The client—server architecture was a development where the application resided on a client desktop and the database on a server allowing the processing to be distributed. This evolved into a multitier architecture incorporating application servers and web servers with the end user interface via a web browser with the database only directly connected to the adjacent tier. A general-purpose DBMS will provide public application programming interfaces API and optionally a processor for database languages such as SQL to allow applications to be written to interact with the database.
For example, an email system performing many of the functions of a general-purpose DBMS such as message insertion, message deletion, attachment handling, blocklist lookup, associating messages an email address and so forth however these functions are limited to what is required to handle email. External interaction with the database will be via an application program that interfaces with the DBMS. A programmer will code interactions to the database sometimes referred to as a datasource via an application program interface API or via a database language.
Database languages are special-purpose languages, which allow one or more of the following tasks, sometimes distinguished as sublanguages :. 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 database engine a. Though typically accessed by a DBMS through the underlying operating system and often using the operating systems' file 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.http://police-risk-management.com/order/reviews/navej-bloccare-telefono.php
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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 character encoding was used to store data, so multiple encodings can be used in the same database.
Various low-level database storage structures 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 column-oriented and correlation databases. Often storage redundancy is employed to increase performance.
A common example is storing materialized views , which consist of frequently needed external views or query results. Storing such views saves the expensive computing of them each time they are needed. The downsides of materialized views are the overhead incurred when updating them to keep them synchronized with their original updated database data, and the cost of storage redundancy.
Occasionally a database employs storage redundancy by database objects replication with one or more copies to increase data availability both to improve performance of simultaneous multiple end-user accesses to a same database object, and to provide resiliency in a case of partial failure of a distributed database. Updates of a replicated object need to be synchronized across the object copies. In many cases, the entire database is replicated. Database 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. 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 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.
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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. 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. Database 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. The acronym ACID describes some ideal properties of a database transaction: atomicity , consistency , isolation , and durability. However, in some situations, it is desirable to 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. After designing a database for an application, the next stage is building the database. Typically, an appropriate general-purpose DBMS can be selected to be used for this purpose. A DBMS provides the needed user interfaces to be used 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. Sometimes it is desired to bring a database back to a previous state for many reasons, e. To achieve this, a backup operation is done occasionally or continuously, where each desired database state i. When it is decided by a database administrator to bring the database back to this state e.
Static analysis techniques for software verification can be applied also in the scenario of query languages. The abstraction of relational database system has many interesting applications, in particular, for security purposes, such as fine grained access control, watermarking, 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". The first task of a database designer is to produce a conceptual data model 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 Unified Modeling Language. 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. 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?
The answers to these questions establish definitions of the terminology used for entities customers, products, flights, flight segments and their relationships and attributes. Producing the conceptual data model sometimes involves input from business processes , or the analysis of workflow 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 schema that implements the relevant data structures within the database. This process is often called logical database design, and the output is a logical data model 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 modeling 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 normalization.
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 physical data model.
A key goal during this stage is data independence , 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.
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 access control to database objects as well as defining security levels and methods for the data itself. A database model is a type of data model that determines the logical structure of a database and fundamentally determines in which manner data can be stored, organized, and manipulated.
The most popular example of a database model is the relational model or the SQL approximation of relational , which uses a table-based format. Physical data models include:. 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 human resources 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 technology has been an active research topic since the s, both in academia and in the research and development groups of companies for example IBM Research. Research activity includes theory and development of prototypes.
Notable research topics have included models , the atomic transaction concept, and related concurrency control techniques, query languages and query optimization methods, RAID , and more. From Wikipedia, the free encyclopedia. For a topical guide to this subject, see Outline of databases. Further information: Navigational database. Main article: Database machine. This section does not cite any sources. Please help improve this section by adding citations to reliable sources.
Hybrid Ontology for Semantic Information Retrieval Model Using Keyword Matching Indexing System
Unsourced material may be challenged and removed. March Learn how and when to remove this template message. Main articles: Computer data storage and Database engine. Main article: Materialized view. Main article: Database replication. This article appears to contradict the article Database security. Please see discussion on the linked talk page.
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Main article: Database security. Further information: Concurrency control. Main article: Database tuning. Main article: Backup.
Main article: Database design. Main article: Database model. 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. Chong et al. OED Online. Oxford University Press. June Retrieved July 12, Subscription required. Retrieved Feb 20, Kahn, D. Rumelhart, and B. Retrieved 23 August The FoxPro History.
Retrieved on Retrieved on August 13, Retrieved 10 December International Business Machines. October 27, Retrieved Retrieved April 15, Bachman, Charles W. Communications of the ACM. Beynon-Davies, Paul Database Systems 3rd ed. Palgrave Macmillan.
Intelligent Knowledge Retrieval from Industrial Repositories
Chapple, Mike Archived from the original on 22 February Retrieved 28 January Childs, David L. Technical Report 3. University of Michigan. Technical Report 6. Chong, Raul F. Retrieved 17 March Codd, Edgar F. Connolly, Thomas M. Date, C. An Introduction to Database Systems 8th ed. Halder, Raju; Cortesi, Agostino Hershey, William; Easthope, Carol A set theoretic data structure and retrieval language. Spring Joint Computer Conference, May A person using such facts and opinions generates more information, some of which is communicated to others during discourse, by instructions, in letters and documents, and through other media.
Information organized according to some logical relationships is referred to as a body of knowledge, to be acquired by systematic exposure or study. Application of knowledge or skills yields expertise, and additional analytic or experiential insights are said to constitute instances of wisdom. Use of the term information is not restricted exclusively to its communication via natural language. Information is also registered and communicated through art and by facial expressions and gestures or by such other physical responses as shivering.
Moreover, every living entity is endowed with information in the form of a genetic code. These information phenomena permeate the physical and mental world, and their variety is such that it has defied so far all attempts at a unified definition of information. Interest in information phenomena increased dramatically in the 20th century, and today they are the objects of study in a number of disciplines , including philosophy, physics, biology, linguistics, information and computer science , electronic and communications engineering , management science , and the social sciences.
On the commercial side, the information service industry has become one of the newer industries worldwide. Almost all other industries—manufacturing and service—are increasingly concerned with information and its handling. This article touches on such concepts as they relate to information processing. In treating the basic elements of information processing, it distinguishes between information in analog and digital form, and it describes its acquisition, recording, organization, retrieval, display, and techniques of dissemination.
A separate article, information system , covers methods for organizational control and dissemination of information. Interest in how information is communicated and how its carriers convey meaning has occupied, since the time of pre-Socratic philosophers, the field of inquiry called semiotics , the study of signs and sign phenomena. Signs are the irreducible elements of communication and the carriers of meaning. The American philosopher, mathematician, and physicist Charles S.
Peirce is credited with having pointed out the three dimensions of signs, which are concerned with, respectively, the body or medium of the sign, the object that the sign designates, and the interpretant or interpretation of the sign. Peirce recognized that the fundamental relations of information are essentially triadic; in contrast, all relations of the physical sciences are reducible to dyadic binary relations.
Another American philosopher, Charles W. Morris , designated these three sign dimensions syntactic, semantic, and pragmatic , the names by which they are known today. Information processes are executed by information processors. For a given information processor, whether physical or biological, a token is an object, devoid of meaning, that the processor recognizes as being totally different from other tokens. Objects that carry meaning are represented by patterns of tokens called symbols.
The latter combine to form symbolic expressions that constitute inputs to or outputs from information processes and are stored in the processor memory. Information processors are components of an information system, which is a class of constructs. An abstract model of an information system features four basic elements: processor, memory, receptor, and effector Figure 1. The memory stores symbolic expressions, including those that represent composite information processes, called programs.
The two other components, the receptor and the effector , are input and output mechanisms whose functions are, respectively, to receive symbolic expressions or stimuli from the external environment for manipulation by the processor and to emit the processed structures back to the environment. The power of this abstract model of an information-processing system is provided by the ability of its component processors to carry out a small number of elementary information processes: reading; comparing; creating, modifying, and naming; copying; storing; and writing.
The model, which is representative of a broad variety of such systems, has been found useful to explicate man-made information systems implemented on sequential information processors. Because it has been recognized that in nature information processes are not strictly sequential, increasing attention has been focused since on the study of the human brain as an information processor of the parallel type. The cognitive sciences , the interdisciplinary field that focuses on the study of the human mind, have contributed to the development of neurocomputers, a new class of parallel, distributed-information processors that mimic the functioning of the human brain , including its capabilities for self-organization and learning.
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