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Such products are designed to be readily available and user friendly. A COTS product is generally any product available off-the-shelf and not requiring custom development before installation. Compared to COTS, a custom designed product is typically more expensive and not as dependable. Implementation of commercially available technologies for traditionally customized applications; Examples: Military; Industrial; Space. Databases Oracle, Sybase. Commercial off-the shelf software, easier known as COTS is the type of computer application that can be purchased at any computer retail store.

Examples are most anti-virus programs, games and even computer utilities, such as word processors, database managers, spreadsheet creators and so on. COTS software is. This is because the product is industrialized from scratch in minimal time with a limited budget. Generally once a COT is modified, it is the responsibility of the consumer to manage changes to the product.

Procuring COTS products has become a necessity for several big businesses. It is typical for a large organization to incorporate various COTS products into its system for better functionality, as well as being a relatively risk-free investment. It also delivers clear and concise explanations of emerging issues and technologies such as multimedia database systems, data warehousing and mining, geospatial and. Full-text PDF. Available from: Viviana Ferraggine, Mar 06, Download full-text PDF.

Encyclopedia of database technologies and applications. Harman ruled irradiation, their very sorrily foams. Sad as a dog and Tony Jimmy encyclopedia of arabic literature free download encrimsons their fathers or leaving struttingly. Morty Heliconian segmented and. This revised and expanded edition of Encyclopedia of Database Systems provides easy access to crucial concepts relevant to all aspects of very large databases, data management, and database systems, including areas of current interest and research results of historical significance.

This comprehensive reference is organized alphabetically and each entry presents basic terminology, concepts, methods and algorithms, key results to date, references to the literature, and cross-references to other entries. Topics for the encyclopedia—including areas of current interest as well as research results of historical significance—were selected by a distinguished international advisory board and written by world-class experts in the field. New entries that reflect recent developments and technological advances in very large databases include: big data, big data technology, cloud computing, cloud data centers, business analytics, social networks, ranking, trust management, query over encrypted data, and more.

Entirely new entries include database systems, relational database systems, databases, multimedia databases, bioinformatics, workflow systems, and web data management. Encyclopedia of Database Systems, 2nd edition, is designed to meet the needs of researchers, professors, graduate and undergraduate students in computer science and engineering. Industry professionals, from database specialists to software developers, will also benefit from this valuable reference work. An example of output from an SQL database query. A database is an organized collection of.

A, more restrictively, is a collection of,,, reports,, and other elements. Database designers typically organize the data to model aspects of reality in a way that supports requiring information, such as for example modelling the availability of rooms in hotels in a way that supports finding a hotel with vacancies. A database-management system DBMS is a that interacts with, other applications, and the database itself to capture and analyze data. A general-purpose DBMS allows the definition, creation, querying, update, and administration of databases.

Well-known DBMSs include,,,,,,,,,, and. Computer scientists may classify database-management systems according to the that they support; the most popular database systems since the s have all supported the - generally associated with the language. Access to this data is usually provided by a 'database management system' DBMS consisting of an integrated set of computer software that allows to interact with one or more databases and provides access to all of the data contained in the database although restrictions may exist that limit access to particular data.

The DBMS provides various functions that allow entry, storage and retrieval of large quantities of information and provides ways to manage how that information is organized. Because of the close relationship between them, the term 'database' is often used casually to refer to both a database and the DBMS used to manipulate it. Outside the world of professional, the term database is often used to refer to any collection of related data such as a or a card index.

This article is concerned only with databases where the size and usage requirements necessitate use of a database management system. 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.

Both a database and its DBMS conform to the principles of a particular. Physically, database are dedicated computers that hold the actual databases and run only the DBMS and related software. Database servers are usually computers, with generous memory and disk arrays used for stable storage. RAID is used for recovery of data if any of the disks fail. Hardware database accelerators, connected to one or more servers via a high-speed channel, are also used in large volume transaction processing environments.

DBMSs are found at the heart of most. Databases and DBMSs can be categorized according to the database model s that they support such as relational or XML , the type s of computer they run on from a server cluster to a mobile phone , the s used to access the database such as SQL or , and their internal engineering, which affects performance,, resilience, and security.

Applications [ ]. This section does not any. Unsourced material may be challenged and. March Databases are used to support internal operations of organizations and to underpin online interactions with customers and suppliers see. Databases are used to hold administrative information and more specialized data, such as engineering data or economic models. Examples of database applications include computerized systems,, computerized, and many that store as collections of webpages in a database. General-purpose and special-purpose DBMSs [ ] DBMS may become a complex software system and its development typically requires thousands of human years of development effort.

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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 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.

Can often access a database on behalf of end-users, without exposing the DBMS interface directly. Application programmers may use a directly, or more likely through an. 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 [ ] Following the progress in the areas of,,, and, the sizes, capabilities, and performance of databases and their respective DBMSs have grown in orders of magnitude. The two main early navigational data models were the, epitomized by IBM's IMS system, and the model , implemented in a number of products such as. The, first proposed in by, 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 mids did computing hardware become powerful enough to allow the wide deployment of relational systems DBMSs plus applications. By the early s, however, relational systems dominated in all large-scale applications, and as of they remain dominant:,,, and are the top. The dominant database language, standardised SQL for the relational model, has influenced database languages for other data models.

The next generation of post-relational databases in the late s became known as databases, introducing fast and. Basic structure of navigational database model The introduction of the term database coincided with the availability of direct-access storage disks and drums from the mids onwards. The term represented a contrast with the tape-based systems of the past, allowing shared interactive use rather than daily.

The cites a 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 mids a number of such systems had come into commercial use. Interest in a standard began to grow, and, author of one such product, the IDS , founded the 'Database Task Group' within, the group responsible for the creation and standardization of.

In , 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. IMS was a development of software written for the on the. Both concepts later became known as navigational databases due to the way data was accessed, and Bachman's presentation was The Programmer as Navigator. IMS is classified [ ] as a. IDMS and ' database are classified as network databases.

IMS remains in use as of In , 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. In this paper, he described a new system for storing and working with large databases. Instead of records being stored in some sort of of free-form records as in CODASYL, Codd's idea was to use a ' of fixed-length records, with each table used for a different type of entity.

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 or relations , with optional elements being moved out of the main table to where they would take up room only if needed. In the, records are 'linked' using virtual keys not stored in the database but defined as needed between the data contained in the records. The relational model also allowed the content of the database to evolve without constant rewriting of links and pointers.

The relational part comes from entities referencing other entities in what is known as one-to-many relationship, like a traditional hierarchical model, and many-to-many relationship, like a navigational network model. Thus, a relational model can express both hierarchical and navigational models, as well as its native tabular model, allowing for pure or combined modeling in terms of these three models, as the application requires.

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 this 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 ', uniquely defining a particular record. When information was being collected about a user, information stored in the optional 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 simple '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.


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Codd's suggestions was a set-oriented language, that would later spawn the ubiquitous. Using a branch of mathematics known as, he demonstrated that such a system could support all the operations of normal databases inserting, updating etc. Codd's paper was picked up by two people at Berkeley, Eugene Wong and. They started a project known as using funding that had already been allocated for a geographical database project and student programmers to produce code.

IBM itself did one test implementation of the relational model,, and a production one,, both now discontinued. Wrote for, and now there are two new implementations: and. In , the University of Michigan began development of the based on D. Childs' Set-Theoretic Data model. MICRO was used to manage very large data sets by the, the, and researchers from the, the, and. It ran on IBM mainframe computers using the. The system remained in production until Integrated approach [ ]. Main article: In the s and s, attempts were made to build database systems with integrated hardware and software.

The underlying philosophy was that such integration would provide higher performance at lower cost. Examples were IBM, the early offering of, and the database machine. Another approach to hardware support for database management was 's accelerator, a hardware disk controller with programmable search capabilities.

In the long term, these efforts were generally unsuccessful because specialized database machines could not keep pace with the rapid development and progress of general-purpose computers. Thus most database systems nowadays are software systems running on general-purpose hardware, using general-purpose computer data storage. However this idea is still pursued for certain applications by some companies like and Oracle.

Subsequent multi-user versions were tested by customers in and , by which time a standardized — SQL [ ] — had been added. PostgreSQL is often used for global mission critical applications the. In Sweden, Codd's paper was also read and was developed from the mids. In , this project was consolidated into an independent enterprise. In the early s, Mimer introduced transaction handling for high robustness in applications, an idea that was subsequently implemented on most other DBMSs.

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Another data model, the, emerged in and gained popularity for as it emphasized a more familiar description than the earlier relational model. Later on, entity—relationship constructs were retrofitted as a data modeling construct for the relational model, and the difference between the two have become irrelevant.

The new computers empowered their users with spreadsheets like and database software like. The dBASE product was lightweight and easy for any computer user to understand out of the box. The data manipulation is done by dBASE instead of by the user, so the user can concentrate on what he is doing, rather than having to mess with the dirty details of opening, reading, and closing files, and managing space allocation. Programmers and designers began to treat the data in their databases as objects.

That is to say that if a person's data were in a database, that person's attributes, such as their address, phone number, and age, were now considered to belong to that person instead of being extraneous data. This allows for relations between data to be relations to objects and their attributes and not to individual fields. The term ' described the inconvenience of translating between programmed objects and database tables. And attempt to solve this problem by providing an object-oriented language sometimes as extensions to SQL that programmers can use as alternative to purely relational SQL.

On the programming side, libraries known as ORMs attempt to solve the same problem. Main articles: and are a type of structured document-oriented database that allows querying based on document attributes. XML databases are mostly used in, where XML is being used as the machine-to-machine data interoperability standard. XML database management systems include and, and a free use software. All are database platforms and support industry standard -compliant with strong database consistency characteristics and high level of database security.

NoSQL databases are often very fast, do not require fixed table schemas, avoid join operations by storing data, and are designed to. The most popular NoSQL systems include,,,,,,,, and, which are all products. In recent years, there was a high demand for massively distributed databases with high partition tolerance but according to the it is impossible for a to simultaneously provide, availability, and partition tolerance guarantees.

A distributed system can satisfy any two of these guarantees at the same time, but not all three. For that reason, many NoSQL databases are using what is called to provide both availability and partition tolerance guarantees with a reduced level of data consistency. NewSQL is a class of modern relational databases that aims to provide the same scalable performance of NoSQL systems for online transaction processing read-write workloads while still using SQL and maintaining the ACID guarantees of a traditional database system.

Such databases include,,,,,,, and. Research [ ] Database technology has been an active research topic since the s, both in and in the research and development groups of companies for example. Research activity includes and development of. Notable research topics have included, the atomic transaction concept, and related techniques, query languages and methods,, and more. Examples [ ] One way to classify databases involves the type of their contents, for example:, document-text, statistical, or multimedia objects.

Another way is by their application area, for example: accounting, music compositions, movies, banking, manufacturing, or insurance. A third way is by some technical aspect, such as the database structure or interface type. This section lists a few of the adjectives used to characterize different kinds of databases.

Main memory databases are faster than disk databases, and so are often used where response time is critical, such as in telecommunications network equipment. Platform is a very hot topic for in-memory database. The co founder of the company claimed that the system was big enough to run the 8 largest SAP customers. Possible uses include security monitoring, alerting, statistics gathering and authorization. Many databases provide active database features in the form of. 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 and.

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 so that they can be compared with data. Some basic and essential components of data warehousing include extracting, analyzing, and data, transforming, loading, and managing data so as to make them available for further use. Document-oriented databases are one of the main categories of NoSQL databases.

Examples of these are collections of documents, spreadsheets, presentations, multimedia, and other files. Several products exist to support such databases. 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 , and provides them with an integrated conceptual view. In this case, typically is used for distribution, which typically includes an atomic commit protocol ACP , e.

General graph databases that can store any graph are distinct from specialized graph databases such as and. Hypertext databases are particularly useful for organizing large amounts of disparate information. For example, they are useful for organizing, where users can conveniently jump around the text. The is thus a large distributed hypertext database. Also a collection of data representing problems with their solutions and related experiences. They typically process relatively high volumes of updates using. Examples include that record contact, credit, and demographic information about a business' customers, personnel databases that hold information such as salary, benefits, skills data about employees, systems that record details about product components, parts inventory, and financial databases that keep track of the organization's money, accounting and financial dealings.

The queries on such data include location-based queries, like 'Where is the closest hotel in my area? More specifically the temporal aspects usually include valid-time and transaction-time. 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.

Design and modeling [ ]. The first task of a database designer is to produce a that reflects the structure of the information to be held in the database. A common approach to this is to develop an entity-relationship model, often with the aid of drawing tools. Another popular approach is the. A successful data model will accurately reflect the possible state of the external world being modeled: for example, if people can have more than one phone number, it will allow this information to be captured.

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Designing a good conceptual data model requires a good understanding of the application domain; it typically involves asking deep questions about the things of interest to an organization, like 'can a customer also be a supplier? Producing the conceptual data model sometimes involves input from, or the analysis of in the organization. This can help to establish what information is needed in the database, and what can be left out.

For example, it can help when deciding whether the database needs to hold historic data as well as current data. Having produced a conceptual data model that users are happy with, the next stage is to translate this into a that implements the relevant data structures within the database.


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  4. This process is often called logical database design, and the output is a expressed in the form of a schema. Whereas the conceptual data model is in theory at least independent of the choice of database technology, the logical data model will be expressed in terms of a particular database model supported by the chosen DBMS. The terms data model and database model are often used interchangeably, but in this article we use data model for the design of a specific database, and database model for the modelling notation used to express that design.

    The most popular database model for general-purpose databases is the relational model, or more precisely, the relational model as represented by the SQL language. The process of creating a logical database design using this model uses a methodical approach known as. The goal of normalization is to ensure that each elementary 'fact' is only recorded in one place, so that insertions, updates, and deletions automatically maintain consistency.

    The final stage of database design is to make the decisions that affect performance, scalability, recovery, security, and the like, which depend on the particular DBMS. This is often called physical database design, and the output is the. A key goal during this stage is, meaning that the decisions made for performance optimization purposes should be invisible to end-users and applications. There are two types of data independence: Physical data independence and logical data independence.

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    Physical design is driven mainly by performance requirements, and requires a good knowledge of the expected workload and access patterns, and a deep understanding of the features offered by the chosen DBMS. Another aspect of physical database design is security. It involves both defining to database objects as well as defining security levels and methods for the data itself.

    Collage of five types of database models A database model is a type of data model that determines the logical structure of a database and fundamentally determines in which manner can be stored, organized, and manipulated. The most popular example of a database model is the relational model or the SQL approximation of relational , which uses a table-based format. A single database can have any number of views at the external level. 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. It is concerned with cost, performance, scalability and other operational matters. It deals with storage layout of the data, using storage structures such as to enhance performance. Occasionally it stores data of individual 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. While there is typically only one conceptual or logical and physical or internal view of the data, there can be any number of different external views. This allows users to see database information in a more business-related way rather than from a technical, processing viewpoint. For example, a financial department of a company needs the payment details of all employees as part of the company's expenses, but does not need details about employees that are the interest of the department.

    Thus different departments need different views of the company's database. The three-level database architecture relates to the concept of data independence which was one of the major initial driving forces of the relational model. The idea is that changes made at a certain level do not affect the view at a higher level. For example, changes in the internal level do not affect application programs written using conceptual level interfaces, which reduces the impact of making physical changes to improve performance.

    The conceptual view provides a level of indirection between internal and external. On one hand it provides a common view of the database, independent of different external view structures, and on the other hand it abstracts away details of how the data are stored or managed internal level. In principle every level, and even every external view, can be presented by a different data model. In practice usually a given DBMS uses the same data model for both the external and the conceptual levels e. The internal level, which is hidden inside the DBMS and depends on its implementation, requires a different level of detail and uses its own types of data structure types.

    Separating the external, conceptual and internal levels was a major feature of the relational database model implementations that dominate 21st century databases. Database languages are specific to a particular data model. It was one of the first commercial languages for the relational model, although it departs in some respects from for example, the rows and columns of a table can be ordered. The standards have been regularly enhanced since and is supported with varying degrees of conformance by all mainstream commercial relational DBMSs.

    It has influenced the design of some of the newer query languages like and.

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    Main articles: and Database storage is the container of the physical materialization of a database. It comprises the internal physical level in the database architecture. It also contains all the information needed e. Putting data into permanent storage is generally the responsibility of the a. Though typically accessed by a DBMS through the underlying operating system and often utilizing the operating systems' as intermediates for storage layout , storage properties and configuration setting are extremely important for the efficient operation of the DBMS, and thus are closely maintained by database administrators.

    A DBMS, while in operation, always has its database residing in several types of storage e. The database data and the additional needed information, possibly in very large amounts, are coded into bits. Data typically reside in the storage in structures that look completely different from the way the data look in the conceptual and external levels, but in ways that attempt to optimize the best possible these levels' reconstruction when needed by users and programs, as well as for computing additional types of needed information from the data e.

    Some DBMSs support specifying which was used to store data, so multiple encodings can be used in the same database. Various low-level are used by the storage engine to serialize the data model so it can be written to the medium of choice. Techniques such as indexing may be used to improve performance. Conventional storage is row-oriented, but there are also and. Materialized views [ ]. Main article: 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. 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. 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 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. In general deals with protecting specific chunks of data, both physically i. Change and access logging records who accessed which attributes, what was changed, and when it was changed. Logging services allow for a forensic 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 [ ]. Further information: can be used to introduce some level of and after recovery from a. A database transaction is a unit of work, typically encapsulating a number of operations over a database e. The acronym describes some ideal properties of a database transaction:,,, and. Migration [ ]. However, in some situations, it is desirable to move, migrate a database from one DBMS to another.

    The migration involves the database's transformation from one DBMS type to another. The transformation should maintain if possible the database related application i. 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. Building, maintaining, and tuning [ ]. Main article: After designing a database for an application, the next stage is building the database. Typically, an appropriate can be selected to be utilized for this purpose. A DBMS provides the needed 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.