\langle A,B,C\right\rangle }\mid {\left\langle A,B,C\right\rangle \in \mathrm {Enterprise} \ \land \ A=\mathrm {'Captain'} }\ \right\}}
In this example, A, B, C denotes both the result set and a set in the table Enterprise.
Names of Enterprise crew members who are in Stellar Cartography:
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{\displaystyle {\begin{aligned}\{{\left\langle B\right\rangle }&\mid {\exists A,C\ \left\langle A,B,C\right\rangle \in \mathrm {Enterprise} }\\&\land \ {\exists D,E,F\ \left\langle D,E,F\right\rangle \in \mathrm {Departments} }\\&\land \ F=C\\&\land \ E=\mathrm {'Stellar\ Cartography'} \}\\\end{aligned}}}
In this example, we're only looking for the name, and that's B. The condition F = C is a requirement that describes the intersection of Enterprise crew members AND members of the Stellar Cartography Department.
An alternate representation of the previous example would be:
{
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B
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A
,
C
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{\displaystyle {\begin{aligned}\{{\left\langle B\right\rangle }&\mid {\exists A,C\ \left\langle A,B,C\right\rangle \in \mathrm {Enterprise} }\\&\land \ {\exists D\ \left\langle D,\mathrm {'Stellar\ Cartography'} ,C\right\rangle \in \mathrm {Departments} }\}\\\end{aligned}}}
In this example, the value of the requested F domain is directly placed in the formula and the C domain variable is re-used in the query for the existence of a department, since it already holds a crew member's ID.
Both of them written in SQL will be like:
== See also ==
Relational calculus
== References ==
== External links ==
DES – An educational tool for working with Domain Relational Calculus and other formal languages
WinRDBI – An educational tool for working with Domain Relational Calculus and other formal languages | Wikipedia/Domain_calculus |
Enterprise modelling is the abstract representation, description and definition of the structure, processes, information and resources of an identifiable business, government body, or other large organization.
It deals with the process of understanding an organization and improving its performance through creation and analysis of enterprise models. This includes the modelling of the relevant business domain (usually relatively stable), business processes (usually more volatile), and uses of information technology within the business domain and its processes.
== Overview ==
Enterprise modelling is the process of building models of whole or part of an enterprise with process models, data models, resource models and/or new ontologies etc. It is based on knowledge about the enterprise, previous models and/or reference models as well as domain ontologies using model representation languages. An enterprise in general is a unit of economic organization or activity. These activities are required to develop and deliver products and/or services to a customer. An enterprise includes a number of functions and operations such as purchasing, manufacturing, marketing, finance, engineering, and research and development. The enterprise of interest are those corporate functions and operations necessary to manufacture current and potential future variants of a product.
The term "enterprise model" is used in industry to represent differing enterprise representations, with no real standardized definition. Due to the complexity of enterprise organizations, a vast number of differing enterprise modelling approaches have been pursued across industry and academia. Enterprise modelling constructs can focus upon manufacturing operations and/or business operations; however, a common thread in enterprise modelling is an inclusion of assessment of information technology. For example, the use of networked computers to trigger and receive replacement orders along a material supply chain is an example of how information technology is used to coordinate manufacturing operations within an enterprise.
The basic idea of enterprise modelling according to Ulrich Frank is "to offer different views on an enterprise, thereby providing a medium to foster dialogues between various stakeholders - both in academia and in practice. For this purpose they include abstractions suitable for strategic planning, organisational (re-) design and software engineering. The views should complement each other and thereby foster a better understanding of complex systems by systematic abstractions. The views should be generic in the sense that they can be applied to any enterprise. At the same time they should offer abstractions that help with designing information systems which are well integrated with a company's long term strategy and its organisation. Hence, enterprise models can be regarded as the conceptual infrastructure that support a high level of integration."
== History ==
Enterprise modelling has its roots in systems modelling and especially information systems modelling. One of the earliest pioneering works in modelling information systems was done by Young and Kent (1958), who argued for "a precise and abstract way of specifying the informational and time characteristics of a data processing problem". They wanted to create "a notation that should enable the analyst to organize the problem around any piece of hardware". Their work was a first effort to create an abstract specification and invariant basis for designing different alternative implementations using different hardware components. A next step in IS modelling was taken by CODASYL, an IT industry consortium formed in 1959, who essentially aimed at the same thing as Young and Kent: the development of "a proper structure for machine independent problem definition language, at the system level of data processing". This led to the development of a specific IS information algebra.
The first methods dealing with enterprise modelling emerged in the 1970s. They were the entity-relationship approach of Peter Chen (1976) and SADT of Douglas T. Ross (1977), the one concentrate on the information view and the other on the function view of business entities. These first methods have been followed end 1970s by numerous methods for software engineering, such as SSADM, Structured Design, Structured Analysis and others. Specific methods for enterprise modelling in the context of Computer Integrated Manufacturing appeared in the early 1980s. They include the IDEF family of methods (ICAM, 1981) and the GRAI method by Guy Doumeingts in 1984 followed by GRAI/GIM by Doumeingts and others in 1992.
These second generation of methods were activity-based methods which have been surpassed on the one hand by process-centred modelling methods developed in the 1990s such as Architecture of Integrated Information Systems (ARIS), CIMOSA and Integrated Enterprise Modeling (IEM). And on the other hand by object-oriented methods, such as Object-oriented analysis (OOA) and Object-modelling technique (OMT).
== Enterprise modelling basics ==
=== Enterprise model ===
An enterprise model is a representation of the structure, activities, processes, information, resources, people, behavior, goals, and constraints of a business, government, or other enterprises. Thomas Naylor (1970) defined a (simulation) model as "an attempt to describe the interrelationships among a corporation's financial, marketing, and production activities in terms of a set of mathematical and logical relationships which are programmed into the computer." These interrelationships should according to Gershefski (1971) represent in detail all aspects of the firm including "the physical operations of the company, the accounting and financial practices followed, and the response to investment in key areas" Programming the modelled relationships into the computer is not always necessary: enterprise models, under different names, have existed for centuries and were described, for example, by Adam Smith, Walter Bagehot, and many others.
According to Fox and Gruninger (1998) from "a design perspective, an enterprise model should provide the language used to explicitly define an enterprise... From an operations perspective, the enterprise model must be able to represent what is planned, what might happen, and what has happened. It must supply the information and knowledge necessary to support the operations of the enterprise, whether they be performed by hand or machine."
In a two-volume set entitled The Managerial Cybernetics of Organization Stafford Beer introduced a model of the enterprise, the Viable System Model (VSM). Volume 2, The Heart of Enterprise, analyzed the VSM as a recursive organization of five systems: System One (S1) through System Five (S5). Beer's model differs from others in that the VSM is recursive, not hierarchical: "In a recursive organizational structure, any viable system contains, and is contained in, a viable system."
=== Function modelling ===
Function modelling in systems engineering is a structured representation of the functions, activities or processes within the modelled system or subject area.
A function model, also called an activity model or process model, is a graphical representation of an enterprise's function within a defined scope. The purposes of the function model are: to describe the functions and processes, assist with discovery of information needs, help identify opportunities, and establish a basis for determining product and service costs. A function model is created with a functional modelling perspective. A functional perspectives is one or more perspectives possible in process modelling. Other perspectives possible are for example behavioural, organisational or informational.
A functional modelling perspective concentrates on describing the dynamic process. The main concept in this modelling perspective is the process, this could be a function, transformation, activity, action, task etc. A well-known example of a modelling language employing this perspective is data flow diagrams. The perspective uses four symbols to describe a process, these being:
Process: Illustrates transformation from input to output.
Store: Data-collection or some sort of material.
Flow: Movement of data or material in the process.
External Entity: External to the modelled system, but interacts with it.
Now, with these symbols, a process can be represented as a network of these symbols. This decomposed process is a DFD, data flow diagram. In Dynamic Enterprise Modeling, for example, a division is made in the Control model, Function Model, Process model and Organizational model.
=== Data modelling ===
Data modelling is the process of creating a data model by applying formal data model descriptions using data modelling techniques. Data modelling is a technique for defining business requirements for a database. It is sometimes called database modelling because a data model is eventually implemented in a database.
The figure illustrates the way data models are developed and used today. A conceptual data model is developed based on the data requirements for the application that is being developed, perhaps in the context of an activity model. The data model will normally consist of entity types, attributes, relationships, integrity rules, and the definitions of those objects. This is then used as the start point for interface or database design.
=== Business process modelling ===
Business process modelling, not to be confused with the wider Business Process Management (BPM) discipline, is the activity of representing processes of an enterprise, so that the current ("as is") process may be analyzed and improved in future ("to be"). Business process modelling is typically performed by business analysts and managers who are seeking to improve process efficiency and quality. The process improvements identified by business process modelling may or may not require Information Technology involvement, although that is a common driver for the need to model a business process, by creating a process master.
Change management programs are typically involved to put the improved business processes into practice. With advances in technology from large platform vendors, the vision of business process modelling models becoming fully executable (and capable of simulations and round-trip engineering) is coming closer to reality every day.
=== Systems architecture ===
The RM-ODP reference model identifies enterprise modelling as providing one of the five viewpoints of an open distributed system. Note that such a system need not be a modern-day IT system: a banking clearing house in the 19th century may be used as an example ().
== Enterprise modelling techniques ==
There are several techniques for modelling the enterprise such as
Active Knowledge Modeling,
Design & Engineering Methodology for Organizations (DEMO)
Dynamic Enterprise Modeling
Enterprise Modelling Methodology/Open Distributed Processing (EMM/ODP)
Extended Enterprise Modeling Language
Multi-Perspective Enterprise Modelling (MEMO),
Process modelling such as BPMN, CIMOSA, DYA, IDEF3, LOVEM, PERA, etc.
Integrated Enterprise Modeling (IEM), and
Modelling the enterprise with multi-agent systems.
More enterprise modelling techniques are developed into Enterprise Architecture framework such as:
ARIS - ARchitecture of Integrated Information Systems
DoDAF - the US Department of Defense Architecture Framework
RM-ODP - Reference Model of Open Distributed Processing
TOGAF - The Open Group Architecture Framework
Zachman Framework - an architecture framework, based on the work of John Zachman at IBM in the 1980s
Service-oriented modeling framework (SOMF), based on the work of Michael Bell
And metamodelling frameworks such as:
Generalised Enterprise Reference Architecture and Methodology
== Enterprise engineering ==
Enterprise engineering is the discipline concerning the design and the engineering of enterprises, regarding both their business and organization. In theory and practice two types of enterprise engineering has emerged. A more general connected to engineering and the management of enterprises, and a more specific related to software engineering, enterprise modelling and enterprise architecture.
In the field of engineering a more general enterprise engineering emerged, defined as the application of engineering principals to the management of enterprises. It encompasses the application of knowledge, principles, and disciplines related to the analysis, design, implementation and operation of all elements associated with an enterprise. In essence this is an interdisciplinary field which combines systems engineering and strategic management as it seeks to engineer the entire enterprise in terms of the products, processes and business operations. The view is one of continuous improvement and continued adaptation as firms, processes and markets develop along their life cycles. This total systems approach encompasses the traditional areas of research and development, product design, operations and manufacturing as well as information systems and strategic management. This fields is related to engineering management, operations management, service management and systems engineering.
In the context of software development a specific field of enterprise engineering has emerged, which deals with the modelling and integration of various organizational and technical parts of business processes. In the context of information systems development it has been the area of activity in the organization of the systems analysis, and an extension of the scope of Information Modelling. It can also be viewed as the extension and generalization of the systems analysis and systems design phases of the software development process. Here Enterprise modelling can be part of the early, middle and late information system development life cycle. Explicit representation of the organizational and technical system infrastructure is being created in order to understand the orderly transformations of existing work practices. This field is also called Enterprise architecture, or defined with Enterprise Ontology as being two major parts of Enterprise architecture.
== Related fields ==
=== Business reference modelling ===
Business reference modelling is the development of reference models concentrating on the functional and organizational aspects of the core business of an enterprise, service organization or government agency. In enterprise engineering a business reference model is part of an enterprise architecture framework. This framework defines in a series of reference models, how to organize the structure and views associated with an Enterprise Architecture.
A reference model in general is a model of something that embodies the basic goal or idea of something and can then be looked at as a reference for various purposes. A business reference model is a means to describe the business operations of an organization, independent of the organizational structure that perform them. Other types of business reference model can also depict the relationship between the business processes, business functions, and the business area’s business reference model. These reference model can be constructed in layers, and offer a foundation for the analysis of service components, technology, data, and performance.
=== Economic modelling ===
Economic modelling is the theoretical representation of economic processes by a set of variables and a set of logical and/or quantitative relationships between them. The economic model is a simplified framework designed to illustrate complex processes, often but not always using mathematical techniques. Frequently, economic models use structural parameters. Structural parameters are underlying parameters in a model or class of models. A model may have various parameters and those parameters may change to create various properties.
In general terms, economic models have two functions: first as a simplification of and abstraction from observed data, and second as a means of selection of data based on a paradigm of econometric study. The simplification is particularly important for economics given the enormous complexity of economic processes. This complexity can be attributed to the diversity of factors that determine economic activity; these factors include: individual and cooperative decision processes, resource limitations, environmental and geographical constraints, institutional and legal requirements and purely random fluctuations. Economists therefore must make a reasoned choice of which variables and which relationships between these variables are relevant and which ways of analyzing and presenting this information are useful.
=== Ontology engineering ===
Ontology engineering or ontology building is a subfield of knowledge engineering that studies the methods and methodologies for building ontologies. In the domain of enterprise architecture, an ontology is an outline or a schema used to structure objects, their attributes and relationships in a consistent manner. As in enterprise modelling, an ontology can be composed of other ontologies. The purpose of ontologies in enterprise modelling is to formalize and establish the sharability, re-usability, assimilation and dissemination of information across all organizations and departments within an enterprise. Thus, an ontology enables integration of the various functions and processes which take place in an enterprise.
One common language with well articulated structure and vocabulary would enable the company to be more efficient in its operations. A common ontology will allow for effective communication, understanding and thus coordination among the various divisions of an enterprise. There are various kinds of ontologies used in numerous environments. While the language example given earlier dealt with the area of information systems and design, other ontologies may be defined for processes, methods, activities, etc., within an enterprise.
Using ontologies in enterprise modelling offers several advantages. Ontologies ensure clarity, consistency, and structure to a model. They promote efficient model definition and analysis. Generic enterprise ontologies allow for reusability of and automation of components. Because ontologies are schemata or outlines, the use of ontologies does not ensure proper enterprise model definition, analysis, or clarity. Ontologies are limited by how they are defined and implemented. An ontology may or may not include the potential or capability to capture all of the aspects of what is being modelled.
=== Systems thinking ===
The modelling of the enterprise and its environment could facilitate the creation of enhanced understanding of the business domain and processes of the extended enterprise, and especially of the relations—both those that "hold the enterprise together" and those that extend across the boundaries of the enterprise. Since enterprise is a system, concepts used in system thinking can be successfully reused in modelling enterprises.
This way a fast understanding can be achieved throughout the enterprise about how business functions are working and how they depend upon other functions in the organization.
== See also ==
Business process modelling
Enterprise architecture
Enterprise Architecture framework
Enterprise integration
Enterprise life cycle
ISO 19439
Enterprise Data Modeling
== References ==
== Further reading ==
August-Wilhelm Scheer (1992). Architecture of Integrated Information Systems: Foundations of Enterprise Modelling. Springer-Verlag. ISBN 3-540-55131-X
François Vernadat (1996) Enterprise Modeling and Integration: Principles and Applications, Chapman & Hall, London, ISBN 0-412-60550-3
== External links ==
Agile Enterprise Modeling. by S.W. Ambler, 2003-2008.
Enterprise Modeling Anti-patterns. by S.W. Ambler, 2005.
Enterprise Modelling and Information Systems Architectures - An International Journal (EMISA) is a scholarly open access journal with a unique focus on novel and innovative research on Enterprise Models and Information Systems Architectures. | Wikipedia/Enterprise_model |
Building information modeling (BIM) is an approach involving the generation and management of digital representations of the physical and functional characteristics of buildings or other physical assets and facilities. BIM is supported by various tools, processes, technologies and contracts. Building information models (BIMs) are computer files (often but not always in proprietary formats and containing proprietary data) which can be extracted, exchanged or networked to support decision-making regarding a built asset. BIM software is used by individuals, businesses and government agencies who plan, design, construct, operate and maintain buildings and diverse physical infrastructures, such as water, refuse, electricity, gas, communication utilities, roads, railways, bridges, ports and tunnels.
The concept of BIM has been in development since the 1970s, but it only became an agreed term in the early 2000s. The development of standards and the adoption of BIM has progressed at different speeds in different countries. Developed by buildingSMART, Industry Foundation Classes (IFCs) – data structures for representing information – became an international standard, ISO 16739, in 2013, and BIM process standards developed in the United Kingdom from 2007 onwards formed the basis of an international standard, ISO 19650, launched in January 2019.
== History ==
The concept of BIM has existed since the 1970s. The first software tools developed for modeling buildings emerged in the late 1970s and early 1980s, and included workstation products such as Chuck Eastman's Building Description System and GLIDE, RUCAPS, Sonata, Reflex and Gable 4D Series. The early applications, and the hardware needed to run them, were expensive, which limited widespread adoption.
The pioneering role of applications such as RUCAPS, Sonata and Reflex has been recognized by Laiserin as well as the UK's Royal Academy of Engineering; former GMW employee Jonathan Ingram worked on all three products. What became known as BIM products differed from architectural drafting tools such as AutoCAD by allowing the addition of further information (time, cost, manufacturers' details, sustainability, and maintenance information, etc.) to the building model.
As Graphisoft had been developing such solutions for longer than its competitors, Laiserin regarded its ArchiCAD application as then "one of the most mature BIM solutions on the market." Following its launch in 1987, ArchiCAD became regarded by some as the first implementation of BIM, as it was the first CAD product on a personal computer able to create both 2D and 3D geometry, as well as the first commercial BIM product for personal computers. However, Graphisoft founder Gábor Bojár has acknowledged to Jonathan Ingram in an open letter, that Sonata "was more advanced in 1986 than ArchiCAD at that time", adding that it "surpassed already the matured definition of 'BIM' specified only about one and a half decade later".
The term 'building model' (in the sense of BIM as used today) was first used in papers in the mid-1980s: in a 1985 paper by Simon Ruffle eventually published in 1986, and later in a 1986 paper by Robert Aish – then at GMW Computers Ltd, developer of RUCAPS software – referring to the software's use at London's Heathrow Airport. The term 'Building Information Model' first appeared in a 1992 paper by G.A. van Nederveen and F. P. Tolman.
However, the terms 'Building Information Model' and 'Building Information Modeling' (including the acronym "BIM") did not become popularly used until some 10 years later. Facilitating exchange and interoperability of information in digital format was variously with differing terminology: by Graphisoft as "Virtual Building" or "Single Building Model", Bentley Systems as "Integrated Project Models", and by Autodesk or Vectorworks as "Building Information Modeling". In 2002, Autodesk released a white paper entitled "Building Information Modeling," and other software vendors also started to assert their involvement in the field. By hosting contributions from Autodesk, Bentley Systems and Graphisoft, plus other industry observers, in 2003, Jerry Laiserin helped popularize and standardize the term as a common name for the digital representation of the building process.
=== Interoperability and BIM standards ===
As some BIM software developers have created proprietary data structures in their software, data and files created by one vendor's applications may not work in other vendor solutions. To achieve interoperability between applications, neutral, non-proprietary or open standards for sharing BIM data among different software applications have been developed.
Poor software interoperability has long been regarded as an obstacle to industry efficiency in general and to BIM adoption in particular. In August 2004 a US National Institute of Standards and Technology (NIST) report conservatively estimated that $15.8 billion was lost annually by the U.S. capital facilities industry due to inadequate interoperability arising from "the highly fragmented nature of the industry, the industry’s continued paper-based business practices, a lack of standardization, and inconsistent technology adoption among stakeholders".
An early BIM standard was the CIMSteel Integration Standard, CIS/2, a product model and data exchange file format for structural steel project information (CIMsteel: Computer Integrated Manufacturing of Constructional Steelwork). CIS/2 enables seamless and integrated information exchange during the design and construction of steel framed structures. It was developed by the University of Leeds and the UK's Steel Construction Institute in the late 1990s, with inputs from Georgia Tech, and was approved by the American Institute of Steel Construction as its data exchange format for structural steel in 2000.
BIM is often associated with Industry Foundation Classes (IFCs) and aecXML – data structures for representing information – developed by buildingSMART. IFC is recognised by the ISO and has been an international standard, ISO 16739, since 2013. OpenBIM is an initiative by buildingSMART that promotes open standards and interoperability. Based on the IFC standard, it allows vendor-neutral BIM data exchange. OpenBIM standards also include BIM Collaboration Format (BCF) for issue tracking and Information Delivery Specification (IDS) for defining model requirements.
Construction Operations Building information exchange (COBie) is also associated with BIM. COBie was devised by Bill East of the United States Army Corps of Engineers in 2007, and helps capture and record equipment lists, product data sheets, warranties, spare parts lists, and preventive maintenance schedules. This information is used to support operations, maintenance and asset management once a built asset is in service. In December 2011, it was approved by the US-based National Institute of Building Sciences as part of its National Building Information Model (NBIMS-US) standard. COBie has been incorporated into software, and may take several forms including spreadsheet, IFC, and ifcXML. In early 2013 BuildingSMART was working on a lightweight XML format, COBieLite, which became available for review in April 2013. In September 2014, a code of practice regarding COBie was issued as a British Standard: BS 1192-4.
In January 2019, ISO published the first two parts of ISO 19650, providing a framework for building information modelling, based on process standards developed in the United Kingdom. UK BS and PAS 1192 specifications form the basis of further parts of the ISO 19650 series, with parts on asset management (Part 3) and security management (Part 5) published in 2020.
The IEC/ISO 81346 series for reference designation has published 81346-12:2018, also known as RDS-CW (Reference Designation System for Construction Works). The use of RDS-CW offers the prospect of integrating BIM with complementary international standards based classification systems being developed for the Power Plant sector.
== Definition ==
ISO 19650-1:2018 defines BIM as:
Use of a shared digital representation of a built asset to facilitate design, construction and operation processes to form a reliable basis for decisions.
The US National Building Information Model Standard Project Committee has the following definition:
Building Information Modeling (BIM) is a digital representation of physical and functional characteristics of a facility. A BIM is a shared knowledge resource for information about a facility forming a reliable basis for decisions during its life-cycle; defined as existing from earliest conception to demolition.
Traditional building design was largely reliant upon two-dimensional technical drawings (plans, elevations, sections, etc.). Building information modeling extends the three primary spatial dimensions (width, height and depth), incorporating information about time (so-called 4D BIM), cost (5D BIM), asset management, sustainability, etc. BIM therefore covers more than just geometry. It also covers spatial relationships, geospatial information, quantities and properties of building components (for example, manufacturers' details), and enables a wide range of collaborative processes relating to the built asset from initial planning through to construction and then throughout its operational life.
BIM authoring tools present a design as combinations of "objects" – vague and undefined, generic or product-specific, solid shapes or void-space oriented (like the shape of a room), that carry their geometry, relations, and attributes. BIM applications allow extraction of different views from a building model for drawing production and other uses. These different views are automatically consistent, being based on a single definition of each object instance. BIM software also defines objects parametrically; that is, the objects are defined as parameters and relations to other objects so that if a related object is amended, dependent ones will automatically also change. Each model element can carry attributes for selecting and ordering them automatically, providing cost estimates as well as material tracking and ordering.
For the professionals involved in a project, BIM enables a virtual information model to be shared by the design team (architects, landscape architects, surveyors, civil, structural and building services engineers, etc.), the main contractor and subcontractors, and the owner/operator. Each professional adds discipline-specific data to the shared model – commonly, a 'federated' model which combines several different disciplines' models into one. Combining models enables visualisation of all models in a single environment, better coordination and development of designs, enhanced clash avoidance and detection, and improved time and cost decision-making.
=== BIM wash ===
"BIM wash" or "BIM washing" is a term sometimes used to describe inflated, and/or deceptive, claims of using or delivering BIM services or products.
== Usage throughout the asset life cycle ==
Use of BIM goes beyond the planning and design phase of a project, extending throughout the life cycle of the asset. The supporting processes of building lifecycle management include cost management, construction management, project management, facility operation and application in green building.
=== Common Data Environment ===
A 'Common Data Environment' (CDE) is defined in ISO 19650 as an:
Agreed source of information for any given project or asset, for collecting, managing and disseminating each information container through a managed process.
A CDE workflow describes the processes to be used while a CDE solution can provide the underlying technologies. A CDE is used to share data across a project or asset lifecycle, supporting collaboration across a whole project team. The concept of a CDE overlaps with enterprise content management, ECM, but with a greater focus on BIM issues.
=== Management of building information models ===
Building information models span the whole concept-to-occupation time-span. To ensure efficient management of information processes throughout this span, a BIM manager might be appointed. The BIM manager is retained by a design build team on the client's behalf from the pre-design phase onwards to develop and to track the object-oriented BIM against predicted and measured performance objectives, supporting multi-disciplinary building information models that drive analysis, schedules, take-off and logistics. Companies are also now considering developing BIMs in various levels of detail, since depending on the application of BIM, more or less detail is needed, and there is varying modeling effort associated with generating building information models at different levels of detail.
=== BIM in construction management ===
Participants in the building process are constantly challenged to deliver successful projects despite tight budgets, limited staffing, accelerated schedules, and limited or conflicting information. The significant disciplines such as architectural, structural and MEP designs should be well-coordinated, as two things can't take place at the same place and time. BIM additionally is able to aid in collision detection, identifying the exact location of discrepancies.
The BIM concept envisages virtual construction of a facility prior to its actual physical construction, in order to reduce uncertainty, improve safety, work out problems, and simulate and analyze potential impacts. Sub-contractors from every trade can input critical information into the model before beginning construction, with opportunities to pre-fabricate or pre-assemble some systems off-site. Waste can be minimised on-site and products delivered on a just-in-time basis rather than being stock-piled on-site.
Quantities and shared properties of materials can be extracted easily. Scopes of work can be isolated and defined. Systems, assemblies and sequences can be shown in a relative scale with the entire facility or group of facilities. BIM also prevents errors by enabling conflict or 'clash detection' whereby the computer model visually highlights to the team where parts of the building (e.g.:structural frame and building services pipes or ducts) may wrongly intersect.
=== BIM in facility operation and asset management ===
BIM can bridge the information loss associated with handing a project from design team, to construction team and to building owner/operator, by allowing each group to add to and reference back to all information they acquire during their period of contribution to the BIM model. Enabling an effective handover of information from design and construction (including via IFC or COBie) can thus yield benefits to the facility owner or operator. BIM-related processes relating to longer-term asset management are also covered in ISO-19650 Part 3.
For example, a building owner may find evidence of a water leak in a building. Rather than exploring the physical building, the owner may turn to the model and see that a water valve is located in the suspect location. The owner could also have in the model the specific valve size, manufacturer, part number, and any other information ever researched in the past, pending adequate computing power. Such problems were initially addressed by Leite and Akinci when developing a vulnerability representation of facility contents and threats for supporting the identification of vulnerabilities in building emergencies.
Dynamic information about the building, such as sensor measurements and control signals from the building systems, can also be incorporated within software to support analysis of building operation and maintenance. As such, BIM in facility operation can be related to internet of things approaches; rapid access to data may also be aided by use of mobile devices (smartphones, tablets) and machine-readable RFID tags or barcodes; while integration and interoperability with other business systems - CAFM, ERP, BMS, IWMS, etc - can aid operational reuse of data.
There have been attempts at creating information models for older, pre-existing facilities. Approaches include referencing key metrics such as the Facility Condition Index (FCI), or using 3D laser-scanning surveys and photogrammetry techniques (separately or in combination) or digitizing traditional building surveying methodologies by using mobile technology to capture accurate measurements and operation-related information about the asset that can be used as the basis for a model. Trying to retrospectively model a building constructed in, say 1927, requires numerous assumptions about design standards, building codes, construction methods, materials, etc, and is, therefore, more complex than building a model during design.
One of the challenges to the proper maintenance and management of existing facilities is understanding how BIM can be utilized to support a holistic understanding and implementation of building management practices and "cost of ownership" principles that support the full product lifecycle of a building. An American National Standard entitled APPA 1000 – Total Cost of Ownership for Facilities Asset Management incorporates BIM to factor in a variety of critical requirements and costs over the life-cycle of the building, including but not limited to: replacement of energy, utility, and safety systems; continual maintenance of the building exterior and interior and replacement of materials; updates to design and functionality; and recapitalization costs.
=== BIM in green building ===
BIM in green building, or "green BIM", is a process that can help architecture, engineering and construction firms to improve sustainability in the built environment. It can allow architects and engineers to integrate and analyze environmental issues in their design over the life cycle of the asset.
In the ERANet projects EPC4SES and FinSESCo projects worked on the digital representation of the energy demand of the building. The nucleus is the XML from issuing Energy Performance Certificates, amended by roof data to be able to retrieve the position and size of PV or PV/T.
== International developments ==
=== Asia ===
==== China ====
China began its exploration on informatisation in 2001. The Ministry of Construction announced BIM was as the key application technology of informatisation in "Ten new technologies of construction industry" (by 2010). The Ministry of Science and Technology (MOST) clearly announced BIM technology as a national key research and application project in "12th Five-Year" Science and Technology Development Planning. Therefore, the year 2011 was described as "The First Year of China's BIM".
==== Hong Kong ====
In 2006 the Hong Kong Housing Authority introduced BIM, and then set a target of full BIM implementation in 2014/2015. BuildingSmart Hong Kong was inaugurated in Hong Kong SAR in late April 2012. The Government of Hong Kong mandates the use of BIM for all government projects over HK$30M since 1 January 2018.
==== India ====
India Building Information Modelling Association (IBIMA) is a national-level society that represents the entire Indian BIM community. In India BIM is also known as VDC: Virtual Design and Construction. Due to its population and economic growth, India has an expanding construction market. In spite of this, BIM usage was reported by only 22% of respondents in a 2014 survey. In 2019, government officials said BIM could help save up to 20% by shortening construction time, and urged wider adoption by infrastructure ministries.
==== Iran ====
The Iran Building Information Modeling Association (IBIMA) was founded in 2012 by professional engineers from five universities in Iran, including the Civil and Environmental Engineering Department at Amirkabir University of Technology. While it is not currently active, IBIMA aims to share knowledge resources to support construction engineering management decision-making.
==== Malaysia ====
BIM implementation is targeted towards BIM Stage 2 by the year 2020 led by the Construction Industry Development Board (CIDB Malaysia). Under the Construction Industry Transformation Plan (CITP 2016–2020), it is hoped more emphasis on technology adoption across the project life-cycle will induce higher productivity.
==== Singapore ====
The Building and Construction Authority (BCA) has announced that BIM would be introduced for architectural submission (by 2013), structural and M&E submissions (by 2014) and eventually for plan submissions of all projects with gross floor area of more than 5,000 square meters by 2015. The BCA Academy is training students in BIM.
==== Japan ====
The Ministry of Land, Infrastructure and Transport (MLIT) has announced "Start of BIM pilot project in government building and repairs" (by 2010). Japan Institute of Architects (JIA) released the BIM guidelines (by 2012), which showed the agenda and expected effect of BIM to architects. MLIT announced " BIM will be mandated for all of its public works from the fiscal year of 2023, except those having particular reasons". The works subject to WTO Government Procurement Agreement shall comply with the published ISO standards related to BIM such as ISO19650 series as determined by the Article 10 (Technical Specification) of the Agreement.
==== South Korea ====
Small BIM-related seminars and independent BIM effort existed in South Korea even in the 1990s. However, it was not until the late 2000s that the Korean industry paid attention to BIM. The first industry-level BIM conference was held in April 2008, after which, BIM has been spread very rapidly. Since 2010, the Korean government has been gradually increasing the scope of BIM-mandated projects. McGraw Hill published a detailed report in 2012 on the status of BIM adoption and implementation in South Korea.
==== United Arab Emirates ====
Dubai Municipality issued a circular (196) in 2014 mandating BIM use for buildings of a certain size, height or type. The one page circular initiated strong interest in BIM and the market responded in preparation for more guidelines and direction. In 2015 the Municipality issued another circular (207) titled 'Regarding the expansion of applying the (BIM) on buildings and facilities in the emirate of Dubai' which made BIM mandatory on more projects by reducing the minimum size and height requirement for projects requiring BIM. This second circular drove BIM adoption further with several projects and organizations adopting UK BIM standards as best practice. In 2016, the UAE's Quality and Conformity Commission set up a BIM steering group to investigate statewide adoption of BIM.
=== Europe ===
==== Austria ====
Austrian standards for digital modeling are summarized in the ÖNORM A 6241, published on 15 March 2015. The ÖNORM A 6241-1 (BIM Level 2), which replaced the ÖNORM A 6240-4, has been extended in the detailed and executive design stages, and corrected in the lack of definitions. The ÖNORM A 6241-2 (BIM Level 3) includes all the requirements for the BIM Level 3 (iBIM).
==== Czech Republic ====
The Czech BIM Council, established in May 2011, aims to implement BIM methodologies into the Czech building and designing processes, education, standards and legislation.
==== Estonia ====
In Estonia digital construction cluster (Digitaalehituse Klaster) was formed in 2015 to develop BIM solutions for the whole life-cycle of construction. The strategic objective of the cluster is to develop an innovative digital construction environment as well as VDC new product development, Grid and e-construction portal to increase the international competitiveness and sales of Estonian businesses in the construction field. The cluster is equally co-funded by European Structural and Investment Funds through Enterprise Estonia and by the members of the cluster with a total budget of 600 000 euros for the period 2016–2018.
==== France ====
The French arm of buildingSMART, called Mediaconstruct (existing since 1989), is supporting digital transformation in France. A building transition digital plan – French acronym PTNB – was created in 2013 (mandated since 2015 to 2017 and under several ministries). A 2013 survey of European BIM practice showed France in last place, but, with government support, in 2017 it had risen to third place with more than 30% of real estate projects carried out using BIM. PTNB was superseded in 2018 by Plan BIM 2022, administered by an industry body, the Association for the Development of Digital in Construction (AND Construction), founded in 2017, and supported by a digital platform, KROQI, developed and launched in 2017 by CSTB (France's Scientific and Technical Centre for Building).
==== Germany ====
In December 2015, the German minister for transport Alexander Dobrindt announced a timetable for the introduction of mandatory BIM for German road and rail projects from the end of 2020. Speaking in April 2016, he said digital design and construction must become standard for construction projects in Germany, with Germany two to three years behind The Netherlands and the UK in aspects of implementing BIM. BIM was piloted in many areas of German infrastructure delivery and in July 2022 Volker Wissing, Federal Minister for Digital and Transport, announced that, from 2025, BIM will be used as standard in the construction of federal trunk roads in addition to the rail sector.
==== Ireland ====
In November 2017, Ireland's Department for Public Expenditure and Reform launched a strategy to increase use of digital technology in delivery of key public works projects, requiring the use of BIM to be phased in over the next four years.
==== Italy ====
Through the new D.l. 50, in April 2016 Italy has included into its own legislation several European directives including 2014/24/EU on Public Procurement. The decree states among the main goals of public procurement the "rationalization of designing activities and of all connected verification processes, through the progressive adoption of digital methods and electronic instruments such as Building and Infrastructure Information Modelling". A norm in 8 parts is also being written to support the transition: UNI 11337-1, UNI 11337-4 and UNI 11337-5 were published in January 2017, with five further chapters to follow within a year.
In early 2018 the Italian Ministry of Infrastructure and Transport issued a decree (DM 01/12/17) creating a governmental BIM Mandate compelling public client organisations to adopt a digital approach by 2025, with an incremental obligation which will start on 1 January 2019.
==== Lithuania ====
Lithuania is moving towards adoption of BIM infrastructure by founding a public body "Skaitmeninė statyba" (Digital Construction), which is managed by 13 associations. Also, there is a BIM work group established by Lietuvos Architektų Sąjunga (a Lithuanian architects body). The initiative intends Lithuania to adopt BIM, Industry Foundation Classes (IFC) and National Construction Classification as standard. An international conference "Skaitmeninė statyba Lietuvoje" (Digital Construction in Lithuania) has been held annually since 2012.
==== The Netherlands ====
On 1 November 2011, the Rijksgebouwendienst, the agency within the Dutch Ministry of Housing, Spatial Planning and the Environment that manages government buildings, introduced the Rgd BIM Standard, which it updated on 1 July 2012.
==== Norway ====
In Norway BIM has been used increasingly since 2008. Several large public clients require use of BIM in open formats (IFC) in most or all of their projects. The Government Building Authority bases its processes on BIM in open formats to increase process speed and quality, and all large and several small and medium-sized contractors use BIM. National BIM development is centred around the local organisation, buildingSMART Norway which represents 25% of the Norwegian construction industry.
==== Poland ====
BIMKlaster (BIM Cluster) is a non-governmental, non-profit organisation established in 2012 with the aim of promoting BIM development in Poland. In September 2016, the Ministry of Infrastructure and Construction began a series of expert meetings concerning the application of BIM methodologies in the construction industry.
==== Portugal ====
Created in 2015 to promote the adoption of BIM in Portugal and its normalisation, the Technical Committee for BIM Standardisation, CT197-BIM, has created the first strategic document for construction 4.0 in Portugal, aiming to align the country's industry around a common vision, integrated and more ambitious than a simple technology change.
==== Russia ====
The Russian government has approved a list of the regulations that provide the creation of a legal framework for the use of information modeling of buildings in construction and encourages the use of BIM in government projects.
==== Slovakia ====
The BIM Association of Slovakia, "BIMaS", was established in January 2013 as the first Slovak professional organisation focused on BIM. Although there are neither standards nor legislative requirements to deliver projects in BIM, many architects, structural engineers and contractors, plus a few investors are already applying BIM. A Slovak implementation strategy created by BIMaS and supported by the Chamber of Civil Engineers and Chamber of Architects has yet to be approved by Slovak authorities due to their low interest in such innovation.
==== Spain ====
A July 2015 meeting at Spain's Ministry of Infrastructure [Ministerio de Fomento] launched the country's national BIM strategy, making BIM a mandatory requirement on public sector projects with a possible starting date of 2018. Following a February 2015 BIM summit in Barcelona, professionals in Spain established a BIM commission (ITeC) to drive the adoption of BIM in Catalonia.
==== Switzerland ====
Since 2009 through the initiative of buildingSmart Switzerland, then 2013, BIM awareness among a broader community of engineers and architects was raised due to the open competition for Basel's Felix Platter Hospital where a BIM coordinator was sought. BIM has also been a subject of events by the Swiss Society for Engineers and Architects, SIA.
==== United Kingdom ====
In May 2011 UK Government Chief Construction Adviser Paul Morrell called for BIM adoption on UK government construction projects. Morrell also told construction professionals to adopt BIM or be "Betamaxed out". In June 2011 the UK government published its BIM strategy, announcing its intention to require collaborative 3D BIM (with all project and asset information, documentation and data being electronic) on its projects by 2016. Initially, compliance would require building data to be delivered in a vendor-neutral 'COBie' format, thus overcoming the limited interoperability of BIM software suites available on the market. The UK Government BIM Task Group led the government's BIM programme and requirements, including a free-to-use set of UK standards and tools that defined 'level 2 BIM'. In April 2016, the UK Government published a new central web portal as a point of reference for the industry for 'level 2 BIM'. The work of the BIM Task Group then continued under the stewardship of the Cambridge-based Centre for Digital Built Britain (CDBB), announced in December 2017 and formally launched in early 2018.
Outside of government, industry adoption of BIM since 2016 has been led by the UK BIM Alliance, an independent, not-for-profit, collaboratively-based organisation formed to champion and enable the implementation of BIM, and to connect and represent organisations, groups and individuals working towards digital transformation of the UK's built environment industry. In November 2017, the UK BIM Alliance merged with the UK and Ireland chapter of BuildingSMART. In October 2019, CDBB, the UK BIM Alliance and the BSI Group launched the UK BIM Framework. Superseding the BIM levels approach, the framework describes an overarching approach to implementing BIM in the UK, giving free guidance on integrating the international ISO 19650 series of standards into UK processes and practice.
National Building Specification (NBS) has published research into BIM adoption in the UK since 2011, and in 2020 published its 10th annual BIM report. In 2011, 43% of respondents had not heard of BIM; in 2020 73% said they were using BIM.
=== North America ===
==== Canada ====
BIM is not mandatory in Canada. Several organizations support BIM adoption and implementation in Canada: the Canada BIM Council (CANBIM, founded in 2008), the Institute for BIM in Canada, and buildingSMART Canada (the Canadian chapter of buildingSMART International). Public Services and Procurement Canada (formerly Public Works and Government Services Canada) is committed to using non-proprietary or "OpenBIM" BIM standards and avoids specifying any specific proprietary BIM format. Designers are required to use the international standards of interoperability for BIM (IFC).
==== United States ====
The Associated General Contractors of America and US contracting firms have developed various working definitions of BIM that describe it generally as:
an object-oriented building development tool that utilizes 5-D modeling concepts, information technology and software interoperability to design, construct and operate a building project, as well as communicate its details.
Although the concept of BIM and relevant processes are being explored by contractors, architects and developers alike, the term itself has been questioned and debated with alternatives including Virtual Building Environment (VBE) also considered. Unlike some countries such as the UK, the US has not adopted a set of national BIM guidelines, allowing different systems to remain in competition. In 2021, the National Institute of Building Sciences (NIBS) looked at applying UK BIM experiences to developing shared US BIM standards and processes. The US National BIM Standard had largely been developed through volunteer efforts; NIBS aimed to create a national BIM programme to drive effective adoption at a national scale.
BIM is seen to be closely related to Integrated Project Delivery (IPD) where the primary motive is to bring the teams together early on in the project. A full implementation of BIM also requires the project teams to collaborate from the inception stage and formulate model sharing and ownership contract documents.
The American Institute of Architects has defined BIM as "a model-based technology linked with a database of project information",[3] and this reflects the general reliance on database technology as the foundation. In the future, structured text documents such as specifications may be able to be searched and linked to regional, national, and international standards.
=== Africa ===
==== Nigeria ====
BIM has the potential to play a vital role in the Nigerian AEC sector. In addition to its potential clarity and transparency, it may help promote standardization across the industry. For instance, Utiome suggests that, in conceptualizing a BIM-based knowledge transfer framework from industrialized economies to urban construction projects in developing nations, generic BIM objects can benefit from rich building information within specification parameters in product libraries, and used for efficient, streamlined design and construction. Similarly, an assessment of the current 'state of the art' by Kori found that medium and large firms were leading the adoption of BIM in the industry. Smaller firms were less advanced with respect to process and policy adherence. There has been little adoption of BIM in the built environment due to construction industry resistance to changes or new ways of doing things. The industry is still working with conventional 2D CAD systems in services and structural designs, although production could be in 3D systems. There is virtually no utilisation of 4D and 5D systems.
BIM Africa Initiative, primarily based in Nigeria, is a non-profit institute advocating the adoption of BIM across Africa. Since 2018, it has been engaging with professionals and the government towards the digital transformation of the built industry. Produced annually by its research and development committee, the African BIM Report gives an overview of BIM adoption across the African continent.
==== South Africa ====
The South African BIM Institute, established in May 2015, aims to enable technical experts to discuss digital construction solutions that can be adopted by professionals working within the construction sector. Its initial task was to promote the SA BIM Protocol.
There are no mandated or national best practice BIM standards or protocols in South Africa. Organisations implement company-specific BIM standards and protocols at best (there are isolated examples of cross-industry alliances).
=== Oceania ===
==== Australia ====
In February 2016, Infrastructure Australia recommended: "Governments should make the use of Building Information Modelling (BIM) mandatory for the design of large-scale complex infrastructure projects. In support of a mandatory rollout, the Australian Government should commission the Australasian Procurement and Construction Council, working with industry, to develop appropriate guidance around the adoption and use of BIM; and common standards and protocols to be applied when using BIM".
==== New Zealand ====
In 2015, many projects in the rebuilding of Christchurch were being assembled in detail on a computer using BIM well before workers set foot on the site. The New Zealand government started a BIM acceleration committee, as part of a productivity partnership with the goal of 20 per cent more efficiency in the construction industry by 2020. Today, BIM use is still not mandated in the country while several challenges have been identified for its implementation in the country. However, members of the AEC industry and academia have developed a national BIM handbook providing definitions, case studies and templates.
== Purposes or dimensionality ==
Some purposes or uses of BIM may be described as 'dimensions'. However, there is little consensus on definitions beyond 5D. Some organisations dismiss the term; for example, the UK Institution of Structural Engineers does not recommend using nD modelling terms beyond 4D, adding "cost (5D) is not really a 'dimension'."
=== 3D ===
3D BIM, an acronym for three-dimensional building information modeling, refers to the graphical representation of an asset's geometric design, augmented by information describing attributes of individual components. 3D BIM work may be undertaken by professional disciplines such as architectural, structural, and MEP, and the use of 3D models enhances coordination and collaboration between disciplines. A 3D virtual model can also be created by creating a point cloud of the building or facility using laser scanning technology.
=== 4D ===
4D BIM, an acronym for 4-dimensional building information modeling, refers to the intelligent linking of individual 3D CAD components or assemblies with time- or scheduling-related information. The term 4D refers to the fourth dimension: time, i.e. 3D plus time.
4D modelling enables project participants (architects, designers, contractors, clients) to plan, sequence the physical activities, visualise the critical path of a series of events, mitigate the risks, report and monitor progress of activities through the lifetime of the project. 4D BIM enables a sequence of events to be depicted visually on a time line that has been populated by a 3D model, augmenting traditional Gantt charts and critical path (CPM) schedules often used in project management. Construction sequences can be reviewed as a series of problems using 4D BIM, enabling users to explore options, manage solutions and optimize results.
As an advanced construction management technique, it has been used by project delivery teams working on larger projects. 4D BIM has traditionally been used for higher end projects due to the associated costs, but technologies are now emerging that allow the process to be used by laymen or to drive processes such as manufacture.
=== 5D ===
5D BIM, an acronym for 5-dimensional building information modeling refers to the intelligent linking of individual 3D components or assemblies with time schedule (4D BIM) constraints and then with cost-related information. 5D models enable participants to visualise construction progress and related costs over time. This BIM-centric project management technique has potential to improve management and delivery of projects of any size or complexity.
In June 2016, McKinsey & Company identified 5D BIM technology as one of five big ideas poised to disrupt construction. It defined 5D BIM as "a five-dimensional representation of the physical and functional characteristics of any project. It considers a project’s time schedule and cost in addition to the standard spatial design parameters in 3-D."
=== 6D ===
6D BIM, an acronym for 6-dimensional building information modeling, is sometimes used to refer to the intelligent linking of individual 3D components or assemblies with all aspects of project life-cycle management information. However, there is less consensus about the definition of 6D BIM; it is also sometimes used to cover use of BIM for sustainability purposes.
In the project life cycle context, a 6D model is usually delivered to the owner when a construction project is finished. The "As-Built" BIM model is populated with relevant building component information such as product data and details, maintenance/operation manuals, cut sheet specifications, photos, warranty data, web links to product online sources, manufacturer information and contacts, etc. This database is made accessible to the users/owners through a customized proprietary web-based environment. This is intended to aid facilities managers in the operation and maintenance of the facility.
The term is less commonly used in the UK and has been replaced with reference to the Asset Information Requirements (AIR) and an Asset Information Model (AIM) as specified in BS EN ISO 19650-3:2020.
== See also ==
Data model
Design computing
Digital twin (the physical manifestation instrumented and connected to the model)
BCF
GIS
Digital Building Logbook
Landscape design software
Lean construction
List of BIM software
Macro BIM
Open-source 3D file formats
OpenStreetMap
Pre-fire planning
System information modelling
Whole Building Design Guide
Facility management (or Building management)
Building automation (and Building management systems)
== Notes ==
== References ==
== Further reading ==
Kensek, Karen (2014). Building Information Modeling, Routledge. ISBN 978-0-415-71774-8
Kensek, Karen and Noble, Douglas (2014). Building Information Modeling: BIM in Current and Future Practice, Wiley. ISBN 978-1-118-76630-9
Eastman, Chuck; Teicholz, Paul; Sacks, Rafael; Liston, Kathleen (2011). 'BIM Handbook: A Guide to Building Information Modeling for Owners, Managers, Designers, Engineers, and Contractors (2 ed.). John Wiley. ISBN 978-0-470-54137-1.
Lévy, François (2011). BIM in Small-Scale Sustainable Design, Wiley. ISBN 978-0470590898
Weygant, Robert S. (2011) BIM Content Development: Standards, Strategies, and Best Practices, Wiley. ISBN 978-0-470-58357-9
Hardin, Brad (2009). Martin Viveros (ed.). BIM and Construction Management: Proven Tools, Methods and Workflows. Sybex. ISBN 978-0-470-40235-1.
Smith, Dana K. and Tardif, Michael (2009). Building Information Modeling: A Strategic Implementation Guide for Architects, Engineers, Constructors, and Real Estate Asset Managers, Wiley. ISBN 978-0-470-25003-7
Underwood, Jason, and Isikdag, Umit (2009). Handbook of Research on Building Information Modeling and Construction Informatics: Concepts and Technologies, Information Science Publishing. ISBN 978-1-60566-928-1
Krygiel, Eddy and Nies, Brad (2008). Green BIM: Successful Sustainable Design with Building Information Modeling, Sybex. ISBN 978-0-470-23960-5
Kymmell, Willem (2008). Building Information Modeling: Planning and Managing Construction Projects with 4D CAD and Simulations, McGraw-Hill Professional. ISBN 978-0-07-149453-3
Jernigan, Finith (2007). BIG BIM little bim. 4Site Press. ISBN 978-0-9795699-0-6. | Wikipedia/Facility_Information_Model |
In computing, the network model is a database model conceived as a flexible way of representing objects and their relationships. Its distinguishing feature is that the schema, viewed as a graph in which object types are nodes and relationship types are arcs, is not restricted to being a hierarchy or lattice.
The network model was adopted by the CODASYL Data Base Task Group in 1969 and underwent a major update in 1971. It is sometimes known as the CODASYL model for this reason. A number of network database systems became popular on mainframe and minicomputers through the 1970s before being widely replaced by relational databases in the 1980s.
== Overview ==
While the hierarchical database model structures data as a tree of records, with each record having one parent record and many children, the network model allows each record to have multiple parent and child records, forming a generalized graph structure. This property applies at two levels: the schema is a generalized graph of record types connected by relationship types (called "set types" in CODASYL), and the database itself is a generalized graph of record occurrences connected by relationships (CODASYL "sets"). Cycles are permitted at both levels. Peer-to-Peer and Client Server are examples of Network Models.
The chief argument in favour of the network model, in comparison to the hierarchical model, was that it allowed a more natural modeling of relationships between entities. Although the model was widely implemented and used, it failed to become dominant for two main reasons. Firstly, IBM chose to stick to the hierarchical model with semi-network extensions in their established products such as IMS and DL/I. Secondly, it was eventually displaced by the relational model, which offered a higher-level, more declarative interface. Until the early 1980s the performance benefits of the low-level navigational interfaces offered by hierarchical and network databases were persuasive for many large-scale applications, but as hardware became faster, the extra productivity and flexibility of the relational model led to the gradual obsolescence of the network model in corporate enterprise usage.
== History ==
The network model's original inventor was Charles Bachman, and it was developed into a standard specification published in 1969 by the Conference on Data Systems Languages (CODASYL) Consortium. This was followed by a second publication in 1971, which became the basis for most implementations. Subsequent work continued into the early 1980s, culminating in an ISO specification, but this had little influence on products.
Bachman's influence is recognized in the term Bachman diagram, a diagrammatic notation that represents a database schema expressed using the network model. In a Bachman diagram, named rectangles represent record types, and arrows represent one-to-many relationship types between records (CODASYL set types).
== Database systems ==
Some well-known database systems that use the network model include:
IMAGE for HP 3000
Integrated Data Store (IDS)
IDMS (Integrated Database Management System)
Univac DMS-1100
Norsk Data SIBAS
Oracle CODASYL DBMS for OpenVMS (originally known as DEC VAX DBMS)
== See also ==
Navigational database
Graph database
== References ==
David M, k., 1997. Fundamentals, Design, and Implementation. database processing ed. s.l.:Prentice-Hall.
== Further reading ==
Charles W. Bachman, The Programmer as Navigator. Turing Award lecture, Communications of the ACM, Volume 16, Issue 11, 1973, pp. 653–658, ISSN 0001-0782, doi:10.1145/355611.362534
== External links ==
"CODASYL Systems Committee "Survey of Data Base Systems"" (PDF). 1968-09-03. Archived from the original (PDF) on 2007-10-12.
Network (CODASYL) Data Model
SIBAS Database running on Norsk Data Servers | Wikipedia/Network_data_model |
Solution architecture is a term used in information technology with various definitions, such as "a description of a discrete and focused business operation or activity and how IS/IT supports that operation".
== Definitions ==
The Open Group's definition of solution architecture, as provided above, is accompanied by the following three from Scaled Agile, Gartner and Greefhorst/Proper. The Open Group does not recognize the role "solution architect" in its TOGAF skills framework; on the other hand, Glassdoor advertised 55,000 Solution Architect roles in August 2020.
Scaled agile (2020): Solution Architect/Engineering is responsible for defining and communicating a shared technical and architectural vision across a "Solution Train" to help ensure the system or Solution under development is fit for its intended purpose.
Gartner (2013): A solution architecture (SA) is an architectural description of a specific solution. SAs combine guidance from different enterprise architecture viewpoints (business, information and technical), as well as from the enterprise solution architecture (ESA).
Greefhorst and Proper (2013): An architecture of a solution, where a solution is a system that offers a coherent set of functionalities to its environment. As such, it concerns those properties of a solution that are necessary and sufficient to meet its essential requirements
A typical property of solution architecture, in contrast to other types of Enterprise Architecture, is that it often seeks to define a solution within the context of a project or initiative. This close association to actual projects and initiatives means that solution architecture is the means to execute or realise a technology strategy.
== Coverage ==
According to Forrester Research, solution architecture is one of the key components by which Enterprise Architecture delivers value to the organization. It entails artifacts such as a solution business context, a solution vision and requirements, solution options (e.g. through RFIs, RFPs or prototype development) and an agreed optimal solution with build and implementation plans ("road-map").
Since The Open Group does not recognize a unique Solution Architect role, a relevant link for these mentioned artifacts can be to the Business and Systems Analyst roles. The Open Group's definition of solution architecture is broader than Forrester's (see aforementioned definition).
According to a 2013 paper published by the Federation of Enterprise Architecture Professional Organizations, solution architecture includes business architecture, information architecture, application architecture, and technology architecture operating at a tactical level and focusing on the scope and span of a selected business problem. In contrast, enterprise architecture, which also includes the aforementioned four types of architecture, operates at the strategic level and its scope and span is the enterprise rather than a specific business problem.
== See also ==
Architecture patterns, hereunder enterprise architecture (EA) reference architectures
Segment architecture
== References ==
== Further reading ==
Banerjee, Jaidip, and Sohel Aziz. "SOA: the missing link between enterprise architecture and solution architecture." SETLabs briefing 5.2 (2007): 69-80.
Chen, Graham, and Qinzheng Kong. "Integrated management solution architecture." Network Operations and Management Symposium, 2000. NOMS 2000. 2000 IEEE/IFIP. IEEE, 2000.
Gulledge, Thomas, et al. "Solution architecture alignment for logistics portfolio management." International Journal of Services and Standards 1.4 (2005): 401-413.
Shan, Tony Chao, and Winnie W. Hua. "Solution architecture for n-tier applications." Services Computing, 2006. SCC'06. IEEE International Conference on. IEEE, 2006.
Slot, Raymond, Guido Dedene, and Rik Maes. "Business value of solution architecture." Advances in Enterprise Engineering II. Springer Berlin Heidelberg, 2009. 84-108. | Wikipedia/Solution_architecture |
A hierarchical database model is a data model in which the data is organized into a tree-like structure. The data are stored as records which is a collection of one or more fields. Each field contains a single value, and the collection of fields in a record defines its type. One type of field is the link, which connects a given record to associated records. Using links, records link to other records, and to other records, forming a tree. An example is a "customer" record that has links to that customer's "orders", which in turn link to "line_items".
The hierarchical database model mandates that each child record has only one parent, whereas each parent record can have zero or more child records. The network model extends the hierarchical by allowing multiple parents and children. In order to retrieve data from these databases, the whole tree needs to be traversed starting from the root node. Both models were well suited to data that was normally stored on tape drives, which had to move the tape from end to end in order to retrieve data.
When the relational database model emerged, one criticism of hierarchical database models was their close dependence on application-specific implementation. This limitation, along with the relational model's ease of use, contributed to the popularity of relational databases, despite their initially lower performance in comparison with the existing network and hierarchical models.
== History ==
The hierarchical structure was developed by IBM in the 1960s and used in early mainframe DBMS. Records' relationships form a treelike model. This structure is simple but inflexible because the relationship is confined to a one-to-many relationship. The IBM Information Management System (IMS) and RDM Mobile are examples of a hierarchical database system with multiple hierarchies over the same data.
The hierarchical data model lost traction as Codd's relational model became the de facto standard used by virtually all mainstream database management systems. A relational-database implementation of a hierarchical model was first discussed in published form in 1992 (see also nested set model). Hierarchical data organization schemes resurfaced with the advent of XML in the late 1990s (see also XML database). The hierarchical structure is used primarily today for storing geographic information and file systems.
Currently, hierarchical databases are still widely used especially in applications that require very high performance and availability such as banking, health care, and telecommunications. One of the most widely used commercial hierarchical databases is IMS.
Another example of the use of hierarchical databases is Windows Registry in the Microsoft Windows operating systems.
== Examples of hierarchical data represented as relational tables ==
An organization could store employee information in a table that contains attributes/columns such as employee number, first name, last name, and department number. The organization provides each employee with computer hardware as needed, but computer equipment may only be used by the employee to which it is assigned. The organization could store the computer hardware information in a separate table that includes each part's serial number, type, and the employee that uses it. The tables might look like this:
In this model, the employee data table represents the "parent" part of the hierarchy, while the computer table represents the "child" part of the hierarchy.
In contrast to tree structures usually found in computer software algorithms, in this model the children point to the parents.
As shown, each employee may possess several pieces of computer equipment, but each individual piece of computer equipment may have only one employee owner.
Consider the following structure:
In this, the "child" is the same type as the "parent". The hierarchy stating EmpNo 10 is boss of 20, and 30 and 40 each report to 20 is represented by the "ReportsTo" column. In Relational database terms, the ReportsTo column is a foreign key referencing the EmpNo column. If the "child" data type were different, it would be in a different table, but there would still be a foreign key referencing the EmpNo column of the employees table.
This simple model is commonly known as the adjacency list model and was introduced by Dr. Edgar F. Codd after initial criticisms surfaced that the relational model could not model hierarchical data. However, the model is only a special case of a general adjacency list for a graph.
== See also ==
Tree structure
Hierarchical query
Hierarchical clustering
== References ==
== External links ==
Troels' links to Hierarchical data in RDBMSs
Managing Hierarchical Data in MySQL (This page is from archive.org as the page has been removed from MySQL.com)
Hierarchical data in MySQL: parents and children in one query
Create Hierarchy Chart from Hierarchical Database | Wikipedia/Hierarchical_data_model |
Business process modeling (BPM) is the action of capturing and representing processes of an enterprise (i.e. modeling them), so that the current business processes may be analyzed, applied securely and consistently, improved, and automated.
BPM is typically performed by business analysts, with subject matter experts collaborating with these teams to accurately model processes. It is primarily used in business process management, software development, or systems engineering.
Alternatively, process models can be directly modeled from IT systems, such as event logs.
== Overview ==
According to the Association of Business Process Management Professionals (ABPMP), business process modeling is one of the five key disciplines within Business Process Management (BPM). (Chapter 1.4 CBOK® structure) ← automatic translation from German The five disciplines are:
Process modeling : Creating visual or structured representations of business processes to better understand how they work.
Process analysis : understanding the as-is processes and their alignment with the company's objectives – analysis of business activities.
Process design : redesign – business process reengineering – or redesign of business processes – business process optimization.
Process performance measurement : can focus on the factors of time, cost, capacity, and quality or on the overarching view of waste.
Process transformation : planned, structured development, technical realization, and transfer to ongoing operations.
However, these disciplines cannot be considered in isolation: Business process modeling always requires a business process analysis for modeling the as-is processes (see section Analysis of business activities) or specifications from process design for modeling the to-be processes (see sections Business process reengineering and Business process optimization).
The focus of business process modeling is on the representation of the flow of actions (activities), according to Hermann J. Schmelzer and Wolfgang Sesselmann consisting "of the cross-functional identification of value-adding activities that generate specific services expected by the customer and whose results have strategic significance for the company. They can extend beyond company boundaries and involve activities of customers, suppliers, or even competitors." (Chapter 2.1 Differences between processes and business processes) ← automatic translation from German
But also other qualities (facts) such as data and business objects (as inputs/outputs, formal organizations and roles (responsible/accountable/consulted/informed persons, see RACI), resources and IT-systems as well as guidelines/instructions (work equipment), requirements, key figures etc. can be modeled.
Incorporating more of these characteristics into business process modeling enhances the accuracy of abstraction but also increases model complexity. "To reduce complexity and improve the comprehensibility and transparency of the models, the use of a view concept is recommended."(Chapter 2.4 Views of process modeling) ← automatic translation from German There is also a brief comparison of the view concepts of five relevant German-speaking schools of business informatics: 1) August W. Scheer, 2) Hubert Österle, 3) Otto K. Ferstl and Elmar J. Sinz, 4) Hermann Gehring and 5) Andreas Gadatsch.
The term views (August W. Scheer, Otto K. Ferstl and Elmar J. Sinz, Hermann Gehring and Andreas Gadatsch) is not used uniformly in all schools of business informatics – alternative terms are design dimensions (Hubert Österle) or perspectives (Zachman).
M. Rosemann, A. Schwegmann, and P. Delfmann also see disadvantages in the concept of views: "It is conceivable to create information models for each perspective separately and thus partially redundantly. However, redundancies always mean increased maintenance effort and jeopardize the consistency of the models." (Chapter 3.2.1 Relevant perspectives on process models) ← automatic translation from German
According to Andreas Gadatsch, business process modeling is understood as a part of business process management alongside process definition and process management. (Chapter 1.1 Process management) ← automatic translation from German
Business process modeling is also a central aspect of holistic company mapping – which also deals with the mapping of the corporate mission statement, corporate policy/corporate governance, organizational structure, process organization, application architecture, regulations and interest groups as well as the market.
According to the European Association of Business Process Management EABPM, there are three different types of end-to-end business processes:
Leadership processes;
Execution processes and
Support processes. (Chapter 2.4 Process types) ← automatic translation from German
These three process types can be identified in every company and are used in practice almost without exception as the top level for structuring business process models. Instead the term leadership processes the term management processes is typically used. Instead of the term execution processes the term core processes has become widely accepted. (Chapter 6.2.1 Objectives and concept) ← automatic translation from German, (Chapter 1.3 The concept of process) ← automatic translation from German, (Chapter 4.12.2 Differentiation between core and support objectives) ← automatic translation from German, (Chapter 6.2.2 Identification and rough draft) ← automatic translation from German
If the core processes are then organized/decomposed at the next level in supply chain management (SCM), customer relationship management (CRM), and product lifecycle management (PLM), standard models of large organizations and industry associations such as the SCOR model can also be integrated into business process modeling.
== History ==
Techniques to model business processes such as the flow chart, functional flow block diagram, control flow diagram, Gantt chart, PERT diagram, and IDEF have emerged since the beginning of the 20th century. The Gantt charts were among the first to arrive around 1899, the flow charts in the 1920s, functional flow block diagram and PERT in the 1950s, and data-flow diagrams and IDEF in the 1970s. Among the modern methods are Unified Modeling Language and Business Process Model and Notation. Still, these represent just a fraction of the methodologies used over the years to document business processes. The term business process modeling was coined in the 1960s in the field of systems engineering by S. Williams in his 1967 article "Business Process Modelling Improves Administrative Control". His idea was that techniques for obtaining a better understanding of physical control systems could be used in a similar way for business processes. It was not until the 1990s that the term became popular.
In the 1990s, the term process became a new productivity paradigm. Companies were encouraged to think in processes instead of functions and procedures. Process thinking looks at the chain of events in the company from purchase to supply, from order retrieval to sales, etc. The traditional modeling tools were developed to illustrate time and cost, while modern tools focus on cross-functional activities. These cross-functional activities have increased significantly in number and importance, due to the growth of complexity and dependence. New methodologies include business process redesign, business process innovation, business process management, integrated business planning, among others, all "aiming at improving processes across the traditional functions that comprise a company".
In the field of software engineering, the term business process modeling opposed the common software process modeling, aiming to focus more on the state of the practice during software development. In that time (the early 1990s) all existing and new modeling techniques to illustrate business processes were consolidated as 'business process modeling languages'. In the Object Oriented approach, it was considered to be an essential step in the specification of business application systems. Business process modeling became the base of new methodologies, for instance, those that supported data collection, data flow analysis, process flow diagrams, and reporting facilities. Around 1995, the first visually oriented tools for business process modeling and implementation were presented.
== Objectives of business process modeling ==
The objective of business process modeling is a – usually graphical – representation of end-to-end processes, whereby complex facts of reality are documented using a uniform (systematized) representation and reduced to the substantial (qualities). Regulatory requirements for the documentation of processes often also play a role here (e.g. document control, traceability, or integrity), for example from quality management, information security management or data protection.
Business process modeling typically begins with determining the environmental requirements: First, the goal of the modeling (applications of business process modeling) must be determined. Business process models are now often used in a multifunctional way (see above). Second the model addressees must be determined, as the properties of the model to be created must meet their requirements. This is followed by the determination of the business processes to be modeled.
The qualities of the business process that are to be represented in the model are specified in accordance with the goal of the modeling. As a rule, these are not only the functions constituting the process, including the relationships between them, but also a number of other qualities, such as formal organization, input, output, resources, information, media, transactions, events, states, conditions, operations and methods.
The objectives of business process modeling may include (compare: Association of Business Process Management Professionals (ABPMP) (Chapter 3.1.2 Process characteristics and properties) ← automatic translation from German):
Documentation of the company's business processes
to gain knowledge of the business processes
to map business unit(s) with the applicable regulations
to transfer business processes to other locations
to determine the requirements of new business activities
to provide an external framework for the set of rules from procedures and work instructions
to meet the requirements of business partners or associations (e.g. certifications)
to gain advantages over competitors (e.g. in tenders)
to comply with legal regulations (e.g. for operators of critical infrastructures, banks or producers of armaments)
to check the fulfillment of standards and compliance requirements
to create the basis for communication and discussion
to train or familiarize employees
to avoid loss of knowledge (e.g. due to staff leaving)
to support quality and environmental management
Definition of process performance indicators and monitoring of process performance
to increase process speed
to reduce cycle time
to increase quality
to reduce costs, such as labor, materials, scrap, or capital costs
Preparation/Implementation of a business process optimization (which usually begins with an analysis of the current situation)
to support the analysis of the current situation
to develop alternative processes
to introduce new organizational structures
to outsource company tasks
to redesign, streamline, or improve company processes (e.g. with the help of the CMM)
Preparation of an information technology project
to support a software evaluation/software selection
to support the customizing of commercial off-the-shelf software
to introduce automation or IT support with a workflow management system
Definition of interfaces and SLAs
Modularization of company processes
Benchmarking between parts of the company, partners and competitors
Performing activity-based costing and simulations
to understand how the process reacts to different stress rituals or expected changes
to evaluate the effectiveness of measures for business process optimization and compare alternatives
Finding the best practice
Accompanying organizational changes
such as the sale or partial sale
such as the acquisition and integration of companies or parts of companies
such as the introduction or change of IT systems or organizational structures
Participation in competitions (such as EFQM).
== Applications of business process modeling ==
Since business process modeling in itself makes no direct contribution to the financial success of a company, there is no motivation for business process modeling from the most important goal of a company, the intention to make a profit. The motivation of a company to engage in business process modeling therefore always results from the respective purpose. Michael Rosemann, Ansgar Schwegmann und Patrick Delfmann lists a number of purposes as motivation for business process modeling:
Organizational documentation, with the "objective of increasing transparency about the processes in order to increase the efficiency of communication about the processes" (Chapter 3.2.1 Relevant perspectives on process models) ← automatic translation from German, (Chapter 2.5.4 Areas of application for process modeling in practice) ← automatic translation from German including the ability to create process templates to relocate or replicate business functions or the objective to create a complete company model
Process-oriented re-organization, both in the sense of "(revolutionary) business process re-engineering and in the sense of continual (evolutionary) process improvement" (Chapter 3.2.1 Relevant perspectives on process models) ← automatic translation from German with the objective of a vulnerability assessment (Chapter 2.5.4 Areas of application for process modeling in practice) ← automatic translation from German, process optimization (e.g. by controlling and reducing total cycle time (TCT), through Kaizen, Six Sigma etc.) or process standardization
Continuous process management, as "planning, implementation and control of processes geared towards sustainability" (Chapter 3.2.1 Relevant perspectives on process models) ← automatic translation from German
Certifications according to DIN ISO/IEC 9001 (or also according to ISO/IEC 14001, ISO/IEC 27001 etc.)
Benchmarking, defined as "comparison of company-specific structures and performance with selected internal or external references. In the context of process modeling, this can include the comparison of process models (structural benchmarking) or the comparison of process key figures" (Chapter 3.2.1 Relevant perspectives on process models) ← automatic translation from German
Knowledge management with the "aim of increasing transparency about the company's knowledge resource in order to improve the process of identifying, acquiring, utilizing, developing and distributing knowledge" (Chapter 3.2.1 Relevant perspectives on process models) ← automatic translation from German
Selection of ERP software, which "often documents its functionality in the form of (software-specific) reference models, so that it makes sense to also use a comparison of the company-specific process models with these software-specific models for software selection" (Chapter 3.2.1 Relevant perspectives on process models) ← automatic translation from German, < (Chapter 2.5.4 Areas of application for process modeling in practice) ← automatic translation from German
Model-based customization, i.e. "the configuration of commercial off-the-shelf software" often by means of "parameterization of the software through configuration of reference models" (Chapter 3.2.1 Relevant perspectives on process models) ← automatic translation from German, (Chapter 2.5.4 Areas of application for process modeling in practice) ← automatic translation from German
Software development, using the processes for "the description of the requirements for the software to be developed at a conceptual level as part of requirements engineering"(Chapter 3.2.1 Relevant perspectives on process models) ← automatic translation from German, (Chapter 3 The path to a process-oriented application landscape) ← automatic translation from German, (Chapter 2.5.4 Areas of application for process modeling in practice) ← automatic translation from German
Workflow management, for which the process models are "the basis for the creation of instantiable workflow models" (Chapter 3.2.1 Relevant perspectives on process models) ← automatic translation from German
Simulation with the aim of "investigating the system behavior over time" and the "identification of weak points that would not be revealed by a pure model view" (Chapter 3.2.1 Relevant perspectives on process models) ← automatic translation from German
=== Business process re-engineering (BPR) ===
Within an extensive research program initiated in 1984 titled "Management in the 1990s" at MIT, the approach of process re-engineering emerged in the early 1990s. The research program was designed to explore the impact of information technology on the way organizations would be able to survive and thrive in the competitive environment of the 1990s and beyond. In the final report, N. Venkat Venkatraman summarizes the result as follows: The greatest increases in productivity can be achieved when new processes are planned in parallel with information technologies.
This approach was taken up by Thomas H. Davenport (Part I: A Framework For Process Innovation, Chapter: Introduction) as well as Michael M. Hammer and James A. Champy and developed it into business process re-engineering (BPR) as we understand it today, according to which business processes are fundamentally restructured in order to achieve an improvement in measurable performance indicators such as costs, quality, service and time.
Business process re-engineering has been criticized in part for starting from a "green field" and therefore not being directly implementable for established companies. Hermann J. Schmelzer and Wolfgang Sesselmann assess this as follows: "The criticism of BPR has an academic character in many respects. ... Some of the points of criticism raised are justified from a practical perspective. This includes pointing out that an overly radical approach carries the risk of failure. It is particularly problematic if the organization and employees are not adequately prepared for BPR." (Chapter 6.2.1 Objectives and concept) ← automatic translation from German
The high-level approach to BPR according to Thomas H. Davenport consists of:
Identifying Process for Innovation
Identifying Change Levers
Developing Process Visions
Understanding Existing Processes
Designing and Prototyping the New Process
=== Certification of the management system according to ISO ===
With ISO/IEC 27001:2022, the standard requirements for management systems are now standardized for all major ISO standards and have a process character.
==== General standard requirements for management systems with regard to processes ====
In the ISO/IEC 9001, ISO/IEC 14001, ISO/IEC 27001 standards, this is anchored in Chapter 4.4 in each case:
Each of these standards requires the organization to establish, implement, maintain and continually improve an appropriate management system "including the processes needed and their interactions"., ,
In the definition of the standard requirements for the processes needed and their interactions, ISO/IEC 9001 is more specific in clause 4.4.1 than any other ISO standard for management systems and defines that "the organization shall determine and apply the processes needed for" an appropriate management system throughout the organization and also lists detailed requirements with regard to processes:
Determine the inputs required and the outputs expected
Determine the sequence and interaction
Define and apply the criteria and methods (including monitoring, measurement, and related performance indicators) for effective operation and control
Determine the resources needed
Assign the responsibilities and authorities
Address the risks and opportunities
Evaluate these processes and implement any changes needed for effective operation and control
Improve
In addition, clause 4.4.2 of the ISO/IEC 9001 lists some more
detailed requirements with regard to processes:
Maintain documented information
Retain documented information for correct implementation
The standard requirements for documented information are also relevant for business process modelling as part of an ISO management system.
==== Specific standard requirements for management systems with regard to documented information ====
In the standards ISO/IEC 9001, ISO/IEC 14001, ISO/IEC 27001 the requirements with regard to documented information are anchored in clause 7.5 (detailed in the respective standard in clauses "7.5.1. General", "7.5.2. Creating and updating" and "7.5.3. Control of documented information").
The standard requirements of ISO/IEC 9001 used here as an example include in clause "7.5.1. General"
Documented information by the standard requirements; and
Documented information on the effectiveness of the management system must be included;
Demand in clause "7.5.2. Creating and updating"
Labelling and description (e.g. with title, date, author or reference number);
Suitable format (e.g. language, software version, graphics) and medium (e.g. paper, electronic); and
Review and approval
And require in clause "7.5.3. Control of documented information"
To ensure suitable and available at the place and time as required;
To ensure protection (e.g. against loss of confidentiality, improper use or loss of integrity);
To consider distribution, access, retrieval,and use;
To consider filing/storage and preservation (including preservation of readability);
To perform monitoring of changes (e.g. version control); and
To consider storage and disposition of further whereabouts.
Based on the standard requirements,
To determine and continuously improve the required processes and their interactions
To determine and maintain the content of the documented information deemed necessary and
To ensure the secure handling of documented information (protection, access, monitoring, and maintenance)
Preparing for ISO certification of a management system is a very good opportunity to establish or promote business process modelling in the organisation.
=== Business process optimization ===
Hermann J. Schmelzer and Wolfgang Sesselmann point out that the field of improvement of the three methods mentioned by them as examples for process optimization (control and reduction of total cycle time (TCT), Kaizen and Six Sigma) are processes: In the case of total cycle time (TCT), it is the business processes (end-to-end processes) and sub-processes, with Kaizen it is the process steps and activity and with Six Sigma it is the sub-processes, process steps and activity. (Chapter 6.3.1 Total Cycle Time (TCT), KAIZEN and Six Sigma in comparison) ← automatic translation from German
For the total cycle time (TCT), Hermann J. Schmelzer and Wolfgang Sesselmann list the following key features: (Chapter 6.3.2 Total Cycle Time (TCT)) ← automatic translation from German
Identify barriers that hinder the process flow
Eliminate barriers and substitute processes
Measure the effects of barrier removal
Comparison of the measured variables with the targets
Consequently, business process modeling for TCT must support adequate documentation of barriers, barrier handling, and measurement.
When examining Kaizen tools, initially, there is no direct connection to business processes or business process modeling. However, Kaizen and business process management can mutually enhance each other. In the realm of business process management, Kaizen's objectives are directly derived from the objectives for business processes and sub-processes. This linkage ensures that Kaizen measures effectively support the overarching business objectives." (Chapter 6.3.3 KAIZEN) ← automatic translation from German
Six Sigma is designed to prevent errors and improve the process capability so that the proportion of process outcomes that meet the requirements is 6σ – or in other words, for every million process outcomes, only 3.4 errors occur. Hermann J. Schmelzer and Wolfgang Sesselmann explain: "Companies often encounter considerable resistance at a level of 4σ, which makes it necessary to redesign business processes in the sense of business process re-engineering (design for Six Sigma)." (Chapter 6.3.4 Six Sigma) ← automatic translation from German For a reproducible measurement of process capability, precise knowledge of the business processes is required and business process modeling is a suitable tool for design for Six Sigma. Six Sigma, therefore, uses business process modeling according to SIPOC as an essential part of the methodology, and business process modeling using SIPOC has established itself as a standard tool for Six Sigma.
=== Inter-company business process modeling ===
The aim of inter-company business process modeling is to include the influences of external stakeholders in the analysis or to achieve inter-company comparability of business processes, e.g. to enable benchmarking.
Martin Kugler lists the following requirements for business process modeling in this context: (Chapter 14.2.1 Requirements for inter-company business process modeling) ← automatic translation from German
Employees from different companies must comprehend business process models, highlighting the critical importance of familiarity with modeling techniques. Acceptance of business process modeling is bolstered by the simplicity of representation. Models should be clear, easy to understand, and as self-explanatory as possible. Standardization of the presentation of inter-company business process models across different companies is essential to ensure consistent comprehensibility and acceptance, particularly given the varied representations used within different organizations. It is imperative to employ an industry-neutral modeling technique to accommodate the diverse backgrounds of companies along the value chain (supplier, manufacturer, retailer, customer), which typically span different industries.
== Topics ==
=== Analysis of business activities ===
==== Define framework conditions ====
The analysis of business activities determines and defines the framework conditions for successful business process modeling. This is where the company should start,
define the relevant applications of business process modeling on the basis of the business model and where it is positioned in the value chain,
derive the strategy for the long-term success of business process modeling from the business strategy and develop an approach for structuring the business process models. Both the relevant purposes and the strategy directly influence the process map.
This strategy for the long-term success of business process modeling can be characterized by the market-oriented view and/or the resource-based view. Jörg Becker and Volker Meise explain: "Whereas in the market view, the industry and the behavior of competitors directly determine a company's strategy, the resource-oriented approach takes an internal view by analyzing the strengths and weaknesses of the company and deriving the direction of development of the strategy from this." (Chapter 4.6 The resource-based view) ← automatic translation from German And further: "The alternative character initially formulated in the literature between the market-based and resource-based view has now given way to a differentiated perspective. The core competence approach is seen as an important contribution to the explanation of success potential, which is used alongside the existing, market-oriented approaches."(Chapter 4.7 Combination of views) ← automatic translation from German Depending on the company's strategy, the process map will therefore be the business process models with a view to market development and to resource optimization in a balanced manner.
==== Identify business processes ====
Following the identification phase, a company's business processes are distinguished from one another through an analysis of their respective business activities (refer also to business process analysis). A business process constitutes a set of interconnected, organized actions (activities) geared towards delivering a specific service or product (to fulfill a specific goal) for a particular customer or customer group.
According to the European Association of Business Process Management (EABPM), establishing a common understanding of the current process and its alignment with the objectives serves as an initial step in process design or reengineering." (Chapter 4 Process analysis) ← automatic translation from German
The effort involved in analysing the as-is processes is repeatedly criticised in the literature, especially by proponents of business process re-engineering (BPR), and it is suggested that the definition of the target state should begin immediately.
Hermann J. Schmelzer and Wolfgang Sesselmann, on the other hand, discuss and evaluate the criticism levelled at the radical approach of business process re-engineering (BPR) in the literature and "recommend carrying out as-is analyses. A reorganisation must know the current weak points in order to be able to eliminate them. The results of the analyses also provide arguments as to why a process re-engineering is necessary. It is also important to know the initial situation for the transition from the current to the target state. However, the analysis effort should be kept within narrow limits. The results of the analyses should also not influence the redesign too strongly." (Chapter 6.2.2 Critical assessment of the BPR) ← automatic translation from German
==== Structure business processes – building a process map ====
Timo Füermann explains: "Once the business processes have been identified and named, they are now compiled in an overview. Such overviews are referred to as process maps." (Chapter 2.4 Creating the process map) ← automatic translation from German
Jörg Becker and Volker Meise provide the following list of activities for structuring business processes:
Enumeration of the main processes,
Definition of the process boundaries,
Determining the strategic relevance of each process,
Analysis of the need for improvement of a process and
Determining the political and cultural significance of the process (Chapter 4.10 Defining the process structure) ← automatic translation from German
The structuring of business processes generally begins with a distinction between management, core, and support processes.
Management processes govern the operation of a company. Typical management processes include corporate governance and strategic management. They define corporate objectives and monitor the achievement of objectives.
Core processes constitute the core business and create the primary value stream. Typical operational processes are purchasing, manufacturing, marketing, and sales. They generate visible, direct customer benefits.
Support processes provide and manage operational resources. They support the core and management processes by ensuring the smooth running of business operations. Examples include accounting, recruitment, and technical support.
==== Structure core processes based on the strategy for the long-term success of business process modeling ====
As the core business processes clearly make up the majority of a company's identified business processes, it has become common practice to subdivide the core processes once again. There are different approaches to this depending on the type of company and business activity. These approaches are significantly influenced by the defined application of business process modeling and the strategy for the long-term success of business process modeling.
In the case of a primarily market-based strategy, end-to-end core business processes are often defined from the customer or supplier to the retailer or customer (e.g. "from offer to order", "from order to invoice", "from order to delivery", "from idea to product", etc.). In the case of a strategy based on resources, the core business processes are often defined on the basis of the central corporate functions ("gaining orders", "procuring and providing materials", "developing products", "providing services", etc.).
In a differentiated view without a clear focus on the market view or the resource view, the core business processes are typically divided into CRM, PLM and SCM.
CRM (customer relationship management) describes the business processes for customer acquisition, quotation and order creation as well as support and maintenance
PLM (product lifecycle management) describes the business processes from product portfolio planning, product planning, product development and product maintenance to product discontinuation and individual developments
SCM (supply chain management) describes the business processes from supplier management through purchasing and all production stages to delivery to the customer, including installation and commissioning where applicable
However, other approaches to structuring core business processes are also common, for example from the perspective of customers, products or sales channels.
"Customers" describes the business processes that can be assigned to specific customer groups (e.g. private customer, business customer, investor, institutional customer)
"Products" describes the business processes that are product-specific (e.g. current account, securities account, loan, issue)
"Sales channels" describe the business processes that are typical for the type of customer acquisition and support (e.g. direct sales, partner sales, online).
The result of structuring a company's business processes is the process map (shown, for example, as a value chain diagram). Hermann J. Schmelzer and Wolfgang Sesselmann add: "There are connections and dependencies between the business processes. They are based on the transfer of services and information. It is important to know these interrelationships in order to understand, manage, and control the business processes." (Chapter 2.4.3 Process map) ← automatic translation from German
=== Definition of business processes ===
The definition of business processes often begins with the company's core processes because they
Fulfill their own market requirements,
Operate largely autonomously/independently and independently of other business areas and
Contribute to the business success of the company,
For the company
Have a strong external impact,
Can be easily differentiated from other business processes and
Offer the greatest potential for business process optimization, both by improving process performance or productivity and by reducing costs.
The scope of a business process should be selected in such a way that it contains a manageable number of sub-processes, while at the same time keeping the total number of business processes within reasonable limits. Five to eight business processes per business unit usually cover the performance range of a company.
Each business process should be independent – but the processes are interlinked.
The definition of a business process includes: What result should be achieved on completion? What activities are necessary to achieve this? Which objects should be processed (orders, raw materials, purchases, products, ...)?
Depending on the prevailing corporate culture, which may either be more inclined towards embracing change or protective of the status quo and the effectiveness of communication, defining business processes can prove to be either straightforward or challenging. This hinges on the willingness of key stakeholders within the organization, such as department heads, to lend their support to the endeavor. Within this context, effective communication plays a pivotal role.
In elucidating this point, Jörg Becker and Volker Meise elucidate that the communication strategy within an organizational design initiative should aim to garner support from members of the organization for the intended structural changes. It is worth noting that business process modeling typically precedes business process optimization, which entails a reconfiguration of process organization – a fact well understood by the involved parties. Therefore, the communication strategy must focus on persuading organizational members to endorse the planned structural adjustments." (Chapter 4.15 Influencing the design of the regulatory framework) ← automatic translation from German In the event of considerable resistance, however, external knowledge can also be used to define the business processes.
==== General process identification and individual process identification ====
Jörg Becker and Volker Meise mention two approaches (general process identification and individual process identification) and state the following about general process identification: "In the general process definition, it is assumed that basic, generally valid processes exist that are the same in all companies." It goes on to say: "Detailed reference models can also be used for general process identification. They describe industry- or application system-specific processes of an organization that still need to be adapted to the individual case, but are already coordinated in their structure." (Chapter 4.11 General process identification) ← automatic translation from German
Jörg Becker and Volker Meise state the following about individual process identification: "In individual or singular process identification, it is assumed that the processes in each company are different according to customer needs and the competitive situation and can be identified inductively based on the individual problem situation." (Chapter 4.12 Individual process identification) ← automatic translation from German
The result of the definition of the business processes is usually a rough structure of the business processes as a value chain diagram.
=== Further structuring of business processes ===
The rough structure of the business processes created so far will now be decomposed – by breaking it down into sub-processes that have their own attributes but also contribute to achieving the goal of the business process. This decomposition should be significantly influenced by the application and strategy for the long-term success of business process modeling and should be continued as long as the tailoring of the sub-processes defined this way contributes to the implementation of the purpose and strategy.
A sub-process created in this way uses a model to describe the way in which procedures are carried out in order to achieve the intended operating goals of the company. The model is an abstraction of reality (or a target state) and its concrete form depends on the intended use (application).
A further decomposition of the sub-processes can then take place during business process modeling if necessary. If the business process can be represented as a sequence of phases, separated by milestones, the decomposition into phases is common. Where possible, the transfer of milestones to the next level of decomposition contributes to general understanding.
The result of the further structuring of business processes is usually a hierarchy of sub-processes, represented in value chain diagrams. It is common that not all business processes have the same depth of decomposition. In particular, business processes that are not safety-relevant, cost-intensive or contribute to the operating goal are broken down to a much lesser depth. Similarly, as a preliminary stage of a decomposition of a process planned for (much) later, a common understanding can first be developed using simpler / less complex means than value chain diagrams – e.g. with a textual description or with a turtle diagram (Chapter 3.1 Defining process details) ← automatic translation from German (not to be confused with turtle graphic!).
=== Assigning the process responsibility ===
Complete, self-contained processes are summarized and handed over to a responsible person or team. The process owner is responsible for success, creates the framework conditions, and coordinates his or her approach with that of the other process owners. Furthermore, he/she is responsible for the exchange of information between the business processes. This coordination is necessary in order to achieve the overall goal orientation.
=== Modeling business process ===
==== Design of the process chains ====
If business processes are documented using a specific IT-system and representation, e.g. graphically, this is generally referred to as modeling. The result of the documentation is the business process model.
As is modeling and to be modeling
The question of whether the business process model should be created through as is modeling or to be modeling is significantly influenced by the defined application and the strategy for the long-term success of business process modeling. The previous procedure with analysis of business activities, defineition of business processes and further structuring of business processes is advisable in any case.
As-is modeling
Ansgar Schwegmann and Michael Laske explain: "Determining the current status is the basis for identifying weaknesses and localizing potential for improvement. For example, weak points such as organizational breaks or insufficient IT penetration can be identified." (Chapter 5.1 Intention of the as is modeling) ← automatic translation from German
The following disadvantages speak against as is modeling:
The creativity of those involved in the project to develop optimal target processes is stifled, as old structures and processes may be adopted without reflection in downstream target modeling and
The creation of detailed as is models represents a considerable effort, also influenced by the effort required to reach a consensus between the project participants at interfaces and responsibility transitions
These arguments weigh particularly heavily if Business process re-engineering (BPR) is planned anyway.
Ansgar Schwegmann and Michael Laske also list a number of advantages of as is modeling: (Chapter 5.1 Intention of as-is modeling) ← automatic translation from German
Modeling the current situation is the basis for identifying weaknesses and potential for improvement
Knowledge of the current state is a prerequisite for developing migration strategies to the target state
Modeling the current state provides an overview of the existing situation, which can be particularly valuable for newly involved and external project participants
The as is modeling can be a starting point for training and introducing project participants to the tools and methods
The as is model can serve as a checklist for later target modeling so that no relevant issues are overlooked
The as is models can be used as starting models for target modeling if the target state is very similar to the current situation, at least in some areas
Other advantages can also be found, such as
The as is model is suitable for supporting certification of the management system
The as is model can serve as a basis for organizational documentation (written rules, specifications and regulations of the organization, ...)
The requirements for workflow management can be checked on the basis of the as is model (definition of processes, repetition rate, ...)
Key figures can be collected on the basis of the as is model in order to be compared with the key figures achieved after a reorganization and to measure the success of the measures.
To be modeling
Mario Speck and Norbert Schnetgöke define the objective of to be modeling as follows: "The target processes are based on the strategic goals of the company. This means that all sub-processes and individual activities of a company must be analyzed with regard to their target contribution. Sub-processes or activities that cannot be identified as value-adding and do not serve at least one non-monetary corporate objective must therefore be eliminated from the business processes." (Chapter 6.2.3 Capturing and documenting to be models
)
They also list five basic principles that have proven their worth in the creation of to be models:
Parallel processing of sub-processes and individual activities is preferable to sequential processing – it contains the greater potential for optimization.
The development of a sub-process should be carried out as consistently as possible by one person or group – this allows the best model quality to be achieved.
Self-monitoring should be made possible for individual sub-processes and individual activities during processing – this reduces quality assurance costs.
If not otherwise possible, at least one internal customer/user should be defined for each process – this strengthens customer awareness and improves the assessability of process performance.
Learning effects that arise during the introduction of the target processes should be taken into account – this strengthens the employees' awareness of value creation.
The business process model created by as is modeling or to be modeling consists of:
==== Sub-processes ====
Delimitation
August W. Scheer is said to have said in his lectures: A process is a process is a process. This is intended to express the recursiveness of the term, because almost every process can be broken down into smaller processes (sub-processes). In this respect, terms such as business process, main process, sub-process or elementary process are only a desperate attempt to name the level of process decomposition. As there is no universally valid agreement on the granularity of a business process, main process, sub-process or elementary process, the terms are not universally defined, but can only be understood in the context of the respective business process model.
In addition, some German-speaking schools of business informatics do not use the terms process (in the sense of representing the sequence of actions) and function (in the sense of a delimited corporate function/action (activity) area that is clearly assigned to a corporate function owner).
For example, in August W. Scheer's ARIS it is possible to use functions from the function view as processes in the control view and vice versa. Although this has the advantage that already defined processes or functions can be reused across the board, it also means that the proper purpose of the function view is diluted and the ARIS user is no longer able to separate processes and functions from one another.
The first image shows as a value chain diagram how the business process Edit sales pipeline has been broken down into sub-processes (in the sense of representing the sequence of actions (activities)) based on its phases.
The second image shows an excerpt of typical functions (in the sense of delimited corporate function/action (activity) areas, which are assigned to a corporate function owner), which are structured based on the areas of competence and responsibility hierarchy. The corporate functions that support the business process Edit sales pipeline are marked in the function tree.
Utilization
A business process can be decomposed into sub-processes until further decomposition is no longer meaningful/possible (smallest meaningful sub-process = elementary process). Usually, all levels of decomposition of a business process are documented in the same methodology: Process symbols. The process symbols used when modeling one level of decomposition then usually refer to the sub-processes of the next level until the level of elementary processes is reached. Value chain diagrams are often used to represent business processes, main processes, sub-processes and elementary processes.
Workflow
A workflow is a representation of a sequence of tasks, declared as work of a person, of a simple or complex mechanism, of a group of persons, of an organization of staff, or of machines (including IT-systems). A workflow is therefore always located at the elementary process level. The workflow may be seen as any abstraction of real work, segregated into workshare, work split, or other types of ordering. For control purposes, the workflow may be a view of real work under a chosen aspect.
==== Functions (Tasks) ====
Delimitation
The term functions is often used synonymously for a delimited corporate function/action (activita) area, which is assigned to a corporate function owner, and the atomic activity (task) at the level of the elementary processes. In order to avoid the double meaning of the term function, the term task can be used for the atomic activities at the level of the elementary processes in accordance with the naming in BPMN. Modern tools also offer the automatic conversion of a task into a process, so that it is possible to create a further level of process decomposition at any time, in which a task must then be upgraded to an elementary process.
Utilization
The graphical elements used at the level of elementary processes then describe the (temporal-logical) sequence with the help of functions (tasks). The sequence of the functions (tasks) within the elementary processes is determined by their logical linking with each other (by logical operators or Gateways), provided it is not already specified by input/output relationships or Milestones. It is common to use additional graphical elements to illustrate interfaces, states (events), conditions (rules), milestones, etc. in order to better clarify the process. Depending on the modeling tool used, very different graphical representation (models) are used.
Furthermore, the functions (tasks) can be supplemented with graphical elements to describe inputs, outputs, systems, roles, etc. with the aim of improving the accuracy of the description and/or increasing the number of details. However, these additions quickly make the model confusing. To resolve the contradiction between accuracy of description and clarity, there are two main solutions: Outsourcing the additional graphical elements for describing inputs, outputs, systems, roles, etc. to a Function Allocation Diagram (FAD) or selectively showing/hiding these elements depending on the question/application.
The function allocation diagram shown in the image illustrates the addition of graphical elements for the description of inputs, outputs, systems, roles, etc. to functions (tasks) very well.
==== Master data (artifacts) ====
The term master data is neither defined by The Open Group (The Open Group Architecture Framework, TOGAF) or John A. Zachman (Zachman Framework) nor any of the five relevant German-speaking schools of business informatics: 1) August W. Scheer, 2) Hubert Österle, 3) Otto K. Ferstl and Elmar J. Sinz, 4) Hermann Gehring and 5) Andreas Gadatsch and is commonly used in the absence of a suitable term in the literature. It is based on the general term for data that represents basic information about operationally relevant objects and refers to basic information that is not primary information of the business process.
For August W. Scheer in ARIS, this would be the basic information of the organization view, data view, function view and performance view. (Chapter 1 The vision: A common language for IT and management) ← automatic translation from German
For Andreas Gadatsch in GPM (Ganzheitliche Prozessmodellierung (German), means holistic process modelling), this would be the basic information of the organizational structure view, activity structure view, data structure view, and application structure view. (Chapter 3.2 GPM – Holistic process modelling) ← automatic translation from German
For Otto K. Ferstl and Elmar J. Sinz in SOM (Semantic Objektmodell), this would be the basic information of the levels Business plan and Resourcen.
Master data can be, for example:
The business unit in whose area of responsibility a process takes place
The business object whose information is required to execute the process
The product that is produced by the process
The policy to be observed when executing the process
The risk that occurs in a process
The measure that is carried out to increase the process capability
The control that is performed to ensure the governance of the process
The IT-system that supports the execution of the business process
The milestone that divides processes into process phases
etc.
By adding master data to the business process modeling, the same business process model can be used for different application and a return on investment for the business process modeling can be achieved more quickly with the resulting synergy.
Depending on how much value is given to master data in business process modeling, it is still possible to embed the master data in the process model without negatively affecting the readability of the model or the master data should be outsourced to a separate view, e.g. Function Allocation Diagrams.
If master data is systematically added to the business process model, this is referred to as an artifact-centric business process model.
Artifact-centric business process
The artifact-centric business process model has emerged as a holistic approach for modeling business processes, as it provides a highly flexible solution to capture operational specifications of business processes. It particularly focuses on describing the data of business processes, known as "artifacts", by characterizing business-relevant data objects, their life-cycles, and related services. The artifact-centric process modelling approach fosters the automation of the business operations and supports the flexibility of the workflow enactment and evolution.
==== Integration of external documents and IT-systems ====
The integration of external documents and IT-systems can significantly increase the added value of a business process model.
For example, direct access to objects in a knowledge database or documents in a rule framework can significantly increase the benefits of the business process model in everyday life and thus the acceptance of business process modeling. All IT-systems involved can exploit their specific advantages and cross-fertilize each other (e.g. link to each other or standardize the filing structure):
short response times of the knowledge database; characterized by a relatively high number of auditors, very fast adaptation of content, and low requirements for the publication of content – e.g. realized with a wiki
Legally compliant documents of the rule framework; characterized by a very small number of specially trained auditors, validated adaptation of content, and high requirements for the release of content – e.g. implemented with a document management system
Integrating graphical representation of processes by a BPM system; characterized by a medium number of auditors, moderately fast adaptation of content, and modest requirements for the release of content
If all relevant objects of the knowledge database and / or documents of the rule framework are connected to the processes, the end users have context-related access to this information and do not need to be familiar with the respective filing structure of the connected systems.
The direct connection of external systems can also be used to integrate current measurement results or system statuses into the processes (and, for example, to display the current operating status of the processes), to display widgets and show output from external systems or to jump to external systems and initiate a transaction there with a preconfigured dialog.
Further connections to external systems can be used, for example, for electronic data interchange (EDI).
=== Model consolidation ===
This is about checking whether there are any redundancies. If so, the relevant sub-processes are combined. Or sub-processes that are used more than once are outsourced to support processes. For a successful model consolidation, it may be necessary to revise the original decomposition of the sub-processes.
Ansgar Schwegmann and Michael Laske explain: "A consolidation of the models of different modeling complexes is necessary in order to obtain an integrated ... model." (Chapter 5.2.4 Model consolidation) ← automatic translation from German They also list a number of aspects for which model consolidation is important:
"Modeling teams need to drive harmonization of models during model creation to facilitate later consolidation."
"If an object-oriented decomposition of the problem domain is carried out, it must be analyzed at an early stage whether similar structures and processes of different objects exist."
"If a function-oriented decomposition of the problem domain is undertaken, the interfaces between the modelled areas in particular must be harmonized."
"In general, a uniform level of detail of the models" (in each decomposition level) "should be aimed for during modeling in order to facilitate the comparability of the submodels and the precise definition of interfaces."
"After completion of the modeling activities in the teams of the individual modeling complexes, [the] created partial models are to be integrated into an overall model."
"In order to facilitate the traceability of the mapped processes, it makes sense to explicitly model selected business transactions that are particularly important for the company and to map them at the top level. ... Colour coding, for example, can also be used to differentiate between associated organizational units." (Chapter 5.2.4 Model consolidation) ← automatic translation from German
=== Process chaining and control flow patterns ===
The chaining of the sub-processes with each other and the chaining of the functions (tasks) in the sub-processes is modeled using Control Flow Patterns.
Material details of the chaining (What does the predecessor deliver to the successor?) are specified in the process interfaces if intended.
=== Process interfaces ===
Process interfaces are defined in order to
Show the relationships between the sub-processes after the decomposition of business processes or
Determine what the business processes or their sub-processes must 'pass on' to each other.
As a rule, this what and its structure is determined by the requirements in the subsequent process.
Process interfaces represent the exit from the current business process/sub-process and the entry into the subsequent business process/sub-process.
Process interfaces are therefore description elements for linking processes section by section. A process interface can
Represent a business process model/sub-process model without the business process model referenced by it already being defined.
Represent a business process model/sub-process model that is referenced from two/multiple superordinate or neighboring business process models.
Represent two/multiple variants of the same business process model/sub-process model.
Process interfaces are agreed between the participants of superordinate/subordinate or neighboring business process models. They are defined and linked once and used as often as required in process models.
Interfaces can be defined by:
Transfer of responsibility/accountability from one business unit to another,
Transfer of data from one IT-system to another,
Original input (information / materials at the beginning of the business process),
Transfer of intermediate results between sub-processes (output at the predecessor and input at the successor are usually identical) or
Final output (the actual result / goal of the business process).
In real terms, the transferred inputs/outputs are often data or information, but any other business objects are also conceivable (material, products in their final or semi-finished state, documents such as a delivery bill). They are provided via suitable transport media (e.g. data storage in the case of data).
=== Business process management ===
See article Business process management.
In order to put improved business processes into practice, change management programs are usually required. With advances in software design, the vision of BPM models being fully executable (enabling simulations and round-trip engineering) is getting closer to reality.
==== Adaptation of process models ====
In business process management, process flows are regularly reviewed and optimized (adapted) if necessary. Regardless of whether this adaptation of process flows is triggered by continuous process improvement or by process reorganization (business process re-engineering), it entails an update of individual sub-processes or an entire business process.
== Representation type and notation ==
In practice, combinations of informal, semiformal and formal models are common: informal textual descriptions for explanation, semiformal graphical representation for visualization, and formal language representation to support simulation and transfer into executable code.
=== Modelling techniques ===
There are various standards for notations; the most common are:
Business Process Model and Notation (BPMN), proposed in 2002 by Stephen A. White, published by the Business Process Management Initiative – merged in June 2005 with Object Management Group
Event-driven process chain (EPC), proposed in 1992 by a working group under the leadership of August-Wilhelm Scheer
Value-added chain diagram (VAD), for visualizing processes mainly at a high level of abstraction
Petri net, developed by Carl Adam Petri in 1962
Follow-up plans (e.g. in the specific form of a Flowchart), proposed in 1997 by Fischermanns and Liebelt
HIPO model, developed by IBM around 1970 as a design aid and documentation technology for software (in a non-technical, but business-oriented form)
Lifecycle Modeling Language (LML), originally designed by the LML steering committee and published in 2013
Subject-oriented business process management (S-BPM)
Cognition enhanced Natural language Information Analysis Method (CogNIAM)
SIPOC diagram, invented in the 1980s as part of the Total Quality Management movement and then adopted by Lean Management and Six Sigma practitioners
Unified Modelling Language (UML), proposed in 1996 by Grady Booch, Ivar Jacobson, and James Rumbaugh, continuously revised under the aegis of the OMG (provides extensions for business process)
ICAM DEFinition (IDEF0), developed for the US Air Force in the early 1980s
Formalized Administrative Notation (FAN), created by Pablo Iacub and Leonardo Mayo in the 1990s
Harbarian process modeling (HPM)
Business Process Execution Language (BPEL), an XML-based language developed in 2002 by OASIS for the description and automation of business processes
Turtle diagram (also turtle method, turtle model, 8W method), a simple, clear and easy-to-understand graphical representation of facts about the process
Furthermore:
Communication structure analysis, proposed in 1989 by Prof. Hermann Krallmann at the Systems Analysis Department of the TU Berlin.
Extended Business Modelling Language (xBML) (seems to be outdated, as the founding company is no longer online)
Notation from OMEGA (object-oriented method for business process modeling and analysis, Objektorientierte Methode zur Geschäftsprozessmodellierung und -analyse in German), presented by Uta Fahrwinkel in 1995
Semantic object model (SOM), proposed in 1990 by Otto K. Ferstl and Elmar J. Sinz
PICTURE-Methode for the documentation and modeling of business processes in public administration
Data-flow diagram, a way of representing a flow of data through a process or a system
Swimlane technique, mainly known through BPMN but also SIPOC, the Process chain diagram (PCD) and other methods use this technique
ProMet, a method set for business engineering
State diagram, used to describe the behavior of systems
In addition, representation types from software architecture can also be used:
Flowchart, standardized in DIN 66001 from September 1966 and last revised in December 1983 or standardized in ISO 5807 from 1985
Nassi-Shneiderman diagram or structure diagram, proposed in 1972/73 by Isaac Nassi and Ben Shneiderman, standardized in DIN 66261.
==== Business Process Model and Notation (BPMN) ====
==== Event-driven process chain (EPC) ====
==== Petri net ====
==== Flowchart ====
==== Hierarchical input process output model (HIPO) ====
==== Lifecycle Modeling Language (LML) ====
==== Subject-oriented business process management ====
==== Cognition enhanced Natural language Information Analysis Method ====
==== SIPOC (suppliers, inputs, process, outputs and customers) ====
==== Unified Modelling Language (UML) ====
==== Integration Definition (IDEF) ====
==== Formalized Administrative Notation (FAN) ====
==== Harbarian process modeling (HPM) ====
==== Business Process Execution Language (BPEL) ====
=== Tools ===
Business process modelling tools provide business users with the ability to model their business processes, implement and execute those models, and refine the models based on as-executed data. As a result, business process modelling tools can provide transparency into business processes, as well as the centralization of corporate business process models and execution metrics. Modelling tools may also enable collaborate modelling of complex processes by users working in teams, where users can share and simulate models collaboratively. Business process modelling tools should not be confused with business process automation systems – both practices have modeling the process as the same initial step and the difference is that process automation gives you an 'executable diagram' and that is drastically different from traditional graphical business process modelling tools.
=== Programming language tools ===
BPM suite software provides programming interfaces (web services, application program interfaces (APIs)) which allow enterprise applications to be built to leverage the BPM engine. This component is often referenced as the engine of the BPM suite.
Programming languages that are being introduced for BPM include:
Business Process Execution Language (BPEL),
Web Services Choreography Description Language (WS-CDL).
XML Process Definition Language (XPDL),
Some vendor-specific languages:
Architecture of Integrated Information Systems (ARIS) supports EPC,
Java Process Definition Language (JBPM),
Other technologies related to business process modelling include model-driven architecture and service-oriented architecture.
=== Simulation ===
The simulation functionality of such tools allows for pre-execution "what-if" modelling (which has particular requirements for this application) and simulation. Post-execution optimization is available based on the analysis of actual as-performed metrics.
Use case diagrams created by Ivar Jacobson, 1992 (integrated into UML)
Activity diagrams (also adopted by UML)
== Related concepts ==
=== Business reference model ===
A business reference model is a reference model, concentrating on the functional and organizational aspects of an enterprise, service organization, or government agency. In general, a reference model is a model of something that embodies the basic goal or idea of something and can then be looked at as a reference for various purposes. A business reference model is a means to describe the business operations of an organization, independent of the organizational structure that performs them. Other types of business reference models can also depict the relationship between the business processes, business functions, and the business area's business reference model. These reference models can be constructed in layers, and offer a foundation for the analysis of service components, technology, data, and performance.
The most familiar business reference model is the Business Reference Model of the US federal government. That model is a function-driven framework for describing the business operations of the federal government independent of the agencies that perform them. The Business Reference Model provides an organized, hierarchical construct for describing the day-to-day business operations of the federal government. While many models exist for describing organizations – organizational charts, location maps, etc. – this model presents the business using a functionally driven approach.
=== Business process integration ===
A business model, which may be considered an elaboration of a business process model, typically shows business data and business organizations as well as business processes. By showing business processes and their information flows, a business model allows business stakeholders to define, understand, and validate their business enterprise. The data model part of the business model shows how business information is stored, which is useful for developing software code. See the figure on the right for an example of the interaction between business process models and data models.
Usually, a business model is created after conducting an interview, which is part of the business analysis process. The interview consists of a facilitator asking a series of questions to extract information about the subject business process. The interviewer is referred to as a facilitator to emphasize that it is the participants, not the facilitator, who provide the business process information. Although the facilitator should have some knowledge of the subject business process, but this is not as important as the mastery of a pragmatic and rigorous method interviewing business experts. The method is important because for most enterprises a team of facilitators is needed to collect information across the enterprise, and the findings of all the interviewers must be compiled and integrated once completed.
Business models are developed to define either the current state of the process, resulting in the 'as is' snapshot model, or a vision of what the process should evolve into, leading to a 'to be' model. By comparing and contrasting the 'as is' and 'to be' models, business analysts can determine if existing business processes and information systems require minor modifications or if reengineering is necessary to enhance efficiency. As a result, business process modeling and subsequent analysis can fundamentally reshape the way an enterprise conducts its operations.
=== Business process re-engineering ===
Business process reengineering (BPR) aims to improve the efficiency and effectiveness of the processes that exist within and across organizations. It examines business processes from a "clean slate" perspective to determine how best to construct them.
Business process re-engineering (BPR) began as a private sector technique to help organizations fundamentally rethink how they do their work. A key stimulus for re-engineering has been the development and deployment of sophisticated information systems and networks. Leading organizations use this technology to support innovative business processes, rather than refining current ways of doing work.
=== Business process management ===
Change management programs are typically involved to put any improved business processes into practice. With advances in software design, the vision of BPM models becoming fully executable (and capable of simulations and round-trip engineering) is coming closer to reality.
==== Adaptation of process models ====
In business process management, process flows are regularly reviewed and, if necessary, optimized (adapted). Regardless of whether this adaptation of process flows is triggered by continual improvement process or business process re-engineering, it entails updating individual sub-processes or an entire business process.
== See also ==
Business architecture
Business Model Canvas
Business plan
Business process mapping
Capability Maturity Model Integration
Drakon-chart
Generalised Enterprise Reference Architecture and Methodology
Model Driven Engineering
Outline of consulting
Value Stream Mapping
== References ==
== Further reading ==
Aguilar-Saven, Ruth Sara. "Business process modelling: Review and framework Archived 2020-08-07 at the Wayback Machine." International Journal of production economics 90.2 (2004): 129–149.
Barjis, Joseph (2008). "The importance of business process modeling in software systems design". Science of Computer Programming. 71: 73–87. doi:10.1016/j.scico.2008.01.002.
Becker, Jörg, Michael Rosemann, and Christoph von Uthmann. "Guidelines of business process modelling." Business Process Management. Springer Berlin Heidelberg, 2000. 30–49.
Hommes, L.J. The Evaluation of Business Process Modelling Techniques. Doctoral thesis. Technische Universiteit Delft.
Håvard D. Jørgensen (2004). Interactive Process Models. Thesis Norwegian University of Science and Technology Trondheim, Norway.
Manuel Laguna, Johan Marklund (2004). Business Process Modeling, Simulation, and Design. Pearson/Prentice Hall, 2004.
Ovidiu S. Noran (2000). Business Modelling: UML vs. IDEF Paper Griffh University
Jan Recker (2005). "Process Modelling in the 21st Century". In: BP Trends, May 2005.
Ryan K. L. Ko, Stephen S. G. Lee, Eng Wah Lee (2009) Business Process Management (BPM) Standards: A Survey. In: Business Process Management Journal, Emerald Group Publishing Limited. Volume 15 Issue 5. ISSN 1463-7154.
Jan Vanthienen, S. Goedertier and R. Haesen (2007). "EM-BrA2CE v0.1: A vocabulary and execution model for declarative business process modelling". DTEW – KBI_0728.
== External links ==
Media related to Business process modeling at Wikimedia Commons
{{|bot=InternetArchiveBot |fix-attempted=yes}} | Wikipedia/Business_process_model |
Computer science and engineering (CSE) or computer science (CS) also integrated as electrical engineering and computer science (EECS) in some universities, is an academic subject comprising approaches of computer science and computer engineering. There is no clear division in computing between science and engineering, just like in the field of materials science and engineering. However, some classes are historically more related to computer science (e.g. data structures and algorithms), and other to computer engineering (e.g. computer architecture). CSE is also a term often used in Europe to translate the name of technical or engineering informatics academic programs. It is offered in both undergraduate as well postgraduate with specializations.
== Academic courses ==
Academic programs vary between colleges, but typically include a combination of topics in computer science,computer engineering, and electrical engineering. Undergraduate courses usually include programming, algorithms and data structures, computer architecture, operating systems, computer networks, parallel computing, embedded systems, algorithms design, circuit analysis and electronics, digital logic and processor design, computer graphics, scientific computing, software engineering, database systems, digital signal processing, virtualization, computer simulations and games programming. CSE programs also include core subjects of theoretical computer science such as theory of computation, numerical methods, machine learning, programming theory and paradigms. Modern academic programs also cover emerging computing fields like image processing, data science, robotics, bio-inspired computing, computational biology, autonomic computing and artificial intelligence. Most CSE programs require introductory mathematical knowledge, hence the first year of study is dominated by mathematical courses, primarily discrete mathematics, mathematical analysis, linear algebra, probability, and statistics, as well as the introduction to physics and electrical and electronic engineering. Students usually also have the opportunity to choose one social science subject.
== See also ==
Computer science
Computer engineering
Computer graphics (computer science)
Bachelor of Technology
== References == | Wikipedia/Computer_science_and_engineering |
Light transport theory deals with the mathematics behind calculating the energy transfers between media that affect visibility. This article is currently specific to light transport in rendering processes such as global illumination and high dynamic range imaging (HDRI).
== Light ==
=== Light Transport ===
The amount of light transported is measured by flux density, or luminous flux per unit area on the point of the surface at which it is measured.
== Radiometry and Energy Transfer ==
Radiometry is the science of measuring electromagnetic radiation, including visible light. It forms the foundation of light transport theory, which models how light interacts with surfaces, volumes, and media.
Energy Transfer Models: Light interacts with media through absorption, reflection, and transmission. These processes are governed by the rendering equation, which models the distribution of light in a scene.
== Geometric Models ==
=== Hemisphere ===
Given a surface S, a hemisphere H can be projected onto S to calculate the amount of incoming and outgoing light. If a point P is selected at random on the surface S, the amount of incoming and outgoing light can be calculated by its projection onto the hemisphere.
=== Hemicube ===
The hemicube model works similarly to the hemisphere model, except that a hemicube is projected instead of a hemisphere. The similarity is only conceptual; the actual calculation, done through numerical integration, has a different form factor.
== Wave-Particle Duality ==
Light transport theory incorporates both wave-based and particle-based descriptions of light. While wave-based models rely on the principles of Maxwell's equations, particle models use ray optics and Monte Carlo methods to simulate light paths.
Monte Carlo Ray Tracing: A stochastic method used in light transport simulations to compute global illumination. This method leverages randomness to estimate solutions to the rendering equation, particularly in complex scenes with multiple light interactions.
== Advanced Models ==
Bidirectional Reflectance Distribution Function (BRDF)
The BRDF models how light is reflected on an opaque surface. It is defined as the ratio of reflected radiance in a given direction to the incident irradiance. BRDFs are crucial in light transport theory for simulating realistic material behavior.
Participating Media
Light transport within volumes (e.g., fog, smoke, or translucent objects) is modeled using the radiative transfer equation (RTE). Participating media are integral to achieving photorealism in scenes involving volumetric light effects.
== Applications in Rendering ==
Rendering converts a model into an image either by simulating a method, such as light transport, to get physically accurate photorealistic images, or by applying some kind of style as non-photorealistic rendering (NPR). The two basic operations in light transport are transport (how much light gets from one place to another) and scattering (how surfaces interact with light).
=== Global Illumination ===
Global illumination simulates all light interactions in a scene, including indirect lighting. It employs light transport theory to compute effects such as color bleeding and soft shadows.
=== Non-Photorealistic Rendering (NPR) ===
Unlike photorealistic rendering, non-photorealistic rendering (NPR) uses light transport models to stylize images, often prioritizing artistic intent over physical accuracy.
== See also ==
Path Tracing
Global illumination
Monte Carlo Method
Photon mapping
Radiosity (computer graphics)
Ray tracing (graphics)
Ray tracing (physics)
Reyes rendering
== References == | Wikipedia/Light_transport_theory |
Cloth modeling is the term used for simulating cloth within a computer program, usually in the context of 3D computer graphics. The main approaches used for this may be classified into three basic types: geometric, physical, and particle/energy.
== Background ==
Most models of cloth are based on "particles" of mass connected in some manner of mesh. Newtonian physics is used to model each particle through the use of a "black box" called a physics engine. This involves using the basic law of motion (Newton's second law):
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In all of these models, the goal is to find the position and shape of a piece of fabric using this basic equation and several other methods.
== Geometric methods ==
Jerry Weil pioneered the first of these, the geometric technique, in 1986. His work was focused on approximating the look of cloth by treating cloth like a collection of cables and using hyperbolic cosine (catenary) curves. Because of this, it is not suitable for dynamic models but works very well for stationary or single-frame renders. This technique creates an underlying shape out of single points; then, it parses through each set of three of these points and maps a catenary curve to the set. It then takes the lowest out of each overlapping set and uses it for the render.
== Physical methods ==
The second technique treats cloth like a grid work of particles connected to each other by springs. Whereas the geometric approach accounted for none of the inherent stretch of a woven material, this physical model accounts for stretch (tension), stiffness, and weight:
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{\displaystyle E(Particle_{i,j})=k_{s}E_{s,i,j}+k_{b}E_{b,i,j}+k_{g}E_{g,i,j}}
s terms are elasticity (by Hooke's law)
b terms are bending
g terms are gravity (see Acceleration due to gravity)
Now we apply the basic principle of mechanical equilibrium in which all bodies seek lowest energy by differentiating this equation to find the minimum energy.
== Particle/energy methods ==
The last method is more complex than the first two. The particle technique takes the physical methods a step further and supposes that we have a network of particles interacting directly. Rather than springs, the energy interactions of the particles are used to determine the cloth's shape. An energy equation that adds onto the following is used:
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{\displaystyle U_{Total}=U_{Repel}+U_{Stretch}+U_{Bend}+U_{Trellis}+U_{Gravity}}
The energy of repelling is an artificial element we add to prevent cloth from intersecting itself.
The energy of stretching is governed by Hooke's law as with the physical method.
The energy of bending describes the stiffness of the fabric
The energy of trellising describes the shearing of the fabric (distortion within the plane of the fabric)
The energy of gravity is based on acceleration due to gravity
Terms for energy added by any source can be added to this equation, then derive and find minima, which generalizes our model. This allows for modeling cloth behavior under any circumstance, and since the cloth is treated as a collection of particles its behavior can be described with the dynamics provided in our physics engine.
== See also ==
Soft body dynamics
Classical mechanics
Physics engine
Rigid body dynamics
Stretched grid method
== External links ==
Cloth Modeling by Kristopher Babic
== Notes == | Wikipedia/Cloth_modeling |
Corel DESIGNER is a vector-based graphics program. It was originally developed by Micrografx, which was bought by Corel in 2001.
The last version developed by Micrografx was 9.0 in 2001. This program was later sold as Corel DESIGNER 9. There are still a number of users who continue working with version 9.0, because newer versions of the product are based on a modified CorelDRAW rather than the original product.
Corel DESIGNER is effective for the creation of engineering drawings, but also offers many functions for graphic design. Starting with version X5, Corel DESIGNER Technical Suite includes Corel Designer, CorelDRAW and Corel Photo-Paint. X6 was the last release for Windows XP.
== Release history and file formats ==
== References ==
== External links ==
Official website
Corel DESIGNER Help (available in HTML and PDF format) | Wikipedia/Corel_Designer |
Affinity Designer is a vector graphics editor developed by Serif for macOS, iPadOS, and Microsoft Windows. It is part of the "Affinity trinity" alongside Affinity Photo and Affinity Publisher. Affinity Designer is available for purchase directly from the company website and in the Mac App Store, iOS App Store, and the Microsoft Store.
== Functionality ==
Affinity Designer serves as a successor to Serif's own DrawPlus software, which the company discontinued in August 2017 in order to focus on the Affinity product range. It has been described as an Adobe Illustrator alternative, and is compatible with common graphics file formats, including Adobe Illustrator (AI), Scalable Vector Graphics (SVG), Adobe Photoshop (PSD), Portable Document Format (PDF), and Encapsulated PostScript (EPS) formats. The application can also import data from some Adobe FreeHand files (specifically versions 10 and MX).
Affinity Designer's core functions include vector pen and shape-drawing tools, support for custom vector and raster brushes (including the ability to import Adobe Photoshop (ABR) brushes), dynamic symbols, stroke stabilization, text style management, and vector/pixel export options.
Affinity Designer provides non-destructive editing features across unlimited layers, with pan and zoom at 60fps, and real-time views for effects and transformations. It supports the RGB, RGB Hex, LAB, CMYK and Grayscale color models, along with PANTONE color swatches and an end-to-end CMYK workflow with ICC color management, and 16-bit per channel editing.
== Development ==
Affinity Designer began as a vector graphics editor solely for macOS. It was developed entirely from scratch for this operating system, allowing it to leverage core native technologies such as OpenGL, Grand Central Dispatch, and Core Graphics.
The first version was released in October 2014, making it the first of the Affinity apps to be released by Serif (and their first macOS release). At that time, Serif's vector graphics application for Windows was DrawPlus; however, following the release of Affinity Designer for Windows, this product has now been discontinued.
Version 1.2, released in April 2015, introduced new tools and features, such as a corner tool and a pixel-alignment mode for GUI design tasks. In December 2015, version 1.4 then introduced new features for managing artboards and printing. With version 1.5 in October 2016, the application received multiple new features, including symbols, constraints, asset management and text styles.
The application began branching out to other platforms in November 2016, when it first launched for Microsoft Windows.
Version 1.6 was released in November 2017, introducing performance improvements and alternative GUI display mode.
The first release of a separate iPad version of Affinity Designer took place in July 2018.
Version 1.7 was released in June 2019 adding some key features such as HDR support, unlimited strokes and fills to a single shape, new point transform tool, new transform mode in Node tool, Lasso selection of nodes, new sculpt mode added to pencil, and also some big performance improvements. Version 1.8, released in February 2020, added the ability for users to define their own document templates and keyboard shortcuts, and a built-in panel for adding stock images. Version 1.9 was released in February 2021, containing "substantial performance gains when working with complex vector documents" and hardware accelerration for Windows.
=== Version 2 ===
In November 2022, Serif launched the second major version of Affinity Designer, incorporating shape builder and knife tools, vector warping, and an x-ray view. However, it received criticism online as some users felt the new feature set was not substantial enough to justify a new purchase. This was followed by version 2.1 in May 2023 and version 2.2 in September 2023, adding various UI improvements and support for macOS Sonoma.
== Reception ==
Affinity Designer was selected as a runner-up in Apple's "Best of 2014" list of Mac App Store and iTunes Store content in the macOS app category. It also was one of the winners of the 2015 Apple Design Award.
In 2018, the Windows version of Affinity Designer won 'Application Creator of the Year' at the Windows Developer Awards (part of Microsoft Build 2018).
Affinity Designer was selected as the winner of the "Best Software For Designers" Award in the 2022 Creative Bloq Awards.
== See also ==
Comparison of vector graphics editors
== References ==
== Further reading ==
Affinity Designer Workbook. Nottingham: Serif Europe Ltd. 2016. ISBN 9781909581036.
== External links ==
Official website | Wikipedia/Affinity_Designer |
The definition of the BSDF (bidirectional scattering distribution function) is not well standardized. The term was probably introduced in 1980 by Bartell, Dereniak, and Wolfe. Most often it is used to name the general mathematical function which describes the way in which the light is scattered by a surface. However, in practice, this phenomenon is usually split into the reflected and transmitted components, which are then treated separately as BRDF (bidirectional reflectance distribution function) and BTDF (bidirectional transmittance distribution function).
BSDF is a superset and the generalization of the BRDF and BTDF. The concept behind all BxDF functions could be described as a black box with the inputs being any two angles, one for incoming (incident) ray and the second one for the outgoing (reflected or transmitted) ray at a given point of the surface. The output of this black box is the value defining the ratio between the incoming and the outgoing light energy for the given couple of angles. The content of the black box may be a mathematical formula which more or less accurately tries to model and approximate the actual surface behavior or an algorithm which produces the output based on discrete samples of measured data. This implies that the function is 4(+1)-dimensional (4 values for 2 3D angles + 1 optional for wavelength of the light), which means that it cannot be simply represented by 2D and not even by a 3D graph. Each 2D or 3D graph, sometimes seen in the literature, shows only a slice of the function.
Some tend to use the term BSDF simply as a category name covering the whole family of BxDF functions.
The term BSDF is sometimes used in a slightly different context, for the function describing the amount of the scatter (not scattered light), simply as a function of the incident light angle. An example to illustrate this context: for perfectly lambertian surface the BSDF (angle)=const. This approach is used for instance to verify the output quality by the manufacturers of the glossy surfaces.
Another recent usage of the term BSDF can be seen in some 3D packages, when vendors use it as a 'smart' category to encompass the simple well known cg algorithms like Phong, Blinn–Phong etc.
Acquisition of the BSDF over the human face in 2000 by Debevec et al. was one of the last key breakthroughs on the way to fully virtual cinematography with its ultra-photorealistic digital look-alikes. The team was the first in the world to isolate the subsurface scattering component (a specialized case of BTDF) using the simplest light stage, consisting on moveable light source, moveable high-res digital camera, 2 polarizers in a few positions and really simple algorithms on a modest computer. The team utilized the existing scientific knowledge that light that is reflected and scattered from the air-to-oil layer retains its polarization while light that travels within the skin loses its polarization. The subsurface scattering component can be simulated as a steady high-scatter glow of light from within the models, without which the skin does not look realistic. ESC Entertainment, a company set up by Warner Brothers Pictures specially to do the visual effects / virtual cinematography system for The Matrix Reloaded and The Matrix Revolutions isolated the parameters for an approximate analytical BRDF which consisted of Lambertian diffusion component and a modified specular Phong component with a Fresnel type of effect.
== Overview of the BxDF functions ==
BRDF (Bidirectional reflectance distribution function) is a simplified BSSRDF, assuming that light enters and leaves at the same point (see the image on the right).
BTDF (Bidirectional transmittance distribution function) is similar to BRDF but for the opposite side of the surface. (see the top image).
BDF (Bidirectional distribution function) is collectively defined by BRDF and BTDF.
BSSRDF (Bidirectional scattering-surface reflectance distribution function or Bidirectional surface scattering RDF) describes the relation between outgoing radiance and the incident flux, including the phenomena like subsurface scattering (SSS). The BSSRDF describes how light is transported between any two rays that hit a surface.
BSSTDF (Bidirectional scattering-surface transmittance distribution function) is like BTDF but with subsurface scattering.
BSSDF (Bidirectional scattering-surface distribution function) is collectively defined by BSSTDF and BSSRDF. Also known as BSDF (Bidirectional scattering distribution function).
== See also ==
BRDF
Radiometry
Reflectance
Radiance
BTF
== References == | Wikipedia/Bidirectional_scattering_distribution_function |
Rendering is the process of generating a photorealistic or non-photorealistic image from input data such as 3D models. The word "rendering" (in one of its senses) originally meant the task performed by an artist when depicting a real or imaginary thing (the finished artwork is also called a "rendering"). Today, to "render" commonly means to generate an image or video from a precise description (often created by an artist) using a computer program.
A software application or component that performs rendering is called a rendering engine, render engine, rendering system, graphics engine, or simply a renderer.
A distinction is made between real-time rendering, in which images are generated and displayed immediately (ideally fast enough to give the impression of motion or animation), and offline rendering (sometimes called pre-rendering) in which images, or film or video frames, are generated for later viewing. Offline rendering can use a slower and higher-quality renderer. Interactive applications such as games must primarily use real-time rendering, although they may incorporate pre-rendered content.
Rendering can produce images of scenes or objects defined using coordinates in 3D space, seen from a particular viewpoint. Such 3D rendering uses knowledge and ideas from optics, the study of visual perception, mathematics, and software engineering, and it has applications such as video games, simulators, visual effects for films and television, design visualization, and medical diagnosis. Realistic 3D rendering requires modeling the propagation of light in an environment, e.g. by applying the rendering equation.
Real-time rendering uses high-performance rasterization algorithms that process a list of shapes and determine which pixels are covered by each shape. When more realism is required (e.g. for architectural visualization or visual effects) slower pixel-by-pixel algorithms such as ray tracing are used instead. (Ray tracing can also be used selectively during rasterized rendering to improve the realism of lighting and reflections.) A type of ray tracing called path tracing is currently the most common technique for photorealistic rendering. Path tracing is also popular for generating high-quality non-photorealistic images, such as frames for 3D animated films. Both rasterization and ray tracing can be sped up ("accelerated") by specially designed microprocessors called GPUs.
Rasterization algorithms are also used to render images containing only 2D shapes such as polygons and text. Applications of this type of rendering include digital illustration, graphic design, 2D animation, desktop publishing and the display of user interfaces.
Historically, rendering was called image synthesis: xxi but today this term is likely to mean AI image generation. The term "neural rendering" is sometimes used when a neural network is the primary means of generating an image but some degree of control over the output image is provided. Neural networks can also assist rendering without replacing traditional algorithms, e.g. by removing noise from path traced images.
== Features ==
=== Photorealistic rendering ===
A large proportion of computer graphics research has worked towards producing images that resemble photographs. Fundamental techniques that make this possible were invented in the 1980s, but at the end of the decade, photorealism for complex scenes was still considered a distant goal.: x Today, photorealism is routinely achievable for offline rendering, but remains difficult for real-time rendering.: 1–2
In order to produce realistic images, rendering must simulate how light travels from light sources, is reflected, refracted, and scattered (often many times) by objects in the scene, passes through a camera lens, and finally reaches the film or sensor of the camera. The physics used in these simulations is primarily geometrical optics, in which particles of light follow (usually straight) lines called rays, but in some situations (such as when rendering thin films, like the surface of soap bubbles) the wave nature of light must be taken into account.
Effects that may need to be simulated include:
Shadows, including both shadows with sharp edges and soft shadows with umbra and penumbra
Reflections in mirrors and smooth surfaces, as well as rough or rippled reflective surfaces
Refraction – the bending of light when it crosses a boundary between two transparent materials such as air and glass. The amount of bending varies with the wavelength of the light, which may cause colored fringes or "rainbows" to appear.
Volumetric effects – Absorption and scattering when light travels through partially transparent or translucent substances (called participating media because they modify the light rather than simply allow rays to pass through): 140
Caustics – bright patches, sometimes with distinct filaments and a folded or twisted appearance, resulting when light is reflected or refracted before illuminating an object.: 109
In realistic scenes, objects are illuminated both by light that arrives directly from a light source (after passing mostly unimpeded through air), and light that has bounced off other objects in the scene. The simulation of this complex lighting is called global illumination. In the past, indirect lighting was often faked (especially when rendering animated films) by placing additional hidden lights in the scene, but today path tracing is used to render it accurately.: 3 : 108
For true photorealism, the camera used to take the photograph must be simulated. The thin lens approximation allows combining perspective projection with depth of field (and bokeh) emulation. Camera lens simulations can be made more realistic by modeling the way light is refracted by the components of the lens. Motion blur is often simulated if film or video frames are being rendered. Simulated lens flare and bloom are sometimes added to make the image appear subjectively brighter (although the design of real cameras tries to reduce these effects).: 12.4
Realistic rendering uses mathematical descriptions of how different surface materials reflect light, called reflectance models or (when physically plausible) bidirectional reflectance distribution functions (BRDFs). Rendering materials such as marble, plant leaves, and human skin requires simulating an effect called subsurface scattering, in which a portion of the light travels into the material, is scattered, and then travels back out again.: 143 The way color, and properties such as roughness, vary over a surface can be represented efficiently using texture mapping.: 6.1
=== Other styles of 3D rendering ===
For some applications (including early stages of 3D modeling), simplified rendering styles such as wireframe rendering may be appropriate, particularly when the material and surface details have not been defined and only the shape of an object is known.: 5.3 Games and other real-time applications may use simpler and less realistic rendering techniques as an artistic or design choice, or to allow higher frame rates on lower-end hardware.
Orthographic and isometric projections can be used for a stylized effect or to ensure that parallel lines are depicted as parallel in CAD rendering.: 4.7 : 3.7
Non-photorealistic rendering (NPR) uses techniques like edge detection and posterization to produce 3D images that resemble technical illustrations, cartoons, or other styles of drawing or painting.: ch 15
== Inputs ==
Before a 3D scene or 2D image can be rendered, it must be described in a way that the rendering software can understand. Historically, inputs for both 2D and 3D rendering were usually text files, which are easier than binary files for humans to edit and debug. For 3D graphics, text formats have largely been supplanted by more efficient binary formats, and by APIs which allow interactive applications to communicate directly with a rendering component without generating a file on disk (although a scene description is usually still created in memory prior to rendering).: 1.2, 3.2.6, 3.3.1, 3.3.7
Traditional rendering algorithms use geometric descriptions of 3D scenes or 2D images. Applications and algorithms that render visualizations of data scanned from the real world, or scientific simulations, may require different types of input data.
The PostScript format (which is often credited with the rise of desktop publishing) provides a standardized, interoperable way to describe 2D graphics and page layout. The Scalable Vector Graphics (SVG) format is also text-based, and the PDF format uses the PostScript language internally. In contrast, although many 3D graphics file formats have been standardized (including text-based formats such as VRML and X3D), different rendering applications typically use formats tailored to their needs, and this has led to a proliferation of proprietary and open formats, with binary files being more common.: 3.2.3, 3.2.5, 3.3.7 : vii : 16.5.2.
=== 2D vector graphics ===
A vector graphics image description may include:
Coordinates and curvature information for line segments, arcs, and Bézier curves (which may be used as boundaries of filled shapes)
Center coordinates, width, and height (or bounding rectangle coordinates) of basic shapes such as rectangles, circles and ellipses
Color, width and pattern (such as dashed or dotted) for rendering lines
Colors, patterns, and gradients for filling shapes
Bitmap image data (either embedded or in an external file) along with scale and position information
Text to be rendered (along with size, position, orientation, color, and font)
Clipping information, if only part of a shape or bitmap image should be rendered
Transparency and compositing information for rendering overlapping shapes
Color space information, allowing the image to be rendered consistently on different displays and printers
=== 3D geometry ===
A geometric scene description may include:: Ch. 4-7, 8.7
Size, position, and orientation of geometric primitives such as spheres and cones (which may be combined in various ways to create more complex objects)
Vertex coordinates and surface normal vectors for meshes of triangles or polygons (often rendered as smooth surfaces by subdividing the mesh)
Transformations for positioning, rotating, and scaling objects within a scene (allowing parts of the scene to use different local coordinate systems).
"Camera" information describing how the scene is being viewed (position, direction, focal length, and field of view)
Light information (location, type, brightness, and color)
Optical properties of surfaces, such as albedo, roughness, and refractive index,
Optical properties of media through which light passes (transparent solids, liquids, clouds, smoke), e.g. absorption and scattering cross sections
Bitmap image data used as texture maps for surfaces
Small scripts or programs for generating complex 3D shapes or scenes procedurally
Description of how object and camera locations and other information change over time, for rendering an animation
Many file formats exist for storing individual 3D objects or "models". These can be imported into a larger scene, or loaded on-demand by rendering software or games. A realistic scene may require hundreds of items like household objects, vehicles, and trees, and 3D artists often utilize large libraries of models. In game production, these models (along with other data such as textures, audio files, and animations) are referred to as "assets".: Ch. 4
=== Volumetric data ===
Scientific and engineering visualization often requires rendering volumetric data generated by 3D scans or simulations. Perhaps the most common source of such data is medical CT and MRI scans, which need to be rendered for diagnosis. Volumetric data can be extremely large, and requires specialized data formats to store it efficiently, particularly if the volume is sparse (with empty regions that do not contain data).: 14.3.1
Before rendering, level sets for volumetric data can be extracted and converted into a mesh of triangles, e.g. by using the marching cubes algorithm. Algorithms have also been developed that work directly with volumetric data, for example to render realistic depictions of the way light is scattered and absorbed by clouds and smoke, and this type of volumetric rendering is used extensively in visual effects for movies. When rendering lower-resolution volumetric data without interpolation, the individual cubes or "voxels" may be visible, an effect sometimes used deliberately for game graphics.: 4.6 : 13.10, Ch. 14, 16.1
=== Photogrammetry and scanning ===
Photographs of real world objects can be incorporated into a rendered scene by using them as textures for 3D objects. Photos of a scene can also be stitched together to create panoramic images or environment maps, which allow the scene to be rendered very efficiently but only from a single viewpoint. Scanning of real objects and scenes using structured light or lidar produces point clouds consisting of the coordinates of millions of individual points in space, sometimes along with color information. These point clouds may either be rendered directly or converted into meshes before rendering. (Note: "point cloud" sometimes also refers to a minimalist rendering style that can be used for any 3D geometry, similar to wireframe rendering.): 13.3, 13.9 : 1.3
=== Neural approximations and light fields ===
A more recent, experimental approach is description of scenes using radiance fields which define the color, intensity, and direction of incoming light at each point in space. (This is conceptually similar to, but not identical to, the light field recorded by a hologram.) For any useful resolution, the amount of data in a radiance field is so large that it is impractical to represent it directly as volumetric data, and an approximation function must be found. Neural networks are typically used to generate and evaluate these approximations, sometimes using video frames, or a collection of photographs of a scene taken at different angles, as "training data".
Algorithms related to neural networks have recently been used to find approximations of a scene as 3D Gaussians. The resulting representation is similar to a point cloud, except that it uses fuzzy, partially-transparent blobs of varying dimensions and orientations instead of points. As with neural radiance fields, these approximations are often generated from photographs or video frames.
== Outputs ==
The output of rendering may be displayed immediately on the screen (many times a second, in the case of real-time rendering such as games) or saved in a raster graphics file format such as JPEG or PNG. High-end rendering applications commonly use the OpenEXR file format, which can represent finer gradations of colors and high dynamic range lighting, allowing tone mapping or other adjustments to be applied afterwards without loss of quality.: Ch. 14, Ap. B
Quickly rendered animations can be saved directly as video files, but for high-quality rendering, individual frames (which may be rendered by different computers in a cluster or render farm and may take hours or even days to render) are output as separate files and combined later into a video clip.: 1.5, 3.11, 8.11
The output of a renderer sometimes includes more than just RGB color values. For example, the spectrum can be sampled using multiple wavelengths of light, or additional information such as depth (distance from camera) or the material of each point in the image can be included (this data can be used during compositing or when generating texture maps for real-time rendering, or used to assist in removing noise from a path-traced image). Transparency information can be included, allowing rendered foreground objects to be composited with photographs or video. It is also sometimes useful to store the contributions of different lights, or of specular and diffuse lighting, as separate channels, so lighting can be adjusted after rendering. The OpenEXR format allows storing many channels of data in a single file. Renderers such as Blender and Pixar RenderMan support a large variety of configurable values called Arbitrary Output Variables (AOVs).: Ch. 14, Ap. B
== Techniques ==
Choosing how to render a 3D scene usually involves trade-offs between speed, memory usage, and realism (although realism is not always desired). The algorithms developed over the years follow a loose progression, with more advanced methods becoming practical as computing power and memory capacity increased. Multiple techniques may be used for a single final image.
An important distinction is between image order algorithms, which iterate over pixels in the image, and object order algorithms, which iterate over objects in the scene. For simple scenes, object order is usually more efficient, as there are fewer objects than pixels.: Ch. 4
2D vector graphics
The vector displays of the 1960s-1970s used deflection of an electron beam to draw line segments directly on the screen. Nowadays, vector graphics are rendered by rasterization algorithms that also support filled shapes. In principle, any 2D vector graphics renderer can be used to render 3D objects by first projecting them onto a 2D image plane. : 93, 431, 505, 553
3D rasterization
Adapts 2D rasterization algorithms so they can be used more efficiently for 3D rendering, handling hidden surface removal via scanline or z-buffer techniques. Different realistic or stylized effects can be obtained by coloring the pixels covered by the objects in different ways. Surfaces are typically divided into meshes of triangles before being rasterized. Rasterization is usually synonymous with "object order" rendering (as described above).: 560-561, 575-590 : 8.5 : Ch. 9
Ray casting
Uses geometric formulas to compute the first object that a ray intersects.: 8 It can be used to implement "image order" rendering by casting a ray for each pixel, and finding a corresponding point in the scene. Ray casting is a fundamental operation used for both graphical and non-graphical purposes,: 6 e.g. determining whether a point is in shadow, or checking what an enemy can see in a game.
Ray tracing
Simulates the bouncing paths of light caused by specular reflection and refraction, requiring a varying number of ray casting operations for each path. Advanced forms use Monte Carlo techniques to render effects such as area lights, depth of field, blurry reflections, and soft shadows, but computing global illumination is usually in the domain of path tracing.: 9-13
Radiosity
A finite element analysis approach that breaks surfaces in the scene into pieces, and estimates the amount of light that each piece receives from light sources, or indirectly from other surfaces. Once the irradiance of each surface is known, the scene can be rendered using rasterization or ray tracing.: 888-890, 1044-1045
Path tracing
Uses Monte Carlo integration with a simplified form of ray tracing, computing the average brightness of a sample of the possible paths that a photon could take when traveling from a light source to the camera (for some images, thousands of paths need to be sampled per pixel: 8 ). It was introduced as a statistically unbiased way to solve the rendering equation, giving ray tracing a rigorous mathematical foundation.: 11-13
Each of the above approaches has many variations, and there is some overlap. Path tracing may be considered either a distinct technique or a particular type of ray tracing.: 846, 1021 Note that the usage of terminology related to ray tracing and path tracing has changed significantly over time.: 7
Ray marching is a family of algorithms, used by ray casting, for finding intersections between a ray and a complex object, such as a volumetric dataset or a surface defined by a signed distance function. It is not, by itself, a rendering method, but it can be incorporated into ray tracing and path tracing, and is used by rasterization to implement screen-space reflection and other effects.: 13
A technique called photon mapping traces paths of photons from a light source to an object, accumulating data about irradiance which is then used during conventional ray tracing or path tracing.: 1037-1039 Rendering a scene using only rays traced from the light source to the camera is impractical, even though it corresponds more closely to reality, because a huge number of photons would need to be simulated, only a tiny fraction of which actually hit the camera.: 7-9 : 587
Some authors call conventional ray tracing "backward" ray tracing because it traces the paths of photons backwards from the camera to the light source, and call following paths from the light source (as in photon mapping) "forward" ray tracing.: 7-9 However, sometimes the meaning of these terms is reversed. Tracing rays starting at the light source can also be called particle tracing or light tracing, which avoids this ambiguity.: 92 : 4.5.4
Real-time rendering, including video game graphics, typically uses rasterization, but increasingly combines it with ray tracing and path tracing.: 2 To enable realistic global illumination, real-time rendering often relies on pre-rendered ("baked") lighting for stationary objects. For moving objects, it may use a technique called light probes, in which lighting is recorded by rendering omnidirectional views of the scene at chosen points in space (often points on a grid to allow easier interpolation). These are similar to environment maps, but typically use a very low resolution or an approximation such as spherical harmonics. (Note: Blender uses the term 'light probes' for a more general class of pre-recorded lighting data, including reflection maps.)
=== Rasterization ===
The term rasterization (in a broad sense) encompasses many techniques used for 2D rendering and real-time 3D rendering. 3D animated films were rendered by rasterization before ray tracing and path tracing became practical.
A renderer combines rasterization with geometry processing (which is not specific to rasterization) and pixel processing which computes the RGB color values to be placed in the framebuffer for display.: 2.1 : 9
The main tasks of rasterization (including pixel processing) are:: 2, 3.8, 23.1.1
Determining which pixels are covered by each geometric shape in the 3D scene or 2D image (this is the actual rasterization step, in the strictest sense)
Blending between colors and depths defined at the vertices of shapes, e.g. using barycentric coordinates (interpolation)
Determining if parts of shapes are hidden by other shapes, due to 2D layering or 3D depth (hidden surface removal)
Evaluating a function for each pixel covered by a shape (shading)
Smoothing edges of shapes so pixels are less visible (anti-aliasing)
Blending overlapping transparent shapes (compositing)
3D rasterization is typically part of a graphics pipeline in which an application provides lists of triangles to be rendered, and the rendering system transforms and projects their coordinates, determines which triangles are potentially visible in the viewport, and performs the above rasterization and pixel processing tasks before displaying the final result on the screen.: 2.1 : 9
Historically, 3D rasterization used algorithms like the Warnock algorithm and scanline rendering (also called "scan-conversion"), which can handle arbitrary polygons and can rasterize many shapes simultaneously. Although such algorithms are still important for 2D rendering, 3D rendering now usually divides shapes into triangles and rasterizes them individually using simpler methods.: 456, 561–569
High-performance algorithms exist for rasterizing 2D lines, including anti-aliased lines, as well as ellipses and filled triangles. An important special case of 2D rasterization is text rendering, which requires careful anti-aliasing and rounding of coordinates to avoid distorting the letterforms and preserve spacing, density, and sharpness.: 9.1.1
After 3D coordinates have been projected onto the image plane, rasterization is primarily a 2D problem, but the 3rd dimension necessitates hidden surface removal. Early computer graphics used geometric algorithms or ray casting to remove the hidden portions of shapes, or used the painter's algorithm, which sorts shapes by depth (distance from camera) and renders them from back to front. Depth sorting was later avoided by incorporating depth comparison into the scanline rendering algorithm. The z-buffer algorithm performs the comparisons indirectly by including a depth or "z" value in the framebuffer. A pixel is only covered by a shape if that shape's z value is lower (indicating closer to the camera) than the z value currently in the buffer. The z-buffer requires additional memory (an expensive resource at the time it was invented) but simplifies the rasterization code and permits multiple passes. Memory is now faster and more plentiful, and a z-buffer is almost always used for real-time rendering.: 553–570 : 2.5.2
A drawback of the basic z-buffer algorithm is that each pixel ends up either entirely covered by a single object or filled with the background color, causing jagged edges in the final image. Early anti-aliasing approaches addressed this by detecting when a pixel is partially covered by a shape, and calculating the covered area. The A-buffer (and other supersampling and multi-sampling techniques) solve the problem less precisely but with higher performance. For real-time 3D graphics, it has become common to use complicated heuristics (and even neural-networks) to perform anti-aliasing.: 9.3 : 5.4.2
In 3D rasterization, color is usually determined by a pixel shader or fragment shader, a small program that is run for each pixel. The shader does not (or cannot) directly access 3D data for the entire scene (this would be very slow, and would result in an algorithm similar to ray tracing) and a variety of techniques have been developed to render effects like shadows and reflections using only texture mapping and multiple passes.: 17.8
Older and more basic 3D rasterization implementations did not support shaders, and used simple shading techniques such as flat shading (lighting is computed once for each triangle, which is then rendered entirely in one color), Gouraud shading (lighting is computed using normal vectors defined at vertices and then colors are interpolated across each triangle), or Phong shading (normal vectors are interpolated across each triangle and lighting is computed for each pixel).: 9.2
Until relatively recently, Pixar used rasterization for rendering its animated films. Unlike the renderers commonly used for real-time graphics, the Reyes rendering system in Pixar's RenderMan software was optimized for rendering very small (pixel-sized) polygons, and incorporated stochastic sampling techniques more typically associated with ray tracing.: 2, 6.3
=== Ray casting ===
One of the simplest ways to render a 3D scene is to test if a ray starting at the viewpoint (the "eye" or "camera") intersects any of the geometric shapes in the scene, repeating this test using a different ray direction for each pixel. This method, called ray casting, was important in early computer graphics, and is a fundamental building block for more advanced algorithms. Ray casting can be used to render shapes defined by constructive solid geometry (CSG) operations.: 8-9 : 246–249
Early ray casting experiments include the work of Arthur Appel in the 1960s. Appel rendered shadows by casting an additional ray from each visible surface point towards a light source. He also tried rendering the density of illumination by casting random rays from the light source towards the object and plotting the intersection points (similar to the later technique called photon mapping).
When rendering scenes containing many objects, testing the intersection of a ray with every object becomes very expensive. Special data structures are used to speed up this process by allowing large numbers of objects to be excluded quickly (such as objects behind the camera). These structures are analogous to database indexes for finding the relevant objects. The most common are the bounding volume hierarchy (BVH), which stores a pre-computed bounding box or sphere for each branch of a tree of objects, and the k-d tree which recursively divides space into two parts. Recent GPUs include hardware acceleration for BVH intersection tests. K-d trees are a special case of binary space partitioning, which was frequently used in early computer graphics (it can also generate a rasterization order for the painter's algorithm). Octrees, another historically popular technique, are still often used for volumetric data.: 16–17 : 36.2
Geometric formulas are sufficient for finding the intersection of a ray with shapes like spheres, polygons, and polyhedra, but for most curved surfaces there is no analytic solution, or the intersection is difficult to compute accurately using limited precision floating point numbers. Root-finding algorithms such as Newton's method can sometimes be used. To avoid these complications, curved surfaces are often approximated as meshes of triangles. Volume rendering (e.g. rendering clouds and smoke), and some surfaces such as fractals, may require ray marching instead of basic ray casting.: 13 : 14, 17.3
=== Ray tracing ===
Ray casting can be used to render an image by tracing light rays backwards from a simulated camera. After finding a point on a surface where a ray originated, another ray is traced towards the light source to determine if anything is casting a shadow on that point. If not, a reflectance model (such as Lambertian reflectance for matte surfaces, or the Phong reflection model for glossy surfaces) is used to compute the probability that a photon arriving from the light would be reflected towards the camera, and this is multiplied by the brightness of the light to determine the pixel brightness. If there are multiple light sources, brightness contributions of the lights are added together. For color images, calculations are repeated for multiple wavelengths of light (e.g. red, green, and blue).: 11.2.2 : 8
Classical ray tracing (also called Whitted-style or recursive ray tracing) extends this method so it can render mirrors and transparent objects. If a ray traced backwards from the camera originates at a point on a mirror, the reflection formula from geometric optics is used to calculate the direction the reflected ray came from, and another ray is cast backwards in that direction. If a ray originates at a transparent surface, rays are cast backwards for both reflected and refracted rays (using Snell's law to compute the refracted direction), and so ray tracing needs to support a branching "tree" of rays. In simple implementations, a recursive function is called to trace each ray.: 11.2.2 : 9
Ray tracing usually performs anti-aliasing by taking the average of multiple samples for each pixel. It may also use multiple samples for effects like depth of field and motion blur. If evenly-spaced ray directions or times are used for each of these features, many rays are required, and some aliasing will remain. Cook-style, stochastic, or Monte Carlo ray tracing avoids this problem by using random sampling instead of evenly-spaced samples. This type of ray tracing is commonly called distributed ray tracing, or distribution ray tracing because it samples rays from probability distributions. Distribution ray tracing can also render realistic "soft" shadows from large lights by using a random sample of points on the light when testing for shadowing, and it can simulate chromatic aberration by sampling multiple wavelengths from the spectrum of light.: 10 : 25
Real surface materials reflect small amounts of light in almost every direction because they have small (or microscopic) bumps and grooves. A distribution ray tracer can simulate this by sampling possible ray directions, which allows rendering blurry reflections from glossy and metallic surfaces. However, if this procedure is repeated recursively to simulate realistic indirect lighting, and if more than one sample is taken at each surface point, the tree of rays quickly becomes huge. Another kind of ray tracing, called path tracing, handles indirect light more efficiently, avoiding branching, and ensures that the distribution of all possible paths from a light source to the camera is sampled in an unbiased way.: 25–27
Ray tracing was often used for rendering reflections in animated films, until path tracing became standard for film rendering. Films such as Shrek 2 and Monsters University also used distribution ray tracing or path tracing to precompute indirect illumination for a scene or frame prior to rendering it using rasterization.: 118–121
Advances in GPU technology have made real-time ray tracing possible in games, although it is currently almost always used in combination with rasterization.: 2 This enables visual effects that are difficult with only rasterization, including reflection from curved surfaces and interreflective objects,: 305 and shadows that are accurate over a wide range of distances and surface orientations.: 159-160 Ray tracing support is included in recent versions of the graphics APIs used by games, such as DirectX, Metal, and Vulkan.
Ray tracing has been used to render simulated black holes, and the appearance of objects moving at close to the speed of light, by taking spacetime curvature and relativistic effects into account during light ray simulation.
=== Radiosity ===
Radiosity (named after the radiometric quantity of the same name) is a method for rendering objects illuminated by light bouncing off rough or matte surfaces. This type of illumination is called indirect light, environment lighting, diffuse lighting, or diffuse interreflection, and the problem of rendering it realistically is called global illumination. Rasterization and basic forms of ray tracing (other than distribution ray tracing and path tracing) can only roughly approximate indirect light, e.g. by adding a uniform "ambient" lighting amount chosen by the artist. Radiosity techniques are also suited to rendering scenes with area lights such as rectangular fluorescent lighting panels, which are difficult for rasterization and traditional ray tracing. Radiosity is considered a physically-based method, meaning that it aims to simulate the flow of light in an environment using equations and experimental data from physics, however it often assumes that all surfaces are opaque and perfectly Lambertian, which reduces realism and limits its applicability.: 10, 11.2.1 : 888, 893 : 6
In the original radiosity method (first proposed in 1984) now called classical radiosity, surfaces and lights in the scene are split into pieces called patches, a process called meshing (this step makes it a finite element method). The rendering code must then determine what fraction of the light being emitted or diffusely reflected (scattered) by each patch is received by each other patch. These fractions are called form factors or view factors (first used in engineering to model radiative heat transfer). The form factors are multiplied by the albedo of the receiving surface and put in a matrix. The lighting in the scene can then be expressed as a matrix equation (or equivalently a system of linear equations) that can be solved by methods from linear algebra.: 46 : 888, 896
Solving the radiosity equation gives the total amount of light emitted and reflected by each patch, which is divided by area to get a value called radiosity that can be used when rasterizing or ray tracing to determine the color of pixels corresponding to visible parts of the patch. For real-time rendering, this value (or more commonly the irradiance, which does not depend on local surface albedo) can be pre-computed and stored in a texture (called an irradiance map) or stored as vertex data for 3D models. This feature was used in architectural visualization software to allow real-time walk-throughs of a building interior after computing the lighting.: 890 : 11.5.1 : 332
The large size of the matrices used in classical radiosity (the square of the number of patches) causes problems for realistic scenes. Practical implementations may use Jacobi or Gauss-Seidel iterations, which is equivalent (at least in the Jacobi case) to simulating the propagation of light one bounce at a time until the amount of light remaining (not yet absorbed by surfaces) is insignificant. The number of iterations (bounces) required is dependent on the scene, not the number of patches, so the total work is proportional to the square of the number of patches (in contrast, solving the matrix equation using Gaussian elimination requires work proportional to the cube of the number of patches). Form factors may be recomputed when they are needed, to avoid storing a complete matrix in memory.: 901, 907
The quality of rendering is often determined by the size of the patches, e.g. very fine meshes are needed to depict the edges of shadows accurately. An important improvement is hierarchical radiosity, which uses a coarser mesh (larger patches) for simulating the transfer of light between surfaces that are far away from one another, and adaptively sub-divides the patches as needed. This allows radiosity to be used for much larger and more complex scenes.: 975, 939
Alternative and extended versions of the radiosity method support non-Lambertian surfaces, such as glossy surfaces and mirrors, and sometimes use volumes or "clusters" of objects as well as surface patches. Stochastic or Monte Carlo radiosity uses random sampling in various ways, e.g. taking samples of incident light instead of integrating over all patches, which can improve performance but adds noise (this noise can be reduced by using deterministic iterations as a final step, unlike path tracing noise). Simplified and partially precomputed versions of radiosity are widely used for real-time rendering, combined with techniques such as octree radiosity that store approximations of the light field.: 979, 982 : 49 : 11.5
=== Path tracing ===
As part of the approach known as physically based rendering, path tracing has become the dominant technique for rendering realistic scenes, including effects for movies. For example, the popular open source 3D software Blender uses path tracing in its Cycles renderer. Images produced using path tracing for global illumination are generally noisier than when using radiosity (the main competing algorithm for realistic lighting), but radiosity can be difficult to apply to complex scenes and is prone to artifacts that arise from using a tessellated representation of irradiance.: 975-976, 1045
Like distributed ray tracing, path tracing is a kind of stochastic or randomized ray tracing that uses Monte Carlo or Quasi-Monte Carlo integration. It was proposed and named in 1986 by Jim Kajiya in the same paper as the rendering equation. Kajiya observed that much of the complexity of distributed ray tracing could be avoided by only tracing a single path from the camera at a time (in Kajiya's implementation, this "no branching" rule was broken by tracing additional rays from each surface intersection point to randomly chosen points on each light source). Kajiya suggested reducing the noise present in the output images by using stratified sampling and importance sampling for making random decisions such as choosing which ray to follow at each step of a path. Even with these techniques, path tracing would not have been practical for film rendering, using computers available at the time, because the computational cost of generating enough samples to reduce variance to an acceptable level was too high. Monster House, the first feature film rendered entirely using path tracing, was not released until 20 years later.
In its basic form, path tracing is inefficient (requiring too many samples) for rendering caustics and scenes where light enters indirectly through narrow spaces. Attempts were made to address these weaknesses in the 1990s. Bidirectional path tracing has similarities to photon mapping, tracing rays from the light source and the camera separately, and then finding ways to connect these paths (but unlike photon mapping it usually samples new light paths for each pixel rather than using the same cached data for all pixels). Metropolis light transport samples paths by modifying paths that were previously traced, spending more time exploring paths that are similar to other "bright" paths, which increases the chance of discovering even brighter paths. Multiple importance sampling provides a way to reduce variance when combining samples from more than one sampling method, particularly when some samples are much noisier than the others.
This later work was summarized and expanded upon in Eric Veach's 1997 PhD thesis, which helped raise interest in path tracing in the computer graphics community. The Arnold renderer, first released in 1998, proved that path tracing was practical for rendering frames for films, and that there was a demand for unbiased and physically based rendering in the film industry; other commercial and open source path tracing renderers began appearing. Computational cost was addressed by rapid advances in CPU and cluster performance.
Path tracing's relative simplicity and its nature as a Monte Carlo method (sampling hundreds or thousands of paths per pixel) have made it attractive to implement on a GPU, especially on recent GPUs that support ray tracing acceleration technology such as Nvidia's RTX and OptiX. However bidirectional path tracing and Metropolis light transport are more difficult to implement efficiently on a GPU.
Research into improving path tracing continues. Many variations of bidirectional path tracing and Metropolis light transport have been explored, and ways of combining path tracing with photon mapping. Recent path guiding approaches construct approximations of the light field probability distribution in each volume of space, so paths can be sampled more effectively. Techniques have been developed to denoise the output of path tracing, reducing the number of paths required to achieve acceptable quality, at the risk of losing some detail or introducing small-scale artifacts that are more objectionable than noise; neural networks are now widely used for this purpose.
=== Neural rendering ===
Neural rendering is a rendering method using artificial neural networks. Neural rendering includes image-based rendering methods that are used to reconstruct 3D models from 2-dimensional images. One of these methods are photogrammetry, which is a method in which a collection of images from multiple angles of an object are turned into a 3D model. There have also been recent developments in generating and rendering 3D models from text and coarse paintings by notably Nvidia, Google and various other companies.
== Scientific and mathematical basis ==
The implementation of a realistic renderer always has some basic element of physical simulation or emulation – some computation which resembles or abstracts a real physical process.
The term "physically based" indicates the use of physical models and approximations that are more general and widely accepted outside rendering. A particular set of related techniques have gradually become established in the rendering community.
The basic concepts are moderately straightforward, but intractable to calculate; and a single elegant algorithm or approach has been elusive for more general purpose renderers. In order to meet demands of robustness, accuracy and practicality, an implementation will be a complex combination of different techniques.
Rendering research is concerned with both the adaptation of scientific models and their efficient application.
Mathematics used in rendering includes: linear algebra, calculus, numerical mathematics, signal processing, and Monte Carlo methods.
=== The rendering equation ===
This is the key academic/theoretical concept in rendering. It serves as the most abstract formal expression of the non-perceptual aspect of rendering. All more complete algorithms can be seen as solutions to particular formulations of this equation.
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{\displaystyle L_{o}(x,\omega )=L_{e}(x,\omega )+\int _{\Omega }L_{i}(x,\omega ')f_{r}(x,\omega ',\omega )(\omega '\cdot n)\,\mathrm {d} \omega '}
Meaning: at a particular position and direction, the outgoing light (Lo) is the sum of the emitted light (Le) and the reflected light. The reflected light being the sum of the incoming light (Li) from all directions, multiplied by the surface reflection and incoming angle. By connecting outward light to inward light, via an interaction point, this equation stands for the whole 'light transport' – all the movement of light – in a scene.
=== The bidirectional reflectance distribution function ===
The bidirectional reflectance distribution function (BRDF) expresses a simple model of light interaction with a surface as follows:
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{\displaystyle f_{r}(x,\omega ',\omega )={\frac {\mathrm {d} L_{r}(x,\omega )}{L_{i}(x,\omega ')(\omega '\cdot {\vec {n}})\mathrm {d} \omega '}}}
Light interaction is often approximated by the even simpler models: diffuse reflection and specular reflection, although both can ALSO be BRDFs.
=== Geometric optics ===
Rendering is practically exclusively concerned with the particle aspect of light physics – known as geometrical optics. Treating light, at its basic level, as particles bouncing around is a simplification, but appropriate: the wave aspects of light are negligible in most scenes, and are significantly more difficult to simulate. Notable wave aspect phenomena include diffraction (as seen in the colours of CDs and DVDs) and polarisation (as seen in LCDs). Both types of effect, if needed, are made by appearance-oriented adjustment of the reflection model.
=== Visual perception ===
Though it receives less attention, an understanding of human visual perception is valuable to rendering. This is mainly because image displays and human perception have restricted ranges. A renderer can simulate a wide range of light brightness and color, but current displays – movie screen, computer monitor, etc. – cannot handle so much, and something must be discarded or compressed. Human perception also has limits, and so does not need to be given large-range images to create realism. This can help solve the problem of fitting images into displays, and, furthermore, suggest what short-cuts could be used in the rendering simulation, since certain subtleties will not be noticeable. This related subject is tone mapping.
=== Sampling and filtering ===
One problem that any rendering system must deal with, no matter which approach it takes, is the sampling problem. Essentially, the rendering process tries to depict a continuous function from image space to colors by using a finite number of pixels. As a consequence of the Nyquist–Shannon sampling theorem (or Kotelnikov theorem), any spatial waveform that can be displayed must consist of at least two pixels, which is proportional to image resolution. In simpler terms, this expresses the idea that an image cannot display details, peaks or troughs in color or intensity, that are smaller than one pixel.
If a naive rendering algorithm is used without any filtering, high frequencies in the image function will cause ugly aliasing to be present in the final image. Aliasing typically manifests itself as jaggies, or jagged edges on objects where the pixel grid is visible. In order to remove aliasing, all rendering algorithms (if they are to produce good-looking images) must use some kind of low-pass filter on the image function to remove high frequencies, a process called antialiasing.
== Hardware ==
Rendering is usually limited by available computing power and memory bandwidth, and so specialized hardware has been developed to speed it up ("accelerate" it), particularly for real-time rendering. Hardware features such as a framebuffer for raster graphics are required to display the output of rendering smoothly in real time.
=== History ===
In the era of vector monitors (also called calligraphic displays), a display processing unit (DPU) was a dedicated CPU or coprocessor that maintained a list of visual elements and redrew them continuously on the screen by controlling an electron beam. Advanced DPUs such as Evans & Sutherland's Line Drawing System-1 (and later models produced into the 1980s) incorporated 3D coordinate transformation features to accelerate rendering of wire-frame images.: 93–94, 404–421 Evans & Sutherland also made the Digistar planetarium projection system, which was a vector display that could render both stars and wire-frame graphics (the vector-based Digistar and Digistar II were used in many planetariums, and a few may still be in operation). A Digistar prototype was used for rendering 3D star fields for the film Star Trek II: The Wrath of Khan – some of the first 3D computer graphics sequences ever seen in a feature film.
Shaded 3D graphics rendering in the 1970s and early 1980s was usually implemented on general-purpose computers, such as the PDP-10 used by researchers at the University of Utah. It was difficult to speed up using specialized hardware because it involves a pipeline of complex steps, requiring data addressing, decision-making, and computation capabilities typically only provided by CPUs (although dedicated circuits for speeding up particular operations were proposed ). Supercomputers or specially designed multi-CPU computers or clusters were sometimes used for ray tracing. In 1981, James H. Clark and Marc Hannah designed the Geometry Engine, a VLSI chip for performing some of the steps of the 3D rasterization pipeline, and started the company Silicon Graphics (SGI) to commercialize this technology.
Home computers and game consoles in the 1980s contained graphics coprocessors that were capable of scrolling and filling areas of the display, and drawing sprites and lines, though they were not useful for rendering realistic images. Towards the end of the 1980s PC graphics cards and arcade games with 3D rendering acceleration began to appear, and in the 1990s such technology became commonplace. Today, even low-power mobile processors typically incorporate 3D graphics acceleration features.
=== GPUs ===
The 3D graphics accelerators of the 1990s evolved into modern GPUs. GPUs are general-purpose processors, like CPUs, but they are designed for tasks that can be broken into many small, similar, mostly independent sub-tasks (such as rendering individual pixels) and performed in parallel. This means that a GPU can speed up any rendering algorithm that can be split into subtasks in this way, in contrast to 1990s 3D accelerators which were only designed to speed up specific rasterization algorithms and simple shading and lighting effects (although tricks could be used to perform more general computations).: ch3
Due to their origins, GPUs typically still provide specialized hardware acceleration for some steps of a traditional 3D rasterization pipeline, including hidden surface removal using a z-buffer, and texture mapping with mipmaps, but these features are no longer always used.: ch3 Recent GPUs have features to accelerate finding the intersections of rays with a bounding volume hierarchy, to help speed up all variants of ray tracing and path tracing, as well as neural network acceleration features sometimes useful for rendering.
GPUs are usually integrated with high-bandwidth memory systems to support the read and write bandwidth requirements of high-resolution, real-time rendering, particularly when multiple passes are required to render a frame, however memory latency may be higher than on a CPU, which can be a problem if the critical path in an algorithm involves many memory accesses. GPU design accepts high latency as inevitable (in part because a large number of threads are sharing the memory bus) and attempts to "hide" it by efficiently switching between threads, so a different thread can be performing computations while the first thread is waiting for a read or write to complete.: ch3
Rendering algorithms will run efficiently on a GPU only if they can be implemented using small groups of threads that perform mostly the same operations. As an example of code that meets this requirement: when rendering a small square of pixels in a simple ray-traced image, all threads will likely be intersecting rays with the same object and performing the same lighting computations. For performance and architectural reasons, GPUs run groups of around 16-64 threads called warps or wavefronts in lock-step (all threads in the group are executing the same instructions at the same time). If not all threads in the group need to run particular blocks of code (due to conditions) then some threads will be idle, or the results of their computations will be discarded, causing degraded performance.: ch3
== Chronology of algorithms and techniques ==
The following is a rough timeline of frequently mentioned rendering techniques, including areas of current research. Note that even in cases where an idea was named in a specific paper, there were almost always multiple researchers or teams working in the same area (including earlier related work). When a method is first proposed it is often very inefficient, and it takes additional research and practical efforts to turn it into a useful technique.: 887
The list focuses on academic research and does not include hardware. (For more history see #External links, as well as Computer graphics#History and Golden age of arcade video games#Technology.)
== See also ==
== References ==
== Further reading ==
== External links ==
SIGGRAPH – the ACMs special interest group in graphics – the largest academic and professional association and conference
vintage3d.org "The way to home 3d" – Extensive history of computer graphics hardware, including research, commercialization, and video games and consoles | Wikipedia/Rendering_algorithm |
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalise to unseen data, and thus perform tasks without explicit instructions. Within a subdiscipline in machine learning, advances in the field of deep learning have allowed neural networks, a class of statistical algorithms, to surpass many previous machine learning approaches in performance.
ML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine. The application of ML to business problems is known as predictive analytics.
Statistics and mathematical optimisation (mathematical programming) methods comprise the foundations of machine learning. Data mining is a related field of study, focusing on exploratory data analysis (EDA) via unsupervised learning.
From a theoretical viewpoint, probably approximately correct learning provides a framework for describing machine learning.
== History ==
The term machine learning was coined in 1959 by Arthur Samuel, an IBM employee and pioneer in the field of computer gaming and artificial intelligence. The synonym self-teaching computers was also used in this time period.
Although the earliest machine learning model was introduced in the 1950s when Arthur Samuel invented a program that calculated the winning chance in checkers for each side, the history of machine learning roots back to decades of human desire and effort to study human cognitive processes. In 1949, Canadian psychologist Donald Hebb published the book The Organization of Behavior, in which he introduced a theoretical neural structure formed by certain interactions among nerve cells. Hebb's model of neurons interacting with one another set a groundwork for how AIs and machine learning algorithms work under nodes, or artificial neurons used by computers to communicate data. Other researchers who have studied human cognitive systems contributed to the modern machine learning technologies as well, including logician Walter Pitts and Warren McCulloch, who proposed the early mathematical models of neural networks to come up with algorithms that mirror human thought processes.
By the early 1960s, an experimental "learning machine" with punched tape memory, called Cybertron, had been developed by Raytheon Company to analyse sonar signals, electrocardiograms, and speech patterns using rudimentary reinforcement learning. It was repetitively "trained" by a human operator/teacher to recognise patterns and equipped with a "goof" button to cause it to reevaluate incorrect decisions. A representative book on research into machine learning during the 1960s was Nilsson's book on Learning Machines, dealing mostly with machine learning for pattern classification. Interest related to pattern recognition continued into the 1970s, as described by Duda and Hart in 1973. In 1981 a report was given on using teaching strategies so that an artificial neural network learns to recognise 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal.
Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E." This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Turing's proposal in his paper "Computing Machinery and Intelligence", in which the question "Can machines think?" is replaced with the question "Can machines do what we (as thinking entities) can do?".
Modern-day machine learning has two objectives. One is to classify data based on models which have been developed; the other purpose is to make predictions for future outcomes based on these models. A hypothetical algorithm specific to classifying data may use computer vision of moles coupled with supervised learning in order to train it to classify the cancerous moles. A machine learning algorithm for stock trading may inform the trader of future potential predictions.
== Relationships to other fields ==
=== Artificial intelligence ===
As a scientific endeavour, machine learning grew out of the quest for artificial intelligence (AI). In the early days of AI as an academic discipline, some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what were then termed "neural networks"; these were mostly perceptrons and other models that were later found to be reinventions of the generalised linear models of statistics. Probabilistic reasoning was also employed, especially in automated medical diagnosis.: 488
However, an increasing emphasis on the logical, knowledge-based approach caused a rift between AI and machine learning. Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation.: 488 By 1980, expert systems had come to dominate AI, and statistics was out of favour. Work on symbolic/knowledge-based learning did continue within AI, leading to inductive logic programming(ILP), but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval.: 708–710, 755 Neural networks research had been abandoned by AI and computer science around the same time. This line, too, was continued outside the AI/CS field, as "connectionism", by researchers from other disciplines including John Hopfield, David Rumelhart, and Geoffrey Hinton. Their main success came in the mid-1980s with the reinvention of backpropagation.: 25
Machine learning (ML), reorganised and recognised as its own field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics, fuzzy logic, and probability theory.
=== Data compression ===
=== Data mining ===
Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases). Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously unknown knowledge. Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.
Machine learning also has intimate ties to optimisation: Many learning problems are formulated as minimisation of some loss function on a training set of examples. Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the preassigned labels of a set of examples).
=== Generalization ===
Characterizing the generalisation of various learning algorithms is an active topic of current research, especially for deep learning algorithms.
=== Statistics ===
Machine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population inferences from a sample, while machine learning finds generalisable predictive patterns. According to Michael I. Jordan, the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics. He also suggested the term data science as a placeholder to call the overall field.
Conventional statistical analyses require the a priori selection of a model most suitable for the study data set. In addition, only significant or theoretically relevant variables based on previous experience are included for analysis. In contrast, machine learning is not built on a pre-structured model; rather, the data shape the model by detecting underlying patterns. The more variables (input) used to train the model, the more accurate the ultimate model will be.
Leo Breiman distinguished two statistical modelling paradigms: data model and algorithmic model, wherein "algorithmic model" means more or less the machine learning algorithms like Random Forest.
Some statisticians have adopted methods from machine learning, leading to a combined field that they call statistical learning.
=== Statistical physics ===
Analytical and computational techniques derived from deep-rooted physics of disordered systems can be extended to large-scale problems, including machine learning, e.g., to analyse the weight space of deep neural networks. Statistical physics is thus finding applications in the area of medical diagnostics.
== Theory ==
A core objective of a learner is to generalise from its experience. Generalisation in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set. The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases.
The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory via the probably approximately correct learning model. Because training sets are finite and the future is uncertain, learning theory usually does not yield guarantees of the performance of algorithms. Instead, probabilistic bounds on the performance are quite common. The bias–variance decomposition is one way to quantify generalisation error.
For the best performance in the context of generalisation, the complexity of the hypothesis should match the complexity of the function underlying the data. If the hypothesis is less complex than the function, then the model has under fitted the data. If the complexity of the model is increased in response, then the training error decreases. But if the hypothesis is too complex, then the model is subject to overfitting and generalisation will be poorer.
In addition to performance bounds, learning theorists study the time complexity and feasibility of learning. In computational learning theory, a computation is considered feasible if it can be done in polynomial time. There are two kinds of time complexity results: Positive results show that a certain class of functions can be learned in polynomial time. Negative results show that certain classes cannot be learned in polynomial time.
== Approaches ==
Machine learning approaches are traditionally divided into three broad categories, which correspond to learning paradigms, depending on the nature of the "signal" or "feedback" available to the learning system:
Supervised learning: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs.
Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).
Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent). As it navigates its problem space, the program is provided feedback that's analogous to rewards, which it tries to maximise.
Although each algorithm has advantages and limitations, no single algorithm works for all problems.
=== Supervised learning ===
Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs. The data, known as training data, consists of a set of training examples. Each training example has one or more inputs and the desired output, also known as a supervisory signal. In the mathematical model, each training example is represented by an array or vector, sometimes called a feature vector, and the training data is represented by a matrix. Through iterative optimisation of an objective function, supervised learning algorithms learn a function that can be used to predict the output associated with new inputs. An optimal function allows the algorithm to correctly determine the output for inputs that were not a part of the training data. An algorithm that improves the accuracy of its outputs or predictions over time is said to have learned to perform that task.
Types of supervised-learning algorithms include active learning, classification and regression. Classification algorithms are used when the outputs are restricted to a limited set of values, while regression algorithms are used when the outputs can take any numerical value within a range. For example, in a classification algorithm that filters emails, the input is an incoming email, and the output is the folder in which to file the email. In contrast, regression is used for tasks such as predicting a person's height based on factors like age and genetics or forecasting future temperatures based on historical data.
Similarity learning is an area of supervised machine learning closely related to regression and classification, but the goal is to learn from examples using a similarity function that measures how similar or related two objects are. It has applications in ranking, recommendation systems, visual identity tracking, face verification, and speaker verification.
=== Unsupervised learning ===
Unsupervised learning algorithms find structures in data that has not been labelled, classified or categorised. Instead of responding to feedback, unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. Central applications of unsupervised machine learning include clustering, dimensionality reduction, and density estimation.
Cluster analysis is the assignment of a set of observations into subsets (called clusters) so that observations within the same cluster are similar according to one or more predesignated criteria, while observations drawn from different clusters are dissimilar. Different clustering techniques make different assumptions on the structure of the data, often defined by some similarity metric and evaluated, for example, by internal compactness, or the similarity between members of the same cluster, and separation, the difference between clusters. Other methods are based on estimated density and graph connectivity.
A special type of unsupervised learning called, self-supervised learning involves training a model by generating the supervisory signal from the data itself.
=== Semi-supervised learning ===
Semi-supervised learning falls between unsupervised learning (without any labelled training data) and supervised learning (with completely labelled training data). Some of the training examples are missing training labels, yet many machine-learning researchers have found that unlabelled data, when used in conjunction with a small amount of labelled data, can produce a considerable improvement in learning accuracy.
In weakly supervised learning, the training labels are noisy, limited, or imprecise; however, these labels are often cheaper to obtain, resulting in larger effective training sets.
=== Reinforcement learning ===
Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximise some notion of cumulative reward. Due to its generality, the field is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimisation, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In reinforcement learning, the environment is typically represented as a Markov decision process (MDP). Many reinforcement learning algorithms use dynamic programming techniques. Reinforcement learning algorithms do not assume knowledge of an exact mathematical model of the MDP and are used when exact models are infeasible. Reinforcement learning algorithms are used in autonomous vehicles or in learning to play a game against a human opponent.
=== Dimensionality reduction ===
Dimensionality reduction is a process of reducing the number of random variables under consideration by obtaining a set of principal variables. In other words, it is a process of reducing the dimension of the feature set, also called the "number of features". Most of the dimensionality reduction techniques can be considered as either feature elimination or extraction. One of the popular methods of dimensionality reduction is principal component analysis (PCA). PCA involves changing higher-dimensional data (e.g., 3D) to a smaller space (e.g., 2D).
The manifold hypothesis proposes that high-dimensional data sets lie along low-dimensional manifolds, and many dimensionality reduction techniques make this assumption, leading to the area of manifold learning and manifold regularisation.
=== Other types ===
Other approaches have been developed which do not fit neatly into this three-fold categorisation, and sometimes more than one is used by the same machine learning system. For example, topic modelling, meta-learning.
==== Self-learning ====
Self-learning, as a machine learning paradigm was introduced in 1982 along with a neural network capable of self-learning, named crossbar adaptive array (CAA). It gives a solution to the problem learning without any external reward, by introducing emotion as an internal reward. Emotion is used as state evaluation of a self-learning agent. The CAA self-learning algorithm computes, in a crossbar fashion, both decisions about actions and emotions (feelings) about consequence situations. The system is driven by the interaction between cognition and emotion.
The self-learning algorithm updates a memory matrix W =||w(a,s)|| such that in each iteration executes the following machine learning routine:
in situation s perform action a
receive a consequence situation s'
compute emotion of being in the consequence situation v(s')
update crossbar memory w'(a,s) = w(a,s) + v(s')
It is a system with only one input, situation, and only one output, action (or behaviour) a. There is neither a separate reinforcement input nor an advice input from the environment. The backpropagated value (secondary reinforcement) is the emotion toward the consequence situation. The CAA exists in two environments, one is the behavioural environment where it behaves, and the other is the genetic environment, wherefrom it initially and only once receives initial emotions about situations to be encountered in the behavioural environment. After receiving the genome (species) vector from the genetic environment, the CAA learns a goal-seeking behaviour, in an environment that contains both desirable and undesirable situations.
==== Feature learning ====
Several learning algorithms aim at discovering better representations of the inputs provided during training. Classic examples include principal component analysis and cluster analysis. Feature learning algorithms, also called representation learning algorithms, often attempt to preserve the information in their input but also transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions. This technique allows reconstruction of the inputs coming from the unknown data-generating distribution, while not being necessarily faithful to configurations that are implausible under that distribution. This replaces manual feature engineering, and allows a machine to both learn the features and use them to perform a specific task.
Feature learning can be either supervised or unsupervised. In supervised feature learning, features are learned using labelled input data. Examples include artificial neural networks, multilayer perceptrons, and supervised dictionary learning. In unsupervised feature learning, features are learned with unlabelled input data. Examples include dictionary learning, independent component analysis, autoencoders, matrix factorisation and various forms of clustering.
Manifold learning algorithms attempt to do so under the constraint that the learned representation is low-dimensional. Sparse coding algorithms attempt to do so under the constraint that the learned representation is sparse, meaning that the mathematical model has many zeros. Multilinear subspace learning algorithms aim to learn low-dimensional representations directly from tensor representations for multidimensional data, without reshaping them into higher-dimensional vectors. Deep learning algorithms discover multiple levels of representation, or a hierarchy of features, with higher-level, more abstract features defined in terms of (or generating) lower-level features. It has been argued that an intelligent machine is one that learns a representation that disentangles the underlying factors of variation that explain the observed data.
Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process. However, real-world data such as images, video, and sensory data has not yielded attempts to algorithmically define specific features. An alternative is to discover such features or representations through examination, without relying on explicit algorithms.
==== Sparse dictionary learning ====
Sparse dictionary learning is a feature learning method where a training example is represented as a linear combination of basis functions and assumed to be a sparse matrix. The method is strongly NP-hard and difficult to solve approximately. A popular heuristic method for sparse dictionary learning is the k-SVD algorithm. Sparse dictionary learning has been applied in several contexts. In classification, the problem is to determine the class to which a previously unseen training example belongs. For a dictionary where each class has already been built, a new training example is associated with the class that is best sparsely represented by the corresponding dictionary. Sparse dictionary learning has also been applied in image de-noising. The key idea is that a clean image patch can be sparsely represented by an image dictionary, but the noise cannot.
==== Anomaly detection ====
In data mining, anomaly detection, also known as outlier detection, is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically, the anomalous items represent an issue such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are referred to as outliers, novelties, noise, deviations and exceptions.
In particular, in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts of inactivity. This pattern does not adhere to the common statistical definition of an outlier as a rare object. Many outlier detection methods (in particular, unsupervised algorithms) will fail on such data unless aggregated appropriately. Instead, a cluster analysis algorithm may be able to detect the micro-clusters formed by these patterns.
Three broad categories of anomaly detection techniques exist. Unsupervised anomaly detection techniques detect anomalies in an unlabelled test data set under the assumption that the majority of the instances in the data set are normal, by looking for instances that seem to fit the least to the remainder of the data set. Supervised anomaly detection techniques require a data set that has been labelled as "normal" and "abnormal" and involves training a classifier (the key difference from many other statistical classification problems is the inherently unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model representing normal behaviour from a given normal training data set and then test the likelihood of a test instance to be generated by the model.
==== Robot learning ====
Robot learning is inspired by a multitude of machine learning methods, starting from supervised learning, reinforcement learning, and finally meta-learning (e.g. MAML).
==== Association rules ====
Association rule learning is a rule-based machine learning method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of "interestingness".
Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilisation of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learning algorithms that commonly identify a singular model that can be universally applied to any instance in order to make a prediction. Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems.
Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliński and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets. For example, the rule
{
o
n
i
o
n
s
,
p
o
t
a
t
o
e
s
}
⇒
{
b
u
r
g
e
r
}
{\displaystyle \{\mathrm {onions,potatoes} \}\Rightarrow \{\mathrm {burger} \}}
found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as promotional pricing or product placements. In addition to market basket analysis, association rules are employed today in application areas including Web usage mining, intrusion detection, continuous production, and bioinformatics. In contrast with sequence mining, association rule learning typically does not consider the order of items either within a transaction or across transactions.
Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component, typically a genetic algorithm, with a learning component, performing either supervised learning, reinforcement learning, or unsupervised learning. They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions.
Inductive logic programming (ILP) is an approach to rule learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that entails all positive and no negative examples. Inductive programming is a related field that considers any kind of programming language for representing hypotheses (and not only logic programming), such as functional programs.
Inductive logic programming is particularly useful in bioinformatics and natural language processing. Gordon Plotkin and Ehud Shapiro laid the initial theoretical foundation for inductive machine learning in a logical setting. Shapiro built their first implementation (Model Inference System) in 1981: a Prolog program that inductively inferred logic programs from positive and negative examples. The term inductive here refers to philosophical induction, suggesting a theory to explain observed facts, rather than mathematical induction, proving a property for all members of a well-ordered set.
== Models ==
A machine learning model is a type of mathematical model that, once "trained" on a given dataset, can be used to make predictions or classifications on new data. During training, a learning algorithm iteratively adjusts the model's internal parameters to minimise errors in its predictions. By extension, the term "model" can refer to several levels of specificity, from a general class of models and their associated learning algorithms to a fully trained model with all its internal parameters tuned.
Various types of models have been used and researched for machine learning systems, picking the best model for a task is called model selection.
=== Artificial neural networks ===
Artificial neural networks (ANNs), or connectionist systems, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules.
An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times.
The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis.
Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.
=== Decision trees ===
Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modelling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels, and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision-making.
=== Random forest regression ===
Random forest regression (RFR) falls under umbrella of decision tree-based models. RFR is an ensemble learning method that builds multiple decision trees and averages their predictions to improve accuracy and to avoid overfitting. To build decision trees, RFR uses bootstrapped sampling, for instance each decision tree is trained on random data of from training set. This random selection of RFR for training enables model to reduce bias predictions and achieve accuracy. RFR generates independent decision trees, and it can work on single output data as well multiple regressor task. This makes RFR compatible to be used in various application.
=== Support-vector machines ===
Support-vector machines (SVMs), also known as support-vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category. An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces.
=== Regression analysis ===
Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularisation methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space.
Multivariate linear regression extends the concept of linear regression to handle multiple dependent variables simultaneously. This approach estimates the relationships between a set of input variables and several output variables by fitting a multidimensional linear model. It is particularly useful in scenarios where outputs are interdependent or share underlying patterns, such as predicting multiple economic indicators or reconstructing images, which are inherently multi-dimensional.
=== Bayesian networks ===
A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalisations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams.
=== Gaussian processes ===
A Gaussian process is a stochastic process in which every finite collection of the random variables in the process has a multivariate normal distribution, and it relies on a pre-defined covariance function, or kernel, that models how pairs of points relate to each other depending on their locations.
Given a set of observed points, or input–output examples, the distribution of the (unobserved) output of a new point as function of its input data can be directly computed by looking like the observed points and the covariances between those points and the new, unobserved point.
Gaussian processes are popular surrogate models in Bayesian optimisation used to do hyperparameter optimisation.
=== Genetic algorithms ===
A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s. Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.
=== Belief functions ===
The theory of belief functions, also referred to as evidence theory or Dempster–Shafer theory, is a general framework for reasoning with uncertainty, with understood connections to other frameworks such as probability, possibility and imprecise probability theories. These theoretical frameworks can be thought of as a kind of learner and have some analogous properties of how evidence is combined (e.g., Dempster's rule of combination), just like how in a pmf-based Bayesian approach would combine probabilities. However, there are many caveats to these beliefs functions when compared to Bayesian approaches in order to incorporate ignorance and uncertainty quantification. These belief function approaches that are implemented within the machine learning domain typically leverage a fusion approach of various ensemble methods to better handle the learner's decision boundary, low samples, and ambiguous class issues that standard machine learning approach tend to have difficulty resolving. However, the computational complexity of these algorithms are dependent on the number of propositions (classes), and can lead to a much higher computation time when compared to other machine learning approaches.
=== Rule-based models ===
Rule-based machine learning (RBML) is a branch of machine learning that automatically discovers and learns 'rules' from data. It provides interpretable models, making it useful for decision-making in fields like healthcare, fraud detection, and cybersecurity. Key RBML techniques includes learning classifier systems, association rule learning, artificial immune systems, and other similar models. These methods extract patterns from data and evolve rules over time.
=== Training models ===
Typically, machine learning models require a high quantity of reliable data to perform accurate predictions. When training a machine learning model, machine learning engineers need to target and collect a large and representative sample of data. Data from the training set can be as varied as a corpus of text, a collection of images, sensor data, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Trained models derived from biased or non-evaluated data can result in skewed or undesired predictions. Biased models may result in detrimental outcomes, thereby furthering the negative impacts on society or objectives. Algorithmic bias is a potential result of data not being fully prepared for training. Machine learning ethics is becoming a field of study and notably, becoming integrated within machine learning engineering teams.
==== Federated learning ====
Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralises the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralised server. This also increases efficiency by decentralising the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.
== Applications ==
There are many applications for machine learning, including:
In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million. Shortly after the prize was awarded, Netflix realised that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly. In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis. In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors jobs would be lost in the next two decades to automated machine learning medical diagnostic software. In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognised influences among artists. In 2019 Springer Nature published the first research book created using machine learning. In 2020, machine learning technology was used to help make diagnoses and aid researchers in developing a cure for COVID-19. Machine learning was recently applied to predict the pro-environmental behaviour of travellers. Recently, machine learning technology was also applied to optimise smartphone's performance and thermal behaviour based on the user's interaction with the phone. When applied correctly, machine learning algorithms (MLAs) can utilise a wide range of company characteristics to predict stock returns without overfitting. By employing effective feature engineering and combining forecasts, MLAs can generate results that far surpass those obtained from basic linear techniques like OLS.
Recent advancements in machine learning have extended into the field of quantum chemistry, where novel algorithms now enable the prediction of solvent effects on chemical reactions, thereby offering new tools for chemists to tailor experimental conditions for optimal outcomes.
Machine Learning is becoming a useful tool to investigate and predict evacuation decision making in large scale and small scale disasters. Different solutions have been tested to predict if and when householders decide to evacuate during wildfires and hurricanes. Other applications have been focusing on pre evacuation decisions in building fires.
Machine learning is also emerging as a promising tool in geotechnical engineering, where it is used to support tasks such as ground classification, hazard prediction, and site characterization. Recent research emphasizes a move toward data-centric methods in this field, where machine learning is not a replacement for engineering judgment, but a way to enhance it using site-specific data and patterns.
== Limitations ==
Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results. Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.
The "black box theory" poses another yet significant challenge. Black box refers to a situation where the algorithm or the process of producing an output is entirely opaque, meaning that even the coders of the algorithm cannot audit the pattern that the machine extracted out of the data. The House of Lords Select Committee, which claimed that such an "intelligence system" that could have a "substantial impact on an individual's life" would not be considered acceptable unless it provided "a full and satisfactory explanation for the decisions" it makes.
In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision. Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested. Microsoft's Bing Chat chatbot has been reported to produce hostile and offensive response against its users.
Machine learning has been used as a strategy to update the evidence related to a systematic review and increased reviewer burden related to the growth of biomedical literature. While it has improved with training sets, it has not yet developed sufficiently to reduce the workload burden without limiting the necessary sensitivity for the findings research themselves.
=== Explainability ===
Explainable AI (XAI), or Interpretable AI, or Explainable Machine Learning (XML), is artificial intelligence (AI) in which humans can understand the decisions or predictions made by the AI. It contrasts with the "black box" concept in machine learning where even its designers cannot explain why an AI arrived at a specific decision. By refining the mental models of users of AI-powered systems and dismantling their misconceptions, XAI promises to help users perform more effectively. XAI may be an implementation of the social right to explanation.
=== Overfitting ===
Settling on a bad, overly complex theory gerrymandered to fit all the past training data is known as overfitting. Many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data but penalising the theory in accordance with how complex the theory is.
=== Other limitations and vulnerabilities ===
Learners can also disappoint by "learning the wrong lesson". A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses. A real-world example is that, unlike humans, current image classifiers often do not primarily make judgements from the spatial relationship between components of the picture, and they learn relationships between pixels that humans are oblivious to, but that still correlate with images of certain types of real objects. Modifying these patterns on a legitimate image can result in "adversarial" images that the system misclassifies.
Adversarial vulnerabilities can also result in nonlinear systems, or from non-pattern perturbations. For some systems, it is possible to change the output by only changing a single adversarially chosen pixel. Machine learning models are often vulnerable to manipulation or evasion via adversarial machine learning.
Researchers have demonstrated how backdoors can be placed undetectably into classifying (e.g., for categories "spam" and well-visible "not spam" of posts) machine learning models that are often developed or trained by third parties. Parties can change the classification of any input, including in cases for which a type of data/software transparency is provided, possibly including white-box access.
== Model assessments ==
Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.
In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning true positive rate (TPR) and true negative rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. Receiver operating characteristic (ROC) along with the accompanying Area Under the ROC Curve (AUC) offer additional tools for classification model assessment. Higher AUC is associated with a better performing model.
== Ethics ==
=== Bias ===
Different machine learning approaches can suffer from different data biases. A machine learning system trained specifically on current customers may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on human-made data, machine learning is likely to pick up the constitutional and unconscious biases already present in society.
Systems that are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitising cultural prejudices. For example, in 1988, the UK's Commission for Racial Equality found that St. George's Medical School had been using a computer program trained from data of previous admissions staff and that this program had denied nearly 60 candidates who were found to either be women or have non-European sounding names. Using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants by similarity to previous successful applicants. Another example includes predictive policing company Geolitica's predictive algorithm that resulted in "disproportionately high levels of over-policing in low-income and minority communities" after being trained with historical crime data.
While responsible collection of data and documentation of algorithmic rules used by a system is considered a critical part of machine learning, some researchers blame lack of participation and representation of minority population in the field of AI for machine learning's vulnerability to biases. In fact, according to research carried out by the Computing Research Association (CRA) in 2021, "female faculty merely make up 16.1%" of all faculty members who focus on AI among several universities around the world. Furthermore, among the group of "new U.S. resident AI PhD graduates," 45% identified as white, 22.4% as Asian, 3.2% as Hispanic, and 2.4% as African American, which further demonstrates a lack of diversity in the field of AI.
Language models learned from data have been shown to contain human-like biases. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases. In 2016, Microsoft tested Tay, a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.
In an experiment carried out by ProPublica, an investigative journalism organisation, a machine learning algorithm's insight into the recidivism rates among prisoners falsely flagged "black defendants high risk twice as often as white defendants". In 2015, Google Photos once tagged a couple of black people as gorillas, which caused controversy. The gorilla label was subsequently removed, and in 2023, it still cannot recognise gorillas. Similar issues with recognising non-white people have been found in many other systems.
Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains. Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good, is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who said that "[t]here's nothing artificial about AI. It's inspired by people, it's created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility."
=== Financial incentives ===
There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is potential for machine learning in health care to provide professionals an additional tool to diagnose, medicate, and plan recovery paths for patients, but this requires these biases to be mitigated.
== Hardware ==
Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of nonlinear hidden units. By 2019, graphics processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI. OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.
=== Tensor Processing Units (TPUs) ===
Tensor Processing Units (TPUs) are specialised hardware accelerators developed by Google specifically for machine learning workloads. Unlike general-purpose GPUs and FPGAs, TPUs are optimised for tensor computations, making them particularly efficient for deep learning tasks such as training and inference. They are widely used in Google Cloud AI services and large-scale machine learning models like Google's DeepMind AlphaFold and large language models. TPUs leverage matrix multiplication units and high-bandwidth memory to accelerate computations while maintaining energy efficiency. Since their introduction in 2016, TPUs have become a key component of AI infrastructure, especially in cloud-based environments.
=== Neuromorphic computing ===
Neuromorphic computing refers to a class of computing systems designed to emulate the structure and functionality of biological neural networks. These systems may be implemented through software-based simulations on conventional hardware or through specialised hardware architectures.
==== physical neural networks ====
A physical neural network is a specific type of neuromorphic hardware that relies on electrically adjustable materials, such as memristors, to emulate the function of neural synapses. The term "physical neural network" highlights the use of physical hardware for computation, as opposed to software-based implementations. It broadly refers to artificial neural networks that use materials with adjustable resistance to replicate neural synapses.
=== Embedded machine learning ===
Embedded machine learning is a sub-field of machine learning where models are deployed on embedded systems with limited computing resources, such as wearable computers, edge devices and microcontrollers. Running models directly on these devices eliminates the need to transfer and store data on cloud servers for further processing, thereby reducing the risk of data breaches, privacy leaks and theft of intellectual property, personal data and business secrets. Embedded machine learning can be achieved through various techniques, such as hardware acceleration, approximate computing, and model optimisation. Common optimisation techniques include pruning, quantisation, knowledge distillation, low-rank factorisation, network architecture search, and parameter sharing.
== Software ==
Software suites containing a variety of machine learning algorithms include the following:
=== Free and open-source software ===
=== Proprietary software with free and open-source editions ===
KNIME
RapidMiner
=== Proprietary software ===
== Journals ==
Journal of Machine Learning Research
Machine Learning
Nature Machine Intelligence
Neural Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
== Conferences ==
AAAI Conference on Artificial Intelligence
Association for Computational Linguistics (ACL)
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)
International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB)
International Conference on Machine Learning (ICML)
International Conference on Learning Representations (ICLR)
International Conference on Intelligent Robots and Systems (IROS)
Conference on Knowledge Discovery and Data Mining (KDD)
Conference on Neural Information Processing Systems (NeurIPS)
== See also ==
Automated machine learning – Process of automating the application of machine learning
Big data – Extremely large or complex datasets
Deep learning — branch of ML concerned with artificial neural networks
Differentiable programming – Programming paradigm
List of datasets for machine-learning research
M-theory (learning framework)
Machine unlearning
Solomonoff's theory of inductive inference – A mathematical theory
== References ==
== Sources ==
Domingos, Pedro (22 September 2015). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books. ISBN 978-0465065707.
Nilsson, Nils (1998). Artificial Intelligence: A New Synthesis. Morgan Kaufmann. ISBN 978-1-55860-467-4. Archived from the original on 26 July 2020. Retrieved 18 November 2019.
Poole, David; Mackworth, Alan; Goebel, Randy (1998). Computational Intelligence: A Logical Approach. New York: Oxford University Press. ISBN 978-0-19-510270-3. Archived from the original on 26 July 2020. Retrieved 22 August 2020.
Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2.
== Further reading ==
== External links ==
International Machine Learning Society
mloss is an academic database of open-source machine learning software. | Wikipedia/machine_learning |
A false positive is an error in binary classification in which a test result incorrectly indicates the presence of a condition (such as a disease when the disease is not present), while a false negative is the opposite error, where the test result incorrectly indicates the absence of a condition when it is actually present. These are the two kinds of errors in a binary test, in contrast to the two kinds of correct result (a true positive and a true negative). They are also known in medicine as a false positive (or false negative) diagnosis, and in statistical classification as a false positive (or false negative) error.
In statistical hypothesis testing, the analogous concepts are known as type I and type II errors, where a positive result corresponds to rejecting the null hypothesis, and a negative result corresponds to not rejecting the null hypothesis. The terms are often used interchangeably, but there are differences in detail and interpretation due to the differences between medical testing and statistical hypothesis testing.
== False positive error ==
A false positive error, or false positive, is a result that indicates a given condition exists when it objectively does not. For example, a pregnancy test which indicates a woman is pregnant when she is not, or the conviction of an innocent person.
A false positive error is a type I error where the test is checking a single condition, and wrongly gives an affirmative (positive) decision. However it is important to distinguish between the type 1 error rate and the probability of a positive result being false. The latter is known as the false positive risk (see Ambiguity in the definition of false positive rate, below).
== False negative error ==
A false negative error, or false negative, is a test result which wrongly indicates that a condition does not hold. For example, when a pregnancy test indicates a woman is not pregnant, but she is, or when a person guilty of a crime is acquitted, these are false negatives. The condition "the woman is pregnant", or "the person is guilty" holds, but the test (the pregnancy test or the trial in a court of law) fails to realize this condition, and wrongly decides that the person is not pregnant or not guilty.
A false negative error is a type II error occurring in a test where a single condition is checked for, and the result of the test is erroneous, that the condition is absent.
== Related terms ==
=== False positive and false negative rates ===
The false positive rate (FPR) is the proportion of all negatives that still yield positive test outcomes, i.e., the conditional probability of a positive test result given an event that was not present.
The false positive rate is equal to the significance level. The specificity of the test is equal to 1 minus the false positive rate.
In statistical hypothesis testing, this fraction is given the Greek letter α, and 1 − α is defined as the specificity of the test. Increasing the specificity of the test lowers the probability of type I errors, but may raise the probability of type II errors (false negatives that reject the alternative hypothesis when it is true).
Complementarily, the false negative rate (FNR) is the proportion of positives which yield negative test outcomes with the test, i.e., the conditional probability of a negative test result given that the condition being looked for is present.
In statistical hypothesis testing, this fraction is given the letter β. The "power" (or the "sensitivity") of the test is equal to 1 − β.
=== Ambiguity in the definition of false positive rate ===
The term false discovery rate (FDR) was used by Colquhoun (2014) to mean the probability that a "significant" result was a false positive. Later Colquhoun (2017) used the term false positive risk (FPR) for the same quantity, to avoid confusion with the term FDR as used by people who work on multiple comparisons. Corrections for multiple comparisons aim only to correct the type I error rate, so the result is a (corrected) p-value. Thus they are susceptible to the same misinterpretation as any other p-value. The false positive risk is always higher, often much higher, than the p-value.
Confusion of these two ideas, the error of the transposed conditional, has caused much mischief. Because of the ambiguity of notation in this field, it is essential to look at the definition in every paper. The hazards of reliance on p-values was emphasized in Colquhoun (2017) by pointing out that even an observation of p = 0.001 was not necessarily strong evidence against the null hypothesis. Despite the fact that the likelihood ratio in favor of the alternative hypothesis over the null is close to 100, if the hypothesis was implausible, with a prior probability of a real effect being 0.1, even the observation of p = 0.001 would have a false positive rate of 8 percent. It wouldn't even reach the 5 percent level. As a consequence, it has been recommended that every p-value should be accompanied by the prior probability of there being a real effect that it would be necessary to assume in order to achieve a false positive risk of 5%. For example, if we observe p = 0.05 in a single experiment, we would have to be 87% certain that there as a real effect before the experiment was done to achieve a false positive risk of 5%.
=== Receiver operating characteristic ===
The article "Receiver operating characteristic" discusses parameters in statistical signal processing based on ratios of errors of various types.
== See also ==
Base rate fallacy
False positive rate
Positive and negative predictive values
Why Most Published Research Findings Are False
== Notes ==
== References == | Wikipedia/False_negative_rate |
A chromosome or genotype in evolutionary algorithms (EA) is a set of parameters which define a proposed solution of the problem that the evolutionary algorithm is trying to solve. The set of all solutions, also called individuals according to the biological model, is known as the population. The genome of an individual consists of one, more rarely of several, chromosomes and corresponds to the genetic representation of the task to be solved. A chromosome is composed of a set of genes, where a gene consists of one or more semantically connected parameters, which are often also called decision variables. They determine one or more phenotypic characteristics of the individual or at least have an influence on them. In the basic form of genetic algorithms, the chromosome is represented as a binary string, while in later variants and in EAs in general, a wide variety of other data structures are used.
== Chromosome design ==
When creating the genetic representation of a task, it is determined which decision variables and other degrees of freedom of the task should be improved by the EA and possible additional heuristics and how the genotype-phenotype mapping should look like. The design of a chromosome translates these considerations into concrete data structures for which an EA then has to be selected, configured, extended, or, in the worst case, created. Finding a suitable representation of the problem domain for a chromosome is an important consideration, as a good representation will make the search easier by limiting the search space; similarly, a poorer representation will allow a larger search space. In this context, suitable mutation and crossover operators must also be found or newly defined to fit the chosen chromosome design. An important requirement for these operators is that they not only allow all points in the search space to be reached in principle, but also make this as easy as possible.
The following requirements must be met by a well-suited chromosome:
It must allow the accessibility of all admissible points in the search space.
Design of the chromosome in such a way that it covers only the search space and no additional areas. so that there is no redundancy or only as little redundancy as possible.
Observance of strong causality: small changes in the chromosome should only lead to small changes in the phenotype. This is also called locality of the relationship between search and problem space.
Designing the chromosome in such a way that it excludes prohibited regions in the search space completely or as much as possible.
While the first requirement is indispensable, depending on the application and the EA used, one usually only has to be satisfied with fulfilling the remaining requirements as far as possible. The evolutionary search is supported and possibly considerably accelerated by a fulfillment as complete as possible.
== Examples of chromosomes ==
=== Chromosomes for binary codings ===
In their classical form, GAs use bit strings and map the decision variables to be optimized onto them. An example for one Boolean and three integer decision variables with the value ranges
0
≤
D
1
≤
60
{\displaystyle 0\leq D_{1}\leq 60}
,
28
≤
D
2
≤
30
{\displaystyle 28\leq D_{2}\leq 30}
and
−
12
≤
D
3
≤
14
{\displaystyle -12\leq D_{3}\leq 14}
may illustrate this:
Note that the negative number here is given in two's complement. This straight forward representation uses five bits to represent the three values of
D
2
{\displaystyle D_{2}}
, although two bits would suffice. This is a significant redundancy. An improved alternative, where 28 is to be added for the genotype-phenotype mapping, could look like this:
with
D
2
=
28
+
D
2
′
=
29
{\displaystyle D_{2}=28+D'_{2}=29}
.
=== Chromosomes with real-valued or integer genes ===
For the processing of tasks with real-valued or mixed-integer decision variables, EAs such as the evolution strategy or the real-coded GAs are suited. In the case of mixed-integer values, rounding is often used, but this represents some violation of the redundancy requirement. If the necessary precisions of the real values can be reasonably narrowed down, this violation can be remedied by using integer-coded GAs. For this purpose, the valid digits of real values are mapped to integers by multiplication with a suitable factor. For example, 12.380 becomes the integer 12380 by multiplying by 1000. This must of course be taken into account in genotype-phenotype mapping for evaluation and result presentation. A common form is a chromosome consisting of a list or an array of integer or real values.
=== Chromosomes for permutations ===
Combinatorial problems are mainly concerned with finding an optimal sequence of a set of elementary items. As an example, consider the problem of the traveling salesman who wants to visit a given number of cities exactly once on the shortest possible tour. The simplest and most obvious mapping onto a chromosome is to number the cities consecutively, to interpret a resulting sequence as permutation and to store it directly in a chromosome, where one gene corresponds to the ordinal number of a city. Then, however, the variation operators may only change the gene order and not remove or duplicate any genes. The chromosome thus contains the path of a possible tour to the cities. As an example the sequence
3
,
5
,
7
,
1
,
4
,
2
,
9
,
6
,
8
{\displaystyle 3,5,7,1,4,2,9,6,8}
of nine cities may serve, to which the following chromosome corresponds:
In addition to this encoding frequently called path representation, there are several other ways of representing a permutation, for example the ordinal representation or the matrix representation.
=== Chromosomes for co-evolution ===
When a genetic representation contains, in addition to the decision variables, additional information that influences evolution and/or the mapping of the genotype to the phenotype and is itself subject to evolution, this is referred to as co-evolution. A typical example is the evolution strategy (ES), which includes one or more mutation step sizes as strategy parameters in each chromosome. Another example is an additional gene to control a selection heuristic for resource allocation in a scheduling tasks.
This approach is based on the assumption that good solutions are based on an appropriate selection of strategy parameters or on control gene(s) that influences genotype-phenotype mapping. The success of the ES gives evidence to this assumption.
=== Chromosomes for complex representations ===
The chromosomes presented above are well suited for processing tasks of continuous, mixed-integer, pure-integer or combinatorial optimization. For a combination of these optimization areas, on the other hand, it becomes increasingly difficult to map them to simple strings of values, depending on the task. The following extension of the gene concept is proposed by the EA GLEAM (General Learning Evolutionary Algorithm and Method) for this purpose: A gene is considered to be the description of an element or elementary trait of the phenotype, which may have multiple parameters. For this purpose, gene types are defined that contain as many parameters of the appropriate data type as are required to describe the particular element of the phenotype. A chromosome now consists of genes as data objects of the gene types, whereby, depending on the application, each gene type occurs exactly once as a gene or can be contained in the chromosome any number of times. The latter leads to chromosomes of dynamic length, as they are required for some problems. The gene type definitions also contain information on the permissible value ranges of the gene parameters, which are observed during chromosome generation and by corresponding mutations, so they cannot lead to lethal mutations. For tasks with a combinatorial part, there are suitable genetic operators that can move or reposition genes as a whole, i.e. with their parameters.
A scheduling task is used as an illustration, in which workflows are to be scheduled that require different numbers of heterogeneous resources. A workflow specifies which work steps can be processed in parallel and which have to be executed one after the other. In this context, heterogeneous resources mean different processing times at different costs in addition to different processing capabilities. Each scheduling operation therefore requires one or more parameters that determine the resource selection, where the value ranges of the parameters depend on the number of alternative resources available for each work step. A suitable chromosome provides one gene type per work step and in this case one corresponding gene, which has one parameter for each required resource. The order of genes determines the order of scheduling operations and, therefore, the precedence in case of allocation conflicts. The exemplary gene type definition of work step 15 with two resources, for which there are four and seven alternatives respectively, would then look as shown in the left image. Since the parameters represent indices in lists of available resources for the respective work step, their value range starts at 0. The right image shows an example of three genes of a chromosome belonging to the gene types in list representation.
=== Chromosomes for tree representations ===
Tree representations in a chromosome are used by genetic programming, an EA type for generating computer programs or circuits. The trees correspond to the syntax trees generated by a compiler as internal representation when translating a computer program. The adjacent figure shows the syntax tree of a mathematical expression as an example. Mutation operators can rearrange, change or delete subtrees depending on the represented syntax structure. Recombination is performed by exchanging suitable subtrees.
== Bibliography ==
Thomas Bäck (1996): Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms, Oxford Univ. Press. ISBN 978-0-19-509971-3
Wolfgang Banzhaf, P. Nordin, R. Keller, F. Francone (1998): Genetic Programming - An Introduction, Morgan Kaufmann, San Francisco. ISBN 1-55860-510-X
Kenneth A. de Jong (2006): Evolutionary Computation: A Unified Approach. MIT Press, Cambridge, MA. ISBN 0-262-04194-4
Melanie Mitchell (1996): An Introduction to Genetic Algorithms. MIT Press, Cambridge MA. ISBN 978-0-262-63185-3
Hans-Paul Schwefel (1995): Evolution and Optimum Seeking. Wiley & Sons, New York. ISBN 0-471-57148-2
== References == | Wikipedia/Chromosome_(genetic_algorithm) |
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