Data virtualizationData virtualization is an approach to data management that allows an application to retrieve and manipulate data without requiring technical details about the data, such as how it is formatted at source, or where it is physically located, and can provide a single customer view (or single view of any other entity) of the overall data. Unlike the traditional extract, transform, load ("ETL") process, the data remains in place, and real-time access is given to the source system for the data.
Data mappingIn computing and data management, data mapping is the process of creating data element mappings between two distinct data models.
Computer data storageComputer data storage is a technology consisting of computer components and recording media that are used to retain digital data. It is a core function and fundamental component of computers. The central processing unit (CPU) of a computer is what manipulates data by performing computations. In practice, almost all computers use a storage hierarchy, which puts fast but expensive and small storage options close to the CPU and slower but less expensive and larger options further away.
Query languageA query language, also known as data query language or database query language (DQL), is a computer language used to make queries in databases and information systems. A well known example is the Structured Query Language (SQL). Broadly, query languages can be classified according to whether they are database query languages or information retrieval query languages. The difference is that a database query language attempts to give factual answers to factual questions, while an information retrieval query language attempts to find documents containing information that is relevant to an area of inquiry.
Data miningData mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information (with intelligent methods) from a data set and transforming the information into a comprehensible structure for further use. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.
Data structureIn computer science, a data structure is a data organization, management, and storage format that is usually chosen for efficient access to data. More precisely, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data, i.e., it is an algebraic structure about data. Data structures serve as the basis for abstract data types (ADT). The ADT defines the logical form of the data type. The data structure implements the physical form of the data type.
Metadata standardA metadata standard is a requirement which is intended to establish a common understanding of the meaning or semantics of the data, to ensure correct and proper use and interpretation of the data by its owners and users. To achieve this common understanding, a number of characteristics, or attributes of the data have to be defined, also known as metadata. Metadata is often defined as data about data.
Magnetic-tape data storageMagnetic-tape data storage is a system for storing digital information on magnetic tape using digital recording. Tape was an important medium for primary data storage in early computers, typically using large open reels of 7-track, later 9-track tape. Modern magnetic tape is most commonly packaged in cartridges and cassettes, such as the widely supported Linear Tape-Open (LTO) and IBM 3592 series. The device that performs the writing or reading of data is called a tape drive.
Data extractionData extraction is the act or process of retrieving data out of (usually unstructured or poorly structured) data sources for further data processing or data storage (data migration). The import into the intermediate extracting system is thus usually followed by data transformation and possibly the addition of metadata prior to export to another stage in the data workflow. Usually, the term data extraction is applied when (experimental) data is first imported into a computer from primary sources, like measuring or recording devices.
Big dataBig data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many entries (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Though used sometimes loosely partly because of a lack of formal definition, the interpretation that seems to best describe big data is the one associated with a large body of information that we could not comprehend when used only in smaller amounts.
Metadata registryA metadata registry is a central location in an organization where metadata definitions are stored and maintained in a controlled method. A metadata repository is the database where metadata is stored. The registry also adds relationships with related metadata types. A metadata engine collects, stores and analyzes information about data and metadata (data about data) in use within a domain. Metadata registries are used whenever data must be used consistently within an organization or group of organizations.
Structure miningStructure mining or structured data mining is the process of finding and extracting useful information from semi-structured data sets. Graph mining, sequential pattern mining and molecule mining are special cases of structured data mining. The growth of the use of semi-structured data has created new opportunities for data mining, which has traditionally been concerned with tabular data sets, reflecting the strong association between data mining and relational databases.
