Database

An example of output from an SQL database query.

• 1Terminology and overview
• 2Applications
• 3General-purpose and special-purpose DBMSs
• 4History
  • 4.11960s, navigational DBMS
  • 4.21970s, relational DBMS
  • 4.3Integrated approach
  • 4.4Late 1970s, SQL DBMS
  • 4.51980s, on the desktop
  • 4.61990s, object-oriented
  • 4.72000s, NoSQL and NewSQL
• 5Research
• 6Examples
• 7Design and modeling
  • 7.1Models
  • 7.2External, conceptual, and internal views
• 8Languages
• 9Performance, security, and availability
  • 9.1Storage
  • 9.2Security
  • 9.3Transactions and concurrency
  • 9.4Migration
  • 9.5Building, maintaining, and tuning
  • 9.6Backup and restore
  • 9.7Static analysis
  • 9.8Other
• 10See also
• 11Notes
• 12References
• 13Sources
• 14Further reading
• 15External links

• Data definition – Creation, modification and removal of definitions that define the organization of the data.
• Update – Insertion, modification, and deletion of the actual data.^[3]
• Retrieval – Providing information in a form directly usable or for further processing by other applications. The retrieved data may be made available in a form basically the same as it is stored in the database or in a new form obtained by altering or combining existing data from the database.^[4]
• Administration – Registering and monitoring users, enforcing data security, monitoring performance, maintaining data integrity, dealing with concurrency control, and recovering information that has been corrupted by some event such as an unexpected system failure.^[5]

| This section does not cite any sources. Please help improve this section by adding citations to reliable sources. Unsourced material may be challenged and removed.(March 2013) (Learn how and when to remove this template message)Databases are used to support internal operations of organizations and to underpin online interactions with customers and suppliers (see Enterprise software).Databases are used to hold administrative information and more specialized data, such as engineering data or economic models. Examples of database applications include computerized library systems, flight reservation systems, computerized parts inventory systems, and many content management systems that store websites as collections of webpages in a database.General-purpose and special-purpose DBMSs[edit]DBMS may become a complex software system and its development typically requires thousands of human years of development effort.^[a] Some general-purpose DBMSs such as Adabas, Oracle and DB2 have been upgraded since the 1970s. General-purpose DBMSs aim to meet the needs of as many applications as possible, which adds to the complexity. However, since their development cost can be spread over a large number of users, they are often the most cost-effective approach. On the other hand, a general-purpose DBMS may introduce unnecessary overhead. Therefore, many systems use a special-purpose DBMS. A common example is an email system that performs many of the functions of a general-purpose DBMS such as the insertion and deletion of messages composed of various items of data or associating messages with a particular email address; but these functions are limited to what is required to handle email and don't provide the user with all of the functionality that would be available using a general-purpose DBMS.Application software can often access a database on behalf of end-users, without exposing the DBMS interface directly. Application programmers may use a wire protocol directly, or more likely through an application programming interface. Database designers and database administrators interact with the DBMS through dedicated interfaces to build and maintain the applications' databases, and thus need some more knowledge and understanding about how DBMSs operate and the DBMSs' external interfaces and tuning parameters.History[edit]Following the technology progress in the areas of processors, computer memory, computer storage, and computer networks, the sizes, capabilities, and performance of databases and their respective DBMSs have grown in orders of magnitude. The development of database technology can be divided into three eras based on data model or structure: navigational,^[9]SQL/relational, and post-relational.The two main early navigational data models were the hierarchical model, epitomized by IBM's IMS system, and the CODASYLmodel (network model), implemented in a number of products such as IDMS.The relational model, first proposed in 1970 by Edgar F. Codd, departed from this tradition by insisting that applications should search for data by content, rather than by following links. The relational model employs sets of ledger-style tables, each used for a different type of entity. Only in the mid-1980s did computing hardware become powerful enough to allow the wide deployment of relational systems (DBMSs plus applications). By the early 1990s, however, relational systems dominated in all large-scale data processing applications, and as of 2015 they remain dominant: IBM DB2, Oracle, MySQL, and Microsoft SQL Server are the top DBMS.^[10] The dominant database language, standardised SQL for the relational model, has influenced database languages for other data models.^{[citation needed]}Object databases were developed in the 1980s to overcome the inconvenience of object-relational impedance mismatch, which led to the coining of the term "post-relational" and also the development of hybrid object-relational databases.The next generation of post-relational databases in the late 2000s became known as NoSQL databases, introducing fast key-value stores and document-oriented databases. A competing "next generation" known as NewSQL databases attempted new implementations that retained the relational/SQL model while aiming to match the high performance of NoSQL compared to commercially available relational DBMSs.1960s, navigational DBMS[edit]Further information: Navigational database

Basic structure of navigational CODASYLdatabase modelThe introduction of the term database coincided with the availability of direct-access storage (disks and drums) from the mid-1960s onwards. The term represented a contrast with the tape-based systems of the past, allowing shared interactive use rather than daily batch processing. The Oxford English Dictionary cites^[11] a 1962 report by the System Development Corporation of California as the first to use the term "data-base" in a specific technical sense.As computers grew in speed and capability, a number of general-purpose database systems emerged; by the mid-1960s a number of such systems had come into commercial use. Interest in a standard began to grow, and Charles Bachman, author of one such product, the Integrated Data Store (IDS), founded the "Database Task Group" within CODASYL, the group responsible for the creation and standardization of COBOL. In 1971, the Database Task Group delivered their standard, which generally became known as the "CODASYL approach", and soon a number of commercial products based on this approach entered the market.The CODASYL approach relied on the "manual" navigation of a linked data set which was formed into a large network. Applications could find records by one of three methods:

1. Use of a primary key (known as a CALC key, typically implemented by hashing)
2. Navigating relationships (called sets) from one record to another
3. Scanning all the records in a sequential order

In the relational model, records are "linked" using virtual keys not stored in the database but defined as needed between the data contained in the records.

• An in-memory database is a database that primarily resides in main memory, but is typically backed-up by non-volatile computer data storage. Main memory databases are faster than disk databases, and so are often used where response time is critical, such as in telecommunications network equipment.^[27] SAP HANA platform is a very hot topic for in-memory database. By May 2012, HANA was able to run on servers with 100TB main memory powered by IBM. The co founder of the company claimed that the system was big enough to run the 8 largest SAP customers.
• An active database includes an event-driven architecture which can respond to conditions both inside and outside the database. Possible uses include security monitoring, alerting, statistics gathering and authorization. Many databases provide active database features in the form of database triggers.
• A cloud database relies on cloud technology. Both the database and most of its DBMS reside remotely, "in the cloud", while its applications are both developed by programmers and later maintained and utilized by (application's) end-users through a web browser and Open APIs.
• Data warehouses archive data from operational databases and often from external sources such as market research firms. The warehouse becomes the central source of data for use by managers and other end-users who may not have access to operational data. For example, sales data might be aggregated to weekly totals and converted from internal product codes to use UPCs so that they can be compared with ACNielsen data. Some basic and essential components of data warehousing include extracting, analyzing, and mining data, transforming, loading, and managing data so as to make them available for further use.
• A deductive database combines logic programming with a relational database, for example by using the Datalog language.
• A distributed database is one in which both the data and the DBMS span multiple computers.
• A document-oriented database is designed for storing, retrieving, and managing document-oriented, or semi structured data, information. Document-oriented databases are one of the main categories of NoSQL databases.
• An embedded database system is a DBMS which is tightly integrated with an application software that requires access to stored data in such a way that the DBMS is hidden from the application's end-users and requires little or no ongoing maintenance.^[28]
• End-user databases consist of data developed by individual end-users. Examples of these are collections of documents, spreadsheets, presentations, multimedia, and other files. Several products exist to support such databases. Some of them are much simpler than full-fledged DBMSs, with more elementary DBMS functionality.
• A federated database system comprises several distinct databases, each with its own DBMS. It is handled as a single database by a federated database management system (FDBMS), which transparently integrates multiple autonomous DBMSs, possibly of different types (in which case it would also be a heterogeneous database system), and provides them with an integrated conceptual view.
• Sometimes the term multi-database is used as a synonym to federated database, though it may refer to a less integrated (e.g., without an FDBMS and a managed integrated schema) group of databases that cooperate in a single application. In this case, typically middleware is used for distribution, which typically includes an atomic commit protocol (ACP), e.g., the two-phase commit protocol, to allow distributed (global) transactions across the participating databases.
• A graph database is a kind of NoSQL database that uses graph structures with nodes, edges, and properties to represent and store information. General graph databases that can store any graph are distinct from specialized graph databases such as triplestores and network databases.
• An array DBMS is a kind of NoSQL DBMS that allows to model, store, and retrieve (usually large) multi-dimensional arrayssuch as satellite images and climate simulation output.
• In a hypertext or hypermedia database, any word or a piece of text representing an object, e.g., another piece of text, an article, a picture, or a film, can be hyperlinked to that object. Hypertext databases are particularly useful for organizing large amounts of disparate information. For example, they are useful for organizing online encyclopedias, where users can conveniently jump around the text. The World Wide Web is thus a large distributed hypertext database.
• A knowledge base (abbreviated KB, kb or Δ^[29][30]) is a special kind of database for knowledge management, providing the means for the computerized collection, organization, and retrieval of knowledge. Also a collection of data representing problems with their solutions and related experiences.
• A mobile database can be carried on or synchronized from a mobile computing device.
• Operational databases store detailed data about the operations of an organization. They typically process relatively high volumes of updates using transactions. Examples include customer databases that record contact, credit, and demographic information about a business' customers, personnel databases that hold information such as salary, benefits, skills data about employees, enterprise resource planning systems that record details about product components, parts inventory, and financial databases that keep track of the organization's money, accounting and financial dealings.
• A parallel database seeks to improve performance through parallelization for tasks such as loading data, building indexes and evaluating queries.

• Shared memory architecture, where multiple processors share the main memory space, as well as other data storage.
• Shared disk architecture, where each processing unit (typically consisting of multiple processors) has its own main memory, but all units share the other storage.
• Shared nothing architecture, where each processing unit has its own main memory and other storage.
• Probabilistic databases employ fuzzy logic to draw inferences from imprecise data.
• Real-time databases process transactions fast enough for the result to come back and be acted on right away.
• A spatial database can store the data with multidimensional features. The queries on such data include location-based queries, like "Where is the closest hotel in my area?".
• A temporal database has built-in time aspects, for example a temporal data model and a temporal version of SQL. More specifically the temporal aspects usually include valid-time and transaction-time.
• A terminology-oriented database builds upon an object-oriented database, often customized for a specific field.
• An unstructured data database is intended to store in a manageable and protected way diverse objects that do not fit naturally and conveniently in common databases. It may include email messages, documents, journals, multimedia objects, etc. The name may be misleading since some objects can be highly structured. However, the entire possible object collection does not fit into a predefined structured framework. Most established DBMSs now support unstructured data in various ways, and new dedicated DBMSs are emerging.

Collage of five types of database models

• Navigational databases
  • Hierarchical database model
  • Network model
  • Graph database
• Relational model
• Entity–relationship model
  • Enhanced entity–relationship model
• Object model
• Document model
• Entity–attribute–value model
• Star schema

• Inverted index
• Flat file

• Associative model
• Multidimensional model
• Array model
• Multivalue model

• XML database
• Semantic model
• Content store
• Event store
• Time series model

Traditional view of data^[31]

• The external level defines how each group of end-users sees the organization of data in the database. A single database can have any number of views at the external level.
• The conceptual level unifies the various external views into a compatible global view.^[32] It provides the synthesis of all the external views. It is out of the scope of the various database end-users, and is rather of interest to database application developers and database administrators.
• The internal level (or physical level) is the internal organization of data inside a DBMS. It is concerned with cost, performance, scalability and other operational matters. It deals with storage layout of the data, using storage structures such as indexes to enhance performance. Occasionally it stores data of individual views (materialized views), computed from generic data, if performance justification exists for such redundancy. It balances all the external views' performance requirements, possibly conflicting, in an attempt to optimize overall performance across all activities.

• Data definition language – defines data types such as creating, altering, or dropping and the relationships among them
• Data manipulation language – performs tasks such as inserting, updating, or deleting data occurrences
• Query language – allows searching for information and computing derived information

• SQL combines the roles of data definition, data manipulation, and query in a single language. It was one of the first commercial languages for the relational model, although it departs in some respects from the relational model as described by Codd (for example, the rows and columns of a table can be ordered). SQL became a standard of the American National Standards Institute (ANSI) in 1986, and of the International Organization for Standardization (ISO) in 1987. The standards have been regularly enhanced since and is supported (with varying degrees of conformance) by all mainstream commercial relational DBMSs.^[33][34]
• OQL is an object model language standard (from the Object Data Management Group). It has influenced the design of some of the newer query languages like JDOQL and EJB QL.
• XQuery is a standard XML query language implemented by XML database systems such as MarkLogic and eXist, by relational databases with XML capability such as Oracle and DB2, and also by in-memory XML processors such as Saxon.
• SQL/XML combines XQuery with SQL.^[35]

• DBMS-specific Configuration and storage engine management
• Computations to modify query results, like counting, summing, averaging, sorting, grouping, and cross-referencing
• Constraint enforcement (e.g. in an automotive database, only allowing one engine type per car)
• Application programming interface version of the query language, for programmer convenience

| This article appears to contradict the article Database security. Please see discussion on the linked talk page. (March 2013) (Learn how and when to remove this template message)Main article: Database securityDatabase security deals with all various aspects of protecting the database content, its owners, and its users. It ranges from protection from intentional unauthorized database uses to unintentional database accesses by unauthorized entities (e.g., a person or a computer program).Database access control deals with controlling who (a person or a certain computer program) is allowed to access what information in the database. The information may comprise specific database objects (e.g., record types, specific records, data structures), certain computations over certain objects (e.g., query types, or specific queries), or utilizing specific access paths to the former (e.g., using specific indexes or other data structures to access information). Database access controls are set by special authorized (by the database owner) personnel that uses dedicated protected security DBMS interfaces.This may be managed directly on an individual basis, or by the assignment of individuals and privileges to groups, or (in the most elaborate models) through the assignment of individuals and groups to roles which are then granted entitlements. Data security prevents unauthorized users from viewing or updating the database. Using passwords, users are allowed access to the entire database or subsets of it called "subschemas". For example, an employee database can contain all the data about an individual employee, but one group of users may be authorized to view only payroll data, while others are allowed access to only work history and medical data. If the DBMS provides a way to interactively enter and update the database, as well as interrogate it, this capability allows for managing personal databases.Data security in general deals with protecting specific chunks of data, both physically (i.e., from corruption, or destruction, or removal; e.g., see physical security), or the interpretation of them, or parts of them to meaningful information (e.g., by looking at the strings of bits that they comprise, concluding specific valid credit-card numbers; e.g., see data encryption).Change and access logging records who accessed which attributes, what was changed, and when it was changed. Logging services allow for a forensic database audit later by keeping a record of access occurrences and changes. Sometimes application-level code is used to record changes rather than leaving this to the database. Monitoring can be set up to attempt to detect security breaches.Transactions and concurrency[edit]Further information: Concurrency controlDatabase transactions can be used to introduce some level of fault tolerance and data integrity after recovery from a crash. A database transaction is a unit of work, typically encapsulating a number of operations over a database (e.g., reading a database object, writing, acquiring lock, etc.), an abstraction supported in database and also other systems. Each transaction has well defined boundaries in terms of which program/code executions are included in that transaction (determined by the transaction's programmer via special transaction commands).The acronym ACID describes some ideal properties of a database transaction: Atomicity, Consistency, Isolation, and Durability.Migration[edit]See also: Data migration § Database migrationA database built with one DBMS is not portable to another DBMS (i.e., the other DBMS cannot run it). However, in some situations, it is desirable to move, migrate a database from one DBMS to another. The reasons are primarily economical (different DBMSs may have different total costs of ownership or TCOs), functional, and operational (different DBMSs may have different capabilities). The migration involves the database's transformation from one DBMS type to another. The transformation should maintain (if possible) the database related application (i.e., all related application programs) intact. Thus, the database's conceptual and external architectural levels should be maintained in the transformation. It may be desired that also some aspects of the architecture internal level are maintained. A complex or large database migration may be a complicated and costly (one-time) project by itself, which should be factored into the decision to migrate. This in spite of the fact that tools may exist to help migration between specific DBMSs. Typically, a DBMS vendor provides tools to help importing databases from other popular DBMSs.Building, maintaining, and tuning[edit]Main article: Database tuningAfter designing a database for an application, the next stage is building the database. Typically, an appropriate general-purpose DBMS can be selected to be utilized for this purpose. A DBMS provides the needed user interfaces to be utilized by database administrators to define the needed application's data structures within the DBMS's respective data model. Other user interfaces are used to select needed DBMS parameters (like security related, storage allocation parameters, etc.).When the database is ready (all its data structures and other needed components are defined), it is typically populated with initial application's data (database initialization, which is typically a distinct project; in many cases using specialized DBMS interfaces that support bulk insertion) before making it operational. In some cases, the database becomes operational while empty of application data, and data are accumulated during its operation.After the database is created, initialised and populated it needs to be maintained. Various database parameters may need changing and the database may need to be tuned (tuning) for better performance; application's data structures may be changed or added, new related application programs may be written to add to the application's functionality, etc.Backup and restore[edit]Main article: BackupSometimes it is desired to bring a database back to a previous state (for many reasons, e.g., cases when the database is found corrupted due to a software error, or if it has been updated with erroneous data). To achieve this, a backup operation is done occasionally or continuously, where each desired database state (i.e., the values of its data and their embedding in database's data structures) is kept within dedicated backup files (many techniques exist to do this effectively). When this state is needed, i.e., when it is decided by a database administrator to bring the database back to this state (e.g., by specifying this state by a desired point in time when the database was in this state), these files are utilized to restore that state.Static analysis[edit]Static analysis techniques for software verification can be applied also in the scenario of query languages. In particular, the *Abstract interpretation framework has been extended to the field of query languages for relational databases as a way to support sound approximation techniques.^[36] The semantics of query languages can be tuned according to suitable abstractions of the concrete domain of data. The abstraction of relational database system has many interesting applications, in particular, for security purposes, such as fine grained access control, watermarking, etc.Other[edit]Other DBMS features might include:

• Database logs
• Graphics component for producing graphs and charts, especially in a data warehouse system
• Query optimizer – Performs query optimization on every query to choose an efficient query plan (a partial order (tree) of operations) to be executed to compute the query result. May be specific to a particular storage engine.
• Tools or hooks for database design, application programming, application program maintenance, database performance analysis and monitoring, database configuration monitoring, DBMS hardware configuration (a DBMS and related database may span computers, networks, and storage units) and related database mapping (especially for a distributed DBMS), storage allocation and database layout monitoring, storage migration, etc.
• Increasingly, there are calls for a single system that incorporates all of these core functionalities into the same build, test, and deployment framework for database management and source control. Borrowing from other developments in the software industry, some market such offerings as "DevOps for database".^[37]

| Book: Databases

• Comparison of database tools
• Comparison of object database management systems
• Comparison of object-relational database management systems
• Comparison of relational database management systems
• Data hierarchy
• Data bank
• Data store
• Database theory
• Database testing
• Database-centric architecture
• Journal of Database Management
• Question-focused dataset

1. Jump up^ This article quotes a development time of 5 years involving 750 people for DB2 release 9 alone.(Chong et al. 2007)

1. Jump up^ "Database – Definition of database by Merriam-Webster". merriam-webster.com.
2. Jump up^ Ullman & Widom 1997, p. 1.
3. Jump up^ "Update – Definition of update by Merriam-Webster". merriam-webster.com.
4. Jump up^ "Retrieval – Definition of retrieval by Merriam-Webster". merriam-webster.com.
5. Jump up^ "Administration – Definition of administration by Merriam-Webster". merriam-webster.com.
6. Jump up^ Tsitchizris & Lochovsky 1982.
7. Jump up^ Beynon–Davies 2003.
8. Jump up^ Nelson & Nelson 2001.
9. Jump up^ Bachman 1973.
10. Jump up^ "TOPDB Top Database index". pypl.github.io.
11. Jump up^ "database, n". OED Online. Oxford University Press. June 2013. Retrieved July 12, 2013.
12. Jump up^ IBM Corporation. "IBM Information Management System (IMS) 13 Transaction and Database Servers delivers high performance and low total cost of ownership". Retrieved Feb 20, 2014.
13. Jump up^ Codd 1970.
14. Jump up^ Hershey & Easthope 1972.
15. Jump up^ North 2010.
16. Jump up^ Childs 1968a.
17. Jump up^ Childs 1968b.
18. Jump up^ MICRO Information Management System (Version 5.0) Reference Manual, M.A. Kahn, D.L. Rumelhart, and B.L. Bronson, October 1977, Institute of Labor and Industrial Relations (ILIR), University of Michigan and Wayne State University
19. Jump up^ "Oracle 30th Anniversary Timeline" (PDF). Retrieved 23 August 2017.
20. Jump up^ Interview with Wayne Ratliff. The FoxPro History. Retrieved on 2013-07-12.
21. Jump up^ Development of an object-oriented DBMS; Portland, Oregon, United States; Pages: 472–482; 1986; ISBN 0-89791-204-7
22. Jump up^ "Oracle Berkeley DB XML" (PDF). Retrieved 10 March2015.
23. Jump up^ "ACID Transactions, MarkLogic". Retrieved 10 March2015.
24. Jump up^ "Clusterpoint Database at a Glance". Archived from the original on 2 April 2015. Retrieved 10 March 2015.
25. Jump up^ "DB-Engines Ranking". January 2013. Retrieved 22 January 2013.
26. Jump up^ Proctor 2013.
27. Jump up^ "TeleCommunication Systems Signs up as a Reseller of TimesTen; Mobile Operators and Carriers Gain Real-Time Platform for Location-Based Services". Business Wire. 2002-06-24.^{[dead link]}
28. Jump up^ Graves, Steve. "COTS Databases For Embedded Systems", Embedded Computing Design magazine, January 2007. Retrieved on August 13, 2008.
29. Jump up^ Argumentation in Artificial Intelligence by Iyad Rahwan, Guillermo R. Simari
30. Jump up^ "OWL DL Semantics". Retrieved 10 December 2010.
31. Jump up^ itl.nist.gov (1993) Integration Definition for Information Modeling (IDEFIX). 21 December 1993.
32. ^ Jump up to:^{a b} Date 2003, pp. 31–32.
33. Jump up^ Chapple 2005.
34. Jump up^ "Structured Query Language (SQL)". International Business Machines. October 27, 2006. Retrieved 2007-06-10.
35. Jump up^ Wagner 2010.
36. Jump up^ Halder & Cortesi 2011.
37. Jump up^ Ben Linders (January 28, 2016). "How Database Administration Fits into DevOps". Retrieved April 15, 2017.

• Bachman, Charles W. (1973). "The Programmer as Navigator". Communications of the ACM. 16 (11): 653–658. doi:10.1145/355611.362534. (Subscription required (help)).
• Beynon–Davies, Paul (2003). Database Systems (3rd ed.). Palgrave Macmillan. ISBN 978-1403916013.
• Chapple, Mike (2005). "SQL Fundamentals". Databases. About.com. Archived from the original on 22 February 2009. Retrieved 28 January 2009.
• Childs, David L. (1968a). "Description of a set-theoretic data structure" (PDF). CONCOMP (Research in Conversational Use of Computers) Project. Technical Report 3. University of Michigan.
• Childs, David L. (1968b). "Feasibility of a set-theoretic data structure: a general structure based on a reconstituted definition" (PDF). CONCOMP (Research in Conversational Use of Computers) Project. Technical Report 6. University of Michigan.
• Chong, Raul F.; Wang, Xiaomei; Dang, Michael; Snow, Dwaine R. (2007). "Introduction to DB2". Understanding DB2: Learning Visually with Examples (2nd ed.). ISBN 978-0131580183. Retrieved 17 March 2013.
• Codd, Edgar F. (1970). "A Relational Model of Data for Large Shared Data Banks" (PDF). Communications of the ACM. 13(6): 377–387. doi:10.1145/362384.362685.
• Date, C. J. (2003). An Introduction to Database Systems (8th ed.). Pearson. ISBN 978-0321197849.
• Halder, Raju; Cortesi, Agostino (2011). "Abstract Interpretation of Database Query Languages" (PDF). COMPUTER LANGUAGES, SYSTEMS & STRUCTURES. Elsevier. 38 (2): 123–157. doi:10.1016/j.cl.2011.10.004. ISSN 1477-8424.
• Hershey, William; Easthope, Carol (1972). A set theoretic data structure and retrieval language. Spring Joint Computer Conference, May 1972. ACM SIGIR Forum. 7 (4). pp. 45–55. doi:10.1145/1095495.1095500.
• Nelson, Anne Fulcher; Nelson, William Harris Morehead (2001). Building Electronic Commerce: With Web Database Constructions. Prentice Hall. ISBN 978-0201741308.
• North, Ken (10 March 2010). "Sets, Data Models and Data Independence". Dr. Dobb's. Archived from the original on 24 October 2010.
• Proctor, Seth (12 July 2013). "Exploring the Architecture of the NuoDB Database, Part 1". Archived from the original on 15 July 2013. Retrieved 12 July 2013.
• Tsitchizris, Dionysios C.; Lochovsky, Fred H. (1982). Data Models. Prentice–Hall. ISBN 978-0131964280.
• Ullman, Jeffrey; Widom, Jennifer (1997). A First Course in Database Systems. Prentice–Hall. ISBN 0138613370.
• Wagner, Michael (2010), SQL/XML:2006 – Evaluierung der Standardkonformität ausgewählter Datenbanksysteme, Diplomica Verlag, ISBN 978-3836696098

• Ling Liu and Tamer M. Özsu (Eds.) (2009). "Encyclopedia of Database Systems, 4100 p. 60 illus. ISBN 978-0-387-49616-0.
• Connolly, Thomas and Carolyn Begg. Database Systems. New York: Harlow, 2002.
• Gray, J. and Reuter, A. Transaction Processing: Concepts and Techniques, 1st edition, Morgan Kaufmann Publishers, 1992.
• Kroenke, David M. and David J. Auer. Database Concepts. 3rd ed. New York: Prentice, 2007.
• Raghu Ramakrishnan and Johannes Gehrke, Database Management Systems
• Abraham Silberschatz, Henry F. Korth, S. Sudarshan, Database System Concepts
• Lightstone, S.; Teorey, T.; Nadeau, T. (2007). Physical Database Design: the database professional's guide to exploiting indexes, views, storage, and more. Morgan Kaufmann Press. ISBN 0-12-369389-6.
• Teorey, T.; Lightstone, S. and Nadeau, T. Database Modeling & Design: Logical Design, 4th edition, Morgan Kaufmann Press, 2005. ISBN 0-12-685352-5

• 

  Definitions from Wiktionary

• 

  Media from Commons

• 

  News from Wikinews

• 

  Quotations from Wikiquote

• 

  Texts from Wikisource

• 

  Textbooks from Wikibooks

• 

  Learning resources from Wikiversity
• DB File extension – information about files with the DB extension

• v
• t
• e

• v
• t
• e

• v
• t
• e

• v
• t
• e

• Database management systems
• Databases

• Not logged in
• Talk
• Contributions
• Create account
• Log in
• Article
• Talk
• Read
• Edit
• View history

• Main page
• Contents
• Featured content
• Current events
• Random article
• Donate to Wikipedia
• Wikipedia store

• Help
• About Wikipedia
• Community portal
• Recent changes
• Contact page

• What links here
• Related changes
• Upload file
• Special pages
• Permanent link
• Page information
• Wikidata item
• Cite this page

• Create a book
• Download as PDF
• Printable version

• Wikimedia Commons
• Wikiversity

• Afrikaans
• العربية
• Aragonés
• Asturianu
• Azərbaycanca
• বাংলা
• Беларуская
• Беларуская (тарашкевіца)‎
• Български
• Boarisch
• Bosanski
• Brezhoneg
• Català
• Čeština
• Cymraeg
• Dansk
• Deutsch
• Eesti
• Ελληνικά
• Español
• Esperanto
• Euskara
• فارسی
• Français
• Frysk
• Gaeilge
• Galego
• 한국어
• Հայերեն
• हिन्दी
• Hrvatski
• Ido
• Bahasa Indonesia
• Interlingua
• Íslenska
• Italiano
• עברית
• Basa Jawa
• ქართული
• Қазақша
• Kurdî
• Кыргызча
• Latina
• Latviešu
• Lietuvių
• Magyar
• Македонски
• മലയാളം
• مصرى
• Bahasa Melayu
• Mirandés
• Монгол
• မြန်မာဘာသာ
• Nederlands
• 日本語
• Norsk
• Norsk nynorsk
• Олык марий
• Oʻzbekcha/ўзбекча
• ਪੰਜਾਬੀ
• پښتو
• Polski
• Português
• Română
• Русский
• Scots
• Seeltersk
• Shqip
• Sicilianu
• සිංහල
• Simple English
• Slovenčina
• Slovenščina
• کوردی
• Српски / srpski
• Srpskohrvatski / српскохрватски
• Suomi
• Svenska
• Tagalog
• தமிழ்
• తెలుగు
• ไทย
• Тоҷикӣ
• Türkçe
• Українська
• اردو
• Tiếng Việt
• Walon
• Winaray
• 粵語
• 中文

• This page was last edited on 27 November 2017, at 07:00.
• Text is available under the Creative Commons Attribution-ShareAlike License; additional terms may apply. By using this site, you agree to the Terms of Use and Privacy Policy. Wikipedia® is a registered trademark of the Wikimedia Foundation, Inc., a non-profit organization.
• Privacy policy
• About Wikipedia
• Disclaimers
• Contact Wikipedia
• Developers
• Cookie statement
• Mobile view
• Enable previews

• 

• 

Database

An example of output from an SQL database query.

• 1Terminology and overview
• 2Applications
• 3General-purpose and special-purpose DBMSs
• 4History
  • 4.11960s, navigational DBMS
  • 4.21970s, relational DBMS
  • 4.3Integrated approach
  • 4.4Late 1970s, SQL DBMS
  • 4.51980s, on the desktop
  • 4.61990s, object-oriented
  • 4.72000s, NoSQL and NewSQL
• 5Research
• 6Examples
• 7Design and modeling
  • 7.1Models
  • 7.2External, conceptual, and internal views
• 8Languages
• 9Performance, security, and availability
  • 9.1Storage
  • 9.2Security
  • 9.3Transactions and concurrency
  • 9.4Migration
  • 9.5Building, maintaining, and tuning
  • 9.6Backup and restore
  • 9.7Static analysis
  • 9.8Other
• 10See also
• 11Notes
• 12References
• 13Sources
• 14Further reading
• 15External links

• Data definition – Creation, modification and removal of definitions that define the organization of the data.
• Update – Insertion, modification, and deletion of the actual data.^[3]
• Retrieval – Providing information in a form directly usable or for further processing by other applications. The retrieved data may be made available in a form basically the same as it is stored in the database or in a new form obtained by altering or combining existing data from the database.^[4]
• Administration – Registering and monitoring users, enforcing data security, monitoring performance, maintaining data integrity, dealing with concurrency control, and recovering information that has been corrupted by some event such as an unexpected system failure.^[5]

| This section does not cite any sources. Please help improve this section by adding citations to reliable sources. Unsourced material may be challenged and removed.(March 2013) (Learn how and when to remove this template message)Databases are used to support internal operations of organizations and to underpin online interactions with customers and suppliers (see Enterprise software).Databases are used to hold administrative information and more specialized data, such as engineering data or economic models. Examples of database applications include computerized library systems, flight reservation systems, computerized parts inventory systems, and many content management systems that store websites as collections of webpages in a database.General-purpose and special-purpose DBMSs[edit]DBMS may become a complex software system and its development typically requires thousands of human years of development effort.^[a] Some general-purpose DBMSs such as Adabas, Oracle and DB2 have been upgraded since the 1970s. General-purpose DBMSs aim to meet the needs of as many applications as possible, which adds to the complexity. However, since their development cost can be spread over a large number of users, they are often the most cost-effective approach. On the other hand, a general-purpose DBMS may introduce unnecessary overhead. Therefore, many systems use a special-purpose DBMS. A common example is an email system that performs many of the functions of a general-purpose DBMS such as the insertion and deletion of messages composed of various items of data or associating messages with a particular email address; but these functions are limited to what is required to handle email and don't provide the user with all of the functionality that would be available using a general-purpose DBMS.Application software can often access a database on behalf of end-users, without exposing the DBMS interface directly. Application programmers may use a wire protocol directly, or more likely through an application programming interface. Database designers and database administrators interact with the DBMS through dedicated interfaces to build and maintain the applications' databases, and thus need some more knowledge and understanding about how DBMSs operate and the DBMSs' external interfaces and tuning parameters.History[edit]Following the technology progress in the areas of processors, computer memory, computer storage, and computer networks, the sizes, capabilities, and performance of databases and their respective DBMSs have grown in orders of magnitude. The development of database technology can be divided into three eras based on data model or structure: navigational,^[9]SQL/relational, and post-relational.The two main early navigational data models were the hierarchical model, epitomized by IBM's IMS system, and the CODASYLmodel (network model), implemented in a number of products such as IDMS.The relational model, first proposed in 1970 by Edgar F. Codd, departed from this tradition by insisting that applications should search for data by content, rather than by following links. The relational model employs sets of ledger-style tables, each used for a different type of entity. Only in the mid-1980s did computing hardware become powerful enough to allow the wide deployment of relational systems (DBMSs plus applications). By the early 1990s, however, relational systems dominated in all large-scale data processing applications, and as of 2015 they remain dominant: IBM DB2, Oracle, MySQL, and Microsoft SQL Server are the top DBMS.^[10] The dominant database language, standardised SQL for the relational model, has influenced database languages for other data models.^{[citation needed]}Object databases were developed in the 1980s to overcome the inconvenience of object-relational impedance mismatch, which led to the coining of the term "post-relational" and also the development of hybrid object-relational databases.The next generation of post-relational databases in the late 2000s became known as NoSQL databases, introducing fast key-value stores and document-oriented databases. A competing "next generation" known as NewSQL databases attempted new implementations that retained the relational/SQL model while aiming to match the high performance of NoSQL compared to commercially available relational DBMSs.1960s, navigational DBMS[edit]Further information: Navigational database

Basic structure of navigational CODASYLdatabase modelThe introduction of the term database coincided with the availability of direct-access storage (disks and drums) from the mid-1960s onwards. The term represented a contrast with the tape-based systems of the past, allowing shared interactive use rather than daily batch processing. The Oxford English Dictionary cites^[11] a 1962 report by the System Development Corporation of California as the first to use the term "data-base" in a specific technical sense.As computers grew in speed and capability, a number of general-purpose database systems emerged; by the mid-1960s a number of such systems had come into commercial use. Interest in a standard began to grow, and Charles Bachman, author of one such product, the Integrated Data Store (IDS), founded the "Database Task Group" within CODASYL, the group responsible for the creation and standardization of COBOL. In 1971, the Database Task Group delivered their standard, which generally became known as the "CODASYL approach", and soon a number of commercial products based on this approach entered the market.The CODASYL approach relied on the "manual" navigation of a linked data set which was formed into a large network. Applications could find records by one of three methods:

1. Use of a primary key (known as a CALC key, typically implemented by hashing)
2. Navigating relationships (called sets) from one record to another
3. Scanning all the records in a sequential order

In the relational model, records are "linked" using virtual keys not stored in the database but defined as needed between the data contained in the records.

• An in-memory database is a database that primarily resides in main memory, but is typically backed-up by non-volatile computer data storage. Main memory databases are faster than disk databases, and so are often used where response time is critical, such as in telecommunications network equipment.^[27] SAP HANA platform is a very hot topic for in-memory database. By May 2012, HANA was able to run on servers with 100TB main memory powered by IBM. The co founder of the company claimed that the system was big enough to run the 8 largest SAP customers.
• An active database includes an event-driven architecture which can respond to conditions both inside and outside the database. Possible uses include security monitoring, alerting, statistics gathering and authorization. Many databases provide active database features in the form of database triggers.
• A cloud database relies on cloud technology. Both the database and most of its DBMS reside remotely, "in the cloud", while its applications are both developed by programmers and later maintained and utilized by (application's) end-users through a web browser and Open APIs.
• Data warehouses archive data from operational databases and often from external sources such as market research firms. The warehouse becomes the central source of data for use by managers and other end-users who may not have access to operational data. For example, sales data might be aggregated to weekly totals and converted from internal product codes to use UPCs so that they can be compared with ACNielsen data. Some basic and essential components of data warehousing include extracting, analyzing, and mining data, transforming, loading, and managing data so as to make them available for further use.
• A deductive database combines logic programming with a relational database, for example by using the Datalog language.
• A distributed database is one in which both the data and the DBMS span multiple computers.
• A document-oriented database is designed for storing, retrieving, and managing document-oriented, or semi structured data, information. Document-oriented databases are one of the main categories of NoSQL databases.
• An embedded database system is a DBMS which is tightly integrated with an application software that requires access to stored data in such a way that the DBMS is hidden from the application's end-users and requires little or no ongoing maintenance.^[28]
• End-user databases consist of data developed by individual end-users. Examples of these are collections of documents, spreadsheets, presentations, multimedia, and other files. Several products exist to support such databases. Some of them are much simpler than full-fledged DBMSs, with more elementary DBMS functionality.
• A federated database system comprises several distinct databases, each with its own DBMS. It is handled as a single database by a federated database management system (FDBMS), which transparently integrates multiple autonomous DBMSs, possibly of different types (in which case it would also be a heterogeneous database system), and provides them with an integrated conceptual view.
• Sometimes the term multi-database is used as a synonym to federated database, though it may refer to a less integrated (e.g., without an FDBMS and a managed integrated schema) group of databases that cooperate in a single application. In this case, typically middleware is used for distribution, which typically includes an atomic commit protocol (ACP), e.g., the two-phase commit protocol, to allow distributed (global) transactions across the participating databases.
• A graph database is a kind of NoSQL database that uses graph structures with nodes, edges, and properties to represent and store information. General graph databases that can store any graph are distinct from specialized graph databases such as triplestores and network databases.
• An array DBMS is a kind of NoSQL DBMS that allows to model, store, and retrieve (usually large) multi-dimensional arrayssuch as satellite images and climate simulation output.
• In a hypertext or hypermedia database, any word or a piece of text representing an object, e.g., another piece of text, an article, a picture, or a film, can be hyperlinked to that object. Hypertext databases are particularly useful for organizing large amounts of disparate information. For example, they are useful for organizing online encyclopedias, where users can conveniently jump around the text. The World Wide Web is thus a large distributed hypertext database.
• A knowledge base (abbreviated KB, kb or Δ^[29][30]) is a special kind of database for knowledge management, providing the means for the computerized collection, organization, and retrieval of knowledge. Also a collection of data representing problems with their solutions and related experiences.
• A mobile database can be carried on or synchronized from a mobile computing device.
• Operational databases store detailed data about the operations of an organization. They typically process relatively high volumes of updates using transactions. Examples include customer databases that record contact, credit, and demographic information about a business' customers, personnel databases that hold information such as salary, benefits, skills data about employees, enterprise resource planning systems that record details about product components, parts inventory, and financial databases that keep track of the organization's money, accounting and financial dealings.
• A parallel database seeks to improve performance through parallelization for tasks such as loading data, building indexes and evaluating queries.

• Shared memory architecture, where multiple processors share the main memory space, as well as other data storage.
• Shared disk architecture, where each processing unit (typically consisting of multiple processors) has its own main memory, but all units share the other storage.
• Shared nothing architecture, where each processing unit has its own main memory and other storage.
• Probabilistic databases employ fuzzy logic to draw inferences from imprecise data.
• Real-time databases process transactions fast enough for the result to come back and be acted on right away.
• A spatial database can store the data with multidimensional features. The queries on such data include location-based queries, like "Where is the closest hotel in my area?".
• A temporal database has built-in time aspects, for example a temporal data model and a temporal version of SQL. More specifically the temporal aspects usually include valid-time and transaction-time.
• A terminology-oriented database builds upon an object-oriented database, often customized for a specific field.
• An unstructured data database is intended to store in a manageable and protected way diverse objects that do not fit naturally and conveniently in common databases. It may include email messages, documents, journals, multimedia objects, etc. The name may be misleading since some objects can be highly structured. However, the entire possible object collection does not fit into a predefined structured framework. Most established DBMSs now support unstructured data in various ways, and new dedicated DBMSs are emerging.

Collage of five types of database models

• Navigational databases
  • Hierarchical database model
  • Network model
  • Graph database
• Relational model
• Entity–relationship model
  • Enhanced entity–relationship model
• Object model
• Document model
• Entity–attribute–value model
• Star schema

• Inverted index
• Flat file

• Associative model
• Multidimensional model
• Array model
• Multivalue model

• XML database
• Semantic model
• Content store
• Event store
• Time series model

Traditional view of data^[31]

• The external level defines how each group of end-users sees the organization of data in the database. A single database can have any number of views at the external level.
• The conceptual level unifies the various external views into a compatible global view.^[32] It provides the synthesis of all the external views. It is out of the scope of the various database end-users, and is rather of interest to database application developers and database administrators.
• The internal level (or physical level) is the internal organization of data inside a DBMS. It is concerned with cost, performance, scalability and other operational matters. It deals with storage layout of the data, using storage structures such as indexes to enhance performance. Occasionally it stores data of individual views (materialized views), computed from generic data, if performance justification exists for such redundancy. It balances all the external views' performance requirements, possibly conflicting, in an attempt to optimize overall performance across all activities.

• Data definition language – defines data types such as creating, altering, or dropping and the relationships among them
• Data manipulation language – performs tasks such as inserting, updating, or deleting data occurrences
• Query language – allows searching for information and computing derived information

• SQL combines the roles of data definition, data manipulation, and query in a single language. It was one of the first commercial languages for the relational model, although it departs in some respects from the relational model as described by Codd (for example, the rows and columns of a table can be ordered). SQL became a standard of the American National Standards Institute (ANSI) in 1986, and of the International Organization for Standardization (ISO) in 1987. The standards have been regularly enhanced since and is supported (with varying degrees of conformance) by all mainstream commercial relational DBMSs.^[33][34]
• OQL is an object model language standard (from the Object Data Management Group). It has influenced the design of some of the newer query languages like JDOQL and EJB QL.
• XQuery is a standard XML query language implemented by XML database systems such as MarkLogic and eXist, by relational databases with XML capability such as Oracle and DB2, and also by in-memory XML processors such as Saxon.
• SQL/XML combines XQuery with SQL.^[35]

• DBMS-specific Configuration and storage engine management
• Computations to modify query results, like counting, summing, averaging, sorting, grouping, and cross-referencing
• Constraint enforcement (e.g. in an automotive database, only allowing one engine type per car)
• Application programming interface version of the query language, for programmer convenience

| This article appears to contradict the article Database security. Please see discussion on the linked talk page. (March 2013) (Learn how and when to remove this template message)Main article: Database securityDatabase security deals with all various aspects of protecting the database content, its owners, and its users. It ranges from protection from intentional unauthorized database uses to unintentional database accesses by unauthorized entities (e.g., a person or a computer program).Database access control deals with controlling who (a person or a certain computer program) is allowed to access what information in the database. The information may comprise specific database objects (e.g., record types, specific records, data structures), certain computations over certain objects (e.g., query types, or specific queries), or utilizing specific access paths to the former (e.g., using specific indexes or other data structures to access information). Database access controls are set by special authorized (by the database owner) personnel that uses dedicated protected security DBMS interfaces.This may be managed directly on an individual basis, or by the assignment of individuals and privileges to groups, or (in the most elaborate models) through the assignment of individuals and groups to roles which are then granted entitlements. Data security prevents unauthorized users from viewing or updating the database. Using passwords, users are allowed access to the entire database or subsets of it called "subschemas". For example, an employee database can contain all the data about an individual employee, but one group of users may be authorized to view only payroll data, while others are allowed access to only work history and medical data. If the DBMS provides a way to interactively enter and update the database, as well as interrogate it, this capability allows for managing personal databases.Data security in general deals with protecting specific chunks of data, both physically (i.e., from corruption, or destruction, or removal; e.g., see physical security), or the interpretation of them, or parts of them to meaningful information (e.g., by looking at the strings of bits that they comprise, concluding specific valid credit-card numbers; e.g., see data encryption).Change and access logging records who accessed which attributes, what was changed, and when it was changed. Logging services allow for a forensic database audit later by keeping a record of access occurrences and changes. Sometimes application-level code is used to record changes rather than leaving this to the database. Monitoring can be set up to attempt to detect security breaches.Transactions and concurrency[edit]Further information: Concurrency controlDatabase transactions can be used to introduce some level of fault tolerance and data integrity after recovery from a crash. A database transaction is a unit of work, typically encapsulating a number of operations over a database (e.g., reading a database object, writing, acquiring lock, etc.), an abstraction supported in database and also other systems. Each transaction has well defined boundaries in terms of which program/code executions are included in that transaction (determined by the transaction's programmer via special transaction commands).The acronym ACID describes some ideal properties of a database transaction: Atomicity, Consistency, Isolation, and Durability.Migration[edit]See also: Data migration § Database migrationA database built with one DBMS is not portable to another DBMS (i.e., the other DBMS cannot run it). However, in some situations, it is desirable to move, migrate a database from one DBMS to another. The reasons are primarily economical (different DBMSs may have different total costs of ownership or TCOs), functional, and operational (different DBMSs may have different capabilities). The migration involves the database's transformation from one DBMS type to another. The transformation should maintain (if possible) the database related application (i.e., all related application programs) intact. Thus, the database's conceptual and external architectural levels should be maintained in the transformation. It may be desired that also some aspects of the architecture internal level are maintained. A complex or large database migration may be a complicated and costly (one-time) project by itself, which should be factored into the decision to migrate. This in spite of the fact that tools may exist to help migration between specific DBMSs. Typically, a DBMS vendor provides tools to help importing databases from other popular DBMSs.Building, maintaining, and tuning[edit]Main article: Database tuningAfter designing a database for an application, the next stage is building the database. Typically, an appropriate general-purpose DBMS can be selected to be utilized for this purpose. A DBMS provides the needed user interfaces to be utilized by database administrators to define the needed application's data structures within the DBMS's respective data model. Other user interfaces are used to select needed DBMS parameters (like security related, storage allocation parameters, etc.).When the database is ready (all its data structures and other needed components are defined), it is typically populated with initial application's data (database initialization, which is typically a distinct project; in many cases using specialized DBMS interfaces that support bulk insertion) before making it operational. In some cases, the database becomes operational while empty of application data, and data are accumulated during its operation.After the database is created, initialised and populated it needs to be maintained. Various database parameters may need changing and the database may need to be tuned (tuning) for better performance; application's data structures may be changed or added, new related application programs may be written to add to the application's functionality, etc.Backup and restore[edit]Main article: BackupSometimes it is desired to bring a database back to a previous state (for many reasons, e.g., cases when the database is found corrupted due to a software error, or if it has been updated with erroneous data). To achieve this, a backup operation is done occasionally or continuously, where each desired database state (i.e., the values of its data and their embedding in database's data structures) is kept within dedicated backup files (many techniques exist to do this effectively). When this state is needed, i.e., when it is decided by a database administrator to bring the database back to this state (e.g., by specifying this state by a desired point in time when the database was in this state), these files are utilized to restore that state.Static analysis[edit]Static analysis techniques for software verification can be applied also in the scenario of query languages. In particular, the *Abstract interpretation framework has been extended to the field of query languages for relational databases as a way to support sound approximation techniques.^[36] The semantics of query languages can be tuned according to suitable abstractions of the concrete domain of data. The abstraction of relational database system has many interesting applications, in particular, for security purposes, such as fine grained access control, watermarking, etc.Other[edit]Other DBMS features might include:

• Database logs
• Graphics component for producing graphs and charts, especially in a data warehouse system
• Query optimizer – Performs query optimization on every query to choose an efficient query plan (a partial order (tree) of operations) to be executed to compute the query result. May be specific to a particular storage engine.
• Tools or hooks for database design, application programming, application program maintenance, database performance analysis and monitoring, database configuration monitoring, DBMS hardware configuration (a DBMS and related database may span computers, networks, and storage units) and related database mapping (especially for a distributed DBMS), storage allocation and database layout monitoring, storage migration, etc.
• Increasingly, there are calls for a single system that incorporates all of these core functionalities into the same build, test, and deployment framework for database management and source control. Borrowing from other developments in the software industry, some market such offerings as "DevOps for database".^[37]

| Book: Databases

• Comparison of database tools
• Comparison of object database management systems
• Comparison of object-relational database management systems
• Comparison of relational database management systems
• Data hierarchy
• Data bank
• Data store
• Database theory
• Database testing
• Database-centric architecture
• Journal of Database Management
• Question-focused dataset

1. Jump up^ This article quotes a development time of 5 years involving 750 people for DB2 release 9 alone.(Chong et al. 2007)

1. Jump up^ "Database – Definition of database by Merriam-Webster". merriam-webster.com.
2. Jump up^ Ullman & Widom 1997, p. 1.
3. Jump up^ "Update – Definition of update by Merriam-Webster". merriam-webster.com.
4. Jump up^ "Retrieval – Definition of retrieval by Merriam-Webster". merriam-webster.com.
5. Jump up^ "Administration – Definition of administration by Merriam-Webster". merriam-webster.com.
6. Jump up^ Tsitchizris & Lochovsky 1982.
7. Jump up^ Beynon–Davies 2003.
8. Jump up^ Nelson & Nelson 2001.
9. Jump up^ Bachman 1973.
10. Jump up^ "TOPDB Top Database index". pypl.github.io.
11. Jump up^ "database, n". OED Online. Oxford University Press. June 2013. Retrieved July 12, 2013.
12. Jump up^ IBM Corporation. "IBM Information Management System (IMS) 13 Transaction and Database Servers delivers high performance and low total cost of ownership". Retrieved Feb 20, 2014.
13. Jump up^ Codd 1970.
14. Jump up^ Hershey & Easthope 1972.
15. Jump up^ North 2010.
16. Jump up^ Childs 1968a.
17. Jump up^ Childs 1968b.
18. Jump up^ MICRO Information Management System (Version 5.0) Reference Manual, M.A. Kahn, D.L. Rumelhart, and B.L. Bronson, October 1977, Institute of Labor and Industrial Relations (ILIR), University of Michigan and Wayne State University
19. Jump up^ "Oracle 30th Anniversary Timeline" (PDF). Retrieved 23 August 2017.
20. Jump up^ Interview with Wayne Ratliff. The FoxPro History. Retrieved on 2013-07-12.
21. Jump up^ Development of an object-oriented DBMS; Portland, Oregon, United States; Pages: 472–482; 1986; ISBN 0-89791-204-7
22. Jump up^ "Oracle Berkeley DB XML" (PDF). Retrieved 10 March2015.
23. Jump up^ "ACID Transactions, MarkLogic". Retrieved 10 March2015.
24. Jump up^ "Clusterpoint Database at a Glance". Archived from the original on 2 April 2015. Retrieved 10 March 2015.
25. Jump up^ "DB-Engines Ranking". January 2013. Retrieved 22 January 2013.
26. Jump up^ Proctor 2013.
27. Jump up^ "TeleCommunication Systems Signs up as a Reseller of TimesTen; Mobile Operators and Carriers Gain Real-Time Platform for Location-Based Services". Business Wire. 2002-06-24.^{[dead link]}
28. Jump up^ Graves, Steve. "COTS Databases For Embedded Systems", Embedded Computing Design magazine, January 2007. Retrieved on August 13, 2008.
29. Jump up^ Argumentation in Artificial Intelligence by Iyad Rahwan, Guillermo R. Simari
30. Jump up^ "OWL DL Semantics". Retrieved 10 December 2010.
31. Jump up^ itl.nist.gov (1993) Integration Definition for Information Modeling (IDEFIX). 21 December 1993.
32. ^ Jump up to:^{a b} Date 2003, pp. 31–32.
33. Jump up^ Chapple 2005.
34. Jump up^ "Structured Query Language (SQL)". International Business Machines. October 27, 2006. Retrieved 2007-06-10.
35. Jump up^ Wagner 2010.
36. Jump up^ Halder & Cortesi 2011.
37. Jump up^ Ben Linders (January 28, 2016). "How Database Administration Fits into DevOps". Retrieved April 15, 2017.

• Bachman, Charles W. (1973). "The Programmer as Navigator". Communications of the ACM. 16 (11): 653–658. doi:10.1145/355611.362534. (Subscription required (help)).
• Beynon–Davies, Paul (2003). Database Systems (3rd ed.). Palgrave Macmillan. ISBN 978-1403916013.
• Chapple, Mike (2005). "SQL Fundamentals". Databases. About.com. Archived from the original on 22 February 2009. Retrieved 28 January 2009.
• Childs, David L. (1968a). "Description of a set-theoretic data structure" (PDF). CONCOMP (Research in Conversational Use of Computers) Project. Technical Report 3. University of Michigan.
• Childs, David L. (1968b). "Feasibility of a set-theoretic data structure: a general structure based on a reconstituted definition" (PDF). CONCOMP (Research in Conversational Use of Computers) Project. Technical Report 6. University of Michigan.
• Chong, Raul F.; Wang, Xiaomei; Dang, Michael; Snow, Dwaine R. (2007). "Introduction to DB2". Understanding DB2: Learning Visually with Examples (2nd ed.). ISBN 978-0131580183. Retrieved 17 March 2013.
• Codd, Edgar F. (1970). "A Relational Model of Data for Large Shared Data Banks" (PDF). Communications of the ACM. 13(6): 377–387. doi:10.1145/362384.362685.
• Date, C. J. (2003). An Introduction to Database Systems (8th ed.). Pearson. ISBN 978-0321197849.
• Halder, Raju; Cortesi, Agostino (2011). "Abstract Interpretation of Database Query Languages" (PDF). COMPUTER LANGUAGES, SYSTEMS & STRUCTURES. Elsevier. 38 (2): 123–157. doi:10.1016/j.cl.2011.10.004. ISSN 1477-8424.
• Hershey, William; Easthope, Carol (1972). A set theoretic data structure and retrieval language. Spring Joint Computer Conference, May 1972. ACM SIGIR Forum. 7 (4). pp. 45–55. doi:10.1145/1095495.1095500.
• Nelson, Anne Fulcher; Nelson, William Harris Morehead (2001). Building Electronic Commerce: With Web Database Constructions. Prentice Hall. ISBN 978-0201741308.
• North, Ken (10 March 2010). "Sets, Data Models and Data Independence". Dr. Dobb's. Archived from the original on 24 October 2010.
• Proctor, Seth (12 July 2013). "Exploring the Architecture of the NuoDB Database, Part 1". Archived from the original on 15 July 2013. Retrieved 12 July 2013.
• Tsitchizris, Dionysios C.; Lochovsky, Fred H. (1982). Data Models. Prentice–Hall. ISBN 978-0131964280.
• Ullman, Jeffrey; Widom, Jennifer (1997). A First Course in Database Systems. Prentice–Hall. ISBN 0138613370.
• Wagner, Michael (2010), SQL/XML:2006 – Evaluierung der Standardkonformität ausgewählter Datenbanksysteme, Diplomica Verlag, ISBN 978-3836696098

• Ling Liu and Tamer M. Özsu (Eds.) (2009). "Encyclopedia of Database Systems, 4100 p. 60 illus. ISBN 978-0-387-49616-0.
• Connolly, Thomas and Carolyn Begg. Database Systems. New York: Harlow, 2002.
• Gray, J. and Reuter, A. Transaction Processing: Concepts and Techniques, 1st edition, Morgan Kaufmann Publishers, 1992.
• Kroenke, David M. and David J. Auer. Database Concepts. 3rd ed. New York: Prentice, 2007.
• Raghu Ramakrishnan and Johannes Gehrke, Database Management Systems
• Abraham Silberschatz, Henry F. Korth, S. Sudarshan, Database System Concepts
• Lightstone, S.; Teorey, T.; Nadeau, T. (2007). Physical Database Design: the database professional's guide to exploiting indexes, views, storage, and more. Morgan Kaufmann Press. ISBN 0-12-369389-6.
• Teorey, T.; Lightstone, S. and Nadeau, T. Database Modeling & Design: Logical Design, 4th edition, Morgan Kaufmann Press, 2005. ISBN 0-12-685352-5

• 

  Definitions from Wiktionary

• 

  Media from Commons

• 

  News from Wikinews

• 

  Quotations from Wikiquote

• 

  Texts from Wikisource

• 

  Textbooks from Wikibooks

• 

  Learning resources from Wikiversity
• DB File extension – information about files with the DB extension

• v
• t
• e

• v
• t
• e

• v
• t
• e

• v
• t
• e

• Database management systems
• Databases

• Not logged in
• Talk
• Contributions
• Create account
• Log in
• Article
• Talk
• Read
• Edit
• View history

• Main page
• Contents
• Featured content
• Current events
• Random article
• Donate to Wikipedia
• Wikipedia store

• Help
• About Wikipedia
• Community portal
• Recent changes
• Contact page

• What links here
• Related changes
• Upload file
• Special pages
• Permanent link
• Page information
• Wikidata item
• Cite this page

• Create a book
• Download as PDF
• Printable version

• Wikimedia Commons
• Wikiversity

• Afrikaans
• العربية
• Aragonés
• Asturianu
• Azərbaycanca
• বাংলা
• Беларуская
• Беларуская (тарашкевіца)‎
• Български
• Boarisch
• Bosanski
• Brezhoneg
• Català
• Čeština
• Cymraeg
• Dansk
• Deutsch
• Eesti
• Ελληνικά
• Español
• Esperanto
• Euskara
• فارسی
• Français
• Frysk
• Gaeilge
• Galego
• 한국어
• Հայերեն
• हिन्दी
• Hrvatski
• Ido
• Bahasa Indonesia
• Interlingua
• Íslenska
• Italiano
• עברית
• Basa Jawa
• ქართული
• Қазақша
• Kurdî
• Кыргызча
• Latina
• Latviešu
• Lietuvių
• Magyar
• Македонски
• മലയാളം
• مصرى
• Bahasa Melayu
• Mirandés
• Монгол
• မြန်မာဘာသာ
• Nederlands
• 日本語
• Norsk
• Norsk nynorsk
• Олык марий
• Oʻzbekcha/ўзбекча
• ਪੰਜਾਬੀ
• پښتو
• Polski
• Português
• Română
• Русский
• Scots
• Seeltersk
• Shqip
• Sicilianu
• සිංහල
• Simple English
• Slovenčina
• Slovenščina
• کوردی
• Српски / srpski
• Srpskohrvatski / српскохрватски
• Suomi
• Svenska
• Tagalog
• தமிழ்
• తెలుగు
• ไทย
• Тоҷикӣ
• Türkçe
• Українська
• اردو
• Tiếng Việt
• Walon
• Winaray
• 粵語
• 中文

• This page was last edited on 27 November 2017, at 07:00.
• Text is available under the Creative Commons Attribution-ShareAlike License; additional terms may apply. By using this site, you agree to the Terms of Use and Privacy Policy. Wikipedia® is a registered trademark of the Wikimedia Foundation, Inc., a non-profit organization.
• Privacy policy
• About Wikipedia
• Disclaimers
• Contact Wikipedia
• Developers
• Cookie statement
• Mobile view
• Enable previews

• 

• 

Database

An example of output from an SQL database query.

• 1Terminology and overview
• 2Applications
• 3General-purpose and special-purpose DBMSs
• 4History
  • 4.11960s, navigational DBMS
  • 4.21970s, relational DBMS
  • 4.3Integrated approach
  • 4.4Late 1970s, SQL DBMS
  • 4.51980s, on the desktop
  • 4.61990s, object-oriented
  • 4.72000s, NoSQL and NewSQL
• 5Research
• 6Examples
• 7Design and modeling
  • 7.1Models
  • 7.2External, conceptual, and internal views
• 8Languages
• 9Performance, security, and availability
  • 9.1Storage
  • 9.2Security
  • 9.3Transactions and concurrency
  • 9.4Migration
  • 9.5Building, maintaining, and tuning
  • 9.6Backup and restore
  • 9.7Static analysis
  • 9.8Other
• 10See also
• 11Notes
• 12References
• 13Sources
• 14Further reading
• 15External links

• Data definition – Creation, modification and removal of definitions that define the organization of the data.
• Update – Insertion, modification, and deletion of the actual data.^[3]
• Retrieval – Providing information in a form directly usable or for further processing by other applications. The retrieved data may be made available in a form basically the same as it is stored in the database or in a new form obtained by altering or combining existing data from the database.^[4]
• Administration – Registering and monitoring users, enforcing data security, monitoring performance, maintaining data integrity, dealing with concurrency control, and recovering information that has been corrupted by some event such as an unexpected system failure.^[5]

| This section does not cite any sources. Please help improve this section by adding citations to reliable sources. Unsourced material may be challenged and removed.(March 2013) (Learn how and when to remove this template message)Databases are used to support internal operations of organizations and to underpin online interactions with customers and suppliers (see Enterprise software).Databases are used to hold administrative information and more specialized data, such as engineering data or economic models. Examples of database applications include computerized library systems, flight reservation systems, computerized parts inventory systems, and many content management systems that store websites as collections of webpages in a database.General-purpose and special-purpose DBMSs[edit]DBMS may become a complex software system and its development typically requires thousands of human years of development effort.^[a] Some general-purpose DBMSs such as Adabas, Oracle and DB2 have been upgraded since the 1970s. General-purpose DBMSs aim to meet the needs of as many applications as possible, which adds to the complexity. However, since their development cost can be spread over a large number of users, they are often the most cost-effective approach. On the other hand, a general-purpose DBMS may introduce unnecessary overhead. Therefore, many systems use a special-purpose DBMS. A common example is an email system that performs many of the functions of a general-purpose DBMS such as the insertion and deletion of messages composed of various items of data or associating messages with a particular email address; but these functions are limited to what is required to handle email and don't provide the user with all of the functionality that would be available using a general-purpose DBMS.Application software can often access a database on behalf of end-users, without exposing the DBMS interface directly. Application programmers may use a wire protocol directly, or more likely through an application programming interface. Database designers and database administrators interact with the DBMS through dedicated interfaces to build and maintain the applications' databases, and thus need some more knowledge and understanding about how DBMSs operate and the DBMSs' external interfaces and tuning parameters.History[edit]Following the technology progress in the areas of processors, computer memory, computer storage, and computer networks, the sizes, capabilities, and performance of databases and their respective DBMSs have grown in orders of magnitude. The development of database technology can be divided into three eras based on data model or structure: navigational,^[9]SQL/relational, and post-relational.The two main early navigational data models were the hierarchical model, epitomized by IBM's IMS system, and the CODASYLmodel (network model), implemented in a number of products such as IDMS.The relational model, first proposed in 1970 by Edgar F. Codd, departed from this tradition by insisting that applications should search for data by content, rather than by following links. The relational model employs sets of ledger-style tables, each used for a different type of entity. Only in the mid-1980s did computing hardware become powerful enough to allow the wide deployment of relational systems (DBMSs plus applications). By the early 1990s, however, relational systems dominated in all large-scale data processing applications, and as of 2015 they remain dominant: IBM DB2, Oracle, MySQL, and Microsoft SQL Server are the top DBMS.^[10] The dominant database language, standardised SQL for the relational model, has influenced database languages for other data models.^{[citation needed]}Object databases were developed in the 1980s to overcome the inconvenience of object-relational impedance mismatch, which led to the coining of the term "post-relational" and also the development of hybrid object-relational databases.The next generation of post-relational databases in the late 2000s became known as NoSQL databases, introducing fast key-value stores and document-oriented databases. A competing "next generation" known as NewSQL databases attempted new implementations that retained the relational/SQL model while aiming to match the high performance of NoSQL compared to commercially available relational DBMSs.1960s, navigational DBMS[edit]Further information: Navigational database

Basic structure of navigational CODASYLdatabase modelThe introduction of the term database coincided with the availability of direct-access storage (disks and drums) from the mid-1960s onwards. The term represented a contrast with the tape-based systems of the past, allowing shared interactive use rather than daily batch processing. The Oxford English Dictionary cites^[11] a 1962 report by the System Development Corporation of California as the first to use the term "data-base" in a specific technical sense.As computers grew in speed and capability, a number of general-purpose database systems emerged; by the mid-1960s a number of such systems had come into commercial use. Interest in a standard began to grow, and Charles Bachman, author of one such product, the Integrated Data Store (IDS), founded the "Database Task Group" within CODASYL, the group responsible for the creation and standardization of COBOL. In 1971, the Database Task Group delivered their standard, which generally became known as the "CODASYL approach", and soon a number of commercial products based on this approach entered the market.The CODASYL approach relied on the "manual" navigation of a linked data set which was formed into a large network. Applications could find records by one of three methods:

1. Use of a primary key (known as a CALC key, typically implemented by hashing)
2. Navigating relationships (called sets) from one record to another
3. Scanning all the records in a sequential order

In the relational model, records are "linked" using virtual keys not stored in the database but defined as needed between the data contained in the records.

• An in-memory database is a database that primarily resides in main memory, but is typically backed-up by non-volatile computer data storage. Main memory databases are faster than disk databases, and so are often used where response time is critical, such as in telecommunications network equipment.^[27] SAP HANA platform is a very hot topic for in-memory database. By May 2012, HANA was able to run on servers with 100TB main memory powered by IBM. The co founder of the company claimed that the system was big enough to run the 8 largest SAP customers.
• An active database includes an event-driven architecture which can respond to conditions both inside and outside the database. Possible uses include security monitoring, alerting, statistics gathering and authorization. Many databases provide active database features in the form of database triggers.
• A cloud database relies on cloud technology. Both the database and most of its DBMS reside remotely, "in the cloud", while its applications are both developed by programmers and later maintained and utilized by (application's) end-users through a web browser and Open APIs.
• Data warehouses archive data from operational databases and often from external sources such as market research firms. The warehouse becomes the central source of data for use by managers and other end-users who may not have access to operational data. For example, sales data might be aggregated to weekly totals and converted from internal product codes to use UPCs so that they can be compared with ACNielsen data. Some basic and essential components of data warehousing include extracting, analyzing, and mining data, transforming, loading, and managing data so as to make them available for further use.
• A deductive database combines logic programming with a relational database, for example by using the Datalog language.
• A distributed database is one in which both the data and the DBMS span multiple computers.
• A document-oriented database is designed for storing, retrieving, and managing document-oriented, or semi structured data, information. Document-oriented databases are one of the main categories of NoSQL databases.
• An embedded database system is a DBMS which is tightly integrated with an application software that requires access to stored data in such a way that the DBMS is hidden from the application's end-users and requires little or no ongoing maintenance.^[28]
• End-user databases consist of data developed by individual end-users. Examples of these are collections of documents, spreadsheets, presentations, multimedia, and other files. Several products exist to support such databases. Some of them are much simpler than full-fledged DBMSs, with more elementary DBMS functionality.
• A federated database system comprises several distinct databases, each with its own DBMS. It is handled as a single database by a federated database management system (FDBMS), which transparently integrates multiple autonomous DBMSs, possibly of different types (in which case it would also be a heterogeneous database system), and provides them with an integrated conceptual view.
• Sometimes the term multi-database is used as a synonym to federated database, though it may refer to a less integrated (e.g., without an FDBMS and a managed integrated schema) group of databases that cooperate in a single application. In this case, typically middleware is used for distribution, which typically includes an atomic commit protocol (ACP), e.g., the two-phase commit protocol, to allow distributed (global) transactions across the participating databases.
• A graph database is a kind of NoSQL database that uses graph structures with nodes, edges, and properties to represent and store information. General graph databases that can store any graph are distinct from specialized graph databases such as triplestores and network databases.
• An array DBMS is a kind of NoSQL DBMS that allows to model, store, and retrieve (usually large) multi-dimensional arrayssuch as satellite images and climate simulation output.
• In a hypertext or hypermedia database, any word or a piece of text representing an object, e.g., another piece of text, an article, a picture, or a film, can be hyperlinked to that object. Hypertext databases are particularly useful for organizing large amounts of disparate information. For example, they are useful for organizing online encyclopedias, where users can conveniently jump around the text. The World Wide Web is thus a large distributed hypertext database.
• A knowledge base (abbreviated KB, kb or Δ^[29][30]) is a special kind of database for knowledge management, providing the means for the computerized collection, organization, and retrieval of knowledge. Also a collection of data representing problems with their solutions and related experiences.
• A mobile database can be carried on or synchronized from a mobile computing device.
• Operational databases store detailed data about the operations of an organization. They typically process relatively high volumes of updates using transactions. Examples include customer databases that record contact, credit, and demographic information about a business' customers, personnel databases that hold information such as salary, benefits, skills data about employees, enterprise resource planning systems that record details about product components, parts inventory, and financial databases that keep track of the organization's money, accounting and financial dealings.
• A parallel database seeks to improve performance through parallelization for tasks such as loading data, building indexes and evaluating queries.

• Shared memory architecture, where multiple processors share the main memory space, as well as other data storage.
• Shared disk architecture, where each processing unit (typically consisting of multiple processors) has its own main memory, but all units share the other storage.
• Shared nothing architecture, where each processing unit has its own main memory and other storage.
• Probabilistic databases employ fuzzy logic to draw inferences from imprecise data.
• Real-time databases process transactions fast enough for the result to come back and be acted on right away.
• A spatial database can store the data with multidimensional features. The queries on such data include location-based queries, like "Where is the closest hotel in my area?".
• A temporal database has built-in time aspects, for example a temporal data model and a temporal version of SQL. More specifically the temporal aspects usually include valid-time and transaction-time.
• A terminology-oriented database builds upon an object-oriented database, often customized for a specific field.
• An unstructured data database is intended to store in a manageable and protected way diverse objects that do not fit naturally and conveniently in common databases. It may include email messages, documents, journals, multimedia objects, etc. The name may be misleading since some objects can be highly structured. However, the entire possible object collection does not fit into a predefined structured framework. Most established DBMSs now support unstructured data in various ways, and new dedicated DBMSs are emerging.

Collage of five types of database models

• Navigational databases
  • Hierarchical database model
  • Network model
  • Graph database
• Relational model
• Entity–relationship model
  • Enhanced entity–relationship model
• Object model
• Document model
• Entity–attribute–value model
• Star schema

• Inverted index
• Flat file

• Associative model
• Multidimensional model
• Array model
• Multivalue model

• XML database
• Semantic model
• Content store
• Event store
• Time series model

Traditional view of data^[31]

• The external level defines how each group of end-users sees the organization of data in the database. A single database can have any number of views at the external level.
• The conceptual level unifies the various external views into a compatible global view.^[32] It provides the synthesis of all the external views. It is out of the scope of the various database end-users, and is rather of interest to database application developers and database administrators.
• The internal level (or physical level) is the internal organization of data inside a DBMS. It is concerned with cost, performance, scalability and other operational matters. It deals with storage layout of the data, using storage structures such as indexes to enhance performance. Occasionally it stores data of individual views (materialized views), computed from generic data, if performance justification exists for such redundancy. It balances all the external views' performance requirements, possibly conflicting, in an attempt to optimize overall performance across all activities.

• Data definition language – defines data types such as creating, altering, or dropping and the relationships among them
• Data manipulation language – performs tasks such as inserting, updating, or deleting data occurrences
• Query language – allows searching for information and computing derived information

• SQL combines the roles of data definition, data manipulation, and query in a single language. It was one of the first commercial languages for the relational model, although it departs in some respects from the relational model as described by Codd (for example, the rows and columns of a table can be ordered). SQL became a standard of the American National Standards Institute (ANSI) in 1986, and of the International Organization for Standardization (ISO) in 1987. The standards have been regularly enhanced since and is supported (with varying degrees of conformance) by all mainstream commercial relational DBMSs.^[33][34]
• OQL is an object model language standard (from the Object Data Management Group). It has influenced the design of some of the newer query languages like JDOQL and EJB QL.
• XQuery is a standard XML query language implemented by XML database systems such as MarkLogic and eXist, by relational databases with XML capability such as Oracle and DB2, and also by in-memory XML processors such as Saxon.
• SQL/XML combines XQuery with SQL.^[35]

• DBMS-specific Configuration and storage engine management
• Computations to modify query results, like counting, summing, averaging, sorting, grouping, and cross-referencing
• Constraint enforcement (e.g. in an automotive database, only allowing one engine type per car)
• Application programming interface version of the query language, for programmer convenience

| This article appears to contradict the article Database security. Please see discussion on the linked talk page. (March 2013) (Learn how and when to remove this template message)Main article: Database securityDatabase security deals with all various aspects of protecting the database content, its owners, and its users. It ranges from protection from intentional unauthorized database uses to unintentional database accesses by unauthorized entities (e.g., a person or a computer program).Database access control deals with controlling who (a person or a certain computer program) is allowed to access what information in the database. The information may comprise specific database objects (e.g., record types, specific records, data structures), certain computations over certain objects (e.g., query types, or specific queries), or utilizing specific access paths to the former (e.g., using specific indexes or other data structures to access information). Database access controls are set by special authorized (by the database owner) personnel that uses dedicated protected security DBMS interfaces.This may be managed directly on an individual basis, or by the assignment of individuals and privileges to groups, or (in the most elaborate models) through the assignment of individuals and groups to roles which are then granted entitlements. Data security prevents unauthorized users from viewing or updating the database. Using passwords, users are allowed access to the entire database or subsets of it called "subschemas". For example, an employee database can contain all the data about an individual employee, but one group of users may be authorized to view only payroll data, while others are allowed access to only work history and medical data. If the DBMS provides a way to interactively enter and update the database, as well as interrogate it, this capability allows for managing personal databases.Data security in general deals with protecting specific chunks of data, both physically (i.e., from corruption, or destruction, or removal; e.g., see physical security), or the interpretation of them, or parts of them to meaningful information (e.g., by looking at the strings of bits that they comprise, concluding specific valid credit-card numbers; e.g., see data encryption).Change and access logging records who accessed which attributes, what was changed, and when it was changed. Logging services allow for a forensic database audit later by keeping a record of access occurrences and changes. Sometimes application-level code is used to record changes rather than leaving this to the database. Monitoring can be set up to attempt to detect security breaches.Transactions and concurrency[edit]Further information: Concurrency controlDatabase transactions can be used to introduce some level of fault tolerance and data integrity after recovery from a crash. A database transaction is a unit of work, typically encapsulating a number of operations over a database (e.g., reading a database object, writing, acquiring lock, etc.), an abstraction supported in database and also other systems. Each transaction has well defined boundaries in terms of which program/code executions are included in that transaction (determined by the transaction's programmer via special transaction commands).The acronym ACID describes some ideal properties of a database transaction: Atomicity, Consistency, Isolation, and Durability.Migration[edit]See also: Data migration § Database migrationA database built with one DBMS is not portable to another DBMS (i.e., the other DBMS cannot run it). However, in some situations, it is desirable to move, migrate a database from one DBMS to another. The reasons are primarily economical (different DBMSs may have different total costs of ownership or TCOs), functional, and operational (different DBMSs may have different capabilities). The migration involves the database's transformation from one DBMS type to another. The transformation should maintain (if possible) the database related application (i.e., all related application programs) intact. Thus, the database's conceptual and external architectural levels should be maintained in the transformation. It may be desired that also some aspects of the architecture internal level are maintained. A complex or large database migration may be a complicated and costly (one-time) project by itself, which should be factored into the decision to migrate. This in spite of the fact that tools may exist to help migration between specific DBMSs. Typically, a DBMS vendor provides tools to help importing databases from other popular DBMSs.Building, maintaining, and tuning[edit]Main article: Database tuningAfter designing a database for an application, the next stage is building the database. Typically, an appropriate general-purpose DBMS can be selected to be utilized for this purpose. A DBMS provides the needed user interfaces to be utilized by database administrators to define the needed application's data structures within the DBMS's respective data model. Other user interfaces are used to select needed DBMS parameters (like security related, storage allocation parameters, etc.).When the database is ready (all its data structures and other needed components are defined), it is typically populated with initial application's data (database initialization, which is typically a distinct project; in many cases using specialized DBMS interfaces that support bulk insertion) before making it operational. In some cases, the database becomes operational while empty of application data, and data are accumulated during its operation.After the database is created, initialised and populated it needs to be maintained. Various database parameters may need changing and the database may need to be tuned (tuning) for better performance; application's data structures may be changed or added, new related application programs may be written to add to the application's functionality, etc.Backup and restore[edit]Main article: BackupSometimes it is desired to bring a database back to a previous state (for many reasons, e.g., cases when the database is found corrupted due to a software error, or if it has been updated with erroneous data). To achieve this, a backup operation is done occasionally or continuously, where each desired database state (i.e., the values of its data and their embedding in database's data structures) is kept within dedicated backup files (many techniques exist to do this effectively). When this state is needed, i.e., when it is decided by a database administrator to bring the database back to this state (e.g., by specifying this state by a desired point in time when the database was in this state), these files are utilized to restore that state.Static analysis[edit]Static analysis techniques for software verification can be applied also in the scenario of query languages. In particular, the *Abstract interpretation framework has been extended to the field of query languages for relational databases as a way to support sound approximation techniques.^[36] The semantics of query languages can be tuned according to suitable abstractions of the concrete domain of data. The abstraction of relational database system has many interesting applications, in particular, for security purposes, such as fine grained access control, watermarking, etc.Other[edit]Other DBMS features might include:

• Database logs
• Graphics component for producing graphs and charts, especially in a data warehouse system
• Query optimizer – Performs query optimization on every query to choose an efficient query plan (a partial order (tree) of operations) to be executed to compute the query result. May be specific to a particular storage engine.
• Tools or hooks for database design, application programming, application program maintenance, database performance analysis and monitoring, database configuration monitoring, DBMS hardware configuration (a DBMS and related database may span computers, networks, and storage units) and related database mapping (especially for a distributed DBMS), storage allocation and database layout monitoring, storage migration, etc.
• Increasingly, there are calls for a single system that incorporates all of these core functionalities into the same build, test, and deployment framework for database management and source control. Borrowing from other developments in the software industry, some market such offerings as "DevOps for database".^[37]

| Book: Databases

• Comparison of database tools
• Comparison of object database management systems
• Comparison of object-relational database management systems
• Comparison of relational database management systems
• Data hierarchy
• Data bank
• Data store
• Database theory
• Database testing
• Database-centric architecture
• Journal of Database Management
• Question-focused dataset

1. Jump up^ This article quotes a development time of 5 years involving 750 people for DB2 release 9 alone.(Chong et al. 2007)

1. Jump up^ "Database – Definition of database by Merriam-Webster". merriam-webster.com.
2. Jump up^ Ullman & Widom 1997, p. 1.
3. Jump up^ "Update – Definition of update by Merriam-Webster". merriam-webster.com.
4. Jump up^ "Retrieval – Definition of retrieval by Merriam-Webster". merriam-webster.com.
5. Jump up^ "Administration – Definition of administration by Merriam-Webster". merriam-webster.com.
6. Jump up^ Tsitchizris & Lochovsky 1982.
7. Jump up^ Beynon–Davies 2003.
8. Jump up^ Nelson & Nelson 2001.
9. Jump up^ Bachman 1973.
10. Jump up^ "TOPDB Top Database index". pypl.github.io.
11. Jump up^ "database, n". OED Online. Oxford University Press. June 2013. Retrieved July 12, 2013.
12. Jump up^ IBM Corporation. "IBM Information Management System (IMS) 13 Transaction and Database Servers delivers high performance and low total cost of ownership". Retrieved Feb 20, 2014.
13. Jump up^ Codd 1970.
14. Jump up^ Hershey & Easthope 1972.
15. Jump up^ North 2010.
16. Jump up^ Childs 1968a.
17. Jump up^ Childs 1968b.
18. Jump up^ MICRO Information Management System (Version 5.0) Reference Manual, M.A. Kahn, D.L. Rumelhart, and B.L. Bronson, October 1977, Institute of Labor and Industrial Relations (ILIR), University of Michigan and Wayne State University
19. Jump up^ "Oracle 30th Anniversary Timeline" (PDF). Retrieved 23 August 2017.
20. Jump up^ Interview with Wayne Ratliff. The FoxPro History. Retrieved on 2013-07-12.
21. Jump up^ Development of an object-oriented DBMS; Portland, Oregon, United States; Pages: 472–482; 1986; ISBN 0-89791-204-7
22. Jump up^ "Oracle Berkeley DB XML" (PDF). Retrieved 10 March2015.
23. Jump up^ "ACID Transactions, MarkLogic". Retrieved 10 March2015.
24. Jump up^ "Clusterpoint Database at a Glance". Archived from the original on 2 April 2015. Retrieved 10 March 2015.
25. Jump up^ "DB-Engines Ranking". January 2013. Retrieved 22 January 2013.
26. Jump up^ Proctor 2013.
27. Jump up^ "TeleCommunication Systems Signs up as a Reseller of TimesTen; Mobile Operators and Carriers Gain Real-Time Platform for Location-Based Services". Business Wire. 2002-06-24.^{[dead link]}
28. Jump up^ Graves, Steve. "COTS Databases For Embedded Systems", Embedded Computing Design magazine, January 2007. Retrieved on August 13, 2008.
29. Jump up^ Argumentation in Artificial Intelligence by Iyad Rahwan, Guillermo R. Simari
30. Jump up^ "OWL DL Semantics". Retrieved 10 December 2010.
31. Jump up^ itl.nist.gov (1993) Integration Definition for Information Modeling (IDEFIX). 21 December 1993.
32. ^ Jump up to:^{a b} Date 2003, pp. 31–32.
33. Jump up^ Chapple 2005.
34. Jump up^ "Structured Query Language (SQL)". International Business Machines. October 27, 2006. Retrieved 2007-06-10.
35. Jump up^ Wagner 2010.
36. Jump up^ Halder & Cortesi 2011.
37. Jump up^ Ben Linders (January 28, 2016). "How Database Administration Fits into DevOps". Retrieved April 15, 2017.

• Bachman, Charles W. (1973). "The Programmer as Navigator". Communications of the ACM. 16 (11): 653–658. doi:10.1145/355611.362534. (Subscription required (help)).
• Beynon–Davies, Paul (2003). Database Systems (3rd ed.). Palgrave Macmillan. ISBN 978-1403916013.
• Chapple, Mike (2005). "SQL Fundamentals". Databases. About.com. Archived from the original on 22 February 2009. Retrieved 28 January 2009.
• Childs, David L. (1968a). "Description of a set-theoretic data structure" (PDF). CONCOMP (Research in Conversational Use of Computers) Project. Technical Report 3. University of Michigan.
• Childs, David L. (1968b). "Feasibility of a set-theoretic data structure: a general structure based on a reconstituted definition" (PDF). CONCOMP (Research in Conversational Use of Computers) Project. Technical Report 6. University of Michigan.
• Chong, Raul F.; Wang, Xiaomei; Dang, Michael; Snow, Dwaine R. (2007). "Introduction to DB2". Understanding DB2: Learning Visually with Examples (2nd ed.). ISBN 978-0131580183. Retrieved 17 March 2013.
• Codd, Edgar F. (1970). "A Relational Model of Data for Large Shared Data Banks" (PDF). Communications of the ACM. 13(6): 377–387. doi:10.1145/362384.362685.
• Date, C. J. (2003). An Introduction to Database Systems (8th ed.). Pearson. ISBN 978-0321197849.
• Halder, Raju; Cortesi, Agostino (2011). "Abstract Interpretation of Database Query Languages" (PDF). COMPUTER LANGUAGES, SYSTEMS & STRUCTURES. Elsevier. 38 (2): 123–157. doi:10.1016/j.cl.2011.10.004. ISSN 1477-8424.
• Hershey, William; Easthope, Carol (1972). A set theoretic data structure and retrieval language. Spring Joint Computer Conference, May 1972. ACM SIGIR Forum. 7 (4). pp. 45–55. doi:10.1145/1095495.1095500.
• Nelson, Anne Fulcher; Nelson, William Harris Morehead (2001). Building Electronic Commerce: With Web Database Constructions. Prentice Hall. ISBN 978-0201741308.
• North, Ken (10 March 2010). "Sets, Data Models and Data Independence". Dr. Dobb's. Archived from the original on 24 October 2010.
• Proctor, Seth (12 July 2013). "Exploring the Architecture of the NuoDB Database, Part 1". Archived from the original on 15 July 2013. Retrieved 12 July 2013.
• Tsitchizris, Dionysios C.; Lochovsky, Fred H. (1982). Data Models. Prentice–Hall. ISBN 978-0131964280.
• Ullman, Jeffrey; Widom, Jennifer (1997). A First Course in Database Systems. Prentice–Hall. ISBN 0138613370.
• Wagner, Michael (2010), SQL/XML:2006 – Evaluierung der Standardkonformität ausgewählter Datenbanksysteme, Diplomica Verlag, ISBN 978-3836696098

• Ling Liu and Tamer M. Özsu (Eds.) (2009). "Encyclopedia of Database Systems, 4100 p. 60 illus. ISBN 978-0-387-49616-0.
• Connolly, Thomas and Carolyn Begg. Database Systems. New York: Harlow, 2002.
• Gray, J. and Reuter, A. Transaction Processing: Concepts and Techniques, 1st edition, Morgan Kaufmann Publishers, 1992.
• Kroenke, David M. and David J. Auer. Database Concepts. 3rd ed. New York: Prentice, 2007.
• Raghu Ramakrishnan and Johannes Gehrke, Database Management Systems
• Abraham Silberschatz, Henry F. Korth, S. Sudarshan, Database System Concepts
• Lightstone, S.; Teorey, T.; Nadeau, T. (2007). Physical Database Design: the database professional's guide to exploiting indexes, views, storage, and more. Morgan Kaufmann Press. ISBN 0-12-369389-6.
• Teorey, T.; Lightstone, S. and Nadeau, T. Database Modeling & Design: Logical Design, 4th edition, Morgan Kaufmann Press, 2005. ISBN 0-12-685352-5

• 

  Definitions from Wiktionary

• 

  Media from Commons

• 

  News from Wikinews

• 

  Quotations from Wikiquote

• 

  Texts from Wikisource

• 

  Textbooks from Wikibooks

• 

  Learning resources from Wikiversity
• DB File extension – information about files with the DB extension

• v
• t
• e

• v
• t
• e

• v
• t
• e

• v
• t
• e

• Database management systems
• Databases

• Not logged in
• Talk
• Contributions
• Create account
• Log in
• Article
• Talk
• Read
• Edit
• View history

• Main page
• Contents
• Featured content
• Current events
• Random article
• Donate to Wikipedia
• Wikipedia store

• Help
• About Wikipedia
• Community portal
• Recent changes
• Contact page

• What links here
• Related changes
• Upload file
• Special pages
• Permanent link
• Page information
• Wikidata item
• Cite this page

• Create a book
• Download as PDF
• Printable version

• Wikimedia Commons
• Wikiversity

• Afrikaans
• العربية
• Aragonés
• Asturianu
• Azərbaycanca
• বাংলা
• Беларуская
• Беларуская (тарашкевіца)‎
• Български
• Boarisch
• Bosanski
• Brezhoneg
• Català
• Čeština
• Cymraeg
• Dansk
• Deutsch
• Eesti
• Ελληνικά
• Español
• Esperanto
• Euskara
• فارسی
• Français
• Frysk
• Gaeilge
• Galego
• 한국어
• Հայերեն
• हिन्दी
• Hrvatski
• Ido
• Bahasa Indonesia
• Interlingua
• Íslenska
• Italiano
• עברית
• Basa Jawa
• ქართული
• Қазақша
• Kurdî
• Кыргызча
• Latina
• Latviešu
• Lietuvių
• Magyar
• Македонски
• മലയാളം
• مصرى
• Bahasa Melayu
• Mirandés
• Монгол
• မြန်မာဘာသာ
• Nederlands
• 日本語
• Norsk
• Norsk nynorsk
• Олык марий
• Oʻzbekcha/ўзбекча
• ਪੰਜਾਬੀ
• پښتو
• Polski
• Português
• Română
• Русский
• Scots
• Seeltersk
• Shqip
• Sicilianu
• සිංහල
• Simple English
• Slovenčina
• Slovenščina
• کوردی
• Српски / srpski
• Srpskohrvatski / српскохрватски
• Suomi
• Svenska
• Tagalog
• தமிழ்
• తెలుగు
• ไทย
• Тоҷикӣ
• Türkçe
• Українська
• اردو
• Tiếng Việt
• Walon
• Winaray
• 粵語
• 中文

• This page was last edited on 27 November 2017, at 07:00.
• Text is available under the Creative Commons Attribution-ShareAlike License; additional terms may apply. By using this site, you agree to the Terms of Use and Privacy Policy. Wikipedia® is a registered trademark of the Wikimedia Foundation, Inc., a non-profit organization.
• Privacy policy
• About Wikipedia
• Disclaimers
• Contact Wikipedia
• Developers
• Cookie statement
• Mobile view
• Enable previews

• 

• 

Database

An example of output from an SQL database query.

• 1Terminology and overview
• 2Applications
• 3General-purpose and special-purpose DBMSs
• 4History
  • 4.11960s, navigational DBMS
  • 4.21970s, relational DBMS
  • 4.3Integrated approach
  • 4.4Late 1970s, SQL DBMS
  • 4.51980s, on the desktop
  • 4.61990s, object-oriented
  • 4.72000s, NoSQL and NewSQL
• 5Research
• 6Examples
• 7Design and modeling
  • 7.1Models
  • 7.2External, conceptual, and internal views
• 8Languages
• 9Performance, security, and availability
  • 9.1Storage
  • 9.2Security
  • 9.3Transactions and concurrency
  • 9.4Migration
  • 9.5Building, maintaining, and tuning
  • 9.6Backup and restore
  • 9.7Static analysis
  • 9.8Other
• 10See also
• 11Notes
• 12References
• 13Sources
• 14Further reading
• 15External links

• Data definition – Creation, modification and removal of definitions that define the organization of the data.
• Update – Insertion, modification, and deletion of the actual data.^[3]
• Retrieval – Providing information in a form directly usable or for further processing by other applications. The retrieved data may be made available in a form basically the same as it is stored in the database or in a new form obtained by altering or combining existing data from the database.^[4]
• Administration – Registering and monitoring users, enforcing data security, monitoring performance, maintaining data integrity, dealing with concurrency control, and recovering information that has been corrupted by some event such as an unexpected system failure.^[5]

| This section does not cite any sources. Please help improve this section by adding citations to reliable sources. Unsourced material may be challenged and removed.(March 2013) (Learn how and when to remove this template message)Databases are used to support internal operations of organizations and to underpin online interactions with customers and suppliers (see Enterprise software).Databases are used to hold administrative information and more specialized data, such as engineering data or economic models. Examples of database applications include computerized library systems, flight reservation systems, computerized parts inventory systems, and many content management systems that store websites as collections of webpages in a database.General-purpose and special-purpose DBMSs[edit]DBMS may become a complex software system and its development typically requires thousands of human years of development effort.^[a] Some general-purpose DBMSs such as Adabas, Oracle and DB2 have been upgraded since the 1970s. General-purpose DBMSs aim to meet the needs of as many applications as possible, which adds to the complexity. However, since their development cost can be spread over a large number of users, they are often the most cost-effective approach. On the other hand, a general-purpose DBMS may introduce unnecessary overhead. Therefore, many systems use a special-purpose DBMS. A common example is an email system that performs many of the functions of a general-purpose DBMS such as the insertion and deletion of messages composed of various items of data or associating messages with a particular email address; but these functions are limited to what is required to handle email and don't provide the user with all of the functionality that would be available using a general-purpose DBMS.Application software can often access a database on behalf of end-users, without exposing the DBMS interface directly. Application programmers may use a wire protocol directly, or more likely through an application programming interface. Database designers and database administrators interact with the DBMS through dedicated interfaces to build and maintain the applications' databases, and thus need some more knowledge and understanding about how DBMSs operate and the DBMSs' external interfaces and tuning parameters.History[edit]Following the technology progress in the areas of processors, computer memory, computer storage, and computer networks, the sizes, capabilities, and performance of databases and their respective DBMSs have grown in orders of magnitude. The development of database technology can be divided into three eras based on data model or structure: navigational,^[9]SQL/relational, and post-relational.The two main early navigational data models were the hierarchical model, epitomized by IBM's IMS system, and the CODASYLmodel (network model), implemented in a number of products such as IDMS.The relational model, first proposed in 1970 by Edgar F. Codd, departed from this tradition by insisting that applications should search for data by content, rather than by following links. The relational model employs sets of ledger-style tables, each used for a different type of entity. Only in the mid-1980s did computing hardware become powerful enough to allow the wide deployment of relational systems (DBMSs plus applications). By the early 1990s, however, relational systems dominated in all large-scale data processing applications, and as of 2015 they remain dominant: IBM DB2, Oracle, MySQL, and Microsoft SQL Server are the top DBMS.^[10] The dominant database language, standardised SQL for the relational model, has influenced database languages for other data models.^{[citation needed]}Object databases were developed in the 1980s to overcome the inconvenience of object-relational impedance mismatch, which led to the coining of the term "post-relational" and also the development of hybrid object-relational databases.The next generation of post-relational databases in the late 2000s became known as NoSQL databases, introducing fast key-value stores and document-oriented databases. A competing "next generation" known as NewSQL databases attempted new implementations that retained the relational/SQL model while aiming to match the high performance of NoSQL compared to commercially available relational DBMSs.1960s, navigational DBMS[edit]Further information: Navigational database

Basic structure of navigational CODASYLdatabase modelThe introduction of the term database coincided with the availability of direct-access storage (disks and drums) from the mid-1960s onwards. The term represented a contrast with the tape-based systems of the past, allowing shared interactive use rather than daily batch processing. The Oxford English Dictionary cites^[11] a 1962 report by the System Development Corporation of California as the first to use the term "data-base" in a specific technical sense.As computers grew in speed and capability, a number of general-purpose database systems emerged; by the mid-1960s a number of such systems had come into commercial use. Interest in a standard began to grow, and Charles Bachman, author of one such product, the Integrated Data Store (IDS), founded the "Database Task Group" within CODASYL, the group responsible for the creation and standardization of COBOL. In 1971, the Database Task Group delivered their standard, which generally became known as the "CODASYL approach", and soon a number of commercial products based on this approach entered the market.The CODASYL approach relied on the "manual" navigation of a linked data set which was formed into a large network. Applications could find records by one of three methods:

1. Use of a primary key (known as a CALC key, typically implemented by hashing)
2. Navigating relationships (called sets) from one record to another
3. Scanning all the records in a sequential order

In the relational model, records are "linked" using virtual keys not stored in the database but defined as needed between the data contained in the records.

• An in-memory database is a database that primarily resides in main memory, but is typically backed-up by non-volatile computer data storage. Main memory databases are faster than disk databases, and so are often used where response time is critical, such as in telecommunications network equipment.^[27] SAP HANA platform is a very hot topic for in-memory database. By May 2012, HANA was able to run on servers with 100TB main memory powered by IBM. The co founder of the company claimed that the system was big enough to run the 8 largest SAP customers.
• An active database includes an event-driven architecture which can respond to conditions both inside and outside the database. Possible uses include security monitoring, alerting, statistics gathering and authorization. Many databases provide active database features in the form of database triggers.
• A cloud database relies on cloud technology. Both the database and most of its DBMS reside remotely, "in the cloud", while its applications are both developed by programmers and later maintained and utilized by (application's) end-users through a web browser and Open APIs.
• Data warehouses archive data from operational databases and often from external sources such as market research firms. The warehouse becomes the central source of data for use by managers and other end-users who may not have access to operational data. For example, sales data might be aggregated to weekly totals and converted from internal product codes to use UPCs so that they can be compared with ACNielsen data. Some basic and essential components of data warehousing include extracting, analyzing, and mining data, transforming, loading, and managing data so as to make them available for further use.
• A deductive database combines logic programming with a relational database, for example by using the Datalog language.
• A distributed database is one in which both the data and the DBMS span multiple computers.
• A document-oriented database is designed for storing, retrieving, and managing document-oriented, or semi structured data, information. Document-oriented databases are one of the main categories of NoSQL databases.
• An embedded database system is a DBMS which is tightly integrated with an application software that requires access to stored data in such a way that the DBMS is hidden from the application's end-users and requires little or no ongoing maintenance.^[28]
• End-user databases consist of data developed by individual end-users. Examples of these are collections of documents, spreadsheets, presentations, multimedia, and other files. Several products exist to support such databases. Some of them are much simpler than full-fledged DBMSs, with more elementary DBMS functionality.
• A federated database system comprises several distinct databases, each with its own DBMS. It is handled as a single database by a federated database management system (FDBMS), which transparently integrates multiple autonomous DBMSs, possibly of different types (in which case it would also be a heterogeneous database system), and provides them with an integrated conceptual view.
• Sometimes the term multi-database is used as a synonym to federated database, though it may refer to a less integrated (e.g., without an FDBMS and a managed integrated schema) group of databases that cooperate in a single application. In this case, typically middleware is used for distribution, which typically includes an atomic commit protocol (ACP), e.g., the two-phase commit protocol, to allow distributed (global) transactions across the participating databases.
• A graph database is a kind of NoSQL database that uses graph structures with nodes, edges, and properties to represent and store information. General graph databases that can store any graph are distinct from specialized graph databases such as triplestores and network databases.
• An array DBMS is a kind of NoSQL DBMS that allows to model, store, and retrieve (usually large) multi-dimensional arrayssuch as satellite images and climate simulation output.
• In a hypertext or hypermedia database, any word or a piece of text representing an object, e.g., another piece of text, an article, a picture, or a film, can be hyperlinked to that object. Hypertext databases are particularly useful for organizing large amounts of disparate information. For example, they are useful for organizing online encyclopedias, where users can conveniently jump around the text. The World Wide Web is thus a large distributed hypertext database.
• A knowledge base (abbreviated KB, kb or Δ^[29][30]) is a special kind of database for knowledge management, providing the means for the computerized collection, organization, and retrieval of knowledge. Also a collection of data representing problems with their solutions and related experiences.
• A mobile database can be carried on or synchronized from a mobile computing device.
• Operational databases store detailed data about the operations of an organization. They typically process relatively high volumes of updates using transactions. Examples include customer databases that record contact, credit, and demographic information about a business' customers, personnel databases that hold information such as salary, benefits, skills data about employees, enterprise resource planning systems that record details about product components, parts inventory, and financial databases that keep track of the organization's money, accounting and financial dealings.
• A parallel database seeks to improve performance through parallelization for tasks such as loading data, building indexes and evaluating queries.

• Shared memory architecture, where multiple processors share the main memory space, as well as other data storage.
• Shared disk architecture, where each processing unit (typically consisting of multiple processors) has its own main memory, but all units share the other storage.
• Shared nothing architecture, where each processing unit has its own main memory and other storage.
• Probabilistic databases employ fuzzy logic to draw inferences from imprecise data.
• Real-time databases process transactions fast enough for the result to come back and be acted on right away.
• A spatial database can store the data with multidimensional features. The queries on such data include location-based queries, like "Where is the closest hotel in my area?".
• A temporal database has built-in time aspects, for example a temporal data model and a temporal version of SQL. More specifically the temporal aspects usually include valid-time and transaction-time.
• A terminology-oriented database builds upon an object-oriented database, often customized for a specific field.
• An unstructured data database is intended to store in a manageable and protected way diverse objects that do not fit naturally and conveniently in common databases. It may include email messages, documents, journals, multimedia objects, etc. The name may be misleading since some objects can be highly structured. However, the entire possible object collection does not fit into a predefined structured framework. Most established DBMSs now support unstructured data in various ways, and new dedicated DBMSs are emerging.

Collage of five types of database models

• Navigational databases
  • Hierarchical database model
  • Network model
  • Graph database
• Relational model
• Entity–relationship model
  • Enhanced entity–relationship model
• Object model
• Document model
• Entity–attribute–value model
• Star schema

• Inverted index
• Flat file

• Associative model
• Multidimensional model
• Array model
• Multivalue model

• XML database
• Semantic model
• Content store
• Event store
• Time series model

Traditional view of data^[31]

• The external level defines how each group of end-users sees the organization of data in the database. A single database can have any number of views at the external level.
• The conceptual level unifies the various external views into a compatible global view.^[32] It provides the synthesis of all the external views. It is out of the scope of the various database end-users, and is rather of interest to database application developers and database administrators.
• The internal level (or physical level) is the internal organization of data inside a DBMS. It is concerned with cost, performance, scalability and other operational matters. It deals with storage layout of the data, using storage structures such as indexes to enhance performance. Occasionally it stores data of individual views (materialized views), computed from generic data, if performance justification exists for such redundancy. It balances all the external views' performance requirements, possibly conflicting, in an attempt to optimize overall performance across all activities.

• Data definition language – defines data types such as creating, altering, or dropping and the relationships among them
• Data manipulation language – performs tasks such as inserting, updating, or deleting data occurrences
• Query language – allows searching for information and computing derived information

• SQL combines the roles of data definition, data manipulation, and query in a single language. It was one of the first commercial languages for the relational model, although it departs in some respects from the relational model as described by Codd (for example, the rows and columns of a table can be ordered). SQL became a standard of the American National Standards Institute (ANSI) in 1986, and of the International Organization for Standardization (ISO) in 1987. The standards have been regularly enhanced since and is supported (with varying degrees of conformance) by all mainstream commercial relational DBMSs.^[33][34]
• OQL is an object model language standard (from the Object Data Management Group). It has influenced the design of some of the newer query languages like JDOQL and EJB QL.
• XQuery is a standard XML query language implemented by XML database systems such as MarkLogic and eXist, by relational databases with XML capability such as Oracle and DB2, and also by in-memory XML processors such as Saxon.
• SQL/XML combines XQuery with SQL.^[35]

• DBMS-specific Configuration and storage engine management
• Computations to modify query results, like counting, summing, averaging, sorting, grouping, and cross-referencing
• Constraint enforcement (e.g. in an automotive database, only allowing one engine type per car)
• Application programming interface version of the query language, for programmer convenience

| This article appears to contradict the article Database security. Please see discussion on the linked talk page. (March 2013) (Learn how and when to remove this template message)Main article: Database securityDatabase security deals with all various aspects of protecting the database content, its owners, and its users. It ranges from protection from intentional unauthorized database uses to unintentional database accesses by unauthorized entities (e.g., a person or a computer program).Database access control deals with controlling who (a person or a certain computer program) is allowed to access what information in the database. The information may comprise specific database objects (e.g., record types, specific records, data structures), certain computations over certain objects (e.g., query types, or specific queries), or utilizing specific access paths to the former (e.g., using specific indexes or other data structures to access information). Database access controls are set by special authorized (by the database owner) personnel that uses dedicated protected security DBMS interfaces.This may be managed directly on an individual basis, or by the assignment of individuals and privileges to groups, or (in the most elaborate models) through the assignment of individuals and groups to roles which are then granted entitlements. Data security prevents unauthorized users from viewing or updating the database. Using passwords, users are allowed access to the entire database or subsets of it called "subschemas". For example, an employee database can contain all the data about an individual employee, but one group of users may be authorized to view only payroll data, while others are allowed access to only work history and medical data. If the DBMS provides a way to interactively enter and update the database, as well as interrogate it, this capability allows for managing personal databases.Data security in general deals with protecting specific chunks of data, both physically (i.e., from corruption, or destruction, or removal; e.g., see physical security), or the interpretation of them, or parts of them to meaningful information (e.g., by looking at the strings of bits that they comprise, concluding specific valid credit-card numbers; e.g., see data encryption).Change and access logging records who accessed which attributes, what was changed, and when it was changed. Logging services allow for a forensic database audit later by keeping a record of access occurrences and changes. Sometimes application-level code is used to record changes rather than leaving this to the database. Monitoring can be set up to attempt to detect security breaches.Transactions and concurrency[edit]Further information: Concurrency controlDatabase transactions can be used to introduce some level of fault tolerance and data integrity after recovery from a crash. A database transaction is a unit of work, typically encapsulating a number of operations over a database (e.g., reading a database object, writing, acquiring lock, etc.), an abstraction supported in database and also other systems. Each transaction has well defined boundaries in terms of which program/code executions are included in that transaction (determined by the transaction's programmer via special transaction commands).The acronym ACID describes some ideal properties of a database transaction: Atomicity, Consistency, Isolation, and Durability.Migration[edit]See also: Data migration § Database migrationA database built with one DBMS is not portable to another DBMS (i.e., the other DBMS cannot run it). However, in some situations, it is desirable to move, migrate a database from one DBMS to another. The reasons are primarily economical (different DBMSs may have different total costs of ownership or TCOs), functional, and operational (different DBMSs may have different capabilities). The migration involves the database's transformation from one DBMS type to another. The transformation should maintain (if possible) the database related application (i.e., all related application programs) intact. Thus, the database's conceptual and external architectural levels should be maintained in the transformation. It may be desired that also some aspects of the architecture internal level are maintained. A complex or large database migration may be a complicated and costly (one-time) project by itself, which should be factored into the decision to migrate. This in spite of the fact that tools may exist to help migration between specific DBMSs. Typically, a DBMS vendor provides tools to help importing databases from other popular DBMSs.Building, maintaining, and tuning[edit]Main article: Database tuningAfter designing a database for an application, the next stage is building the database. Typically, an appropriate general-purpose DBMS can be selected to be utilized for this purpose. A DBMS provides the needed user interfaces to be utilized by database administrators to define the needed application's data structures within the DBMS's respective data model. Other user interfaces are used to select needed DBMS parameters (like security related, storage allocation parameters, etc.).When the database is ready (all its data structures and other needed components are defined), it is typically populated with initial application's data (database initialization, which is typically a distinct project; in many cases using specialized DBMS interfaces that support bulk insertion) before making it operational. In some cases, the database becomes operational while empty of application data, and data are accumulated during its operation.After the database is created, initialised and populated it needs to be maintained. Various database parameters may need changing and the database may need to be tuned (tuning) for better performance; application's data structures may be changed or added, new related application programs may be written to add to the application's functionality, etc.Backup and restore[edit]Main article: BackupSometimes it is desired to bring a database back to a previous state (for many reasons, e.g., cases when the database is found corrupted due to a software error, or if it has been updated with erroneous data). To achieve this, a backup operation is done occasionally or continuously, where each desired database state (i.e., the values of its data and their embedding in database's data structures) is kept within dedicated backup files (many techniques exist to do this effectively). When this state is needed, i.e., when it is decided by a database administrator to bring the database back to this state (e.g., by specifying this state by a desired point in time when the database was in this state), these files are utilized to restore that state.Static analysis[edit]Static analysis techniques for software verification can be applied also in the scenario of query languages. In particular, the *Abstract interpretation framework has been extended to the field of query languages for relational databases as a way to support sound approximation techniques.^[36] The semantics of query languages can be tuned according to suitable abstractions of the concrete domain of data. The abstraction of relational database system has many interesting applications, in particular, for security purposes, such as fine grained access control, watermarking, etc.Other[edit]Other DBMS features might include:

• Database logs
• Graphics component for producing graphs and charts, especially in a data warehouse system
• Query optimizer – Performs query optimization on every query to choose an efficient query plan (a partial order (tree) of operations) to be executed to compute the query result. May be specific to a particular storage engine.
• Tools or hooks for database design, application programming, application program maintenance, database performance analysis and monitoring, database configuration monitoring, DBMS hardware configuration (a DBMS and related database may span computers, networks, and storage units) and related database mapping (especially for a distributed DBMS), storage allocation and database layout monitoring, storage migration, etc.
• Increasingly, there are calls for a single system that incorporates all of these core functionalities into the same build, test, and deployment framework for database management and source control. Borrowing from other developments in the software industry, some market such offerings as "DevOps for database".^[37]

| Book: Databases

• Comparison of database tools
• Comparison of object database management systems
• Comparison of object-relational database management systems
• Comparison of relational database management systems
• Data hierarchy
• Data bank
• Data store
• Database theory
• Database testing
• Database-centric architecture
• Journal of Database Management
• Question-focused dataset

1. Jump up^ This article quotes a development time of 5 years involving 750 people for DB2 release 9 alone.(Chong et al. 2007)

1. Jump up^ "Database – Definition of database by Merriam-Webster". merriam-webster.com.
2. Jump up^ Ullman & Widom 1997, p. 1.
3. Jump up^ "Update – Definition of update by Merriam-Webster". merriam-webster.com.
4. Jump up^ "Retrieval – Definition of retrieval by Merriam-Webster". merriam-webster.com.
5. Jump up^ "Administration – Definition of administration by Merriam-Webster". merriam-webster.com.
6. Jump up^ Tsitchizris & Lochovsky 1982.
7. Jump up^ Beynon–Davies 2003.
8. Jump up^ Nelson & Nelson 2001.
9. Jump up^ Bachman 1973.
10. Jump up^ "TOPDB Top Database index". pypl.github.io.
11. Jump up^ "database, n". OED Online. Oxford University Press. June 2013. Retrieved July 12, 2013.
12. Jump up^ IBM Corporation. "IBM Information Management System (IMS) 13 Transaction and Database Servers delivers high performance and low total cost of ownership". Retrieved Feb 20, 2014.
13. Jump up^ Codd 1970.
14. Jump up^ Hershey & Easthope 1972.
15. Jump up^ North 2010.
16. Jump up^ Childs 1968a.
17. Jump up^ Childs 1968b.
18. Jump up^ MICRO Information Management System (Version 5.0) Reference Manual, M.A. Kahn, D.L. Rumelhart, and B.L. Bronson, October 1977, Institute of Labor and Industrial Relations (ILIR), University of Michigan and Wayne State University
19. Jump up^ "Oracle 30th Anniversary Timeline" (PDF). Retrieved 23 August 2017.
20. Jump up^ Interview with Wayne Ratliff. The FoxPro History. Retrieved on 2013-07-12.
21. Jump up^ Development of an object-oriented DBMS; Portland, Oregon, United States; Pages: 472–482; 1986; ISBN 0-89791-204-7
22. Jump up^ "Oracle Berkeley DB XML" (PDF). Retrieved 10 March2015.
23. Jump up^ "ACID Transactions, MarkLogic". Retrieved 10 March2015.
24. Jump up^ "Clusterpoint Database at a Glance". Archived from the original on 2 April 2015. Retrieved 10 March 2015.
25. Jump up^ "DB-Engines Ranking". January 2013. Retrieved 22 January 2013.
26. Jump up^ Proctor 2013.
27. Jump up^ "TeleCommunication Systems Signs up as a Reseller of TimesTen; Mobile Operators and Carriers Gain Real-Time Platform for Location-Based Services". Business Wire. 2002-06-24.^{[dead link]}
28. Jump up^ Graves, Steve. "COTS Databases For Embedded Systems", Embedded Computing Design magazine, January 2007. Retrieved on August 13, 2008.
29. Jump up^ Argumentation in Artificial Intelligence by Iyad Rahwan, Guillermo R. Simari
30. Jump up^ "OWL DL Semantics". Retrieved 10 December 2010.
31. Jump up^ itl.nist.gov (1993) Integration Definition for Information Modeling (IDEFIX). 21 December 1993.
32. ^ Jump up to:^{a b} Date 2003, pp. 31–32.
33. Jump up^ Chapple 2005.
34. Jump up^ "Structured Query Language (SQL)". International Business Machines. October 27, 2006. Retrieved 2007-06-10.
35. Jump up^ Wagner 2010.
36. Jump up^ Halder & Cortesi 2011.
37. Jump up^ Ben Linders (January 28, 2016). "How Database Administration Fits into DevOps". Retrieved April 15, 2017.

• Bachman, Charles W. (1973). "The Programmer as Navigator". Communications of the ACM. 16 (11): 653–658. doi:10.1145/355611.362534. (Subscription required (help)).
• Beynon–Davies, Paul (2003). Database Systems (3rd ed.). Palgrave Macmillan. ISBN 978-1403916013.
• Chapple, Mike (2005). "SQL Fundamentals". Databases. About.com. Archived from the original on 22 February 2009. Retrieved 28 January 2009.
• Childs, David L. (1968a). "Description of a set-theoretic data structure" (PDF). CONCOMP (Research in Conversational Use of Computers) Project. Technical Report 3. University of Michigan.
• Childs, David L. (1968b). "Feasibility of a set-theoretic data structure: a general structure based on a reconstituted definition" (PDF). CONCOMP (Research in Conversational Use of Computers) Project. Technical Report 6. University of Michigan.
• Chong, Raul F.; Wang, Xiaomei; Dang, Michael; Snow, Dwaine R. (2007). "Introduction to DB2". Understanding DB2: Learning Visually with Examples (2nd ed.). ISBN 978-0131580183. Retrieved 17 March 2013.
• Codd, Edgar F. (1970). "A Relational Model of Data for Large Shared Data Banks" (PDF). Communications of the ACM. 13(6): 377–387. doi:10.1145/362384.362685.
• Date, C. J. (2003). An Introduction to Database Systems (8th ed.). Pearson. ISBN 978-0321197849.
• Halder, Raju; Cortesi, Agostino (2011). "Abstract Interpretation of Database Query Languages" (PDF). COMPUTER LANGUAGES, SYSTEMS & STRUCTURES. Elsevier. 38 (2): 123–157. doi:10.1016/j.cl.2011.10.004. ISSN 1477-8424.
• Hershey, William; Easthope, Carol (1972). A set theoretic data structure and retrieval language. Spring Joint Computer Conference, May 1972. ACM SIGIR Forum. 7 (4). pp. 45–55. doi:10.1145/1095495.1095500.
• Nelson, Anne Fulcher; Nelson, William Harris Morehead (2001). Building Electronic Commerce: With Web Database Constructions. Prentice Hall. ISBN 978-0201741308.
• North, Ken (10 March 2010). "Sets, Data Models and Data Independence". Dr. Dobb's. Archived from the original on 24 October 2010.
• Proctor, Seth (12 July 2013). "Exploring the Architecture of the NuoDB Database, Part 1". Archived from the original on 15 July 2013. Retrieved 12 July 2013.
• Tsitchizris, Dionysios C.; Lochovsky, Fred H. (1982). Data Models. Prentice–Hall. ISBN 978-0131964280.
• Ullman, Jeffrey; Widom, Jennifer (1997). A First Course in Database Systems. Prentice–Hall. ISBN 0138613370.
• Wagner, Michael (2010), SQL/XML:2006 – Evaluierung der Standardkonformität ausgewählter Datenbanksysteme, Diplomica Verlag, ISBN 978-3836696098

• Ling Liu and Tamer M. Özsu (Eds.) (2009). "Encyclopedia of Database Systems, 4100 p. 60 illus. ISBN 978-0-387-49616-0.
• Connolly, Thomas and Carolyn Begg. Database Systems. New York: Harlow, 2002.
• Gray, J. and Reuter, A. Transaction Processing: Concepts and Techniques, 1st edition, Morgan Kaufmann Publishers, 1992.
• Kroenke, David M. and David J. Auer. Database Concepts. 3rd ed. New York: Prentice, 2007.
• Raghu Ramakrishnan and Johannes Gehrke, Database Management Systems
• Abraham Silberschatz, Henry F. Korth, S. Sudarshan, Database System Concepts
• Lightstone, S.; Teorey, T.; Nadeau, T. (2007). Physical Database Design: the database professional's guide to exploiting indexes, views, storage, and more. Morgan Kaufmann Press. ISBN 0-12-369389-6.
• Teorey, T.; Lightstone, S. and Nadeau, T. Database Modeling & Design: Logical Design, 4th edition, Morgan Kaufmann Press, 2005. ISBN 0-12-685352-5

• 

  Definitions from Wiktionary

• 

  Media from Commons

• 

  News from Wikinews

• 

  Quotations from Wikiquote

• 

  Texts from Wikisource

• 

  Textbooks from Wikibooks

• 

  Learning resources from Wikiversity
• DB File extension – information about files with the DB extension

• v
• t
• e

• v
• t
• e

• v
• t
• e

• v
• t
• e

• Database management systems
• Databases

• Not logged in
• Talk
• Contributions
• Create account
• Log in
• Article
• Talk
• Read
• Edit
• View history

• Main page
• Contents
• Featured content
• Current events
• Random article
• Donate to Wikipedia
• Wikipedia store

• Help
• About Wikipedia
• Community portal
• Recent changes
• Contact page

• What links here
• Related changes
• Upload file
• Special pages
• Permanent link
• Page information
• Wikidata item
• Cite this page

• Create a book
• Download as PDF
• Printable version

• Wikimedia Commons
• Wikiversity

• Afrikaans
• العربية
• Aragonés
• Asturianu
• Azərbaycanca
• বাংলা
• Беларуская
• Беларуская (тарашкевіца)‎
• Български
• Boarisch
• Bosanski
• Brezhoneg
• Català
• Čeština
• Cymraeg
• Dansk
• Deutsch
• Eesti
• Ελληνικά
• Español
• Esperanto
• Euskara
• فارسی
• Français
• Frysk
• Gaeilge
• Galego
• 한국어
• Հայերեն
• हिन्दी
• Hrvatski
• Ido
• Bahasa Indonesia
• Interlingua
• Íslenska
• Italiano
• עברית
• Basa Jawa
• ქართული
• Қазақша
• Kurdî
• Кыргызча
• Latina
• Latviešu
• Lietuvių
• Magyar
• Македонски
• മലയാളം
• مصرى
• Bahasa Melayu
• Mirandés
• Монгол
• မြန်မာဘာသာ
• Nederlands
• 日本語
• Norsk
• Norsk nynorsk
• Олык марий
• Oʻzbekcha/ўзбекча
• ਪੰਜਾਬੀ
• پښتو
• Polski
• Português
• Română
• Русский
• Scots
• Seeltersk
• Shqip
• Sicilianu
• සිංහල
• Simple English
• Slovenčina
• Slovenščina
• کوردی
• Српски / srpski
• Srpskohrvatski / српскохрватски
• Suomi
• Svenska
• Tagalog
• தமிழ்
• తెలుగు
• ไทย
• Тоҷикӣ
• Türkçe
• Українська
• اردو
• Tiếng Việt
• Walon
• Winaray
• 粵語
• 中文

• This page was last edited on 27 November 2017, at 07:00.
• Text is available under the Creative Commons Attribution-ShareAlike License; additional terms may apply. By using this site, you agree to the Terms of Use and Privacy Policy. Wikipedia® is a registered trademark of the Wikimedia Foundation, Inc., a non-profit organization.
• Privacy policy
• About Wikipedia
• Disclaimers
• Contact Wikipedia
• Developers
• Cookie statement
• Mobile view
• Enable previews

• 

•