Key–value databaseA key–value database, or key–value store, is a data storage paradigm designed for storing, retrieving, and managing associative arrays, and a data structure more commonly known today as a dictionary or hash table. Dictionaries contain a collection of objects, or records, which in turn have many different fields within them, each containing data. These records are stored and retrieved using a key that uniquely identifies the record, and is used to find the data within the database.
Materialized viewIn computing, a materialized view is a database object that contains the results of a query. For example, it may be a local copy of data located remotely, or may be a subset of the rows and/or columns of a table or join result, or may be a summary using an aggregate function. The process of setting up a materialized view is sometimes called materialization. This is a form of caching the results of a query, similar to memoization of the value of a function in functional languages, and it is sometimes described as a form of precomputation.
Conceptual schemaA conceptual schema or conceptual data model is a high-level description of informational needs underlying the design of a database. It typically includes only the main concepts and the main relationships among them. Typically this is a first-cut model, with insufficient detail to build an actual database. This level describes the structure of the whole database for a group of users. The conceptual model is also known as the data model that can be used to describe the conceptual schema when a database system is implemented.
Entity–attribute–value modelAn entity–attribute–value model (EAV) is a data model optimized for the space-efficient storage of sparse—or ad-hoc—property or data values, intended for situations where runtime usage patterns are arbitrary, subject to user variation, or otherwise unforseeable using a fixed design. The use-case targets applications which offer a large or rich system of defined property types, which are in turn appropriate to a wide set of entities, but where typically only a small, specific selection of these are instantated (or persisted) for a given entity.
R-treeR-trees are tree data structures used for spatial access methods, i.e., for indexing multi-dimensional information such as geographical coordinates, rectangles or polygons. The R-tree was proposed by Antonin Guttman in 1984 and has found significant use in both theoretical and applied contexts. A common real-world usage for an R-tree might be to store spatial objects such as restaurant locations or the polygons that typical maps are made of: streets, buildings, outlines of lakes, coastlines, etc.
Logical schemaA logical data model or logical schema is a data model of a specific problem domain expressed independently of a particular database management product or storage technology (physical data model) but in terms of data structures such as relational tables and columns, object-oriented classes, or XML tags. This is as opposed to a conceptual data model, which describes the semantics of an organization without reference to technology. Logical data models represent the abstract structure of a domain of information.
DatalogDatalog is a declarative logic programming language. While it is syntactically a subset of Prolog, Datalog generally uses a bottom-up rather than top-down evaluation model. This difference yields significantly different behavior and properties from Prolog. It is often used as a query language for deductive databases. Datalog has been applied to problems in data integration, networking, program analysis, and more. A Datalog program consists of facts, which are statements that are held to be true, and rules, which say how to deduce new facts from known facts.
Business logicIn computer software, business logic or domain logic is the part of the program that encodes the real-world business rules that determine how data can be created, stored, and changed. It is contrasted with the remainder of the software that might be concerned with lower-level details of managing a database or displaying the user interface, system infrastructure, or generally connecting various parts of the program.
Logging (computing)In computing, logging is the act of keeping a log of events that occur in a computer system, such as problems, errors or just information on current operations. These events may occur in the operating system or in other software. A message or log entry is recorded for each such event. These log messages can then be used to monitor and understand the operation of the system, to debug problems, or during an audit. Logging is particularly important in multi-user software, to have a central overview of the operation of the system.
Data securityData security means protecting digital data, such as those in a database, from destructive forces and from the unwanted actions of unauthorized users, such as a cyberattack or a data breach. Disk encryption Disk encryption refers to encryption technology that encrypts data on a hard disk drive. Disk encryption typically takes form in either software (see disk encryption software) or hardware (see disk encryption hardware). Disk encryption is often referred to as on-the-fly encryption (OTFE) or transparent encryption.
