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World Wide Web Consortium (W3C) : international community that develops guidelines to ensure the long-term growth and accessibility of the World Wide Web | https://openstax.org/books/introduction-computer-science/pages/2-key-terms |
Complex problems are situations that are difficult because they involve many different parts or factors. | https://openstax.org/books/introduction-computer-science/pages/2-summary |
Computational thinking means breaking these problems into smaller parts, understanding how these parts relate to each other, and then coming up with effective strategies or steps to solve each part. | https://openstax.org/books/introduction-computer-science/pages/2-summary |
Computational thinking is a set of tools or strategies for solving (and learning how to solve) complex problems that relate to mathematical thinking in its use of abstraction, decomposition, measurement, and modeling. | https://openstax.org/books/introduction-computer-science/pages/2-summary |
Characterization of computational thinking is the three As: abstraction, automation, and analysis. | https://openstax.org/books/introduction-computer-science/pages/2-summary |
Decomposition is a fundamental concept in computational thinking, representing the process of systematically breaking down a complex problem or system into smaller, more manageable parts or subproblems. | https://openstax.org/books/introduction-computer-science/pages/2-summary |
Logical thinking and pattern recognition are computational thinking techniques that involve the process of identifying similarities among and within problems. | https://openstax.org/books/introduction-computer-science/pages/2-summary |
Abstraction is a computational thinking technique that centers on focusing on important information while ignoring irrelevant details. | https://openstax.org/books/introduction-computer-science/pages/2-summary |
Algorithms are detailed sets of instructions to solve a problem step-by-step. | https://openstax.org/books/introduction-computer-science/pages/2-summary |
Testing and debugging is about finding and fixing mistakes in the step-by-step instructions or algorithms used to solve a problem. | https://openstax.org/books/introduction-computer-science/pages/2-summary |
Computational thinking commonly employs a bottom-up strategy for crafting well-structured components. | https://openstax.org/books/introduction-computer-science/pages/2-summary |
A business solution architecture is a structural design that is meant to address the needs of prospective solution users. | https://openstax.org/books/introduction-computer-science/pages/2-summary |
Business solutions are strategies/systems created to solve specific challenges in a business. Designing business solutions can be described as a complex systemic process that requires expertise in various spheres of technology as well as the concerned business. A blueprint is a detailed plan or design that outlines the... | https://openstax.org/books/introduction-computer-science/pages/2-summary |
Two heuristics are inherent to the design of business solutions and the creation of business solution architectures. Layering in business solution architecture involves creating distinct layers that abstract specific aspects of the overall architecture. The layering approach relies on the principle of separation of con... | https://openstax.org/books/introduction-computer-science/pages/2-summary |
User experience (UX) refers to the overall experience that a person has when interacting with a product, service, or system. | https://openstax.org/books/introduction-computer-science/pages/2-summary |
A monolithic structure is a system or application architecture where all the components are tightly integrated into a single unit. | https://openstax.org/books/introduction-computer-science/pages/2-summary |
Enterprise-level architecture encompasses various domains that define the structure, components, and operations of an entire organization. Enterprise architecture (EA) views the enterprise as a system or a system of systems. | https://openstax.org/books/introduction-computer-science/pages/2-summary |
The enterprise business architecture (EBA) is a comprehensive framework that defines the structure and operation of an entire organization. A business model is a framework that outlines how a business creates, delivers, and captures value. The organizational model is the structure and design of an organization, outlini... | https://openstax.org/books/introduction-computer-science/pages/2-summary |
The business process is a series of interrelated tasks, activities, or steps performed in a coordinated manner within an organization to achieve a specific business goal. | https://openstax.org/books/introduction-computer-science/pages/2-summary |
Location model refers to a set of rules used to analyze and make decisions related to the positioning of entities, activities, or resources. | https://openstax.org/books/introduction-computer-science/pages/2-summary |
The enterprise technology architecture (ETA) is a comprehensive framework that defines the structure, components, and interrelationships of an organizationâs technology systems to support its business processes and objectives. | https://openstax.org/books/introduction-computer-science/pages/2-summary |
The application architecture is a subset of the enterprise solution architecture. | https://openstax.org/books/introduction-computer-science/pages/2-summary |
A data architecture model is a conceptual framework that outlines how an organization structures, organizes, and manages its data assets. | https://openstax.org/books/introduction-computer-science/pages/2-summary |
Data modeling is the collaborative process wherein IT and business stakeholders establish a shared understanding of essential business terms, known as entities. | https://openstax.org/books/introduction-computer-science/pages/2-summary |
Architecture views are representations of the overall system design that matter to different stakeholders. | https://openstax.org/books/introduction-computer-science/pages/2-summary |
The combination of top-down, adaptive design reuse and bottom-up, computational thinking optimizes modern software development. | https://openstax.org/books/introduction-computer-science/pages/2-summary |
Model-View-Controller (MVC) is a software architectural pattern commonly used in the design of interactive applications, providing a systematic way to organize and structure code. | https://openstax.org/books/introduction-computer-science/pages/2-summary |
The adaptive design reuse approach is a strategy in software development that emphasizes the efficient reuse of existing design solutions to create new systems or applications. | https://openstax.org/books/introduction-computer-science/pages/2-summary |
World Wide Web Consortium (W3C) is an international community that develops guidelines to ensure the long-term growth and accessibility of the World Wide Web. | https://openstax.org/books/introduction-computer-science/pages/2-summary |
Web 2.0 is the second generation of the World Wide Web when we shift from static web pages to dynamic content. | https://openstax.org/books/introduction-computer-science/pages/2-summary |
Web 3.0 is the third generation of the World Wide Web and represents a vision for the future of the Internet characterized by advanced technologies. | https://openstax.org/books/introduction-computer-science/pages/2-summary |
A web application (web app) refers to a software application that is accessed and interacted through a web browser over the Internet. | https://openstax.org/books/introduction-computer-science/pages/2-summary |
Blockchain is a secure and transparent way of recording transactions. It uses a chain of blocks, each storing a list of transactions. | https://openstax.org/books/introduction-computer-science/pages/2-summary |
Microservices is a way of building software by breaking it into small, independent pieces. Each piece, or service, does a specific job and works on its own. | https://openstax.org/books/introduction-computer-science/pages/2-summary |
Migrating legacy business solutions means upgrading or replacing old systems with newer, more efficient ones. | https://openstax.org/books/introduction-computer-science/pages/2-summary |
Innovative cloud mashups refer to creative combinations of different innovative business solutions that leverage disruptive technologies. | https://openstax.org/books/introduction-computer-science/pages/2-summary |
abstract data type (ADT) : consists of all data structures that share common functionality | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
abstraction : process of simplifying a concept in order to represent it in a computer | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
adjacent : in a graph abstract data type, the relationship between two vertices connected by an edge | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
algorithm analysis : study of the results produced by the outputs as well as how the algorithm produces those outputs | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
algorithm design pattern : solution to well-known computing problems | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
algorithmic paradigm : common concept and ideas behind algorithm design patterns | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
algorithmic problem solving : refers to a particular set of approaches and methods for designing algorithms that draws on computingâs historical connections to the study of mathematical problem solving | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
array list : data structure that stores elements next to each other in memory | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
asymptotic analysis : evaluates the time that an algorithm takes to produce a result as the size of the input increases | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
asymptotic notation : mathematical notation that formally defines the order of growth | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
AVL tree : balanced binary search tree data structure often used to implement sets or maps that organizes elements according to the AVL tree property | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
AVL tree property : requires the left and right subtrees to be balanced at every node in the tree | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
balanced binary search tree : introduces additional properties that ensure that the tree will never enter a worst-case situation by reorganizing elements to maintain balance | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
Big O notation : most common type of asymptotic notation in computer science used to measure worst case complexity | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
binary heap : binary tree data structure used to implement priority queues that organizes elements according to the heap property | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
binary logarithm : tells how many times a large number needs to be divided by 2 until it reaches 1 | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
binary search algorithm : recursively narrows down the possible locations for the target in the sorted list | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
binary search tree : tree data structure often used to implement sets and maps that organizes elements according to the binary tree property and the search tree property | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
binary tree property : requires that each node can have either zero, one, or two children | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
breadth-first search : iteratively explores each neighbor, expanding the search level-by-level breadth-first | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
brute-force algorithm : solves combinatorial problems by systematically enumerating all potential solutions in order to identify the best candidate solution | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
canonical algorithm : well-known algorithm | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
case analysis : way to account for variation in runtime based on factors other than the size of the problem | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
child node : descendant of another node | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
collision : situation where multiple objects hash to the same integer index value | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
combinatorial explosion : exponential number of solutions to a combinatorial problem that makes brute-force algorithms unusable in practice | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
combinatorial problem : involves identifying the best candidate solution out of a space of many potential solutions | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
comparison sorting : sorting of a list of elements where elements are not assigned numeric values but rather defined in relation to other elements | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
complexity : condition based on the degree of computational resources that an algorithm consumes during its execution in relation to the size of the input | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
compression : problem of representing information using less data storage | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
constant : type of order of growth that does not take more resources as the size of the problem increases | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
correctness : whether the outputs produced by an algorithm match the expected or desired results across the range of possible inputs | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
cost model : characterization of runtime in terms of more abstract operations such as the number of repetitions | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
count sorting : sorting a list of elements by organizing elements into categories and rearranging the categories into a logical order | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
cryptography : problem of masking or obfuscating text to make it unintelligible | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
data structure : complex data type with specific representation and specific functionality | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
data structure problem : computational problem involving the storage and retrieval of elements for implementing abstract data
types such as lists, sets, maps, and priority queues | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
data type : determines how computers process data by defining the possible values for data and the possible functionality or operations on that data | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
depth-first search : graph traversal algorithm that recursively explores each neighbor, continuing as far possible along each subproblem depth-first | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
Dijkstraâs algorithm : maintains a priority queue of vertices in the graph ordered by distance from the start and repeatedly selects the next shortest path to an unconnected part of the graph | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
divide and conquer algorithm : algorithmic paradigm that breaks down a problem into smaller subproblems (divide), recursively solves each subproblem (conquer), and then combines the result of each subproblem in order to inform the overall solution | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
edge : relationship between vertices or nodes | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
element : individual data point | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
experimental analysis : evaluates an algorithmâs runtime by recording how long it takes to run a program implementation of it | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
functionality : operations such as adding, retrieving, and removing elements | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
graph : represents binary relations among collection of entities, specifically vertices and edges | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
graph problem : computational problem involving graphs that represent relationships between data | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
greedy algorithm : solves combinatorial problems by repeatedly applying a simple rule to select the next element to include in the solution | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
hash table : implements sets and maps by applying the concept of hashing | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
hashing : problem of assigning a meaningful integer index for each object | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
heap property : requires that the priority value of each node in the heap is greater than or equal to the priority values of its children | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
heapsort algorithm : adds all elements to a binary heap priority queue data structure using the comparison operation to determine priority value and returns the sorted list by repeatedly removing from the priority queue element-by-element | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
index : position or address for an element in a list | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
interval scheduling problem : combinatorial problem involving a list of scheduled tasks with the goal of finding the largest non-overlapping set of tasks that can be completed | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
intractable : problems that do not have efficient, polynomial-time algorithms | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
Kruskalâs algorithm : greedy algorithm that sorts the list of edges in the graph by weight | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
leaf node : node at the bottom of a tree that has no children | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
linear : type of order of growth where the resources required to run the algorithm increases at about the same rate as the size of the problem increases | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
linear data structure : category of data structures where elements are ordered in a line | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
linked list : data structure that does not necessarily store elements next to each other and instead works by maintaining, for each element, a link to the next element in the list | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
list : ordered sequence of elements and allows adding, retrieving, and removing elements from any position in the list | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
logarithm : tells how many times a large number needs to be divided by a small number until it reaches 1 | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
longest path : problem of finding the highest-cost way to get from one vertex to another without repeating vertices | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
map : represents unordered associations between key-value pairs of elements, where each key can only appear once in the map | https://openstax.org/books/introduction-computer-science/pages/3-key-terms |
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