(computer science) A special data processing system, or part of a data processing system, which aids in the storage, manipulation, reporting, management, and control of data. Abbreviated DBMS.
(computer science) A special data processing system, or part of a data processing system, which aids in the storage, manipulation, reporting, management, and control of data. Abbreviated DBMS.
A collection of interrelated data together with a set of programs to access the data, also called database system, or simply database. The primary goal of such a system is to provide an environment that is both convenient and efficient to use in retrieving and storing information.
A database management system (DBMS) is designed to manage a large body of information. Data management involves both defining structures for storing information and providing mechanisms for manipulating the information. In addition, the database system must provide for the safety of the stored information, despite system crashes or attempts at unauthorized access. If data are to be shared among several users, the system must avoid possible anomalous results due to multiple users concurrently accessing the same data.
Examples of the use of database systems include airline reservation systems, company payroll and employee information systems, banking systems, credit card processing systems, and sales and order tracking systems.
A major purpose of a database system is to provide users with an abstract view of the data. That is, the system hides certain details of how the data are stored and maintained. Thereby, data can be stored in complex data structures that permit efficient retrieval, yet users see a simplified and easy-to-use view of the data. The lowest level of abstraction, the physical level, describes how the data are actually stored and details the data structures. The next-higher level of abstraction, the logical level, describes what data are stored, and what relationships exist among those data. The highest level of abstraction, the view level, describes parts of the database that are relevant to each user; application programs used to access a database form part of the view level.
The overall structure of the database is called the database schema. The schema specifies data, data relationships, data semantics, and consistency constraints on the data.
Underlying the structure of a database is the logical data model: a collection of conceptual tools for describing the schema.
The entity-relationship data model is based on a collection of basic objects, called entities, and of relationships among these objects. An entity is a “thing” or “object” in the real world that is distinguishable from other objects. For example, each person is an entity, and bank accounts can be considered entities. Entities are described in a database by a set of attributes. For example, the attributes account-number and balance describe one particular account in a bank. A relationship is an association among several entities. For example, a depositor relationship associates a customer with each of her accounts. The set of all entities of the same type and the set of all relationships of the same type are termed an entity set and a relationship set, respectively.
Like the entity-relationship model, the object-oriented model is based on a collection of objects. An object contains values stored in instance variables within the object. An object also contains bodies of code that operate on the object. These bodies of code are called methods. The only way in which one object can access the data of another object is by invoking a method of that other object. This action is called sending a message to the object. Thus, the call interface of the methods of an object defines that object's externally visible part. The internal part of the object—the instance variables and method code—are not visible externally. The result is two levels of data abstraction, which are important to abstract away (hide) internal details of objects. Object-oriented data models also provide object references which can be used to identify (refer to) objects.
In record-based models, the database is structured in fixed-format records of several types. Each record has a fixed set of fields. The three most widely accepted record-based data models are the relational, network, and hierarchical models. The latter two were widely used once, but are of declining importance. The relational model is very widely used. Databases based on the relational model are called relational databases.
The relational model uses a collection of tables (called relations) to represent both data and the relationships among those data. Each table has multiple columns, and each column has a unique name. Each row of the table is called a tuple, and each column represents the value of an attribute of the tuple.
The size of a database can vary widely, from a few megabytes for personal databases, to gigabytes (a gigabyte is 1000 megabytes) or even terabytes (a terabyte is 1000 gigabytes) for large corporate databases.
The information in a database is stored on a nonvolatile medium that can accommodate large amounts of data; the most commonly used such media are magnetic disks. Magnetic disks can store significantly larger amounts of data than main memory, at much lower costs per unit of data.
To improve reliability in mission-critical systems, disks can be organized into structures generically called redundant arrays of independent disks (RAID). In a RAID system, data are organized with some amount of redundancy (such as replication) across several disks. Even if one of the disks in the RAID system were to be damaged and lose data, the lost data can be reconstructed from the other disks in the RAID system. See also Computer storage technology.
Logically, data in a relational database are organized as a set of relations, each relation consisting of a set of records. This is the view given to database users. The underlying implementation on disk (hidden from the user) consists of a set of files. Each file consists of a set of fixed-size pieces of disk storage, called blocks. Records of a relation are stored within blocks. Each relation is associated with one or more files. Generally a file contains records from only one relation, but organizations where a file contains records from more than one relation are also used for performance reasons.
One way to retrieve a desired record in a relational database is to perform a scan on the corresponding relation; a scan fetches all the records from the relation, one at a time.
Accessing desired records from a large relation using a scan on the relation can be very expensive. Indices are data structures that permit more efficient access of records. An index is built on one or more attributes of a relation; such attributes constitute the search key. Given a value for each of the search-key attributes, the index structure can be used to retrieve records with the specified search-key values quickly. Indices may also support other operations, such as fetching all records whose search-key values fall in a specified range of values.
A database schema is specified by a set of definitions expressed by a data-definition language. The result of execution of data-definition language statements is a set of information stored in a special file called a data dictionary. The data dictionary contains metadata, that is, data about data. This file is consulted before actual data are read or modified in the database system. The data-definition language is also used to specify storage structures and access methods.
Data manipulation is the retrieval, insertion, deletion, and modification of information stored in the database. A data-manipulation language enables users to access or manipulate data as organized by the appropriate data model. There are basically two types of data-manipulation languages: Procedural data-manipulation languages require a user to specify what data are needed and how to get those data; nonprocedural data-manipulation languages require a user to specify what data are needed without specifying how to get those data.
A query is a statement requesting the retrieval of information. The portion of a data-manipulation language that involves information retrieval is called a query language. Although technically incorrect, it is common practice to use the terms query language and data-manipulation language synonymously.
Database languages support both data-definition and data-manipulation functions. Although many database languages have been proposed and implemented, SQL has become a standard language supported by most relational database systems. Databases based on the object-oriented model also support declarative query languages that are similar to SQL.
SQL provides a complete data-definition language, including the ability to create relations with specified attribute types, and the ability to define integrity constraints on the data.
Query By Example (QBE) is a graphical language for specifying queries. It is widely used in personal database systems, since it is much simpler than SQL for nonexpert users.
Forms interfaces present a screen view that looks like a form, with fields to be filled in by users. Some of the fields may be filled automatically by the forms system. Report writers permit report formats to be defined, along with queries to fetch data from the database; the results of the queries are shown formatted in the report. These tools in effect provide a new language for building database interfaces and are often referred to as fourth-generation languages (4GLs). See also Human-computer interaction.
Often, several operations on the database form a single logical unit of work, called a transaction. An example of a transaction is the transfer of funds from one account to another. Transactions in databases mirror the corresponding transactions in the commercial world.
Traditionally database systems have been designed to support commercial data, consisting mainly of structured alphanumeric data. In recent years, database systems have added support for a number of nontraditional data types such as text documents, images, and maps and other spatial data. The goal is to make databases universal servers, which can store all types of data. Rather than add support for all such data types into the core database, vendors offer add-on packages that integrate with the database to provide such functionality.
(DataBase Management System) Software that controls the organization, storage, retrieval, security and integrity of data in a database. It accepts requests from the application and instructs the operating system to transfer the appropriate data. The major DBMS vendors are Oracle, IBM, Microsoft and Sybase (see Oracle database, DB2, SQL Server and ASE). MySQL is a very popular open source product (see MySQL).
DBMSs may work with traditional programming languages (COBOL, C, etc.) or they may include their own programming language for application development.
DBMSs let information systems be changed more easily as the organization's requirements change. New categories of data can be added to the database without disruption to the existing system. Adding a field to a record does not require changing any of the programs that do not use the data in that new field.
Major Features of a DBMS
Data Security
The DBMS can prevent unauthorized users from viewing or updating the database. Using passwords, users are allowed access to the entire database or a subset of it known as a "subschema." For example, in an employee database, some users may be able to view salaries while others may view only work history and medical data.
Data Integrity
The DBMS can ensure that no more than one user can update the same record at the same time. It can keep duplicate records out of the database; for example, no two customers with the same customer number can be entered.
Interactive Query
Most DBMSs provide query languages and report writers that let users interactively interrogate the database and analyze its data. This important feature gives users access to all management information as needed.
Interactive Data Entry and Updating
Many DBMSs provide a way to interactively enter and edit data, allowing you to manage your own files and databases. However, interactive operation does not leave an audit trail and does not provide the controls necessary in a large organization. These controls must be programmed into the data entry and update programs of the application.
This is a common misconception about desktop computer DBMSs. Complex business systems can be developed, but not without programming. This is not the same as creating lists of data for your own record keeping.
Data Independence
With DBMSs, the details of the data structure are not stated in each application program. The program asks the DBMS for data by field name; for example, a coded equivalent of "give me customer name and balance due" would be sent to the DBMS. Without a DBMS, the programmer must reserve space for the full structure of the record in the program. Any change in data structure requires changing all application programs.
Database Design
A business information system is made up of subjects (customers, employees, vendors, etc.) and activities (orders, payments, purchases, etc.). Database design is the process of organizing this data into related record types. The DBMS that is chosen is the one that can support the organization's data structure while efficiently processing the transaction volume.
Organizations may use one kind of DBMS for daily transaction processing and then move the detail to another DBMS better suited for random inquiries and analysis.
Overall systems design decisions are performed by data administrators and systems analysts. Detailed database design is performed by database administrators.
Hierarchical, Network & Relational
Information systems are made up of related files: customers and orders, vendors and purchases, etc. A key DBMS feature is its ability to manage these relationships.
Hierarchical databases link records like an organization chart. A record type can be owned by only one owner. In the following example, orders are owned by only one customer. Hierarchical structures were widely used with early mainframe systems; however, they are often restrictive in linking real-world structures.
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In network databases, a record type can have multiple owners. In the example below, orders are owned by both customers and products, reflecting their natural relationship in business.
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Relational databases do not link records together physically, but the design of the records must provide a common field, such as account number, to allow for matching. Often, the fields used for matching are indexed in order to speed up the process.
In the following example, customers, orders and products are linked by comparing data fields and/or indexes when information from more than one record type is needed. This method is more flexible for ad hoc inquiries. Many hierarchical and network DBMSs also provide this capability.
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Object Databases
Certain information systems may have complex data structures not easily modeled by traditional data structures. An "object database" can be employed when hierarchical, network and relational structures are too restrictive. Object databases can easily handle many-to-many relationships.
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Intelligent Databases
All DBMSs provide some data validation; for example, they can reject invalid dates or alphabetic data entered into money fields. But most validation is left up to the application programs.
Intelligent databases provide more validation; for example, table lookups can reject bad spelling or coding of items. Common algorithms can also be used such as one that computes sales tax for an order based on zip code.
When validation is left up to each application program, one program could allow an item to be entered while another program rejects it. Data integrity is better served when data validation is done in only one place. Mainframe DBMSs were the first to become intelligent, and all the others followed suit.
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| This diagram shows the interaction between the DBMS with other system and application software running in memory. |
Three Related Articles from CMP's TechWeb
System for quick search and retrieval of information from a database. The DBMS determines how data are stored and retrieved. It must address problems such as security, accuracy, consistency among different records, response time, and memory requirements. These issues are most significant for database systems on computer networks. Ever-higher processing speeds are required for efficient database management. Relational DBMSs, in which data are organized into a series of tables ("relations") that are easily reorganized for accessing data in different ways, are the most widely used today.
For more information on database management system, visit Britannica.com.
| Meaning | Category |
| Data Base Management System | Business->Accounting |
| Database Management System | Academic & Science->Electronics Governmental->Military |
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The noun database management system has one meaning:
Meaning #1:
a software system that facilitates the creation and maintenance and use of an electronic database
Synonym: DBMS
A database management system (DBMS) is computer software designed for
the purpose of managing databases. Typical examples of DBMSs include Oracle, Microsoft SQL Server, MySQL, and FileMaker. DBMSs are typically used by Database administrators in the creation of Database
systems.
A DBMS is a complex set of software programs that controls the organization, storage and retrieval of data in a database. A DBMS includes:
The DBMS accepts requests for data from the application program and instructs the operating system to transfer the appropriate data.
When a DBMS is used, information systems can be changed much more easily as the organization's information requirements change. New categories of data can be added to the database without disruption to the existing system.
Organizations may use one kind of DBMS for daily transaction processing and then move the detail onto another computer that uses another DBMS better suited for random inquiries and analysis. Overall systems design decisions are performed by data administrators and systems analysts. Detailed database design is performed by database administrators.
Database servers are specially designed computers that hold the actual databases and run only the DBMS and related software. Database servers are usually multiprocessor computers, with RAID disk arrays used for stable storage. Connected to one or more servers via a high-speed channel, hardware database accelerators are also used in large volume transaction processing environments.
DBMS's are found at the heart of most database applications. Sometimes DBMSs are built around a private multitasking kernel with built-in networking support although nowadays these functions are left to the operating system.
Features and Abilities Of DBMS: One can characterize a DBMS as an "attribute management system" where attributes are small chunks of information that describe something. For example, "color" is an attribute of a car. The value of the attribute may be a color such as "red", "blue", "silver", etc. Lately databases have been modified to accept large or unstructured (pre-digested or pre-categorized) information as well, such as images and text documents. However, the main focus is still on descriptive attributes.
DBMS roll together frequently-needed services or features of attribute management. This allows one to get powerful functionality "out of the box" rather than program each from scratch or add and integrate them incrementally. Such features include:
Querying is the process of requesting attribute information from various perspectives and combinations of factors. Example: "How many 2-door cars in Texas are green?"
A database query language and report writer to allow users to interactively interrogate the database, analyse its data and update it according to the users privileges on data. It also controls the security of the database. Data security prevents unauthorised 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. However, it may not leave an audit trail of actions or provide the kinds of controls necessary in a multi-user organisation. These controls are only available when a set of application programs are customised for each data entry and updating function.
Often copies of attributes need to be made in case primary disks or other equipment fails. A periodic copy of attributes may also be created for a distant organization that cannot readily access the original. DBMS usually provide utilities to facilitate the process of extracting and disseminating attribute sets.
When data is replicated between database servers, so that the information remains consistent throughout the database system and users cannot tell or even know which server in the DBMS they are using, the system is said to exhibit replication transparency.
Often one wants to apply rules to attributes so that the attributes are clean and reliable. For example, we may have a rule that says each car can have only one engine associated with it (identified by Engine Number). If somebody tries to associate a second engine with a given car, we want the DBMS to deny such a request and display an error message. However, with changes in the model specification such as, in this example, hybrid gas-electric cars, rules may need to change. Ideally such rules should be able to be added and removed as needed without significant data layout redesign.
Often it is desirable to limit who can see or change which attributes or groups of attributes. After all, you don't want anybody on the street to be able to change your license plate number in government automobile databases.
There are common computations requested on attributes such as counting, summing, averaging, sorting, grouping, cross-referencing, etc. Rather than have each computer application implement these from scratch, they can rely on the DBMS to supply such calculations.
Often one wants to know who accessed what attributes, what was changed, and when it was changed. Logging services allow this by keeping a record of access occurrences and changes.
If there are frequently occurring usage patterns or requests, some DBMS can adjust themselves to improve the speed of those interactions. In some cases the DBMS will merely provide tools to monitor performance, allowing a human expert to make the necessary adjustments after reviewing the statistics collected.
Meta-data is information about information. For example, a listing that describes what attributes are allowed to be in data sets is called "meta-information".
Databases have been in use since the earliest days of electronic computing. Unlike modern systems which can be applied to widely different databases and needs, the vast majority of older systems were tightly linked to the custom databases in order to gain speed at the expense of flexibility. Originally DBMSs was found only in large organizations with the computer hardware needed to support large data sets.
As computers grew in capability, this tradeoff became increasingly unnecessary and a number of general-purpose database systems emerged; by the mid-1960s there were a number of such systems in commercial use. Interest in a standard began to grow, and Charles Bachman, author of one such product, IDS, founded the Database Task Group within CODASYL, the group responsible for the creation and standardization of COBOL. In 1971 they delivered their standard, which generally became known as the Codasyl approach, and soon there were a number of commercial products based on it available.
The Codasyl approach was based on the "manual" navigation of a linked dataset which was formed into a large network. When the database was first opened, the program was handed back a link to the first record in the database, which also contained pointers to other pieces of data. To find any particular record the programmer had to step through these pointers one at a time until the required record was returned. Simple queries like "find all the people in Sweden" required the program to walk the entire data set and collect the matching results. There was, essentially, no concept of "find" or "search". This might sound like a serious limitation today, but in an era when the data was most often stored on magnetic tape such operations were too expensive to contemplate anyway.
IBM also had their own DBMS system in 1968, known as IMS. IMS was a development of software written for the Apollo program on the System/360. IMS was generally similar in concept to Codasyl, but used a strict hierarchy for its model of data navigation instead of Codasyl's network model.
Both concepts later became known as navigational databases due to the way data was accessed, and Bachman's 1973 Turing Award award presentation was The Programmer as Navigator.
IMS is classified as a hierarchical database. IDS and IDMS (both CODASYL databases) as well as CINCOMs TOTAL database are classified as network databases.
Edgar Codd worked at IBM in San Jose, California, in one of their offshoot offices that was primarily involved in the development of hard disk systems. He was unhappy with the navigational model of the Codasyl approach, notably the lack of a "search" facility which was becoming increasingly useful when the database was stored on disk instead of tape. In 1970, he wrote a number of papers that outlined a new approach to database construction that eventually culminated in the groundbreaking A Relational Model of Data for Large Shared Data Banks. [1]
In this paper, he described a new system for storing and working with large databases. Instead of records being stored in some sort of linked list of free-form records as in Codasyl, Codd's idea was to use a "table" of fixed-length records. A linked-list system would be very inefficient when storing "sparse" databases where some of the data for any one record could be left empty. The relational model solved this by splitting the data into a series of normalized tables, with optional elements being moved out of the main table to where they would take up room only if needed.
For instance, a common use of a database system is to track information about users, their name, login information, various addresses and phone numbers. In the navigational approach all of these data would be placed in a single record, and unused items would simply not be placed in the database. In the relational approach, the data would be normalized into a user table, an address table and a phone number table (for instance). Records would be created in these optional tables only if the address or phone numbers were actually provided.
Linking the information back together is the key to this system. In the relational model, some bit of information was used as a "key", uniquely defining a particular record. When information was being collected about a user, information stored in the optional (or related) tables would be found by searching for this key. For instance, if the login name of a user is unique, addresses and phone numbers for that user would be recorded with the login name as its key. This "re-linking" of related data back into a single collection is something that traditional computer languages are not designed for.
Just as the navigational approach would require programs to loop in order to collect records, the relational approach would require loops to collect information about any one record. Codd's solution to the necessary looping was a set-oriented language, a suggestion that would later spawn the ubiquitous SQL. Using a branch of mathematics known as tuple calculus, he demonstrated that such a system could support all the operations of normal databases (inserting, updating etc.) as well as providing a simple system for finding and returning sets of data in a single operation.
Codd's paper was picked up by two people at Berkeley, Eugene Wong and Michael Stonebraker. They started a project known as INGRES using funding that had already been allocated for a geographical database project, using student programmers to produce code. Beginning in 1973, INGRES delivered its first test products which were generally ready for widespread use in 1979. During this time, a number of people had moved "through" the group — perhaps as many as 30 people worked on the project, about five at a time. INGRES was similar to System R in a number of ways, including the use of a "language" for data access, known as QUEL — QUEL was in fact relational, having been based on Codd's own Alpha language, but has since been corrupted to follow SQL, thus violating much the same concepts of the relational model as SQL itself.
IBM itself did only one test implementation of the relational model, PRTV, and a production one, Business System 12, both now discontinued. Honeywell did MRDS for Multics, and now there are two new implementations: Alphora Dataphor and Rel. All other DBMS implementations usually called relational are actually SQL DBMSs.
In 1968, the [2] University of Michigan began development of the Micro relational database management system. It was used to manage very large datasets by the US Department of Labor, the Environmental Protection Agency and researchers from University of Alberta, the University of Michigan and Wayne State University. It ran on mainframe computers using Michigan Terminal System. The system remained in production until 1996.
IBM started working on a prototype system loosely based on Codd's concepts as System R in the early 1970s — unfortunately, System R was conceived as a way of proving Codd's ideas unimplementable[citation needed], and thus the project was delivered to a group of programmers who were not under Codd's supervision, never understood his ideas fully and ended up violating several fundamentals of the relational model. The first "quickie" version was ready in 1974/5, and work then started on multi-table systems in which the data could be broken down so that all of the data for a record (much of which is often optional) did not have to be stored in a single large "chunk". Subsequent multi-user versions were tested by customers in 1978 and 1979, by which time a standardized query language, SQL, had been added. Codd's ideas were establishing themselves as both workable and superior to Codasyl, pushing IBM to develop a true production version of System R, known as SQL/DS, and, later, Database 2 (DB2).
Many of the people involved with INGRES became convinced of the future commercial success of such systems, and formed their own companies to commercialize the work but with an SQL interface. Sybase, Informix, NonStop SQL and eventually Ingres itself were all being sold as offshoots to the original INGRES product in the 1980s. Even Microsoft SQL Server is actually a re-built version of Sybase, and thus, INGRES. Only Larry Ellison's Oracle started from a different chain, based on IBM's papers on System R, by beating them to market when the first version was released in 1978.
Stonebraker went on to apply the lessons from INGRES to develop a new database, Postgres, now known as PostgreSQL. PostgreSQL is now one of the most widely used databases in the world [citation needed], primarily for global mission critical applications (the .org and .info domain name registries use it as their primary data store, as do many large companies and financial institutions).
In Sweden, Codd's paper was also read and Mimer SQL was developed from the mid-70s at Uppsala University. In 1984, this project was consolidated into an independent enterprise. In the early 1980s, Mimer introduced transaction handling for high robustness in applications, an idea that was subsequently implemented on most other DBMS.
This article was originally based on material from the Free On-line Dictionary of Computing, which is licensed under the GFDL.
This entry is from Wikipedia, the leading user-contributed encyclopedia. It may not have been reviewed by professional editors (see full disclaimer)
Dansk (Danish)
abbr. - databasesoftware, databasesystem
Français (French)
abbr. - (abrév = database management system) (Comput) système de gestion des données SGBD
Deutsch (German)
abbr. - (Comp.) DBMS, Datenbank-Managementsystem
Ελληνική (Greek)
abbr. - (Η/Υ) σύστημα διαχείρισης βάσης δεδομένων
Español (Spanish)
abbr. - sistema de administración de bases de datos
Svenska (Swedish)
abbr. - (database management system) förvaltningssystem för databaser
中文（简体） (Chinese (Simplified))
数据库管理系统
中文（繁體） (Chinese (Traditional))
abbr. - 資料庫管理系統
한국어 (Korean)
abbr. - 데이터 베이스의 중앙관리 시스템
العربيه (Arabic)
(اختصار) مختصر جمله Data Base Management System , نظام إدارة قاعدة البيانات ( في الحاسوب)
עברית (Hebrew)
abbr. - מערכת ניהול של מסד-נתונים (שם)
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 | Sci-Tech Dictionary definition of database management system McGraw-Hill Dictionary of Scientific and Technical Terms. Copyright © 2003, 1994, 1989, 1984, 1978, 1976, 1974 by McGraw-Hill Companies, Inc. All rights reserved. More from Sci-Tech Dictionary |
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| Sci-Tech Encyclopedia information about database management system McGraw-Hill Encyclopedia of Science and Technology. Copyright © 2005 by The McGraw-Hill Companies, Inc. All rights reserved. More from Sci-Tech Encyclopedia |
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| Computer Encyclopedia information about database management system THIS COPYRIGHTED DEFINITION IS FOR PERSONAL USE ONLY. All other reproduction is strictly prohibited without permission from the publisher. © 1981-2007 Computer Language Company Inc. All rights reserved. More from Computer Encyclopedia |
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| Britannica Concise Encyclopedia information about database management system Britannica Concise Encyclopedia. © 2006 Encyclopædia Britannica, Inc. All rights reserved. More from Britannica Concise Encyclopedia |
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| Abbreviations information about database management system STANDS4.com - The source for acronyms and abbreviations. Copyright ©2006 STANDS4 LLC. All rights reserved. More from Abbreviations |
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| WordNet information about database management system WordNet 1.7.1 Copyright © 2001 by Princeton University. All rights reserved. More from WordNet |
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| Wikipedia information about database management system This article is licensed under the GNU Free Documentation License. It uses material from the Wikipedia article "Database management system". More from Wikipedia |
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