Computer Science: August 2012

Tuesday, 28 August 2012

Computer architecture and engineering

Main articles: Computer architecture 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. The field often involves disciplines of computer engineering and electrical engineering, selecting and interconnection hardware components to create computers that meet functional, performance, and cost goals.


Digital logic	Microarchitecture	Multiprocessing

Operating systems	Computer networks	Databases	Computer security

Ubiquitous computing	Systems architecture	Compiler design	Programming languages

[edit]Computer graphics and visualization

Main article: Computer graphics (computer science)

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

[edit]Computer security and cryptography

Main articles: Computer security and Cryptography

Computer security is a branch of computer technology, whose objective includes protection of information from unauthorized access, disruption, or modification while maintaining the accessibility and usability of the system for its intended users. Cryptography is the practice and study of hiding (encryption) and therefore deciphering (decryption) information. Modern cryptography is largely related to computer science, for many encryption and decryption algorithms are based on their computational complexity.

[edit]Computational science

Computational science (or scientific computing) is the field of study concerned with constructing mathematical models and quantitative analysis techniques and using computers to analyze and solve scientific problems. In practical use, it is typically the application of computer simulation and other forms of computation to problems in various scientific disciplines.


Numerical analysis	Computational physics	Computational chemistry	Bioinformatics

[edit]Health Informatics

Main article: Health Informatics

Health Informatics in computer science is referred to as Computational health informatics and deals with computational techniques for solving problems in health care. It is a sub-branch of both computer science and health informatics.

[edit]Information science

Main article: Information science


Information retrieval	Knowledge representation	Natural language processing	Human–computer interaction

[edit]Software engineering

Main article: Software engineering

Software engineering is the study of designing, implementing, and modifying 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 software to get the best out of them. It doesn't just deal with the creation or manufacture of new software, but its internal maintenance and arrangement.

Areas 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.^[26]^[27] CSAB, formerly called Computing Sciences Accreditation Board – which is made up of representatives of the Association for Computing Machinery (ACM), and theIEEE Computer Society (IEEE-CS)^[28] – 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, computer-human interaction, computer graphics, operating systems, and numerical and symbolic computation as being important areas of computer science.^[26]

[edit]Theoretical computer science

Main article: Theoretical computer science

The broader field of theoretical computer science encompasses both the classical theory of computation and a wide range of other topics that focus on the more abstract, logical, and mathematical aspects of computing.

[edit]Theory of computation

Main article: Theory of computation

According to Peter J. Denning, the fundamental question underlying computer science is, "What can be (efficiently) automated?"^[6] The study of the 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 theoryexamines 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,^[29] is an open problem in the theory of computation.

		P = NP ?	GNITIRW-TERCES
Automata theory	Computability theory	Computational complexity theory	Cryptography	Quantum computing theory

[edit]Information and coding theory

Main articles: Information theory and Coding theory

Information theory is related to the quantification of information. This was developed by Claude E. 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.

[edit]Algorithms and data structures

$O(n^2)$
Analysis of algorithms	Algorithms	Data structures	Computational geometry

[edit]Programming language theory

Main article: Programming language theory

Programming language theory (PLT) 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.

$\Gamma\vdash x: \text{Int}$
Type theory	Compiler design	Programming languages

[edit]Formal methods

Main article: Formal methods

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. 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 orsecurity 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.

[edit]Concurrent, parallel and distributed systems

Main articles: Concurrency (computer science) and Distributed computing

Concurrency is a property of systems in which several computations are executing simultaneously, and potentially interacting with each other. A number of mathematical models have been developed for general concurrent computation including Petri nets, process calculi and the Parallel Random Access Machine model. A distributed system extends the idea of concurrency onto multiple computers connected through a network. Computers within the same distributed system have their own private memory, and information is often exchanged amongst themselves to achieve a common goal.

[edit]Databases and information retrieval

Main articles: Database and Database management systems

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.

[edit]Applied computer science

[edit]Artificial intelligence

Main article: Artificial intelligence

This branch of computer science aims to or is required to synthesise goal-orientated processes such as problem-solving, decision-making, environmental adaptation, learning and communication which are found in humans and animals. From its origins in cybernetics and in the Dartmouth Conference (1956), artificial intelligence (AI) 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 and modeling such as finance and economics, data mining and the physical sciences. 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.


Machine learning	Computer vision	Image processing	Pattern recognition

Cognitive science	Data mining	Evolutionary computation	Information retrieval

Knowledge representation	Natural language processing	Robotics

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