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The batch size refers to the number of work units to be processed within one batch operation. Some examples are:
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The IBM mainframe z/OS operating system or platform has arguably the most highly refined and evolved set of batch processing facilities owing to its origins, long history, and continuing evolution. Today such systems commonly support hundreds or even thousands of concurrent online and batch tasks within a single operating system image. Technologies that aid concurrent batch and online processing include Job Control Language , scripting languages such as REXX, Job Entry Subsystem , Workload Manager , Automatic Restart Manager , Resource Recovery Services , IBM Db2 data sharing, Parallel Sysplex, unique performance optimizations such as HiperDispatch, I/O channel architecture, and several others.
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The Unix programs cron, at, and batch allow for complex scheduling of jobs. Windows has a job scheduler. Most high-performance computing clusters use batch processing to maximize cluster usage.
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Computer science education is essential to preparing students for the 21st century workforce. As technology becomes increasingly integrated into all aspects of society, the demand for skilled computer scientists is growing. According to the Bureau of Labor Statistics, employment of computer and information technology occupations is projected to "grow 21 percent from 2021 to 2031", much faster than the average for all occupations.
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In addition to preparing students for careers in the technology industry, computer science education also promotes computational thinking skills, which are valuable in many fields, including business, healthcare, and education. By learning to think algorithmically and solve problems systematically, students can become more effective problem solvers and critical thinkers.
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The history of computer science education can be traced back to the early days of computing, when programming was primarily done by scientists and mathematicians. As computers became more widely used in industry and government, the need for skilled programmers grew, and universities began to offer courses in computer science.
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In comparison to science education and mathematics education, computer science education is a much younger field. In the history of computing, digital computers were only built from around the 1940s – although computation has been around for centuries since the invention of analog computers.
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Another differentiator of computer science education is that it has primarily only been taught at university level until recently, with some notable exceptions in Israel, Poland and the United Kingdom with the BBC Micro in the 1980s as part of Computer science education in the United Kingdom. Computer science has been a part of the school curricula from age 14 or age 16 in a few countries for a few decades, but has typically as an elective subject.
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Primary and secondary computer science education is relatively new in the United States with many K-12 CS teachers facing obstacles to integrating CS instruction such as professional isolation, limited CS professional development resources, and low levels of CS teaching self-efficacy. According to a 2021 report, only 51% of high schools in the US offer computer science.
Elementary CS teachers in particular have lower CS teaching efficacy and have fewer chances to implement CS into their instruction than their middle and high school peers. Connecting CS teachers to resources and peers using methods such as Virtual Communities of Practice has been shown to help CS and STEM teachers improve their teaching self-efficacy and implement CS topics into student instruction.
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The curriculum for computer science education varies depending on the level of education and country. At the elementary and middle school level, computer science education usually focuses on block or visual programming languages such as Scratch or python using basic programming concepts, such as loops, conditionals, and variables. At the high school level, students may learn more advanced programming concepts and algorithms, as well as web development, networking, and data analysis.
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In college and graduate school, computer science education may include courses in topics such as artificial intelligence, machine learning, data science, and computer graphics. Many computer science programs also offer courses in computer architecture, operating systems, and computer networks.
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Teaching methods in computer science education vary depending on the level of education and the goals of the program. At the elementary and middle school level, computer science education may focus on interactive games and puzzles to teach programming concepts. In high school and college, computer science education may involve lectures, labs, and hands-on projects that allow students to apply their knowledge to real-world problems.
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Online learning platforms and coding bootcamps have also become popular methods of teaching computer science skills. These programs offer self-paced learning and can be accessed from anywhere with an internet connection, as computer science is more about practicality and real-world application.
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Computing education research or Computer science education research is an interdisciplinary field that focuses on studying the teaching and learning of computer science. It is a subfield of both computer science and education research, and is concerned with understanding how computer science is taught, learned, and assessed in a variety of settings, such as K-12 schools, colleges and universities, and online learning environments.
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Computer science education research emerged as a field of study in the 1970s, when researchers began to explore the effectiveness of different approaches to teaching computer programming. Since then, the field has grown to encompass a wide range of topics related to computer science education, including curriculum design, assessment, pedagogy, and diversity and inclusion.
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One of the primary goals of computer science education research is to improve the teaching and learning of computer science. To this end, researchers study a variety of topics, including:
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Researchers in computer science education seek to design curricula that are effective and engaging for students. This may involve studying the effectiveness of different programming languages, or developing new pedagogical approaches that promote active learning.
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Computer science education researchers are interested in developing effective ways to assess student learning outcomes. This may involve developing new measures of student knowledge or skills, or evaluating the effectiveness of different assessment methods.
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Researchers in computer science education are interested in exploring different teaching methods and instructional strategies. This may involve studying the effectiveness of lectures, online tutorials, or peer-to-peer learning.
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Computer science education researchers are interested in promoting diversity and inclusion in computer science education. This may involve studying the factors that contribute to under representation of certain groups in computer science, and developing interventions to promote inclusivity and equity.
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The Association for Computing Machinery runs a Special Interest Group on Computer science education known as SIGCSE which celebrated its 50th anniversary in 2018, making it one of the oldest and longest running ACM Special Interest Groups. An outcome of computing education research are Parsons problems.
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In many countries, there is a significant gender gap in computer science education. In 2015, 15.3% of computer science students graduating from non-doctoral granting institutions in the US were women while at doctoral granting institutions, the figure was 16.6%. The number of female PhD recipients in the US was 19.3% in 2018. In almost everywhere in the world, less than 20% of the computer science graduates are female.
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This problem mainly arises due to the lack of interests of girls in computing starting from the primary level. Despite numerous efforts by programs specifically designed to increase the ratio of women in this field, no significant improvement has been observed. Furthermore, a declining trend has been noticed in the involvement of women in past decades.
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The main reason for the failure of these programs is because almost all of them focused on girls in high school or higher levels of education. Researchers argue that by then women have already made up their mind and stereotypes start to form about computer scientists. Computer Science is perceived as a male dominated field, pursued by people who are nerdy and lack social skills. All these characteristics seem to be more damaging for a woman as compared to a man. Therefore, in order to break these stereotypes and to engage more women in computer science, it is crucial that there are special outreach programs designed to develop interest in girls starting at the middle school level and prepare them for a academic track towards the hard sciences.
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Evidently, there are a few countries in Asia and Africa where these stereotypes do not exist and women are encouraged for a career in science starting at the primary level, thus resulting in a gender gap that is virtually nonexistent. In 2011, women earned half of the computer science degrees in Malaysia. In 2001, 55 percent of computer science graduates in Guyana were women.
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A less common, broader meaning of proceedings are the acts and happenings of an academic field, a learned society. For example, the title of the Acta Crystallographica journals is Neo-Latin for "Proceedings in Crystallography"; the Proceedings of the National Academy of Sciences of the United States of America is the main journal of that academy. Scientific journals whose ISO 4 title abbreviations start with Proc, Acta, or Trans are journals of the proceedings of a field or of an organization concerned with it, in that secondary meaning of the word.
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Selecting and collecting papers for conferences is organized by one or more persons, who form the editorial team. The quality of the papers is typically ensured by having external people read the papers before they are accepted in the proceedings. The level of quality control varies considerably from conference to conference: some have only a binary accept/reject decision, others go through more thorough feedback and revisions cycles . Depending on the level of the conference, this process can take up to a year. The editors decide about the composition of the proceedings, the order of the papers, and produce the preface and possibly other pieces of text. Although most changes in papers occur on basis of consensus between editors and authors, editors can also single-handedly make changes in papers.
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Since the collection of papers comes from individual researchers, the character of proceedings is distinctly different from an educational textbook. Each paper typically is quite isolated from the other papers in the proceedings. Mostly there is no general argument leading from one contribution to the next.
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In some cases, the editors of the proceedings may decide to further develop the proceedings into a textbook. This may even be a goal at the outset of the conference.
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Conference proceedings are published in-house by the organizing institution of the conference or via an academic publisher. For example, the Lecture Notes in Computer Science by Springer take much of their input from proceedings. Conference proceedings also get published through dedicated proceedings series as an edited volume where all their inputs comes from the conference papers. For example, AIJR Proceedings series published by academic publisher AIJR. Publication of proceedings as edited volume in such series are different from publishing conference paper in the journals; also known as conference issue. Increasingly, proceedings are published in electronic format via the internet or on CD, USB, etc.
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In the sciences, the quality of publications in conference proceedings is usually not as high as that of international scientific journals. However, in computer science, papers published in conference proceedings are accorded a higher status than in other fields, due to the fast-moving nature of the field.
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A number of full-fledged academic journals unconnected to particular conferences also use the word "proceedings" as part of their name, for example, Proceedings of the National Academy of Sciences of the United States of America.
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Conference proceedings may be published as a book or book series, in a journal, or otherwise as a serial publication . In many cases, impact factors are not available, although other journal metrics might exist. Bibliographic indexing often is done in separate bibliographic databases and citation indexes, e.g., Conference Proceedings Citation Index instead of Science Citation Index.
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Conferences accepting a broad range of topics from theoretical computer science, including algorithms, data structures, computability, computational complexity, automata theory and formal languages:
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Conferences whose topic is algorithms and data structures considered broadly, but that do not include other areas of theoretical computer science such as computational complexity theory:
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Conferences on computational geometry, graph drawing, and other application areas of geometric computing:
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GD – International Symposium on Graph Drawing
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SoCG – Symposium on Computational Geometry
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Conferences on programming languages, programming language theory and compilers:
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Conferences on software engineering:
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Conferences on formal methods in software engineering, including formal specification, formal verification, and static code analysis:
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Conferences on concurrent, distributed, and parallel computing, fault-tolerant systems, and dependable systems:
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Conferences on high-performance computing, cluster computing, and grid computing:
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Conferences on operating systems, storage systems and middleware:
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Conferences on computer architecture:
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Conferences on computer-aided design and electronic design automation:
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Conferences on computer networking:
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Wireless networks and mobile computing, including ubiquitous and pervasive computing, wireless ad hoc networks and wireless sensor networks:
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Conferences on computer security and privacy:
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Cryptography conferences:
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Conferences on databases, information systems, information retrieval, data mining and the World Wide Web:
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Conferences on artificial intelligence and machine learning:
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Conferences on Evolutionary computation.
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Conferences on automated reasoning:
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Conferences on computer vision and pattern recognition:
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Conferences on computational linguistics and natural language processing:
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Conferences on computer graphics, geometry processing, image processing, and multimedia:
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Conferences on human–computer interaction and user interfaces:
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Conferences on bioinformatics and computational biology:
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An in-memory service in SOP can be transparently externalized as a web service operation. Due to language and platform independent Web Service standards, SOP embraces all existing programming paradigms, languages and platforms. In SOP, the design of the programs pivot around the semantics of service calls, logical routing and data flow description across well-defined service interfaces. All SOP program modules are encapsulated as services and a service can be composed of other nested services in a hierarchical manner with virtually limitless depth to this service stack hierarchy. A composite service can also contain programming constructs some of which are specific and unique to SOP. A service can be an externalized system component that is accessed via any proprietary API or web service standards utilizing an in-memory plug-in technique.
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While SOP supports the basic programming constructs for sequencing, selection and iteration, it is differentiated with a slew of new programming constructs that provide built-in native ability geared towards data list manipulation, data integration, automated multithreading of service modules, declarative context management and synchronization of services. SOP design enables programmers to semantically synchronize the execution of services in order to guarantee that it is correct, or to declare a service module as a transaction boundary with automated commit/rollback behavior.
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Semantic design tools and runtime automation platforms can be built to support the fundamental concepts of SOP. For example, a service virtual machine that automatically creates service objects as units of work and manages their context can be designed to run based on the SOP program metadata stored in XML and created by a design-time automation tool. In SOA terms, the SVM is both a service producer and a service consumer.
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SOP concepts provide a robust base for a semantic approach to programming integration and application logic. There are three significant benefits to this approach:
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The following are some of the key concepts of SOP:
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In SOP, in-memory software modules are strictly encapsulated through well-defined service interfaces that can be externalized on-demand as web service operations. This minimal unit of encapsulation maximizes the opportunities for reusability within other in-memory service modules as well as across existing and legacy software assets. By using service interfaces for information hiding, SOP extends the service-oriented design principles used in SOA to achieve separation of concerns across in-memory service modules.
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A service interface in SOP is an in-memory object that describes a well-defined software task with well-defined input and output data structures. Service interfaces can be grouped into packages. An SOP service interface can be externalized as a WSDL operation and a single service or a package of services can be described using WSDL. Furthermore, service interfaces can be assigned to one or many service groups based on shared properties.
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In SOP, runtime properties stored on the service interface metadata serve as a contract with the service virtual machine . One example for the use of runtime properties is that in declarative service synchronization. A service interface can be declared as a fully synchronized interface, meaning that only a single instance of that service can run at any given time. Or, it can be synchronized based on the actual value of key inputs at runtime, meaning that no two service instances of that service with the same value for their key input data can run at the same time. Furthermore, synchronization can be declared across services interfaces that belong to the same service group. For example, if two services, 'CreditAccount" and 'DebitAccount", belong to the same synchronization service group and are synchronized on the accountName input field, then no two instances of 'CreditAccount" and 'DebitAccount" with the same account name can execute at the same time.
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A service invoker makes service requests. It is a pluggable in-memory interface that abstracts the location of a service producer as well as the communication protocol, used between the consumer and producer when going across computer memory, from the SOP runtime environment such as an SVM. The producer can be in-process , outside the process on the same server machine, or virtualized across a set of networked server machines. The use of a service invoker in SOP is the key to location transparency and virtualization. Another significant feature of the service invoker layer is the ability to optimize bandwidth and throughput when communicating across machines. For example, a "SOAP Invoker" is the default service invoker for remote communication across machines using the web service standards. This invoker can be dynamically swapped out if, for example, the producer and consumer wish to communicate through a packed proprietary API for better security and more efficient use of bandwidth.
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A service listener receives service requests. It is a pluggable in-memory interface that abstracts the communication protocol for incoming service requests made to the SOP runtime environment such as the SVM. Through this abstract layer, the SOP runtime environment can be virtually embedded within the memory address of any traditional programming environment or application service.
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In SOP, a service module can be either implemented as a Composite or Atomic service. It is important to note that Service modules built through the SOP paradigm have an extroverted nature and can be transparently externalized through standards such as SOAP or any proprietary protocol.
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One of the most important characteristic of SOP is that it can support a fully semantic-based approach to programming. Furthermore, this semantic-based approach can be layered into a visual environment built on top of a fully metadata-driven layer for storing the service interface and service module definitions. Furthermore, if the SOP runtime is supported by a SVM capable of interpreting the metadata layer, the need for automatic code generation can be eliminated. The result is tremendous productivity gain during development, ease of testing and significant agility in deployment.
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A composite service implementation is the semantic definition of a service module based on SOP techniques and concepts. If you look inside of a black-boxed interface definition of a composite service, you may see other service interfaces connected to each other and connected to SOP programming constructs. A Composite service has a recursive definition meaning that any service inside may be another atomic or composite service. An inner service may be a recursive reference to the same containing composite service.
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SOP supports the basic programming constructs for sequencing, selection and iteration as well as built-in, advance behavior. Furthermore, SOP supports semantic constructs for automatic data mapping, translation, manipulation and flow across inner services of a composite service.
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A service inside of the definition of a composite service is implicitly sequenced through the semantic connectivity of built-in success or failure ports of other inner services with its built-in activation port. When an inner service runs successfully, all the inner services connected to its success port will run next. If an inner service fails, all the services connected to its failure port will run next.
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Logical selection is accomplished through data-driven branching constructs and other configurable constructs. In general, configurable constructs are services built into the SOP platform with inputs and outputs that can assume the input/output shape of other connected services. For example, a configurable construct used for filtering output data of services can take a list of Sales orders, Purchase orders or any other data structure, and filter its data based on user declared filter properties stored on the interface of that instance of the filter construct. In this example, the structure to be filtered becomes the input of the particular instance of the filter construct and the same structure representing the filtered data becomes the output of the configurable construct.
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A composite service can be declared to loop. The loop can be bound by a fixed number of iterations with an optional built-in delay between iterations and it can dynamically terminate using a "service exit with success" or "service exit with failure" construct inside of the looping composite service. Furthermore, any service interface can automatically run in a loop or "foreach" mode, if it is supplied with two or more input components upon automatic preparation. This behavior is supported at design-time when a data list structure from one service is connected to a service that takes a single data structure as its input. If a runtime property of the composite service interface is declared to support "foreach" in parallel, then the runtime automation environment can automatically multi-thread the loop and run it in parallel. This is an example of how SOP programming constructs provide built-in advanced functionality.
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Data mapping, translation, and transformation constructs enable automatic transfer of data across inner services. An inner-service is prepared to run, when it is activated and all of its input dependencies are resolved. All the prepared inner-services within a composite service run in a parallel burst called a "hypercycle". This is one of the means by which automatic parallel-processing is supported in SOP. The definition of a composite service contains an implicit directed graph of inner service dependencies. The runtime environment for SOP can create an execution graph based on this directed graph by automatically instantiating and running inner services in parallel whenever possible.
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Exception handling is a run-time error in Java. Exception handling in SOP is simply accomplished by connecting the failure port of inner services to another inner service, or to a programming construct. "Exit with failure" and "exit with success" constructs are examples of constructs used for exception handling. If no action is taken on the failure port of a service, then the outer service will automatically fail and the standard output messages from the failed inner service will automatically bubble up to the standard output of the parent.
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A composite service can be declared as a transaction boundary. The runtime environment for SOP automatically creates and manages a hierarchical context for composite service objects which are used as a transaction boundary. This context automatically commits or rollbacks upon the successful execution of the composite service.
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Special composite services, called compensation services, can be associated with any service within SOP. When a composite service that is declared as a transaction boundary fails without an exception handling routing, the SOP runtime environment automatically dispatches the compensation services associated with all the inner services which have already executed successfully.
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An atomic service is an in-memory extension of the SOP runtime environment through a service native interface it is essentially a plug-in mechanism. For example, if SOP is automated through an SVM, a service plug-in is dynamically loaded into the SVM when any associated service is consumed. An example of a service plug-in would be a SOAP communicator plug-in that can on-the-fly translate any in-memory service input data to a Web Service SOAP request, post it to a service producer, and then translate the corresponding SOAP response to in-memory output data on the service. Another example of a service plug-in is a standard database SQL plug-in that supports data access, modification and query operations. A further example that can help establish the fundamental importance of atomic services and service plug-ins is using a service invoker as a service plug-in to transparently virtualize services across different instances of an SOP platform. This unique, component-level virtualization is termed "service grid virtualization" in order to distinguish it from traditional application, or process-level virtualization.
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SOP presents significant opportunities to support cross-cutting concerns for all applications built using the SOP technique. The following sections define some of these opportunities:
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The SOP runtime environment can systematically provide built-in and optimized profiling, logging and metering for all services in real-time.
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Based on declared key input values of a service instance, the outputs of a non time-sensitive inner service can be cached by the SOP runtime environment when running in the context of a particular composite service. When a service is cached for particular key input values, the SOP runtime environment fetches the cached outputs corresponding to the keyed inputs from its service cache instead of consuming the service. Availability of this built-in mechanism to the SOP application developer can significantly reduce the load on back-end systems.
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SOP provides a mechanism for associating a special kind of composite service, trigger service, to any other service. When that service is consumed, the SOP platform automatically creates and consumes an instance of the associated trigger service with an in-memory copy of the inputs of the triggering service. This consumption is non-intrusive to the execution of the triggering service. A service trigger can be declared to run upon activation, failure or success completion of the triggering service.
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In addition to the ability to call any service, Service Request Events and Shared Memory are two of the SOP built-in mechanisms provided for inter-service communication. The consumption of a service is treated as an Event in SOP. SOP provides a correlation-based event mechanism that results in the pre-emption of a running composite that has declared, through a "wait" construct, the need to wait for one or more other service consumption events to happen with specified input data values. The execution of the composite service continues when services are consumed with specific correlation key inputs associated with the wait construct. SOP also provides a shared memory space with access control where services can access and update a well-defined data structure that is similar to the input/output structure of services. The shared memory mechanism within SOP can be programmatically accessed through service interfaces.
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In SOP, customizations are managed through an inventive feature called Service Overrides. Through this feature, a service implementation can be statically or dynamically overridden by one of many possible implementations at runtime. This feature is analogous to polymorphism in object-oriented programming. Each possible override implementation can be associated to one or more override configuration portfolios in order to manage activation of groups of related overrides throughout different SOP application installations at the time of deployment.
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Select services can be deployed securely for external programmatic consumption by a presentation layer, or other applications. Once service accounts are defined, the SOP runtime environment automatically manages access through consumer account provisioning mechanisms.
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The SOP runtime environment can systematically provide built-in authentication and service authorization. For the purpose of authorization, SOP development projects, consumer accounts, packages and services are treated as resources with access control. In this way, the SOP runtime environment can provide built-in authorization. Standards or proprietary authorization and communication security is customized through service overrides, plug-in invoker and service listener modules.
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Since all artifacts of SOP are well-encapsulated services and all SOP mechanisms, such as shared memory, can be provided as distributable services, large-scale virtualization can be automated by the SOP runtime environment. Also, the hierarchical service stack of a composite service with the multiple execution graphs associated to its inner services, at each level, provides tremendous opportunities for automated multi-threading to the SOP runtime environment.
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The term service-oriented programming was first published in 2002 by Alberto Sillitti, Tullio Vernazza and Giancarlo Succi in a book called "Software Reuse: Methods, Techniques, and Tools." SOP, as described above, reflects some aspects of the use of the term proposed by Sillitti, Vernazza and Succi.
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Today, the SOP paradigm is in the early stages of mainstream adoption. There are four market drivers fueling this adoption:
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Service-Oriented Programming: A New Paradigm of Software Reuse
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Grid Virtualization and Itanium®-based Solutions
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Object-oriented programming is a programming paradigm based on the concept of objects, which can contain data and code: data in the form of fields , and code in the form of procedures . In OOP, computer programs are designed by making them out of objects that interact with one another.
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Many of the most widely used programming languages are multi-paradigm and they support object-oriented programming to a greater or lesser degree, typically in combination with imperative programming, procedural programming and functional programming.
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Significant object-oriented languages include Ada, ActionScript, C++, Common Lisp, C#, Dart, Eiffel, Fortran 2003, Haxe, Java, JavaScript, Kotlin, Logo, MATLAB, Objective-C, Object Pascal, Perl, PHP, Python, R, Raku, Ruby, Scala, SIMSCRIPT, Simula, Smalltalk, Swift, Vala and Visual Basic.NET.
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Terminology invoking "objects" in the modern sense of object-oriented programming made its first appearance at the artificial intelligence group at MIT in the late 1950s and early 1960s. "Object" referred to LISP atoms with identified properties . Another early MIT example was Sketchpad created by Ivan Sutherland in 1960–1961; in the glossary of the 1963 technical report based on his dissertation about Sketchpad, Sutherland defined notions of "object" and "instance" , albeit specialized to graphical interaction. Also, in 1968, an MIT ALGOL version, AED-0, established a direct link between data structures and procedures, prefiguring what were later termed "messages", "methods", and "member functions". Topics such as data abstraction and modular programming were common points of discussion at this time.
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Independently of later MIT work such as AED, Simula was developed during the years 1961–1967. Simula introduced important concepts that are today an essential part of object-oriented programming, such as class and object, inheritance, and dynamic binding. The object-oriented Simula programming language was used mainly by researchers involved with physical modelling, such as models to study and improve the movement of ships and their content through cargo ports.
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Influenced by the work at MIT and the Simula language, in November 1966 Alan Kay began working on ideas that would eventually be incorporated into the Smalltalk programming language. Kay used the term "object-oriented programming" in conversation as early as 1967. Although sometimes called "the father of object-oriented programming", Alan Kay has differentiated his notion of OO from the more conventional abstract data type notion of object, and has implied that the computer science establishment did not adopt his notion. A 1976 MIT memo co-authored by Barbara Liskov lists Simula 67, CLU, and Alphard as object-oriented languages, but does not mention Smalltalk.
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