Glossary of Computer Science

Mathematical foundations

Coding theory: useful in networking, programming and other areas where computers communicate with each other.
Discrete Mathematics.
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.

Parallel computing: the theory and practice of simultaneous computation; data safety in any multitasking or multithreaded environment.
Concurrency: computing using multiple concurrent threads of execution, devising algorithms for solving problems on multiple 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.

Compiler theory: theory of compiler design, based on Automata theory.
Programming language theory (PLT): is a branch of computer science that deals with the design, implementation, analysis, characterization, and classification of formal languages known as programming languages and of their individual features.
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.

Computer architecture: the design, organization, optimization and verification of a computer system, mostly about CPUs and Memory subsystem (and the bus connecting them).
Operating systems: systems for managing computer programs and providing the basis of a usable system.

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

Over-fetching (trazer mais informação do que o necessário)
Under-fetching (trazer menos informação que o necessário, obrigando a realização de chamadas adicionais)
Two-way databinding