Computer science

Algorithms 

Algorithms and data structures are central to computer science. The theory of computation concerns abstract models of computation and general classes of problems that can be solved using them. The fields of cryptography and computer security involve studying the means for secure communication and preventing security vulnerabilities.

Graphics
Computer graphics and computational geometry address the generation of images. Programming language theory considers different ways to describe computational processes, and database theory concerns the management of repositories of data. Human–computer interaction investigates the interfaces through which humans and computers interact, and software engineering focuses on the design and principles behind developing software.

Operating Systems
Areas such as operating systems, networks and embedded systems investigate the principles and design behind complex systems. Computer architecture describes the construction of computer components and computer-operated equipment.

Artificial intelligence and machine learning
Artificial intelligence and machine learning aim to synthesize goal-orientated processes such as problem-solving, decision-making, environmental adaptation, planning and learning found in humans and animals. Within artificial intelligence, computer vision aims to understand and process image and video data, while natural language processing aims to understand and process textual and linguistic data.

 

Ada Lovelace published the first algorithm intended for processing on a computer.

The earliest foundations of what would become computer science predate the invention of the modern digital computer. Machines for calculating fixed numerical tasks such as the abacus have existed since antiquity, aiding in computations such as multiplication and division.

Algorithms in the 17th Century
Algorithms for performing computations have existed since antiquity, even before the development of sophisticated computing equipment. Wilhelm Schickard designed and constructed the first working mechanical calculator in 1623. In 1673, Gottfried Leibniz demonstrated a digital mechanical calculator, called the Stepped Reckoner.

Etymology and scope

Although first proposed in 1956, the term "computer science" appears in a 1959 article in Communications of the ACM, in which Louis Fein argues for the creation of a Graduate School in Computer Sciences analogous to the creation of Harvard Business School in 1921. Louis justifies the name by arguing that, like management science, the subject is applied and interdisciplinary in nature, while having the characteristics typical of an academic discipline. His efforts, and those of others such as numerical analyst George Forsythe, were rewarded: universities went on to create such departments, starting with Purdue in 1962.

Machines Never Be Conscious
Despite its name, a significant amount of computer science does not involve the study of computers themselves. Because of this, several alternative names have been proposed. Certain departments of major universities prefer the term computing science, to emphasize precisely that difference. Danish scientist Peter Naur suggested the term datalogy, to reflect the fact that the scientific discipline revolves around data and data treatment, while not necessarily involving computers. The first scientific institution to use the term was the Department of Datalogy at the University of Copenhagen, founded in 1969, with Peter Naur being the first professor in datalogy. The term is used mainly in the Scandinavian countries. An alternative term, also proposed by Naur, is data science; this is now used for a multi-disciplinary field of data analysis, including statistics and databases.

 

In the early days of computing, a number of terms for the practitioners of the field of computing were suggested in the Communications of the ACM—turingineer, turologist, flow-charts-man, applied meta-mathematician, and applied epistemologist. Three months later in the same journal, comptologist was suggested, followed next year by hypologist. The term computics has also been suggested. In Europe, terms derived from contracted translations of the expression "automatic information" (e.g. "informazione automatica" in Italian) or "information and mathematics" are often used, e.g. informatique (French), Informatik (German), informatica (Italian, Dutch), informática (Spanish, Portuguese), informatika (Slavic languages and Hungarian) or pliroforiki (πληροφορική, which means informatics) in Greek. Similar words have also been adopted in the UK (as in the School of Informatics, University of Edinburgh). "In the U.S., however, informatics is linked with applied computing, or computing in the context of another domain."

Computer Science is Mathematics
Computer science is considered by some to have a much closer relationship with mathematics than many scientific disciplines, with some observers saying that computing is a mathematical science. Early computer science was strongly influenced by the work of mathematicians such as Kurt Gödel, Alan Turing, John von Neumann, Rózsa Péter and Alonzo Church and there continues to be a useful interchange of ideas between the two fields in areas such as mathematical logic, category theory, domain theory, and algebra.[36]

 

The relationship between computer science and software engineering is a contentious issue, which is further muddied by disputes over what the term "software engineering" means, and how computer science is defined. David Parnas, taking a cue from the relationship between other engineering and science disciplines, has claimed that the principal focus of computer science is studying the properties of computation in general, while the principal focus of software engineering is the design of specific computations to achieve practical goals, making the two separate but complementary disciplines.

 

The academic, political, and funding aspects of computer science tend to depend on whether a department is formed with a mathematical emphasis or with an engineering emphasis. Computer science departments with a mathematics emphasis and with a numerical orientation consider alignment with computational science. Both types of departments tend to make efforts to bridge the field educationally if not across all research.

 

Despite the word science in its name, there is debate over whether or not computer science is a discipline of science,[49] mathematics,[50] or engineering. Allen Newell and Herbert A. Simon argued in 1975,

Computer science is an empirical discipline. We would have called it an experimental science, but like astronomy, economics, and geology, some of its unique forms of observation and experience do not fit a narrow stereotype of the experimental method. Nonetheless, they are experiments. Each new machine that is built is an experiment. Actually constructing the machine poses a question to nature; and we listen for the answer by observing the machine in operation and analyzing it by all analytical and measurement means available.

 

It has since been argued that computer science can be classified as an empirical science since it makes use of empirical testing to evaluate the correctness of programs, but a problem remains in defining the laws and theorems of computer science (if any exist) and defining the nature of experiments in computer science. Proponents of classifying computer science as an engineering discipline argue that the reliability of computational systems is investigated in the same way as bridges in civil engineering and airplanes in aerospace engineering. They also argue that while empirical sciences observe what presently exists, computer science observes what is possible to exist and while scientists discover laws from observation, no proper laws have been found in computer science and it is instead concerned with creating phenomena.

 

Proponents of classifying computer science as a mathematical discipline argue that computer programs are physical realizations of mathematical entities and programs that can be deductively reasoned through mathematical formal methods.[51] Computer scientists Edsger W. Dijkstra and Tony Hoare regard instructions for computer programs as mathematical sentences and interpret formal semantics for programming languages as mathematical axiomatic systems.

 

A number of computer scientists have argued for the distinction of three separate paradigms in computer science. Peter Wegner argued that those paradigms are science, technology, and mathematics. Peter Denning's working group argued that they are theory, abstraction (modeling), and design. Amnon H. Eden described them as the "rationalist paradigm" (which treats computer science as a branch of mathematics, which is prevalent in theoretical computer science, and mainly employs deductive reasoning), the "technocratic paradigm" (which might be found in engineering approaches, most prominently in software engineering), and the "scientific paradigm" (which approaches computer-related artifacts from the empirical perspective of natural sciences, identifiable in some branches of artificial intelligence). Computer science focuses on methods involved in design, specification, programming, verification, implementation and testing of human-made computing systems.

 

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Further information: Outline of computer science

As a discipline, computer science spans a range of topics from theoretical studies of algorithms and the limits of computation to the practical issues of implementing computing systems in hardware and software. CSAB, formerly called Computing Sciences Accreditation Board—which is made up of representatives of the Association for Computing Machinery (ACM), and the IEEE Computer Society (IEEE CS)—identifies four areas that it considers crucial to the discipline of computer science: theory of computation, algorithms and data structures, programming methodology and languages, and computer elements and architecture. In addition to these four areas, CSAB also identifies fields such as software engineering, artificial intelligence, computer networking and communication, database systems, parallel computation, distributed computation, human–computer interaction, computer graphics, operating systems, and numerical and symbolic computation as being important areas of computer science.

 

Theoretical Computer Science is mathematical and abstract in spirit, but it derives its motivation from practical and everyday computation. It aims to understand the nature of computation and, as a consequence of this understanding, provide more efficient methodologies.

 

According to Peter Denning, the fundamental question underlying computer science is, "What can be automated?" Theory of computation is focused on answering fundamental questions about what can be computed and what amount of resources are required to perform those computations. In an effort to answer the first question, computability theory examines which computational problems are solvable on various theoretical models of computation. The second question is addressed by computational complexity theory, which studies the time and space costs associated with different approaches to solving a multitude of computational problems.

 

The famous P = NP? problem, one of the Millennium Prize Problems, is an open problem in the theory of computation.

 

Automata theory Formal languages Computability theory Computational complexity theory

Models of computation Quantum computing theory Logic circuit theory Cellular automata

Information and coding theory

Main articles: Information theory and Coding theory

Information theory, closely related to probability and statistics, is related to the quantification of information. This was developed by Claude Shannon to find fundamental limits on signal processing operations such as compressing data and on reliably storing and communicating data. Coding theory is the study of the properties of codes (systems for converting information from one form to another) and their fitness for a specific application. Codes are used for data compression, cryptography, error detection and correction, and more recently also for network coding. Codes are studied for the purpose of designing efficient and reliable data transmission methods. 

 

Coding theory Channel capacity Algorithmic information theory Signal detection theory Kolmogorov complexity

Data structures and algorithms

Main articles: Data structure and Algorithm

Data structures and algorithms are the studies of commonly used computational methods and their computational efficiency.

 

O(n2) 

Analysis of algorithms Algorithm design Data structures Combinatorial optimization Computational geometry Randomized algorithms

Programming language theory and formal methods

Main articles: Programming language theory and Formal methods

Programming language theory is a branch of computer science that deals with the design, implementation, analysis, characterization, and classification of programming languages and their individual features. It falls within the discipline of computer science, both depending on and affecting mathematics, software engineering, and linguistics. It is an active research area, with numerous dedicated academic journals.

 

Formal methods are a particular kind of mathematically based technique for the specification, development and verification of software and hardware systems. The use of formal methods for software and hardware design is motivated by the expectation that, as in other engineering disciplines, performing appropriate mathematical analysis can contribute to the reliability and robustness of a design. They form an important theoretical underpinning for software engineering, especially where safety or security is involved. Formal methods are a useful adjunct to software testing since they help avoid errors and can also give a framework for testing. For industrial use, tool support is required. However, the high cost of using formal methods means that they are usually only used in the development of high-integrity and life-critical systems, where safety or security is of utmost importance. Formal methods are best described as the application of a fairly broad variety of theoretical computer science fundamentals, in particular logic calculi, formal languages, automata theory, and program semantics, but also type systems and algebraic data types to problems in software and hardware specification and verification.

 

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Formal semantics Type theory Compiler design Programming languages Formal verification Automated theorem proving

Applied computer science

Computer graphics and visualization

Main article: Computer graphics (computer science)

Computer graphics is the study of digital visual contents and involves the synthesis and manipulation of image data. The study is connected to many other fields in computer science, including computer vision, image processing, and computational geometry, and is heavily applied in the fields of special effects and video games.

 

2D computer graphics Computer animation Rendering Mixed reality Virtual reality Solid modeling

Image and sound processing

Information can take the form of images, sound, video or other multimedia. Bits of information can be streamed via signals. Its processing is the central notion of informatics, the European view on computing, which studies information processing algorithms independently of the type of information carrier – whether it is electrical, mechanical or biological. This field plays important role in information theory, telecommunications, information engineering and has applications in medical image computing and speech synthesis, among others. What is the lower bound on the complexity of fast Fourier transform algorithms? is one of the unsolved problems in theoretical computer science.

 

FFT algorithms Image processing Speech recognition Data compression Medical image computing Speech synthesis

Computational science, finance and engineering

Main articles: Computational science, Computational finance, and Computational engineering

See also: List of computer-aided engineering software

Scientific computing (or computational science) is the field of study concerned with constructing mathematical models and quantitative analysis techniques and using computers to analyze and solve scientific problems. A major usage of scientific computing is simulation of various processes, including computational fluid dynamics, physical, electrical, and electronic systems and circuits, as well as societies and social situations (notably war games) along with their habitats, among many others. Modern computers enable optimization of such designs as complete aircraft. Notable in electrical and electronic circuit design are SPICE,[63] as well as software for physical realization of new (or modified) designs. The latter includes essential design software for integrated circuits.[64]

 

Numerical analysis Computational physics Computational chemistry Bioinformatics Neuroinformatics Psychoinformatics Medical informatics Computational engineering Computational musicology

Social computing and human–computer interaction

Main articles: Social computing and Human–computer interaction

Social computing is an area that is concerned with the intersection of social behavior and computational systems. Human–computer interaction research develops theories, principles, and guidelines for user interface designers.

 

Software engineering is the study of designing, implementing, and modifying the software in order to ensure it is of high quality, affordable, maintainable, and fast to build. It is a systematic approach to software design, involving the application of engineering practices to software. Software engineering deals with the organizing and analyzing of software—it does not just deal with the creation or manufacture of new software, but its internal arrangement and maintenance. For example software testing, systems engineering, technical debt and software development processes.

 

Artificial intelligence (AI) aims to or is required to synthesize goal-orientated processes such as problem-solving, decision-making, environmental adaptation, learning, and communication found in humans and animals. From its origins in cybernetics and in the Dartmouth Conference (1956), artificial intelligence research has been necessarily cross-disciplinary, drawing on areas of expertise such as applied mathematics, symbolic logic, semiotics, electrical engineering, philosophy of mind, neurophysiology, and social intelligence. AI is associated in the popular mind with robotic development, but the main field of practical application has been as an embedded component in areas of software development, which require computational understanding. The starting point in the late 1940s was Alan Turing's question "Can computers think?", and the question remains effectively unanswered, although the Turing test is still used to assess computer output on the scale of human intelligence. But the automation of evaluative and predictive tasks has been increasingly successful as a substitute for human monitoring and intervention in domains of computer application involving complex real-world data.

 

Computational learning theory Computer vision Neural networks Planning and scheduling

Natural language processing Computational game theory Evolutionary computation Autonomic computing

Representation and reasoning Pattern recognition Robotics Swarm intelligence

Computer systems

Computer architecture and organization

Main articles: Computer architecture, Computer organization, and Computer engineering

Computer architecture, or digital computer organization, is the conceptual design and fundamental operational structure of a computer system. It focuses largely on the way by which the central processing unit performs internally and accesses addresses in memory.[65] Computer engineers study computational logic and design of computer hardware, from individual processor components, microcontrollers, personal computers to supercomputers and embedded systems. The term "architecture" in computer literature can be traced to the work of Lyle R. Johnson and Frederick P. Brooks Jr., members of the Machine Organization department in IBM's main research center in 1959.

 

Processing unit Microarchitecture Multiprocessing Processor design

Ubiquitous computing Systems architecture Operating systems Input/output

Embedded system Real-time computing Dependability Interpreter

Concurrent, parallel and distributed computing

 

Concurrency is a property of systems in which several computations are executing simultaneously, and potentially interacting with each other.[66] A number of mathematical models have been developed for general concurrent computation including Petri nets, process calculi and the parallel random access machine model.When multiple computers are connected in a network while using concurrency, this is known as a distributed system. Computers within that distributed system have their own private memory, and information can be exchanged to achieve common goals.

 

This branch of computer science aims to manage networks between computers worldwide.

 

Computer security is a branch of computer technology with the objective of protecting information from unauthorized access, disruption, or modification while maintaining the accessibility and usability of the system for its intended users.

 

Historical cryptography is the art of writing and deciphering secret messages. Modern cryptography is the scientific study of problems relating to distributed computations that can be attacked.[69] Technologies studied in modern cryptography include symmetric and asymmetric encryption, digital signatures, cryptographic hash functions, key-agreement protocols, blockchain, zero-knowledge proofs, and garbled circuits.

 

A database is intended to organize, store, and retrieve large amounts of data easily. Digital databases are managed using database management systems to store, create, maintain, and search data, through database models and query languages. Data mining is a process of discovering patterns in large data sets.

 

Discoveries

The philosopher of computing Bill Rapaport noted three Great Insights of Computer Science:[70]

 

Gottfried Wilhelm Leibniz's, George Boole's, Alan Turing's, Claude Shannon's, and Samuel Morse's insight: there are only two objects that a computer has to deal with in order to represent "anything".[note 4]

 

All the information about any computable problem can be represented using only 0 and 1 (or any other bistable pair that can flip-flop between two easily distinguishable states, such as "on/off", "magnetized/de-magnetized", "high-voltage/low-voltage", etc.).

See also: Digital physics

 

Alan Turing's insight: there are only five actions that a computer has to perform in order to do "anything".

 

Every algorithm can be expressed in a language for a computer consisting of only five basic instructions:

_: move left one location;

_: move right one location;

_: read symbol at current location;

_: print 0 at current location;

_: print 1 at current location.

 

Corrado Böhm and Giuseppe Jacopini's insight: there are only three ways of combining these actions (into more complex ones) that are needed in order for a computer to do "anything".[72]

Only three rules are needed to combine any set of basic instructions into more complex ones:

sequence: first do this, then do that;

IF such-and-such is the case,
THEN do this,
ELSE do that;

repetition:
WHILE such-and-such is the case,
DO this.

Functional programming, a style of building the structure and elements of computer programs that treats computation as the evaluation of mathematical functions and avoids state and mutable data. It is a declarative programming paradigm, which means programming is done with expressions or declarations instead of statements.

Imperative Programming

Imperative programming, a programming paradigm that uses statements that change a program's state.[74] In much the same way that the imperative mood in natural languages expresses commands, an imperative program consists of commands for the computer to perform. Imperative programming focuses on describing how a program operates.

 

Object-oriented programming, a programming paradigm based on the concept of "objects", which may contain data, in the form of fields, often known as attributes; and code, in the form of procedures, often known as methods. A feature of objects is that an object's procedures can access and often modify the data fields of the object with which they are associated. Thus object-oriented computer programs are made out of objects that interact with one another.

Service-oriented programming, a programming paradigm that uses "services" as the unit of computer work, to design and implement integrated business applications and mission critical software programs

Many languages offer support for multiple paradigms, making the distinction more a matter of style than of technical capabilities.[76]

 

Research

Further information: List of computer science conferences and Category:Computer science journals

Conferences are important events for computer science research. During these conferences, researchers from the public and private sectors present their recent work and meet. Unlike in most other academic fields, in computer science, the prestige of conference papers is greater than that of journal publications. One proposed explanation for this is the quick development of this relatively new field requires rapid review and distribution of results, a task better handled by conferences than by journals

 

 

Computer science (also called computing science) is the study of the theoretical foundations of information and computation and their implementation and application in computer systems. One well known subject classification system for computer science is the ACM Computing Classification System devised by the Association for Computing Machinery.

 

Computer science can be described as all of the following:

 

Academic discipline

Science

Applied science

Subfields

Mathematical foundations

Coding theory – Useful in networking, programming, system development, and other areas where computers communicate with each other.

Game theory – Useful in artificial intelligence and cybernetics.

Discrete mathematics - Study of discrete structures. Used in digital computer systems.

Graph theory – Foundations for data structures and searching algorithms.

Mathematical logic – Boolean logic and other ways of modeling logical queries; the uses and limitations of formal proof methods.

Number theory – Theory of the integers. Used in cryptography as well as a test domain in artificial intelligence.

Algorithms and data structures

Algorithms – Sequential and parallel computational procedures for solving a wide range of problems.

Data structures – The organization and manipulation of data.

Artificial intelligence

Outline of artificial intelligence

 

Artificial intelligence – The implementation and study of systems that exhibit an autonomous intelligence or behavior of their own.

Automated reasoning – Solving engines, such as used in Prolog, which produce steps to a result given a query on a fact and rule database, and automated theorem provers that aim to prove mathematical theorems with some assistance from a programmer.

Computer vision – Algorithms for identifying three-dimensional objects from a two-dimensional picture.

Soft computing, the use of inexact solutions for otherwise extremely difficult problems:

Machine learning - Development of models that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyse and draw inferences from patterns in data.

Evolutionary computing - Biologically inspired algorithms.

Natural language processing - Building systems and algorithms that analyze, understand, and generate natural (human) languages.

Robotics – Algorithms for controlling the behaviour of robots.

Communication and security

Networking – Algorithms and protocols for reliably communicating data across different shared or dedicated media, often including error correction.

Computer security – Practical aspects of securing computer systems and computer networks.

Cryptography – Applies results from complexity, probability, algebra and number theory to invent and break codes, and analyze the security of cryptographic protocols.

Computer architecture

Computer architecture – The design, organization, optimization and verification of a computer system, mostly about CPUs and Memory subsystems (and the bus connecting them).

Operating systems – Systems for managing computer programs and providing the basis of a usable system.

Computer graphics

Computer graphics – Algorithms both for generating visual images synthetically, and for integrating or altering visual and spatial information sampled from the real world.

Image processing – Determining information from an image through computation.

Information visualization – Methods for representing and displaying abstract data to facilitate human interaction for exploration and understanding.

Concurrent, parallel, and distributed systems

Parallel computing - The theory and practice of simultaneous computation; data safety in any multitasking or multithreaded environment.

Concurrency (computer science) – Computing using multiple concurrent threads of execution, devising algorithms for solving problems on various processors to achieve maximal speed-up compared to sequential execution.

Distributed computing – Computing using multiple computing devices over a network to accomplish a common objective or task and thereby reducing the latency involved in single processor contributions for any task.

Relational databases – the set theoretic and algorithmic foundation of databases.

Structured Storage - non-relational databases such as NoSQL databases.

Data mining – Study of algorithms for searching and processing information in documents and databases; closely related to information retrieval.

Programming languages and compilers

Compiler theory – Theory of compiler design, based on Automata theory.

Programming language pragmatics – Taxonomy of programming languages, their strength and weaknesses. Various programming paradigms, such as object-oriented programming.

Programming language theory - Theory of programming language design

Formal semantics – rigorous mathematical study of the meaning of programs.

Type theory – Formal analysis of the types of data, and the use of these types to understand properties of programs — especially program safety.

Computational science – constructing mathematical models and quantitative analysis techniques and using computers to analyze and solve scientific problems.

Numerical analysis – Approximate numerical solution of mathematical problems such as root-finding, integration, the solution of ordinary differential equations; the approximation of special functions.

Symbolic computation – Manipulation and solution of expressions in symbolic form, also known as Computer algebra.

Computational physics – Numerical simulations of large non-analytic systems

Computational chemistry – Computational modelling of theoretical chemistry in order to determine chemical structures and properties

Bioinformatics and Computational biology – The use of computer science to maintain, analyse, store biological data and to assist in solving biological problems such as Protein folding, function prediction and Phylogeny.

Computational neuroscience – Computational modelling of neurophysiology.

Computational linguistics

Computational logic

Computational engineering

Formal methods – Mathematical approaches for describing and reasoning about software design.

Software engineering – The principles and practice of designing, developing, and testing programs, as well as proper engineering practices.

Algorithm design – Using ideas from algorithm theory to creatively design solutions to real tasks.

Computer programming – The practice of using a programming language to implement algorithms.

Human–computer interaction – The study and design of computer interfaces that people use.

Reverse engineering – The application of the scientific method to the understanding of arbitrary existing software.

Theory of computation

Main article: Theory of computation

Automata theory – Different logical structures for solving problems.

Computability theory – What is calculable with the current models of computers. Proofs developed by Alan Turing and others provide insight into the possibilities of what may be computed and what may not.

List of unsolved problems in computer science

Computational complexity theory – Fundamental bounds (especially time and storage space) on classes of computations.

Quantum computing theory – Explores computational models involving quantum superposition of bits.