query: where is data stored in a relational database

pos: Relational database called "base relations", and in implementations are called "tables". Other relations do not store data, but are computed by applying relational operations to other relations. These relations are sometimes called "derived relations". In implementations these are called "views" or "queries". Derived relations are convenient in that they act as a single relation, even though they may grab information from several relations. Also, derived relations can be used as an abstraction layer. A domain describes the set of possible values for a given attribute, and can be considered a constraint on the value of the attribute. Mathematically, attaching a domain to

pos: Relational model Relational model The relational model (RM) for database management is an approach to managing data using a structure and language consistent with first-order predicate logic, first described in 1969 by English computer scientist Edgar F. Codd, where all data is represented in terms of tuples, grouped into relations. A database organized in terms of the relational model is a relational database. The purpose of the relational model is to provide a declarative method for specifying data and queries: users directly state what information the database contains and what information they want from it, and let the database management system software

pos: Relational database values or be derived from a query. Similarly, queries identify tuples for updating or deleting. Tuples by definition are unique. If the tuple contains a candidate or primary key then obviously it is unique; however, a primary key need not be defined for a row or record to be a tuple. The definition of a tuple requires that it be unique, but does not require a primary key to be defined. Because a tuple is unique, its attributes by definition constitute a superkey. In a relational database, all data are stored and accessed via relations. Relations that store data are

pos: Relational database 1988 to 1994. DRDA enables network connected relational databases to cooperate to fulfill SQL requests. The messages, protocols, and structural components of DRDA are defined by the Distributed Data Management Architecture. Relational database A relational database is a digital database based on the relational model of data, as proposed by E. F. Codd in 1970. A software system used to maintain relational databases is a relational database management system (RDBMS). Virtually all relational database systems use SQL (Structured Query Language) for querying and maintaining the database. This model organizes data into one or more tables (or "relations") of columns and

pos: Relational database database and support subsequent data use within the application layer. SQL implements constraint functionality in the form of check constraints. Constraints restrict the data that can be stored in relations. These are usually defined using expressions that result in a boolean value, indicating whether or not the data satisfies the constraint. Constraints can apply to single attributes, to a tuple (restricting combinations of attributes) or to an entire relation. Since every attribute has an associated domain, there are constraints (domain constraints). The two principal rules for the relational model are known as entity integrity and referential integrity. A primary key

pos: Data structure diagram or numerical representation of the cardinality. A Bachman diagram is a certain type of data structure diagram, and is used to design the data with a network or relational "logical" model, separating the data model from the way the data is stored in the system. The model is named after database pioneer Charles Bachman, and mostly used in computer software design. In a relational model, a relation is the cohesion of attributes that are fully and not of every key in that relation. The coupling between the relations is based on accordant attributes. For every relation, a rectangle has to

pos: Configuration management database take on multiple forms. Two of the most common forms are those of a relational data model and a semantic data model. Relational data models are based on first-order predicate logic and all data is represented in terms of tuples that are grouped into relations. In the relational model, related records are linked together with a "key", where the key is unique to an entry's data type definition. Such relational models provide declarative methods for specifying data and queries. In other words, users directly state what information the database contains and what information they want from it, and let the

pos: Relational database Relational database A relational database is a digital database based on the relational model of data, as proposed by E. F. Codd in 1970. A software system used to maintain relational databases is a relational database management system (RDBMS). Virtually all relational database systems use SQL (Structured Query Language) for querying and maintaining the database. This model organizes data into one or more tables (or "relations") of columns and rows, with a unique key identifying each row. Rows are also called records or tuples. Columns are also called attributes. Generally, each table/relation represents one "entity type" (such as customer or

pos: Unnormalized form by a user through a form. If the form contains atomic fields, then the model can be considered in 1NF form. However, if the form contains dependent attributes, they are stored as a data structure (typically another table) within that attribute. Data in unnormalized form would not be considered to be relational since it lacks atomic data. Some basic uses of unnormalized relations include dealing with form data in offices. One form would correspond to one record in the database. In 1970, E.F. Codd proposed the relational data model which was widely accepted as the standard data model. At that

pos: GermaNet into a number of semantic fields closely related to major nodes in the semantic network: "Ort", or "location", "Körper", or "body", etc. The following is an up-to-date statistics of GermaNet's version 11.0 contents (release May 2016): All GermaNet data is stored in a relational PostgreSQL 5 database. The database model follows the internal structure of GermaNet: there are tables to store synsets, lexical units, conceptual and lexical relations, etc. The distribution format of all GermaNet data is XML. The two types of files, one for synsets and the other for relations, represent all data that is available in the GermaNet

neg: Online analytical processing directly with relational databases and does not require pre-computation. The base data and the dimension tables are stored as relational tables and new tables are created to hold the aggregated information. It depends on a specialized schema design. This methodology relies on manipulating the data stored in the relational database to give the appearance of traditional OLAP's slicing and dicing functionality. In essence, each action of slicing and dicing is equivalent to adding a "WHERE" clause in the SQL statement. ROLAP tools do not use pre-calculated data cubes but instead pose the query to the standard relational database and its

neg: ADO.NET ADO.NET ADO.NET is a data access technology from the Microsoft .NET Framework that provides communication between relational and non-relational systems through a common set of components. ADO.NET is a set of computer software components that programmers can use to access data and data services from a database. It is a part of the base class library that is included with the Microsoft .NET Framework. It is commonly used by programmers to access and modify data stored in relational database systems, though it can also access data in non-relational data sources. ADO.NET is sometimes considered an evolution of ActiveX Data Objects

neg: Oracle Data Mining Oracle Data Mining Oracle Data Mining (ODM) is an option of Oracle Database Enterprise Edition. It contains several data mining and data analysis algorithms for classification, prediction, regression, associations, feature selection, anomaly detection, feature extraction, and specialized analytics. It provides means for the creation, management and operational deployment of data mining models inside the database environment. Oracle Corporation has implemented a variety of data mining algorithms inside its Oracle Database relational database product. These implementations integrate directly with the Oracle database kernel and operate natively on data stored in the relational database tables. This eliminates the need for extraction or

neg: Terminology-oriented database relational database schema, or even an XML schema. Typically, terminology-oriented databases are not bound on a specific database type. Since the information content, which can be stored in object-oriented databases and in relational databases, is identical, data for a terminology-oriented database can be stored theoretically in any type of database as well as in an XML file. Thus, terminology-oriented databases may support several database systems for storing application data. Terminology Databases, when these contain Terms and Vocabularies, these become valuable for Ontologies and in turn ontologies can help process associated triples or complex predicates thus going deeper than hierarchies or

neg: Database model Database model A database model is a type of data model that determines the logical structure of a database and fundamentally determines in which manner data can be stored, organized and manipulated. The most popular example of a database model is the relational model, which uses a table-based format. Common logical data models for databases include: An object-relational database combines the two related structures. Physical data models include: Other models include: A given database management system may provide one or more models. The optimal structure depends on the natural organization of the application's data, and on the application's requirements, which

neg: Open Dental secure), and the dentist has control of and access to all data. Both local preferences and those which apply to every computer in the office are stored in the mySQL database. The database schema is published and publicly viewable at http://opendental.com/manual/programmingresources.html. There are documented benefits to using a relational database when storing and retrieving data: the relational model offers "advantages over the hierarchical and network models through its simpler data representation, superior data independence and easy to use query language". Relational databases like Oracle and MySQL have mechanisms that can be used to keep the availability (of the database) very

neg: TerraView TerraView TerraView is a GIS application built on the TerraLib GIS library. TerraView handles vector data (polygons, lines, points) and raster data (grids and images), both of them stored in a relational or geo-relational database, including ACCESS, PostgreSQL, MySQL and Oracle Spatial. TerraView has a visualization interface that allows attribute and spatial queries on object in geographical database. The interface allows different views on the database, producing thematic maps with different types of legends. TerraView is able to manage raster data in geographical database and allows the visualization and manipulation of raster data together with vector data. Raster data can

neg: Multi-model database of the underlying data store. Multi-model database Most database management systems are organized around a single data model that determines how data can be organized, stored, and manipulated. In contrast, a multi-model database is designed to support multiple data models against a single, integrated backend. Document, graph, relational, and key-value models are examples of data models that may be supported by a multi-model database. The relational data model became popular after its publication by Edgar F. Codd in 1970. Due to increasing requirements for horizontal scalability and fault tolerance, NoSQL databases became prominent after 2009. NoSQL databases use a variety

neg: Multi-model database Multi-model database Most database management systems are organized around a single data model that determines how data can be organized, stored, and manipulated. In contrast, a multi-model database is designed to support multiple data models against a single, integrated backend. Document, graph, relational, and key-value models are examples of data models that may be supported by a multi-model database. The relational data model became popular after its publication by Edgar F. Codd in 1970. Due to increasing requirements for horizontal scalability and fault tolerance, NoSQL databases became prominent after 2009. NoSQL databases use a variety of data models, with document,

neg: Correlation database Correlation database A correlation database is a database management system (DBMS) that is data-model-independent and designed to efficiently handle unplanned, ad hoc queries in an analytical system environment. Unlike row-oriented relational database management systems, which use a records-based storage approach, or column-oriented databases which use a column-based storage method, a correlation database uses a value-based storage (VBS) architecture in which each unique data value is stored only once and an auto-generated indexing system maintains the context for all values. Because a correlation DBMS stores each unique data value only once, the physical database size is significantly smaller than relational or

neg: Data manipulation language Language (SQL), which is used to retrieve and manipulate data in a relational database. Other forms of DML are those used by IMS/DLI, CODASYL databases, such as IDMS and others. In SQL, the data manipulation language comprises the "SQL-data change" statements, which modify stored data but not the schema or database objects. Manipulation of persistent database objects, e.g., tables or stored procedures, via the SQL schema statements, rather than the data stored within them, is considered to be part of a separate data definition language (DDL). In SQL these two categories are similar in their detailed syntax, data types, expressions

neg: Information technology data they contain is defined and stored separately from the data itself, in a database schema. The extensible markup language (XML) has become a popular format for data representation in recent years. Although XML data can be stored in normal file systems, it is commonly held in relational databases to take advantage of their "robust implementation verified by years of both theoretical and practical effort". As an evolution of the Standard Generalized Markup Language (SGML), XML's text-based structure offers the advantage of being both machine and human-readable. The relational database model introduced a programming-language independent Structured Query Language (SQL), based

neg: Staging (data) areas can be implemented in the form of tables in relational databases, text-based flat files (or XML files) stored in file systems or proprietary formatted binary files stored in file systems. Staging area architectures range in complexity from a set of simple relational tables in a target database to self-contained database instances or file systems. Though the source systems and target systems supported by ETL processes are often relational databases, the staging areas that sit between data sources and targets need not also be relational databases. Staging areas can be designed to provide many benefits, but the primary motivations for

neg: Relational database to operate efficiently and accurately, it must use ACID transactions. Most of the programming within a RDBMS is accomplished using stored procedures (SPs). Often procedures can be used to greatly reduce the amount of information transferred within and outside of a system. For increased security, the system design may grant access to only the stored procedures and not directly to the tables. Fundamental stored procedures contain the logic needed to insert new and update existing data. More complex procedures may be written to implement additional rules and logic related to processing or selecting the data. The relational database was first

neg: Stored procedure Stored procedure A stored procedure (also termed proc, storp, sproc, StoPro, StoredProc, StoreProc, sp, or SP) is a subroutine available to applications that access a relational database management system (RDBMS). Such procedures are stored in the database data dictionary. Uses for stored procedures include data-validation (integrated into the database) or access-control mechanisms. Furthermore, stored procedures can consolidate and centralize logic that was originally implemented in applications. To save time and memory, extensive or complex processing that requires execution of several SQL statements can be saved into stored procedures, and all applications call the procedures. One can use nested stored procedures

neg: Graph database fast because they are perpetually stored within the database itself. Relationships can be intuitively visualized using graph databases, making it useful for heavily inter-connected data. Graph databases are part of the NoSQL databases created to address the limitations of the existing relational databases. While the graph model explicitly lays out the dependencies between nodes of data, the relational model and other NoSQL database models link the data by implicit connections. Graph databases, by design, allow simple and fast retrieval of complex hierarchical structures that are difficult to model in relational systems. Graph databases are similar to 1970s network model databases

neg: Stored procedure server for efficiency and provide some protection from SQL injection attacks. Although simpler and more declarative, prepared statements are not ordinarily written to use procedural logic and cannot operate on variables. Because of their simple interface and client-side implementations, prepared statements are more widely reusable between DBMS. Stored procedure A stored procedure (also termed proc, storp, sproc, StoPro, StoredProc, StoreProc, sp, or SP) is a subroutine available to applications that access a relational database management system (RDBMS). Such procedures are stored in the database data dictionary. Uses for stored procedures include data-validation (integrated into the database) or access-control mechanisms. Furthermore,

neg: Graph database without loss of application functionality. There is no need for the designer of the database to plan out extensive details of the databases's future use-cases. The underlying storage mechanism of graph databases can vary. Some depend on a relational engine and “store” the graph data in a table (although a table is a logical element, therefore this approach imposes another level of abstraction between the graph database, the graph database management system and the physical devices where the data is actually stored). Others use a key-value store or document-oriented database for storage, making them inherently NoSQL structures. One such NoSQL

neg: Relational algebra Relational algebra Relational algebra, first created by Edgar F. Codd while at IBM, is a family of algebras with a well-founded semantics used for modelling the data stored in relational databases, and defining queries on it. The main application of relational algebra is providing a theoretical foundation for relational databases, particularly query languages for such databases, chief among which is SQL. Relational algebra received little attention outside of pure mathematics until the publication of E.F. Codd's relational model of data in 1970. Codd proposed such an algebra as a basis for database query languages. (See section Implementations.) Five primitive operators

neg: Data integration been of greater interest than fully structured (typically relational) Enterprise Data Warehouses. Since 2013, data lake approaches have risen to the level of Data Hubs. (See all three search terms popularity on Google Trends.) These approaches combine unstructured or varied data into one location, but do not necessarily require an (often complex) master relational schema to structure and define all data in the Hub. Consider a web application where a user can query a variety of information about cities (such as crime statistics, weather, hotels, demographics, etc.). Traditionally, the information must be stored in a single database with a single

neg: Graph database in that both represent general graphs, but network-model databases operate at a lower level of abstraction and lack easy traversal over a chain of edges. The underlying storage mechanism of graph databases can vary. Some depend on a relational engine and “store” the graph data in a table (although a table is a logical element, therefore this approach imposes another level of abstraction between the graph database, the graph database management system and the physical devices where the data is actually stored). Others use a key-value store or document-oriented database for storage, making them inherently NoSQL structures. Most graph databases

neg: Connected toys as a "Relational Database Management System – RDBMS"),” to get full authorization to the database where he can access children and parents’ personal data. According to VTech's public data release, around 4.8 million parent accounts and approximately 6.4 million children related profiles were leaked worldwide in several of their products. Data that were compromised during the breach included name, email address, password, secret question and answer for password retrieval, IP address, mailing address and download history; no credit card information or social security numbers were stored in the same database. The United States suffered the most due to the data

neg: Table (database) identify rows is called the primary key. "Table" is another term for "relation"; although there is the difference in that a table is usually a multiset (bag) of rows where a relation is a set and does not allow duplicates. Besides the actual data rows, tables generally have associated with them some metadata, such as constraints on the table or on the values within particular columns. The data in a table does not have to be physically stored in the database. Views also function as relational tables, but their data are calculated at query time. External tables (in Informix or

neg: Scalability hand, Base One makes the case for extreme scalability without departing from mainstream relational database technology. For specialized applications, NoSQL architectures such as Google's Bigtable can further enhance scalability. Google's horizontally distributed Spanner technology, positioned as an relational alternative to Bigtable, supports general-purpose database transactions and provides a more conventional SQL-based query language. In the context of scale-out data storage, scalability is defined as the maximum storage cluster size which guarantees full data consistency, meaning there is only ever one valid version of stored data in the whole cluster, independently from the number of redundant physical data copies. Clusters which

neg: Database virtualization a fundamental concept. With greater numbers of database sources, inserting a horizontal data virtualization layer between the sources and consumers helps address this complexity. Rick van der Lans, author of multiple books on SQL and relational databases, has defined data virtualization as "the process of offering data consumers a data access interface that hides the technical aspects of stored data, such as location, storage structure, API, access language, and storage technology." Database virtualization Database virtualization is the decoupling of the database layer, which lies between the storage and application layers within the application stack. Virtualization of the database layer enables

neg: Graph database transactions, separating data into many tables. Relational models enforce heavy data normalization in order to guarantee consistency. One of the relational model's design motivation was to achieve a fast row-by-row access. Problems arise with when there is a need to form complex relationships between the stored data. Although relationships can be analyzed with the relational model, complex queries performing many join operations on many different attributes over several tables are required. In working with relational models, foreign key constraints and should also be considered when retrieving relationships, causing additional overhead. Compared with relational databases, graph databases are often faster for

neg: History of Microsoft SQL Server x86 clients. The first release of SQL IA-64 was version 8.00.760, with a build date of February 6, 2003. SQL Server 2005 (formerly codenamed "Yukon") released in November 2005. It included native support for managing XML data, in addition to relational data. For this purpose, it defined an codice_1 data type that could be used either as a data type in database columns or as literals in queries. XML columns can be associated with XSD schemas; XML data being stored is verified against the schema. XML is converted to an internal binary data type before being stored in the database.

neg: Object-relational database Object-relational database An object-relational database (ORD), or object-relational database management system (ORDBMS), is a database management system (DBMS) similar to a relational database, but with an object-oriented database model: objects, classes and inheritance are directly supported in database schemas and in the query language. In addition, just as with pure relational systems, it supports extension of the data model with custom data-types and methods. An object-relational database can be said to provide a middle ground between relational databases and "object-oriented databases" (object database). In object-relational databases, the approach is essentially that of relational databases: the data resides in the database

neg: View (SQL) View (SQL) In a database, a view is the result set of a "stored" query on the data, which the database users can query just as they would in a persistent database collection object. This pre-established query command is kept in the database dictionary. Unlike ordinary "base tables" in a relational database, a view does not form part of the physical schema: as a result set, it is a virtual table computed or collated dynamically from data in the database when access to that view is requested. Changes applied to the data in a relevant "underlying table" are reflected in

neg: Virtual Database Manager of the platform, such as security control, license compliance or document relationships, by using standard reporting tools which are used standard SQL. As said Jacob Nikom of MIT's Lincoln Laboratory in 2009, ""real-time data warehousing", .. was so accurate,", to have the best analysis of original data, VDM must provide a real time access to the most recent data. SAP BusinessObjects Enterprise repository's structure does not use a normalized design, although it is physically stored in a relational database. For performance reason, all data which represents an object (Folders, Documents, Users, Groups) is stored in a flat table. Each row

neg: Cost database Company, Cost Data Online (Richardson Estimating Standards), Frank R. Walker Company, BNI Building News, and Design & Construction Resources. The cost database may be stored in relational database management system, which may be in either an open or proprietary format, serving the data to the cost estimating software. The cost database may be hosted in the cloud. Estimators use a cost database to store data in structured way which is easy to manage and retrieve. The most basic element of a cost estimate and therefore the cost database is the estimate line item or work item. An example is "Concrete,

neg: Oracle Data Mining transfer of data into standalone mining/analytic servers. The relational database platform is leveraged to securely manage models and to efficiently execute SQL queries on large volumes of data. The system is organized around a few generic operations providing a general unified interface for data-mining functions. These operations include functions to create, apply, test, and manipulate data-mining models. Models are created and stored as database objects, and their management is done within the database - similar to tables, views, indexes and other database objects. In data mining, the process of using a model to derive predictions or descriptions of behavior that

neg: Microsoft Access Server 2000, and continues with the SQL Server Express versions of SQL Server 2005 and 2008. Microsoft Access is a file server-based database. Unlike client–server relational database management systems (RDBMS), Microsoft Access does not implement database triggers, stored procedures, or transaction logging. Access 2010 includes table-level triggers and stored procedures built into the ACE data engine. Thus a Client-server database system is not a requirement for using stored procedures or table triggers with Access 2010. Tables, queries, forms, reports and macros can now be developed specifically for web based applications in Access 2010. Integration with Microsoft SharePoint 2010 is also

neg: Rasdaman to the client. Both tiling strategy and compression comprise database tuning parameters. Tiles and tile index are stored as BLOBs in a relational database which also holds the data dictionary needed by rasdaman's dynamic type system. Adapters are available for several relational systems, among them open-source PostgreSQL. For arrays larger than disk space, hierarchical storage management (HSM) support has been developed. Queries are parsed, optimised, and executed in the rasdaman server. The parser receives the query string and generates the operation tree. Further, it applies algebraic optimisation rules to the query tree where applicable; of the 150 algebraic rewriting rules,

neg: Key-value database Key-value database A key-value database, or key-value store, is a data storage paradigm designed for storing, retrieving, and managing associative arrays, 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 quickly find the data within the database. Key-value databases work in a very different fashion from the better known relational databases (RDB). RDBs pre-define the data structure in

neg: NitrosBase NitrosBase NitrosBase is a Russian high-performance multi-model database system. The database system supports relational, graph and document database models. The developer initially implemented the database as a triplestore, being a Semantic Web pioneer in Russia. Remodelling into a multi-model database was supported by the Skolkovo Innovation Center in 2017. The database is used in information systems that support the health-care reform in modern Russia. In NitrosBase, all data are stored in the format of the internal graph model, while data in other models are their views (representations; similar to SQL views). Regardless of the model in which format data were

neg: Database preservation Database preservation Database preservation usually involves converting the information stored in a database to a form likely to be accessible in the long term as technology changes, without losing the initial characteristics (context, content, structure, appearance and behaviour) of the data. Version 1.0 of the Software Independent Archiving of Relational Databases (SIARD) format was developed by the Swiss Federal Archives in 2007. It was designed for archiving relational databases in a vendor-neutral form. A SIARD archive is a ZIP-based package of files based on XML and SQL:1999. A SIARD file incorporates both the database content and also machine-processable structural metadata

neg: Database encryption the database. The transparent element of TDE has to do with the fact that TDE encrypts on "the page level", which essentially means that data is encrypted when stored and decrypted when it is called into the system's memory. The contents of the database are encrypted using a symmetric key that is often referred to as a "database encryption key". In order to explain column-level encryption it is important to outline basic database structure. A typical relational database is divided into tables that are divided into columns that each have rows of data. Whilst TDE usually encrypts an entire database,

neg: Entity–attribute–value model model, in a relational database. On the other hand, database vendors have begun to include JSON and XML support into their data structures and query features, like in IBM DB2, where XML data is stored as XML separate from the tables, using Xpath queries as part of SQL statements, or in PostgreSQL, with a JSON data type that can be indexed and queried. These developments accomplish, improve or substitute the EAV model approach. It should be noted, however, that the uses of JSON and XML are not necessarily the same as the use of an EAV model, though they can

neg: Graph database phone number fields for the string "311". This can be a time consuming process in large tables, so relational databases offer the concept of a database index, which allows data like this to be stored in a smaller subtable, containing only the selected data and a unique key (or "primary key") of the record it is part of. If the phone numbers are indexed, the same search would occur in the smaller index table, gathering the keys of matching records, and then looking in the main data table for the records with those keys. Generally, the tables are physically stored

neg: Business intelligence BLOB (binary large object), a catch-all data type available in most relational database management systems. Unstructured data may also refer to irregularly or randomly repeated (nonrepetitive) column patterns that vary from row to row within each file or document. Many of these data types, however, like e-mails, word processing text files, PPTs, image-files, and video-files conform to a standard that offers the possibility of metadata. Metadata can include information such as author and time of creation, and this can be stored in a relational database. Therefore, it may be more accurate to talk about this as semi-structured documents or data,

neg: Data access layer can talk to. In either circumstance, having a data access layer provides a centralized location for all calls into the database, and thus makes it easier to port the application to other database systems (assuming that 100% of the database interaction is done in the DAL for a given application). Object-Relational Mapping tools provide data layers in this fashion, following the active record model. The ORM/active-record model is popular with web frameworks. Data access layer A data access layer (DAL) in computer software, is a layer of a computer program which provides simplified access to data stored in persistent storage

neg: Table (database) the file and each column in a row. This structure implies that a row can have repeating information, generally in the child data segments. Data are stored in sequence of physical records. Unlike a spreadsheet, the datatype of a column is ordinarily defined by the schema describing the table. Some SQL systems, such as SQLite, are less strict about column datatype definitions. Table (database) A table is a collection of related data held in a structured format within a database. It consists of columns, and rows. In relational databases, and flat file databases, a "table" is a set of data

neg: Resource Description Framework model and ability to model disparate, abstract concepts has also led to its increasing use in knowledge management applications unrelated to Semantic Web activity. A collection of RDF statements intrinsically represents a labeled, directed multi-graph. This in theory makes an RDF data model better suited to certain kinds of knowledge representation than are other relational or ontological models. However, in practice, RDF data is often stored in relational database or native representations (also called Triplestores—or Quad stores, if context such as the named graph is also stored for each RDF triple). As RDFS and OWL demonstrate, one can build additional

neg: Emigma changed the manner in which data was stored. The basic stored data structure was changed from an ASCII text file structure in a full relational database. With a database structure it was now possible to add many associated tools such as a range of filtering and editing tools. New geophysical features added included source conductivity depth imaging, 1d TEM inversion, Euler deconvolution, FFT tools for magnetic and gravity data, 3d resistivity inversion and magnetization vector inversion. Full compatibility with Windows Vista was added to EMIGMA with the April 2008 release of version 8. Support was added for new data collection

neg: Distributed data store Distributed data store A distributed data store is a computer network where information is stored on more than one node, often in a replicated fashion. It is usually specifically used to refer to either a distributed database where users store information on a "number of nodes", or a computer network in which users store information on a "number of peer network nodes". Distributed databases are usually non-relational databases that enable a quick access to data over a large number of nodes. Some distributed databases expose rich query abilities while others are limited to a key-value store semantics. Examples of limited

neg: Correlation database use" of a CDBMS is generally similar to or somewhat faster than alternative structures. Correlation database A correlation database is a database management system (DBMS) that is data-model-independent and designed to efficiently handle unplanned, ad hoc queries in an analytical system environment. Unlike row-oriented relational database management systems, which use a records-based storage approach, or column-oriented databases which use a column-based storage method, a correlation database uses a value-based storage (VBS) architecture in which each unique data value is stored only once and an auto-generated indexing system maintains the context for all values. Because a correlation DBMS stores each unique

neg: Data-centric programming language Data-centric programming language Data-centric programming language defines a category of programming languages where the primary function is the management and manipulation of data. A data-centric programming language includes built-in processing primitives for accessing data stored in sets, tables, lists, and other data structures and databases, and for specific manipulation and transformation of data required by a programming application. Data-centric programming languages are typically declarative and often dataflow-oriented, and define the processing result desired; the specific processing steps required to perform the processing are left to the language compiler. The SQL relational database language is an example of a declarative, data-centric

neg: Data warehouse the data warehouse architecture. All data warehouses have multiple phases in which the requirements of the organization are modified and fine tuned. Operational systems are optimized for preservation of data integrity and speed of recording of business transactions through use of database normalization and an entity-relationship model. Operational system designers generally follow Codd's 12 rules of database normalization to ensure data integrity. Fully normalized database designs (that is, those satisfying all Codd rules) often result in information from a business transaction being stored in dozens to hundreds of tables. Relational databases are efficient at managing the relationships between these tables.

neg: Odaba Odaba ODABA is a terminology-oriented database management system, which is a conceptual extension of an object-oriented database system, and implements concepts defined in a terminology model. ODABA supports typical standards and technologies for object-oriented databases, but also terminology-oriented database extensions. ODABA also behaves like an object-relational database management system, i.e. data is seen as being stored in a database rather than accessing persistent objects in a programming environment. ODABA supports active data link (ADL) and provides an ADL-based GUI frame work. Database access is supported via an application program interface for C++ or .NET programming languages and via the ODABA

neg: AM/FM/GIS database part which stores the attribute data for the real world entities that need to be captured or managed as a part of the digitization process. This attribute data is often stored in an underlying relational database. A database table often represents a single real world entity or object and will be used to store relevant attribute data. The "database component" is often linked or associated with the "graphical component" to complete an intelligent GIS system. An AM/FM/GIS system's architecture depends solely on the requirements defined by a public utility. These requirements are mostly industry standards and seldom require changes.

neg: Sensage the two primary products marketed by Hexis. The company uses a columnar database architecture instead of the relational database architecture that is more common in the industry. Sensage holds U.S. patent #7,024,414 for parsing table data into columns of values, formatting each column into a data stream, and transferring each data stream to a storage device in a continuous strip of data. In this architecture, the data is stored in columns instead of rows, which eliminates the need for indices when storing event data to increase data compression and retrieval speeds. The event data warehouse software uses an extraction, transformation

neg: Sympa for example, creates one site per list). Data is stored in a relational database such as MySQL, PostgreSQL, or Oracle; some configuration data is still held in text files, but the stated goal of the developers is to eventually hold as much configuration data as possible in the database as well. Sympa consists of at least five concurrent daemons communicating through the database or by placing files in spools: a main daemon accepting incoming mail and controlling the other processes, a bounce daemon managing incoming bounces, an archiver archiving outgoing mail, a task manager doing scheduled maintenance and a bulk

neg: Semi-structured model Semi-structured model The semi-structured model is a database model where there is no separation between the data and the schema, and the amount of structure used depends on the purpose. The advantages of this model are the following: The primary trade-off being made in using a semi-structured database model is that queries cannot be made as efficiently as in a more constrained structure, such as in the relational model. Typically the records in a semi-structured database are stored with unique IDs that are referenced with pointers to their location on disk. This makes navigational or path-based queries quite efficient, but

neg: Papaya CMS supports unicode, multi-language and multi-site installations. All texts, configuration data etc. is stored as XML in a relational database. Media files such as images and videos are located in the file system and will be managed by papaya and referenced as needed. Many functions (such as database access, authentication, build methods for backend menus, etc.) are defined centrally and can be accessed through an abstraction layer. papaya CMS is developed in PHP and object-oriented. Data is stored in XML, XSLT is used as template language. The system does not use self-developed or proprietary script- or templating languages. Unlike many other

neg: Wide column store separately on disk. Wide column stores do often support the notion of column families that are stored separately. However, each such column family typically contains multiple columns that are used together, similar to traditional relational database tables. Within a given column family, all data is stored in a row-by-row fashion, such that the columns for a given row are stored together, rather than each column being stored separately. Wide column stores that support column families are also known as "column family databases". Notable wide column stores include: Wide column store A wide column store is a type of NoSQL database.

neg: Data warehouse the fact is a value corresponding to the coordinates. The main disadvantages of the dimensional approach are the following: In the normalized approach, the data in the data warehouse are stored following, to a degree, database normalization rules. Tables are grouped together by "subject areas" that reflect general data categories (e.g., data on customers, products, finance, etc.). The normalized structure divides data into entities, which creates several tables in a relational database. When applied in large enterprises the result is dozens of tables that are linked together by a web of joins. Furthermore, each of the created entities is converted

neg: Knowledge base was coined to distinguish this form of knowledge store from the more common and widely used term "database". At the time (the 1970s) virtually all large Management Information Systems stored their data in some type of hierarchical or relational database. At this point in the history of Information Technology the distinction between a database and a knowledge base was clear and unambiguous. A database had the following properties: The first knowledge-based systems had data needs that were the opposite of these database requirements. An expert system requires structured data. Not just tables with numbers and strings, but pointers to other

neg: Microsoft Access Microsoft Access Microsoft Access is a database management system (DBMS) from Microsoft that combines the relational Microsoft Jet Database Engine with a graphical user interface and software-development tools. It is a member of the Microsoft Office suite of applications, included in the Professional and higher editions or sold separately. Microsoft Access stores data in its own format based on the Access Jet Database Engine. It can also import or link directly to data stored in other applications and databases. Software developers, data architects and power users can use Microsoft Access to develop application software. Like other Microsoft Office applications, Access

neg: Oracle Multimedia Oracle Multimedia Oracle Multimedia (formerly Oracle "inter"Media from versions 8 to 10gR2) is a feature available for Oracle databases, which provides multimedia utilities in a database environment, generating as a result a multimedia database (MMDB). Oracle Multimedia is a framework which: Oracle Multimedia manages multimedia data within an Oracle database under transaction control (see transaction processing); using by default database schemas named codice_1 and codice_2 (the "ORD" prefix designates "object-relational data"). codice_1 provides four media classes: Alternatively, an Oracle database can use Oracle Multimedia to store and index meta-information together with external references that enable efficient access to media-content stored

neg: SQL adjustments. SQL was initially developed at IBM by Donald D. Chamberlin and Raymond F. Boyce after learning about the relational model from Ted Codd in the early 1970s. This version, initially called "SEQUEL" ("Structured English Query Language"), was designed to manipulate and retrieve data stored in IBM's original quasi-relational database management system, System R, which a group at IBM San Jose Research Laboratory had developed during the 1970s. Chamberlin and Boyce's first attempt of a relational database language was Square, but it was difficult to use due to subscript notation. After moving to the San Jose Research Laboratory in 1973,

neg: ERIL They may or may not be implemented physically in the system. ERIL is supposed to model any kind of data regardless of the storage. The same ERIL diagram can represent data stored in a relational database, in a NoSQL database, XML file or in the memory. ERIL diagrams serve two purposes. The primary purpose is to explain the data structure of an existing or future system or component. The secondary purpose is to automatically generate source code from the model. Code that can be generated includes specialized collection classes, hash and comparison functions, data retrieval and modification procedures, SQL data-definition

neg: Recursive join is perhaps the most obvious way to implement them when the XML data is stored in a relational database. The standard way to define recursive joins in the standard is by way of recursive common table expressions. Database management systems that support recursive CTEs include Microsoft SQL Server, Oracle, PostgreSQL and others. Recursive join The recursive join is an operation used in relational databases, also sometimes called a "fixed-point join". It is a compound operation that involves repeating the join operation, typically accumulating more records each time, until a repetition makes no change to the results (as compared to the

neg: Document-oriented database the programmer, and a single object may be spread across several tables. Document databases store all information for a given object in a single instance in the database, and every stored object can be different from every other. This eliminates the need for object-relational mapping while loading data into the database. The central concept of a document-oriented database is the notion of a "document". While each document-oriented database implementation differs on the details of this definition, in general, they all assume documents encapsulate and encode data (or information) in some standard format or encoding. Encodings in use include XML, YAML,

neg: Altibase of memory tables for high performance and disk tables for economical storage in a single unified engine. As a result, data can be stored and manipulated in main memory alone, on physical disk alone or a combination of both. Consequently, Altibase eliminates the need to purchase an in-memory database and an on-disk database separately. Altibase is relational and ACID and SQL compliant. Thus, Altibase can replace or interoperate with Oracle and other legacy databases. In addition, migration from other legacy databases to Altibase is exponentially less complex. Altibase is currently among a very small subset of relational databases that provide

neg: IDEF incompatible, data model methods for storing computer data — sequential (VSAM), hierarchical (IMS), network (Cincom's TOTAL and CODASYL, and Cullinet's IDMS). The relational data model was just emerging as a promising way of thinking about structuring data for easy, efficient, and accurate access. Relational database management systems had not yet emerged as a general standard for data management. The ICAM program office deemed it valuable to create a "neutral" way of describing the data content of large-scale systems. The emerging academic literature suggested that methods were needed to process data independently of the way it was physically stored. Thus the

neg: ADO.NET feature. Entity Framework (EF) is an open source object-relational mapping (ORM) framework for ADO.NET, part of .NET Framework. It is a set of technologies in ADO.NET that supports the development of data-oriented software applications. Architects and developers of data-oriented applications have typically struggled with the need to achieve two very different objectives. The Entity Framework enables developers to work with data in the form of domain-specific objects and properties, such as customers and customer addresses, without having to concern themselves with the underlying database tables and columns where this data is stored. With the Entity Framework, developers can work at

neg: Multi-model database graph, and key-value models being popular. A Multi-model database is a database that can store, index and query data in more than one model. For some time, databases have primarily supported only one model, such as: relational database, document-oriented database, graph database or triplestore. A database that combines many of these is multi-model. For some time, it was all but forgotten (or considered irrelevant) that there were any other database models besides Relational. The Relational model and notion of third normal form were the de facto standard for all data storage. However, prior to the dominance of Relational data modeling

neg: WinFS WinFS WinFS (short for Windows Future Storage) was the code name for a canceled data storage and management system project based on relational databases, developed by Microsoft and first demonstrated in 2003 as an advanced storage subsystem for the Microsoft Windows operating system, designed for persistence and management of structured, semi-structured and unstructured data. WinFS includes a relational database for storage of information, and allows any type of information to be stored in it, provided there is a well defined schema for the type. Individual data items could then be related together by relationships, which are either inferred by the

neg: Business intelligence and assessing unstructured or semi-structured data, organizations may not draw upon these vast reservoirs of information, which could influence a particular decision, task or project. This can ultimately lead to poorly informed decision making. Therefore, when designing a business intelligence/DW-solution, the specific problems associated with semi-structured and unstructured data must be accommodated for as well as those for the structured data. Unstructured and semi-structured data have different meanings depending on their context. In the context of relational database systems, unstructured data cannot be stored in predictably ordered columns and rows. One type of unstructured data is typically stored in a

neg: Data modeling software engineering to produce a type of conceptual data model (or semantic data model) of a system, often a relational database, and its requirements in a top-down fashion. These models are being used in the first stage of information system design during the requirements analysis to describe information needs or the type of information that is to be stored in a database. The data modeling technique can be used to describe any ontology (i.e. an overview and classifications of used terms and their relationships) for a certain universe of discourse i.e. area of interest. Several techniques have been developed for

neg: Entity–relationship model system design uses these models during the requirements analysis to describe information needs or the type of information that is to be stored in a database. The data modeling technique can be used to describe any ontology (i.e. an overview and classifications of used terms and their relationships) for a certain area of interest. In the case of the design of an information system that is based on a database, the conceptual data model is, at a later stage (usually called logical design), mapped to a logical data model, such as the relational model; this in turn is mapped to

neg: Database design their system requirements for the database are as they are unaccustomed to thinking in terms of the discrete data elements which must be stored. Data to be stored can be determined by Requirement Specification. Once a database designer is aware of the data which is to be stored within the database, they must then determine where dependency is within the data. Sometimes when data is changed you can be changing other data that is not visible. For example, in a list of names and addresses, assuming a situation where multiple people can have the same address, but one person cannot

neg: Data model hierarchical data model, were proposed during this period of time". Towards the end of the 1960s, Edgar F. Codd worked out his theories of data arrangement, and proposed the relational model for database management based on first-order predicate logic. In the 1970s entity relationship modeling emerged as a new type of conceptual data modeling, originally proposed in 1976 by Peter Chen. Entity relationship models were being used in the first stage of information system design during the requirements analysis to describe information needs or the type of information that is to be stored in a database. This technique can describe

neg: Database Workbench Windows platforms. Under Linux, FreeBSD or Mac OS X Database Workbench can operate using Wine. Database Workbench can be used to view, create and edit tables, indexes, stored procedures and other database meta data objects. It also supports: It includes several productivity features: Database Workbench Database Workbench is a software application for development and administration of multiple relational databases using SQL, with interoperationality between different database systems, developed by Upscene Productions. Because Databases Workbench supports multiple database systems, it can provide software developers with the same interface and development environment for these otherwise different database systems and also includes cross

neg: Ensembl Genomes Ensembl Genomes Ensembl Genomes is a scientific project to provide genome-scale data from non-vertebrate species. The project is run by the European Bioinformatics Institute, and was launched in 2009 using the Ensembl technology. The main objective of the Ensembl Genomes database is to complement the main Ensembl database by introducing five additional web pages to include genome data for bacteria, fungi, invertebrate metazoa, plants, and protists. For each of the domains, the Ensembl tools are available for manipulation, analysis and visualization of genome data. Most Ensembl Genomes data is stored in MySQL relational databases and can be accessed by the

neg: Document-oriented database the contact's name, image, and any contact info, all in a single record. That entry is accessed through its key, which allows the database to retrieve and return the document to the application. No additional work is needed to retrieve the related data; all of this is returned in a single object. A key difference between the document-oriented and relational models is that the data formats are not predefined in the document case. In most cases, any sort of document can be stored in any database, and those documents can change in type and form at any time. If one

neg: Database organized, and manipulated. The most popular example of a database model is the relational model (or the SQL approximation of relational), which uses a table-based format. Common logical data models for databases include: An object-relational database combines the two related structures. Physical data models include: Other models include: Specialized models are optimized for particular types of data: A database management system provides three views of the database data: While there is typically only one conceptual (or logical) and physical (or internal) view of the data, there can be any number of different external views. This allows users to see database

neg: Data access layer Data access layer A data access layer (DAL) in computer software, is a layer of a computer program which provides simplified access to data stored in persistent storage of some kind, such as an entity-relational database. This acronym is prevalently used in Microsoft environments. For example, the DAL might return a reference to an object (in terms of object-oriented programming) complete with its attributes instead of a row of fields from a database table. This allows the client (or user) modules to be created with a higher level of abstraction. This kind of model could be implemented by creating a

neg: Graph database on the relational model, relational databases have been the de-facto industry standard for large-scale data storage systems. However, relational model's requirement of a strict schema and data normalization imposed limitations on how relationships can be queried. The increasing amount of data needing to be processed became an additional problem posed by the relational model. Traditionally, databases have been designed with the relational model. In a relational model, data is normalized to support ACID transactions. The data normalization process removes any duplicate data within the database. The goal of data normalization is to preserve data consistency. The relational model enforces ACID

neg: Oracle metadata Oracle metadata Oracle Database provides information about all of the tables, views, columns, and procedures in a database. This information about the information is known as metadata. It is stored in two locations: data dictionary tables (accessed via built-in views) and a metadata registry. Other relational database management systems support an ANSI-standard equivalent called information schema. The total number of these views depends on the Oracle version, but is in a 1000 range. The main built-in views accessing Oracle RDBMS data dictionary tables are few, and are as follows: In addition there are equivalent views prefixed "USER_" which show only

neg: In-memory processing such as in-memory data grid like Hazelcast, Infinispan, Oracle Coherence or ScaleOut Software. With both in-memory database and data grid, all information is initially loaded into memory RAM or flash memory instead of hard disks. With a data grid processing occurs at three order of magnitude faster than relational databases which have advanced functionality such as ACID which degrade performance in compensation for the additional functionality. The arrival of column centric databases, which store similar information together, allow data to be stored more efficiently and with greater compression ratios. This allows huge amounts of data to be stored in the

neg: TerraView be shared in different formats such as GeoTIFF, TIFF, JPEG, RAW, ASCII-Grid or ASCIISpring. TerraView supports vector operations including intersection and buffer maps. It also has statistical analysis functions: local and global autocorrelation indexes, semivariograms, and regionalisation. TerraView is free software distributed under the GPL license and is available on the Internet . TerraView TerraView is a GIS application built on the TerraLib GIS library. TerraView handles vector data (polygons, lines, points) and raster data (grids and images), both of them stored in a relational or geo-relational database, including ACCESS, PostgreSQL, MySQL and Oracle Spatial. TerraView has a visualization interface

neg: In-memory processing In-memory processing In computer science, in-memory processing is an emerging technology for processing of data stored in an in-memory database. Older systems have been based on disk storage and relational databases using SQL query language, but these are increasingly regarded as inadequate to meet business intelligence (BI) needs. Because stored data is accessed much more quickly when it is placed in random-access memory (RAM) or flash memory, in-memory processing allows data to be analysed in real time, enabling faster reporting and decision-making in business. With the hitherto prevalent disk-based technology, data is loaded on to the computer's hard disk in

neg: ObjectDatabase++ one record that reduce the amount of file reads and writes, which enables the object design to maintain efficiency with very large and very complex database designs. Looking at the images to the right, the on above depicts the relational model and has the data spread over two tables, with the parent in amber and the children in blue. In the object model, both the parent and children are stored in the one data record, the information that was previously stored within the related table is now stored within the sub or nested table of Foo. ODBPP implements a transaction

neg: MultiValue file, there is an attribute called "eMailAddress". The eMailAddress field can store a variable number of email address values in a single record. The list [joe@example.com, jdb@example.net, joe_bacde@example.org] can be stored and accessed via a single query when accessing the associated record. Achieving the same (one-to-many) relationship within a traditional relational database system would include creating an additional table to store the variable number of email addresses associated with a single "PERSON" record. However, modern relational database systems support this multi-value data model too. For example, in PostgreSQL, a column can be an array of any base type. Like the

neg: GLite-AMGA GLite-AMGA The ARDA Metadata Grid Application (AMGA) is a general purpose metadata catalogue and part of the European Middleware Initiative middleware distribution. It was originally developed by the EGEE project as part of its gLite middleware, when it became clear that many Grid applications needed metadata information on files and to organize a work-flow. AMGA is now developed and supported by the European Middleware Initiative. AMGA as a metadata service allows users to attach metadata information to files stored on the Grid, where metadata can be any relationally organized data typically stored in a relational database system (RDBMS). In addition,

neg: Relational database management system Relational database management system A relational database management system (RDBMS) is a database management system (DBMS) based on the relational model of data. Most databases in widespread use today are based on this model. RDBMSs have been a common option for the storage of information in databases used for financial records, manufacturing and logistical information, personnel data, and other applications since the 1980s. Relational databases have often replaced legacy hierarchical databases and network databases because they were easier to implement and administer. Nonetheless, relational databases received continued, unsuccessful challenges by object database management systems in the 1980s and 1990s, (which

neg: Productivity improving technologies at checkout increased ringing speed by 30% and reduced labor requirements of cashiers and baggers by 10-15%. Data storage became better organized after the development of relational database software that allowed data to be stored in different tables. For example, a theoretical airline may have numerous tables such as: airplanes, employees, maintenance contractors, caterers, flights, airports, payments, tickets, etc. each containing a narrower set of more specific information than would a flat file, such as a spreadsheet. These tables are related by common data fields called "keys". (See: Relational model) Data can be retrieved in various specific configurations by posing

neg: Conjunctive query is the problem of evaluating a conjunctive query on a relational database where both the query and the database are considered part of the input. The complexity of this problem is usually referred to as combined complexity, while the complexity of the problem of evaluating a query on a relational database, where the query is assumed fixed, is called data complexity. Conjunctive queries are NP-complete with respect to combined complexity, while the data complexity of conjunctive queries is very low, in the parallel complexity class AC0, which is contained in LOGSPACE and thus in polynomial time. The NP-hardness of conjunctive

neg: SAP HANA HANA incorporates the open source statistical programming language R as a supported language within stored procedures. The column-store database offers graph database capabilities. The graph engine supports the Cypher Query Language as well as a visual graph manipulation via a tool called Graph Viewer. Graph data structures are stored directly in relational tables in HANA’s column store. Pre-built algorithms in the graph engine include pattern matching, neighborhood search, single shortest path, and strongly connected components. Use cases for the Graph Engine include things like supply chain traceability, fraud detection, and logistics and route planning. HANA also includes a spatial database