Data item descriptions

Data protection act

Data reporting is the process of collecting and submitting data which gives rise to accurate analyses of the facts on the ground; inaccurate data reporting can lead to vastly uninformed decision-making based on erroneous evidence. When data is not reported, the problem is known as underreporting; the opposite problem leads to false positives.Data reporting can be an incredibly difficult endeavor. Census bureaus may hire even hundreds of thousands of workers to achieve the task of counting all of the residents of a country. Teachers use data from student assessments to determine grades; cellphone manufacturers rely on sales data from retailers to point the way to which models to increase production of. The effective management of nearly any company relies on accurate data.

Data janitor

Data Sovereignty is the idea that data are subject to the laws and governance structures within the nation it is collected. The concept of data sovereignty is closely linked with data security, cloud computing and technological sovereignty. Unlike technological sovereignty, which is vaguely defined and can be used as an umbrella term in policymaking, data sovereignty is specifically concerned with questions surrounding the data itself. Data sovereignty is usually discussed in two ways: in relation to Indigenous groups and Indigenous autonomy from post-colonial states or in relation to transnational data flow. With the rise of cloud computing, many countries have passed various laws around control and storage of data, which all reflects measures of data sovereignty.

Data shredder

Data structure persistance

Data padding

Data safety monitoring board

Data series

Data rate (disambiguation)

Data security means protecting digital data, such as those in a database, from destructive forces and from the unwanted actions of unauthorized users, such as a cyberattack or a data breach.

Data monetization, a form of monetization, may refer to the act of generating measurable economic benefits from available data sources (analytics). Less commonly, it may also refer to the act of monetizing data services. In the case of analytics, typically, these benefits accrue as revenue or expense savings, but may also include market share or corporate market value gains.

Sungai Sarawak Bridge

Data strobe encoding

Data localisation

Data quality control

In computer science, data stream clustering is defined as the clustering of data that arrive continuously such as telephone records, multimedia data, financial transactions etc. Data stream clustering is usually studied as a streaming algorithm and the objective is, given a sequence of points, to construct a good clustering of the stream, using a small amount of memory and time.

Data matrix code

Data normalization

Data model

Data matrix (statistics)

Data member

A data set (or dataset) is a collection of data. Most commonly a data set corresponds to the contents of a single database table, or a single statistical data matrix, where every column of the table represents a particular variable, and each row corresponds to a given member of the data set in question. The data set lists values for each of the variables, such as height and weight of an object, for each member of the data set. Each value is known as a datum. The data set may comprise data for one or more members, corresponding to the number of rows. The term data set may also be used more loosely, to refer to the data in a collection of closely related tables, corresponding to a particular experiment or event. An example of this type is the data sets collected by space agencies performing experiments with instruments aboard space probes. Data sets that are so large that traditional data processing applications are inadequate to deal with them are known as big data.In the open data discipline, data set is the unit to measure the information released in a public open data repository. The European Open Data portal aggregates more than half a million data sets. In this field other definitions have been proposed but currently there is not an official one. Some other issues (real-time data sources, non-relational data sets, etc.) increases the difficulty to reach a consensus about it.

Data star trek

In ArcGIS, a data model a set of database design specifications for objects in a GIS application. A data model describes the thematic layers used in the applications (for example, hamburger stands, roads, and counties); their spatial representation (for example, point, line, or polygon); their attributes; their integrity rules and relationships (for example, counties must nest within states); their cartographic portrayal; and their metadata requirements.

A data quality firewall is the use of software to protect a computer system from the entry of erroneous, duplicated or poor quality data. Gartner estimates that poor quality data causes failure in up to 50% of customer relationship management systems. Older technology required the tight integration of data quality software, whereas this can now be accomplished by loosely coupling technology in a service-oriented architecture.

Data management system

Data processor

Data loss

Data locality

A data hub is a collection of data from multiple sources organized for distribution, sharing, and often subsetting and sharing. Generally this data distribution is in the form of a hub and spoke architecture.

Datah Dawai Airport or Datah Dawai Airport (IATA: DTD, ICAO: WALJ) is an airport in Long Lunuk, Long Pahangai, Mahakam Ulu Regency, East Kalimantan, Indonesia. The airport is the only airport in Mahakam Ulu, serving the only air service in the area. Susi Air has 1 daily flight from Datah Dawai to Samarinda, for a total of 12 seats daily. The airport was located near Kapuas River, the longest river in Borneo and the longest river in Indonesia. It was surrounded by high trees and green forest.

A data stream management system (DSMS) is a computer software system to manage continuous data streams. It is similar to a database management system (DBMS), which is, however, designed for static data in conventional databases. A DSMS also offers a flexible query processing so that the information need can be expressed using queries. However, in contrast to a DBMS, a DSMS executes a continuous query that is not only performed once, but is permanently installed.

Data security

The data link connector (DLC) is the multi-pin diagnostic connection port for automobiles, trucks, and motorcycles used to interface a scan tool with the control modules of a given vehicle and access on-board diagnostics and live data streams.Prior to 1996, many OBD-I data link connector's were in the engine compartment, usually near the fuse block. Also, prior to 1996, there was no standardization for these connectors, and each manufacturer had its own shape with a unique pin arrangement. After 1996, many manufacturers retained their proprietary connectors in addition to the OBD-II interface, because OBD-II ports are only required to transmit emission-related codes and data.J1708 is a DLC used on heavy duty vehicles.

Data storage (disambiguation)

Data stewardship

Data stealing malware

Data plane

Data Stream Mining is the process of extracting knowledge structures from continuous, rapid data records.

Data reduction is the transformation of numerical or alphabetical digital information derived empirically or experimentally into a corrected, ordered, and simplified form. The basic concept is the reduction of multitudinous amounts of data down to the meaningful parts.When information is derived from instrument readings there may also be a transformation from analog to digital form. When the data are already in digital form the 'reduction' of the data typically involves some editing, scaling, coding, sorting, collating, and producing tabular summaries. When the observations are discrete but the underlying phenomenon is continuous then smoothing and interpolation are often needed. Often the data reduction is undertaken in the presence of reading or measurement errors. Some idea of the nature of these errors is needed before the most likely value may be determined.An example in astronomy is the data reduction in the Kepler satellite. This satellite records 95-megapixel images once every six seconds, generating tens of megabytes of data per second, which is orders of magnitudes more than the downlink bandwidth of 550 KBps. The on-board data reduction encompasses co-adding the raw frames for thirty minutes, reducing the bandwidth by a factor of 300. Furthermore, interesting targets are pre-selected and only the relevant pixels are processed, which is 6% of the total. This reduced data is then sent to Earth where it is processed further.Research has also been carried out on the use of data reduction in wearable (wireless) devices for health monitoring and diagnosis applications. For example, in the context of epilepsy diagnosis, data reduction has been used to increase the battery lifetime of a wearable EEG device by selecting, and only transmitting, EEG data that is relevant for diagnosis and discarding background activity.

Data stewards

Data mediation

A data jam is a technically oriented workshop exercise where people solve problems using data sets. The events can produce visual data, analytics and applications.Examples include the Hudson Data Jam, hosted by Cary Institute of Environmental Systems, in which students are asked to tell a story based on raw data collected from the watershed of the Hudson River and the Capitol Code, a data jam based on the Office of the Minnesota Secretary of State.

Daugavgrīva Lighthouse

Data snooping

Data merge

Data rate units

Data stream clustering

Data sharing

In connection-oriented communication, a data stream is a sequence of digitally encoded coherent signals (packets of data or data packets) used to transmit or receive information that is in the process of being transmitted.

Data steward

Data refinement

Data onboarding

Data mask

Data smog

Masarautar Daura

Data network

Data reference model

Data swamp

In computer data storage, data striping is the technique of segmenting logically sequential data, such as a file, so that consecutive segments are stored on different physical storage devices.Striping is useful when a processing device requests data more quickly than a single storage device can provide it. By spreading segments across multiple devices which can be accessed concurrently, total data throughput is increased. It is also a useful method for balancing I/O load across an array of disks. Striping is used across disk drives in redundant array of independent disks (RAID) storage, network interface controllers, different computers in clustered file systems and grid-oriented storage, and RAM in some systems.

Data networks

Data position measurement

A data link connection identifier (DLCI) is a Frame Relay 10-bit-wide link-local virtual circuit identifier used to assign frames to a specific PVC or SVC. Frame Relay networks use DLCIs to statistically multiplex frames. DLCIs are preloaded into each switch and act as road signs to the traveling frames.The standard allows the existence of 1024 DLCIs. DLCI 0 is reserved for the ANSI/q993a LMI standard—only numbers 16 to 976 are usable for end-user equipment.

Jambatan Datuk Patinggi Haji Abdul Rahman

Data plot

A data monitoring switch is a networking hardware appliance that provides a pool of monitoring tools with access to traffic from a large number of network links.

Data source

Data loss prevention products

A data item describes an atomic state of a particular object concerning a specific property at a certain time point. A collection of data items for the same object at the same time forms an object instance (or table row). Any type of complex information can be broken down to elementary data items (atomic state). Data items are identified by object (o), property (p) and time (t), while the value (v) is a function of o, p and t: v = F(o,p,t).Values typically are represented by symbols like numbers, texts, images, sounds, videos or others. Values are not necessarily atomic. The complexity of a value depends on the complexity of the property and time component.When looking at databases or XML files, the object is usually identified by an object name or other type of object identifier, which is part of the "data". Properties are defined as columns (table row), properties (object instance) or tags (XML). Often, time is not explicitly expressed and is an attribute applying to the complete data set. Other data collections provide time on the instance level (time series), on column level or even on attribute/property level.

Data sharing is the practice of making data used for scholarly research available to other investigators. Replication has a long history in science.

Data storage capacity

Data privacy law

Datahand

Data integrity is the maintenance of, and the assurance of the accuracy and consistency of, data over its entire life-cycle, and is a critical aspect to the design, implementation and usage of any system which stores, processes, or retrieves data. The term is broad in scope and may have widely different meanings depending on the specific context – even under the same general umbrella of computing.

A Data Refuge is a public and collaborative project designed to address concerns about federal climate and environmental data that is in danger of being lost.

Data search engine

Data recovery hardware

Data systems

A data monitoring switch is a networking hardware appliance that provides a pool of monitoring tools with access to traffic from a large number of network links. It provides a combination of functionality that may include aggregating monitoring traffic from multiple links, regenerating traffic to multiple tools, pre-filtering traffic to offload tools, and directing traffic according to one-to-one and many-to-many port mappings.Data monitoring switches enable organizations to use their monitoring tools more efficiently, to centralize traffic monitoring functions, and to share tools and traffic access between groups. Some of these devices also provide functionality that helps justify tool purchases and simplify deployment and management of the device itself.Several other terms have been used to describe this class of device, including data access switch, tool aggregator, network packet broker, net tool optimizer, and distributed filter tap.

Data intensive tasks

Data shadow

Data literacy is the ability to read, understand, create and communicate data as information. Much like literacy as a general concept, data literacy focuses on the competencies involved in working with data. As data collection and sharing become routine and data analysis and big data become common ideas in the news, business, government and society, it becomes more and more important for students, citizens, and readers to have some data literacy.

A data island is a data store, such as on a PDA or other computing device, that has non-existent or limited external connectivity. This limits the ability of the user to synchronize with or copy the data to other devices. Though new data can be added to the system, the ability to move that data elsewhere is impractical or impossible.

Datah Dawai Airport

A Data Refuge is a public and collaborative project designed to address concerns about federal climate and environmental data that is in danger of being lost. In particular, the initiative addresses five main concerns:What are the best ways to safeguard data?How do federal agencies play a crucial role in collecting, managing, and distributing data?How do government priorities impact data's accessibility?Which projects and research fields depend on federal data?And, Which data sets are of value to research and local communities, and why?.Data Refuge began as a grassroots organization in opposition to government data on climate change and the environment not being archived systemically. Data Refuge's main goal is to collect and allocate data in multiple safe locations to create a sustainable way of archiving old and new data.Data Refuge was initiated in 2016 to protect federal climate and environmental data that is vulnerable under an administration that denies climate change. The system aims to make public research-quality copies of federal climate and environmental data. Data Refuge is supported by the National Geographic Foundation, private donors, Libraries+ Network, Preserving Electronic Governance Initiative (PEGI), the Union of Concerned Scientists (USC), and the Penn Program in Environmental Humanities (PPEH).

Data pipeline

In computing and data management, data mapping is the process of creating data element mappings between two distinct data models. Data mapping is used as a first step for a wide variety of data integration tasks including:Data transformation or data mediation between a data source and a destinationIdentification of data relationships as part of data lineage analysisDiscovery of hidden sensitive data such as the last four digits of a social security number hidden in another user id as part of a data masking or de-identification projectConsolidation of multiple databases into a single data base and identifying redundant columns of data for consolidation or eliminationFor example, a company that would like to transmit and receive purchases and invoices with other companies might use data mapping to create data maps from a company's data to standardized ANSI ASC X12 messages for items such as purchase orders and invoices.

Data scientists

Data replication

Data matrix

Data model (ArcGIS)

In software engineering, the data mapper pattern is an architectural pattern. It was named by Martin Fowler in his 2003 book Patterns of Enterprise Application Architecture. The interface of an object conforming to this pattern would include functions such as Create, Read, Update, and Delete, that operate on objects that represent domain entity types in a data store.A Data Mapper is a Data Access Layer that performs bidirectional transfer of data between a persistent data store (often a relational database) and an in-memory data representation (the domain layer). The goal of the pattern is to keep the in-memory representation and the persistent data store independent of each other and the data mapper itself. The layer is composed of one or more mappers (or Data Access Objects), performing the data transfer. Mapper implementations vary in scope. Generic mappers will handle many different domain entity types, dedicated mappers will handle one or a few.

Data packets

Data monitoring switch

A data janitor is a person who works to take big data and condense it into useful amounts of information. Also known as a "data wrangler," a data janitor sifts through data for companies in the information technology industry. A multitude of start-ups rely on large amounts of data, so a data janitor works to help these businesses with this basic, but difficult process of interpreting data.While it is a commonly held belief that data janitor work is fully automated, many data scientists are employed primarily as data janitors. The Information technology industry has been increasingly turning towards new sources of data gathered on consumers, so data janitors have become more commonplace in recent years. There has also been a movement to bring awareness to the working conditions and labor issues of data janitors who often do this work via unregulated online "marketplaces" such as Amazon Mechanical Turk.Data janitors work in a process that largely consists of four steps: selection and defining relationships, extraction and organization, loading, and interpretation. Data janitors identify sources of data before selecting which data is relevant and find the relationships between the data that will be useful to the company's projects. Next, they structure the data in an effort to extract the information and put it into a format that can be stored in a secure place for the business. Last, the data janitors work with other employees to create visual aids to present to managers and executives who will eventually benefit from the conclusions that can be made from them. In this way, the work of data janitors is integral to the functioning of businesses that rely on large amounts data to function.

Data over Signaling

A United States data item description (DID) is a completed document defining the data deliverables required of a United States Department of Defense contractor. A DID specifically defines the data content, format, and intended use of the data with a primary objective of achieving standardization objectives by the U.S. Department of Defense. The content and format requirements for DIDs are defined within MIL-STD-963C, Data Item Descriptions (2014).

Data storage density

Data link Layer

Data privacy on HealthCare.gov

Data quality assurance

In telecommunications, data over signalling, or DoS, a technique in which primary data is sent over a signalling channel instead of the subscriber's bearer channel.

Data processing is, generally, "the collection and manipulation of items of data to produce meaningful information." In this sense it can be considered a subset of information processing, "the change (processing) of information in any manner detectable by an observer." The term Data Processing (DP) has also been used to refer to a department within an organization responsible for the operation of data processing applications.

Data recorder

A data infrastructure is a digital infrastructure promoting data sharing and consumption.Similarly to other infrastructures, it is a structure needed for the operation of a society as well as the services and facilities necessary for an economy to function, the data economy in this case.

Data stick

Data processing technician

Data oriented design

Data in motion

Data market

The Data processing inequality is an information theoretic concept which states that the information content of a signal cannot be increased via a local physical operation. This can be expressed concisely as 'post-processing cannot increase information'. As explained by Kinney and Atwal, the DPI means that information is generally lost (never gained) when transmitted through a noisy channel.

Data hoarding

Data link escape character

Data preservation is the act of conserving and maintaining both the safety and integrity of data. Preservation is done through formal activities that are governed by policies, regulations and strategies directed towards protecting and prolonging the existence and authenticity of data and its metadata. Data can be described as the elements or units in which knowledge and information is created, and metadata are the summarizing subsets of the elements of data; or the data about the data.

Data serialization

Data sanitization

Data link connection identifier

Data mart

Data link university college

Data processing

Data parallel computation

Data site

Data loss prevention software detects potential data breaches/data ex-filtration transmissions and prevents them by monitoring, detecting and blocking sensitive data while in-use (endpoint actions), in-motion (network traffic), and at-rest (data storage).The terms "data loss" and "data leak" are related and are often used interchangeably. Data loss incidents turn into data leak incidents in cases where media containing sensitive information is lost and subsequently acquired by an unauthorized party. However, a data leak is possible without losing the data on the originating side. Other terms associated with data leakage prevention are information leak detection and prevention (ILDP), information leak prevention (ILP), content monitoring and filtering (CMF), information protection and control (IPC) and extrusion prevention system (EPS), as opposed to intrusion prevention system.

Data reporting is the process of collecting and submitting data which gives rise to accurate analyses of the facts on the ground; inaccurate data reporting can lead to vastly uninformed decision-making based on erroneous evidence. When data is not reported, the problem is known as underreporting; the opposite problem leads to false positives.Data reporting can be an incredibly difficult endeavor.

Data mule

Data retention in the United Kingdom

Datok Keramat

Data source name

Data recovery software

Data quality

Data philanthropy describes a form of collaboration in which private sector companies share data for public benefit. There are multiple uses of data philanthropy being explored from humanitarian, corporate, human rights, and academic use. Since introducing the term in 2011, the United Nations Global Pulse has advocated for a global "data philanthropy movement".

The data link connector (DLC) is the multi-pin diagnostic connection port for automobiles, trucks, and motorcycles used to interface a scan tool with the control modules of a given vehicle and access on-board diagnostics and live data streams.Prior to 1996, many OBD-I data link connector's were in the engine compartment, usually near the fuse block. Also, prior to 1996, there was no standardization for these connectors, and each manufacturer had its own shape with a unique pin arrangement.

Data structure augmentation

The Daura Emirate is a religious and traditional state in Northern Nigeria, the Emir of Daura still rules as a ceremonial hereditary monarch, and maintains a palace. Muhammad Bashar became the emir in 1966, reigning for 41 years until his death in 2007, On 28 February 2007, Umar Faruk Umar became Emir of Daura succeeding Muhammad Bashar.

Data processing system

Data leakage prevention

Data residency

A data mule is a vehicle that physically carries a computer with storage between remote locations to effectively create a data communication link. A data mule is a special case of a sneakernet, where the data is automatically loaded and unloaded when the data mule arrives at its terminal locations.

Emir

Data storage devices

Data politics encompasses the political aspects of data including topics ranging from data activism, open data and open government. The ways in which data is collected, accessed, and what we do with that data has changed in contemporary society due to a number of factors surrounding issues of politics. An issue that arises from political data is often how disconnected people are from their own data, rarely gaining access to the data they produce. Large platforms like Google have a "better to ask forgiveness than permission" stance on data collection to which the greater population is largely ignorant, leading to movements within data activism.

Data storage media

A data storage tag (DST), also sometimes known as an archival tag, is a data logger that uses sensors to record data at predetermined intervals. Data storage tags usually have a large memory size and a long lifetime. Most archival tags are supported by batteries that allow the tag to record positions for several years. Alternatively some tags are solar powered and allow the scientist to set their own interval; this then allows data to be recorded for significantly longer than battery-only powered tags.

Data privacy

Data libraries

Data integrity

Data sink

Data historian

Data room

Data store network

Data politics

Data object

Data in transit

Data position measurement (DPM) is a copy protection mechanism that operates by measuring the physical location of data on an optical disc. Stamped CDs are perfect clones and always have the data at the expected location, while a burned copy would exhibit physical differences. DPM detects these differences to identify user-made copies. DPM was first used publicly in 1996 by Link Data Security's CD-Cops. It was used in volume on Lademans Leksikon published by Egmont in November 1996.

Data journal

The United States Navy occupational rating of data processing technician (abbreviated as DP) was a designation given by the Bureau of Naval Personnel (BUPERS) to enlisted members who satisfactorily completed initial data processing technician "A" school training.This navy rating originated as the machine accountant (MA) rating in large number of navy rating changes implemented in 1948. The rating name and designation was changed to data processing technician (DP) in 1967. The Navy disestablished the DP rating on 1 October 1997, combining DPs who had not previously changed ratings into the radioman(RM) rating. In 1999 the (RM) rating was re-designated information systems technician (IT). The radioman rating specialty mark was retained for use by the IT rating members. In 2005 the cryptologic technician communications (CTO) rating merged into the IT rating.Data processing technicians operated data processing equipment including keypunch machines, sorters, collators, reproducers, tabulating printers, and computers; performed the administrative tasks for operating computer facility, including the handling of all classified material passing into or out of a computer system; designed, developed, tested, and maintained computer software.

Data modeling in software engineering is the process of creating a data model for an information system by applying certain formal techniques.

Data rooms are spaces used for housing data, usually of a secure or privileged nature. They can be physical data rooms, virtual data rooms, or data centers.

Data remanence is the residual representation of digital data that remains even after attempts have been made to remove or erase the data. This residue may result from data being left intact by a nominal file deletion operation, by reformatting of storage media that does not remove data previously written to the media, or through physical properties of the storage media that allow previously written data to be recovered. Data remanence may make inadvertent disclosure of sensitive information possible should the storage media be released into an uncontrolled environment (e.g., thrown in the trash or lost).Various techniques have been developed to counter data remanence. These techniques are classified as clearing, purging/sanitizing, or destruction. Specific methods include overwriting, degaussing, encryption, and media destruction.Effective application of countermeasures can be complicated by several factors, including media that are inaccessible, media that cannot effectively be erased, advanced storage systems that maintain histories of data throughout the data's life cycle, and persistence of data in memory that is typically considered volatile.Several standards exist for the secure removal of data and the elimination of data remanence.

Data management plan

Data points

Data page atomic radii of the elements

Al Amir Al Daura

Data storage medium

Data set ready

Data retention (telecommunications)

Data mirroring

A data model in geographic information systems is a mathematical construct for representing geographic objects or surfaces as data. For example, the vector data model represents geography as collections of points, lines, and polygons; the raster data model represent geography as cell matrices that store numeric values; and the TIN data model represents geography as sets of contiguous, nonoverlapping triangles.

Data scrub

Data protection directive

Date notation

Data protection application programming interface

Data rate unit

Data models

Data strobe encoding (or D/S encoding) is an encoding scheme for transmitting data in digital circuits. It uses two signal lines (e.g. wires in a cable or traces on a printed circuit board), Data and Strobe. These have the property that either Data or Strobe changes its logical value in one clock cycle, but never both. More precisely data is transmitted as-is and strobe changes its state if and only if data stays constant between two data bits.

Data scraping is a technique in which a computer program extracts data from human-readable output coming from another program.

Data system is a term used to refer to an organized collection of symbols and processes that may be used to operate on such symbols. Any organised collection of symbols and symbol-manipulating operations can be considered a data system. Hence, human-speech analysed at the level of phonemes can be considered a data system as can the Incan artefact of the khipu and an image stored as pixels. A data system is defined in terms of some data model and bears a resemblance to the idea of a physical symbol system.Symbols within some data systems may be persistent or not. Hence, the sounds of human speech are non-persistent symbols because they decay rapidly in air. In contrast, pixels stored on some peripheral storage device are persistent symbols.

Data manipulation

Data parallelism is a form of parallelization across multiple processors in parallel computing environments. It focuses on distributing the data across different nodes, which operate on the data in parallel. It can be applied on regular data structures like arrays and matrices by working on each element in parallel. It contrasts to task parallelism as another form of parallelism.A data parallel job on an array of 'n' elements can be divided equally among all the processors. Let us assume we want to sum all the elements of the given array and the time for a single addition operation is Ta time units. In the case of sequential execution, the time taken by the process will be n*Ta time units as it sums up all the elements of an array. On the other hand, if we execute this job as a data parallel job on 4 processors the time taken would reduce to (n/4)*Ta + merging overhead time units. Parallel execution results in a speedup of 4 over sequential execution. One important thing to note is that the locality of data references plays an important part in evaluating the performance of a data parallel programming model. Locality of data depends on the memory accesses performed by the program as well as the size of the cache.

Data page

Data literacy

Data munging

Data set

Data matrix (disambiguation)

A data store is a repository for persistently storing and managing collections of data which include not just repositories like databases, but also simpler store types such as simple files, emails etc.A database is a series of bytes that is managed by a database management system (DBMS). A file is a series of bytes that is managed by a file system.

Data mode (modem)

Data logging

A data processing system is a combination of machines, people, and processes that for a set of inputs produces a defined set of outputs. The inputs and outputs are interpreted as data, facts, information, ... depending on the interpreter's relation to the system. A common synonymous term is "information system".A data processing system may involve some combination of:Conversion converting data to another form or Language.Validation – Ensuring that supplied data is "clean, correct and useful."Sorting – "arranging items in some sequence and/or in different sets."Summarization – reducing detail data to its main points.Aggregation – combining multiple pieces of data.Analysis – the "collection, organization, analysis, interpretation and presentation of data.".Reporting – list detail or summary data or computed information.

Data profiling is the process of examining the data available from an existing information source (e.g. a database or a file) and collecting statistics or informative summaries about that data. The purpose of these statistics may be to:Find out whether existing data can be easily used for other purposesImprove the ability to search data by tagging it with keywords, descriptions, or assigning it to a categoryAssess data quality, including whether the data conforms to particular standards or patternsAssess the risk involved in integrating data in new applications, including the challenges of joinsDiscover metadata of the source database, including value patterns and distributions, key candidates, foreign-key candidates, and functional dependenciesAssess whether known metadata accurately describes the actual values in the source databaseUnderstanding data challenges early in any data intensive project, so that late project surprises are avoided. Finding data problems late in the project can lead to delays and cost overruns.Have an enterprise view of all data, for uses such as master data management, where key data is needed, or data governance for improving data quality.

Data retrieval means obtaining data from a database management system such as ODBMS. In this case, it is considered that data is represented in a structured way, and there is no ambiguity in data.In order to retrieve the desired data the user present a set of criteria by a query. Then the Database Management System (DBMS), software for managing databases, selects the demanded data from the database. The retrieved data may be stored in a file, printed, or viewed on the screen.A query language, such as Structured Query Language (SQL), is used to prepare the queries. SQL is an American National Standards Institute (ANSI) standardized query language developed specifically to write database queries. Each DBMS may have its own language, but most relational .

Data reporting

Data mile

Data scientist

Data recovery lab

Data projector

Data in use is an information technology term referring to active data which is stored in a non-persistent digital state typically in computer random-access memory (RAM), CPU caches, or CPU registers.Data in use is used as a complement to the terms data in transit and data at rest which together define the three states of digital data.

Data politics encompasses the political aspects of data including topics ranging from data activism, open data and open government. The ways in which data is collected, accessed, and what we do with that data has changed in contemporary society due to a number of factors surrounding issues of politics. An issue that arises from political data is often how disconnected people are from their own data, rarely gaining access to the data they produce.

Datuk Nicol Ann David

Data structure diagram

Data in use

Daugavgrīvas bāka

In radar-related subjects and in JTIDS, a data mile is a unit of distance equal to 6000 feet (1.8288 kilometres or 0.987 nautical miles). An international mile is exactly 0.88 of a data mile.

Data restoration

A data model (or datamodel) is an abstract model that organizes elements of data and standardizes how they relate to one another and to properties of the real world entities. For instance, a data model may specify that the data element representing a car be composed of a number of other elements which, in turn, represent the color and size of the car and define its owner.The term data model is used in two distinct but closely related senses. Sometimes it refers to an abstract formalization of the objects and relationships found in a particular application domain, for example the customers, products, and orders found in a manufacturing organization. At other times it refers to a set of concepts used in defining such formalizations: for example concepts such as entities, attributes, relations, or tables. So the "data model" of a banking application may be defined using the entity-relationship "data model". This article uses the term in both senses.A data model explicitly determines the structure of data. Data models are specified in a data modeling notation, which is often graphical in form.A data model can sometimes be referred to as a data structure, especially in the context of programming languages. Data models are often complemented by function models, especially in the context of enterprise models.

Data peeking

Data preservation is the act of conserving and maintaining both the safety and integrity of data. Preservation is done through formal activities that are governed by policies, regulations and strategies directed towards protecting and prolonging the existence and authenticity of data and its metadata. Data can be described as the elements or units in which knowledge and information is created, and metadata are the summarizing subsets of the elements of data; or the data about the data. The main goal of data preservation is to protect data from being lost or destroyed and to contribute to the reuse and progression of the data.

Data monetization, a form of monetization, may refer to the act of generating measurable economic benefits from available data sources (analytics). Less commonly, it may also refer to the act of monetizing data services. In the case of analytics, typically, these benefits accrue as revenue or expense savings, but may also include market share or corporate market value gains. Data monetization leverages data generated through business operations, available exogenous data or content, as well as data associated with individual actors such as that collected via electronic devices and sensors participating in the internet of things. For example, the ubiquity of the internet of things is generating location data and other data from sensors and mobile devices at an ever-increasing rate. When this data is collated against traditional databases, the value and utility of both sources of data increases, leading to tremendous potential to mine data for social good, research and discovery, and achievement of business objectives. Closely associated with data monetization are the emerging data as a service models for transactions involving data by the data item.There are three ethical and regulatory vectors involved in data monetization due to the sometimes conflicting interests of actors involved in the data supply chain. The individual data creator who generates files and records through his own efforts or owns a device such as a sensor or a mobile phone that generates data has a claim to ownership of data. The business entity that generates data in the course of its operations, such as its transactions with financial institutions or risk factors discovered through feedback from customers also has a claim on data captured through their systems and platforms. However, the person that contributed the data may also have a legitimate claim on the data. Internet platforms and service providers, such as Google or Facebook that require a user to forgo some ownership interest in their data in exchange for use of the platform also have a legitimate claim on the data. Thus the practice of data monetization, although common since 2000, is now getting increasing attention from regulators. The European Union and the United States Congress have begun to address these issues. For instance, in the financial services industry, regulations involving data are included in the Gramm–Leach–Bliley Act and Dodd-Frank. Some individual creators of data are shifting to using personal data vaults and implementing vendor relationship management concepts as a reflection of an increasing resistance to their data being federated or aggregated and resold without compensation. Groups such as the Personal Data Ecosystem Consortium, Patient privacy rights, and others are also challenging corporate cooptation of data without compensation.Financial services companies are a relatively good example of an industry focused on generating revenue by leveraging data. Credit card issuers and retail banks use customer transaction data to improve targeting of cross-sell offers. Partners are increasingly promoting merchant based reward programs which leverage a bank’s data and provide discounts to customers at the same time.

Data model (GIS)

A data janitor is a person who works to take big data and condense it into useful amounts of information. Also known as a "data wrangler," a data janitor sifts through data for companies in the information technology industry.

Data map

Data reduction is the transformation of numerical or alphabetical digital information derived empirically or experimentally into a corrected, ordered, and simplified form. The basic concept is the reduction of multitudinous amounts of data down to the meaningful parts.When information is derived from instrument readings there may also be a transformation from analog to digital form.

Data safety and monitoring board

Data in transit is defined into two categories, information that flows over the public or untrusted network such as the internet and data which flows in the confines of a private network such as a corporate or enterprise Local Area Network (LAN). Data in transit is used as a complement to the terms data in use, and data at rest which together define the three states of digital data.

Data sets

Data salvaging

A data logger (also datalogger or data recorder) is an electronic device that records data over time or in relation to location either with a built in instrument or sensor or via external instruments and sensors. Increasingly, but not entirely, they are based on a digital processor (or computer). They generally are small, battery powered, portable, and equipped with a microprocessor, internal memory for data storage, and sensors.

Data marshalling

Data masking or data obfuscation is the process of hiding original data with random characters or data.The main reason for applying masking to a data field is to protect data that is classified as personal identifiable data, personal sensitive data or commercially sensitive data, however the data must remain usable for the purposes of undertaking valid test cycles. It must also look real and appear consistent. It is more common to have masking applied to data that is represented outside of a corporate production system. In other words, where data is needed for the purpose of application development, building program extensions and conducting various test cycles. It is common practice in enterprise computing to take data from the production systems to fill the data component, required for these non-production environments. However the practice is not always restricted to non-production environments. In some organizations, data that appears on terminal screens to call centre operators may have masking dynamically applied based on user security permissions. (e.g.: Preventing call centre operators from viewing Credit Card Numbers in billing systems)The primary concern from a corporate governance perspective is that personnel conducting work in these non-production environments are not always security cleared to operate with the information contained in the production data. This practice represents a security hole where data can be copied by unauthorised personnel and security measures associated with standard production level controls can be easily bypassed. This represents an access point for a data security breach.The overall practice of Data Masking at an organisational level should be tightly coupled with the Test Management Practice and underlying Methodology and should incorporate processes for the distribution of masked test data subsets.

Data matrix (computer science)

Data snooping bias

A data store is a repository for persistently storing and managing collections of data which include not just repositories like databases, but also simpler store types such as simple files, emails etc.A database is a series of bytes that is managed by a database management system (DBMS). A file is a series of bytes that is managed by a file system. Thus, any database or file is a series of bytes that, once stored, is called a data store.MATLAB and Cloud Storage systems like VMware, Firefox OS use datastore as a term for abstracting collections of data inside their respective applications.

In computing, a data source name (DSN, sometimes known as a database source name, though "data sources" can comprise other repositories apart from databases) is a string that has an associated data structure used to describe a connection to a data source. Most commonly used in connection with ODBC, DSNs also exist for JDBC and for other data access mechanisms. The term often overlaps with "connection string".

Daukša

Data science competition platforms

Data rot

Data scraper

A data pool is a Gateway to access the GDSN Networks, where all necessary information to perform business transactions between trading partners is exchanged in a standardized way.A data pool is the common point in the communication between the trading partners, provide synchronization capability of their data.

A data storage tag (DST), also sometimes known as an archival tag, is a data logger that uses sensors to record data at predetermined intervals. Data storage tags usually have a large memory size and a long lifetime. Most archival tags are supported by batteries that allow the tag to record positions for several years.

Data sequence

Daura Emirate

A data pool is a Gateway to access the GDSN Networks, where all necessary information to perform business transactions between trading partners is exchanged in a standardized way.A data pool is the common point in the communication between the trading partners, provide synchronization capability of their data. This information is accessible synchronized trading partners in a common, simple, fast and accurate manner.The information exchanged in a typical data pools master data Global Trade Item Number and GLN, as well as other core attributes, necessary for the smooth transaction of goods. For example, product description, dimensions, packaging levels, product category, company address, minimum order volume, etc.Suppliers upload data to a data pool, which is pushed to synchronized retailers. This communication takes place under common rules and standards, with the consideration the business agreements between trading partners.

A data management plan or DMP is a formal document that outlines how data are to be handled both during a research project, and after the project is completed. The goal of a data management plan is to consider the many aspects of data management, metadata generation, data preservation, and analysis before the project begins; this ensures that data are well-managed in the present, and prepared for preservation in the future.

Data parallelism

Data protection policy

Data profiling is the process of examining the data available from an existing information source (e.g. a database or a file) and collecting statistics or informative summaries about that data.

Data havens

Data Stream Mining is the process of extracting knowledge structures from continuous, rapid data records. A data stream is an ordered sequence of instances that in many applications of data stream mining can be read only once or a small number of times using limited computing and storage capabilities.In many data stream mining applications, the goal is to predict the class or value of new instances in the data stream given some knowledge about the class membership or values of previous instances in the data stream. Machine learning techniques can be used to learn this prediction task from labeled examples in an automated fashion. Often, concepts from the field of incremental learning are applied to cope with structural changes, on-line learning and real-time demands. In many applications, especially operating within non-stationary environments, the distribution underlying the instances or the rules underlying their labeling may change over time, i.e. the goal of the prediction, the class to be predicted or the target value to be predicted, may change over time. This problem is referred to as concept drift.Examples of data streams include computer network traffic, phone conversations, ATM transactions, web searches, and sensor data. Data stream mining can be considered a subfield of data mining, machine learning, and knowledge discovery.

Data miner

In the context of IBM mainframe computers, a data set (IBM preferred) or dataset is a computer file having a record organization. Use of this term began with OS/360 and is still used by its successors, including the current z/OS.

Data loading

Data proliferation refers to the prodigious amount of data, structured and unstructured, that businesses and governments continue to generate at an unprecedented rate and the usability problems that result from attempting to store and manage that data. While originally pertaining to problems associated with paper documentation, data proliferation has become a major problem in primary and secondary data storage on computers.While digital storage has become cheaper, the associated costs, from raw power to maintenance and from metadata to search engines, have not kept up with the proliferation of data. Although the power required to maintain a unit of data has fallen, the cost of facilities which house the digital storage has tended to rise.Data proliferation has been documented as a problem for the U.S. military since August 1971, in particular regarding the excessive documentation submitted during the acquisition of major weapon systems. Efforts to mitigate data proliferation and the problems associated with it are ongoing.

Data philanthropy

Data provenance

Data protection authority

Data mining system

Data scraping

Data profiling

Data recovery hardware was developed because data recovery software lacks the ability to deal with all lost or corrupted data files. Often the failures, such as media files with bad sectors, firmware failures, PCB (Printed circuit board) failures, hard drive head failures, etc., cannot be fixed.

Data mapping

Data modeling

Data quality firewall

In computer data storage, data striping is the technique of segmenting logically sequential data, such as a file, so that consecutive segments are stored on different physical storage devices.Striping is useful when a processing device requests data more quickly than a single storage device can provide it. By spreading segments across multiple devices which can be accessed concurrently, total data throughput is increased.

Data table

Data signal

Data monetization

Data persistence

Data protection api

Data reliability

Data of the sword

A data item describes an atomic state of a particular object concerning a specific property at a certain time point. A collection of data items for the same object at the same time forms an object instance (or table row). Any type of complex information can be broken down to elementary data items (atomic state).

Data query language

Data representation

Data organization for low power

Data synchronisation

A data service unit, sometimes called a digital service unit, is a piece of telecommunications circuit terminating equipment that transforms digital data between telephone company lines and local equipment. The device converts bipolar digital signals coming ultimately from a digital circuit and directly from a Channel service unit (CSU), into a format (e.g. RS- 530) compatible with the piece of data terminal equipment (DTE) (e.g. a router) to which the data is sent. The DSU also performs a similar process in reverse for data heading from the DTE toward the circuit. The telecommunications service a DSU supports can be a point-to-point or multipoint operation in a digital data network.A DSU is a two or more port device; one port is called the WAN (Wide Area Network) port and the other is called a DTE port. The purpose of the DSU is to transfer serial data synchronously between the WAN port and the DTE ports. If more than one DTE port is used, the DSU assigns the DTE data according to time slots (channels) on the WAN side.On the WAN side, the DSU, via a CSU, interfaces with a digital carrier such as DS1 or DS3 or a low speed Digital Data Service. On the DTE side, the DSU provides control lines, timing lines and appropriate physical and electrical interface. To maintain the synchronous relationship between the ports, the DSU manages timing by slaving ports to the bit rate of another or to its internal clock. Typically, the DTE port provides timing to the data terminal equipment while the WAN port dictates the rate.DSUs usually include some maintenance capabilities. At minimum, they can loop data back at either the WAN or DTE ports, or at both. When only one port is looped back, the data received at that port is simultaneously sent back toward the port and passed in normal fashion to the other port. Most DSUs also allow various data patterns to be generated and monitored to measure error rate of the communication link. A DSU may be a separate piece of equipment, or may be combined in a CSU/DSU.

Data model diagram

Daukša's Catechism

Data linkage for distributed data systems

Data maintenance

Data quality refers to the condition of a set of values of qualitative or quantitative variables. There are many definitions of data quality but data is generally considered high quality if it is "fit for [its] intended uses in operations, decision making and planning". Alternatively, data is deemed of high quality if it correctly represents the real-world construct to which it refers.

Data processing unit

Data item

Data preservation

Data intensive science

Data origin authentication

Data library

Data structure diagram (DSD) is a diagram of the conceptual data model which documents the entities and their relationships, as well as the constraints that connect to them.The basic graphic notation elements of DSDs are boxes which represent entities.The arrow symbol represents relationships. Data structure diagrams are most useful for documenting complex data entities.

Data rescue is a name for both a movement among scientists, researchers and others and the processes they use to preserve primarily government-hosted data sets, often scientific in nature, to ward off their removal from publicly available websites. While the concept of preserving federal data existed before, it gained new impetus with the election in 2016 of U.S. President Donald Trump.The concept of harvesting and preserving federal web pages began as early as 2008, at the conclusion of President George W. Bush's second term, under the name "End of Term Presidential Harvest."Soon after Trump's election, scientists, librarians and others in the U.S. and Canada—fearing that the administration of Trump (who had expressed doubts about the validity of the scientific consensus on the existence of climate change ) would act to remove scientific data from government websites—began working to preserve that data.In quick order, the concept of Data Rescue became a grassroots movement, with organized "hackathon" events at cities across the U.S. and elsewhere, often hosted at universities and other institutions of higher education. Among the projects involved are the DataRefuge Project and the Environmental Data and Governance Initiative.

Data hazard

A data stream management system (DSMS) is a computer software system to manage continuous data streams. It is similar to a database management system (DBMS), which is, however, designed for static data in conventional databases. A DSMS also offers a flexible query processing so that the information need can be expressed using queries. However, in contrast to a DBMS, a DSMS executes a continuous query that is not only performed once, but is permanently installed. Therefore, the query is continuously executed until it is explicitly uninstalled. Since most DSMS are data-driven, a continuous query produces new results as long as new data arrive at the system. This basic concept is similar to Complex event processing so that both technologies are partially coalescing.

Data onboarding is the process of transferring offline data to an online environment for marketing needs. Data onboarding is mainly used to connect offline customer records with online users by matching identifying information gathered from offline datasets to retrieve the same customers in an online audience.

Data monitoring committees

Data sample

Data laundering is the conversion of stolen data so that it may be sold or used by ostensibly legitimate databases. ZDNet has described the process as "obscuring, removing, or fabricating the provenance of illegally obtained data such that it may be used for lawful purposes".

Data stores

Data link solutions

Data quality refers to the condition of a set of values of qualitative or quantitative variables. There are many definitions of data quality but data is generally considered high quality if it is "fit for [its] intended uses in operations, decision making and planning". Alternatively, data is deemed of high quality if it correctly represents the real-world construct to which it refers. Furthermore, apart from these definitions, as data volume increases, the question of internal data consistency becomes significant, regardless of fitness for use for any particular external purpose. People's views on data quality can often be in disagreement, even when discussing the same set of data used for the same purpose. Data cleansing may be required in order to ensure data quality.

Data logistics

Data harvesting

Data page melting points of the elements

Data over signaling

Data purging

Datuk Patinggi Haji Abdul Rahman Bridge

Data record

Data journalism is a journalism specialty reflecting the increased role that numerical data is used in the production and distribution of information in the digital era. It reflects the increased interaction between content producers (journalist) and several other fields such as design, computer science and statistics. From the point of view of journalists, it represents "an overlapping set of competencies drawn from disparate fields".Data journalism has been widely used to unite several concepts and link them to journalism. Some see these as levels or stages leading from the simpler to the more complex uses of new technologies in the journalistic process.Designers are not always part of the process. According to author and data journalism trainer Henk van Ess, "Datajournalism can be based on any data that has to be processed first with tools before a relevant story is possible. It doesn't include visualisation per se".

Data signalling rate

In the context of IBM mainframe computers, a data set (IBM preferred) or dataset is a computer file having a record organization. Use of this term began with OS/360 and is still used by its successors, including the current z/OS. Documentation for these systems historically preferred this term rather than file.A data set is typically stored on a direct access storage device (DASD) or magnetic tape, however unit record devices, such as punch card readers, card punch, and line printers can provide input/output (I/O) for a data set (file).Data sets are not unstructured streams of bytes, but rather are organized in various logical record and block structures determined by the DSORG (data set organization), RECFM (record format), and other parameters. These parameters are specified at the time of the data set allocation (creation), for example with Job Control Language DD statements. Inside a job they are stored in the Data Control Block (DCB), which is a data structure used to access data sets, for example using access methods.

Data retrieval

Data hiding

Data point

A data set (or dataset) is a collection of data. Most commonly a data set corresponds to the contents of a single database table, or a single statistical data matrix, where every column of the table represents a particular variable, and each row corresponds to a given member of the data set in question. The data set lists values for each of the variables, such as height and weight of an object, for each member of the data set. Each value is known as a datum.

Data in Use

Data revision

Data sharing (Science)

Data pool

Data intensive

Data hub

Data silos

Data networking

Data parallelism is a form of parallelization across multiple processors in parallel computing environments. It focuses on distributing the data across different nodes, which operate on the data in parallel. It can be applied on regular data structures like arrays and matrices by working on each element in parallel. It contrasts to task parallelism as another form of parallelism.A data parallel job on an array of 'n' elements can be divided equally among all the processors.

Data paper

Data masking

Data leak

A data steward is a role within an organization responsible for utilizing an organization's data governance processes to ensure fitness of data elements - both the content and metadata. Data stewards have a specialist role that incorporates processes, policies, guidelines and responsibilities for administering organizations' entire data in compliance with policy and/or regulatory obligations. A data steward may share some responsibilities with a data custodian.The overall objective of a data steward is data quality, in regard to the key/critical data elements existing within a specific enterprise operating structure, of the elements in their respective domains. This includes capturing/documenting (meta)information for their elements (such as: definitions, related rules/governance, physical manifestation, related data models, etc. With most of these properties being specific to an attribute/concept relationship), identifying owners/custodians/various responsibilities, relations insight pertaining to attribute quality, aiding with project requirement data facilitation and documentation of capture rules.Data stewards begin the stewarding process with the identification of the elements which they will steward, with the ultimate result being standards, controls and data entry. The steward works closely with business glossary standards analysts (for standards), with data architect/modelers (for standards), with DQ analysts (for controls) and with operations team members (good-quality data going in per business rules) while entering data.Data stewardship roles are common when organizations attempt to exchange data precisely and consistently between computer systems and to reuse data-related resources. Master data management often makes references to the need for data stewardship for its implementation to succeed. Data stewardship must have precise purpose, fit for purpose or fitness.

Data retention directive

Data tape cartridge

Data repository

Datuk Ong Teik Kwong

Data protection (privacy) laws in Russia

Data modeling tools

Data pack

Data remanence

Data sharing is the practice of making data used for scholarly research available to other investigators. Replication has a long history in science. The motto of The Royal Society is 'Nullius in verba', translated "Take no man's word for it." Many funding agencies, institutions, and publication venues have policies regarding data sharing because transparency and openness are considered by many to be part of the scientific method.A number of funding agencies and science journals require authors of peer-reviewed papers to share any supplemental information (raw data, statistical methods or source code) necessary to understand, develop or reproduce published research. A great deal of scientific research is not subject to data sharing requirements, and many of these policies have liberal exceptions. In the absence of any binding requirement, data sharing is at the discretion of the scientists themselves. In addition, in certain situations governments and institutions prohibit or severely limit data sharing to protect proprietary interests, national security, and subject/patient/victim confidentiality. Data sharing may also be restricted to protect institutions and scientists from use of data for political purposes.Data and methods may be requested from an author years after publication. In order to encourage data sharing and prevent the loss or corruption of data, a number of funding agencies and journals established policies on data archiving. Access to publicly archived data is a recent development in the history of science made possible by technological advances in communications and information technology. To take full advantage of modern rapid communication may require consensual agreement on the criteria underlying mutual recognition of respective contributions. Models recognized for improving the timely sharing of data for more effective response to emergent infectious disease threats include the data sharing mechanism introduced by the GISAID Initiative.Despite policies on data sharing and archiving, data withholding still happens. Authors may fail to archive data or they only archive a portion of the data. Failure to archive data alone is not data withholding. When a researcher requests additional information, an author sometimes refuses to provide it. When authors withhold data like this, they run the risk of losing the trust of the science community.Data sharing may also indicate the sharing of personal information on a social media platform.

Data proliferation refers to the prodigious amount of data, structured and unstructured, that businesses and governments continue to generate at an unprecedented rate and the usability problems that result from attempting to store and manage that data.

A data steward is a role within an organization responsible for utilizing an organization's data governance processes to ensure fitness of data elements - both the content and metadata. Data stewards have a specialist role that incorporates processes, policies, guidelines and responsibilities for administering organizations' entire data in compliance with policy and/or regulatory obligations.

A data library, data archive, or data repository is a collection of numeric and/or geospatial data sets for secondary use in research. A data library is normally part of a larger institution (academic, corporate, scientific, medical, governmental, etc.) established for research data archiving and to serve the data users of that organisation. The data library tends to house local data collections and provides access to them through various means (CD-/DVD-ROMs or central server for download).

Data mapper pattern

Datoga language

Data protection (privacy) laws in Russia are a rapidly developing branch in Russian legislation that have mostly been enacted in the 2005 and 2006. The Russian Federal Law on Personal Data (No. 152-FZ), implemented on July 27, 2006, constitutes the backbone of Russian privacy laws and requires data operators to take "all the necessary organizational and technical measures required for protecting personal data against unlawful or accidental access".

Data retention hardware

Data leakage protection

Data sheet

Data sovereignty

Data retention

Daura (surname)

A data mart is the access layer of the data warehouse environment that is used to get data out to the users. The data mart is a subset of the data warehouse and is usually oriented to a specific business line or team. Whereas data warehouses have an enterprise-wide depth, the information in data marts pertains to a single department. In some deployments, each department or business unit is considered the owner of its data mart including all the hardware, software and data.

Data retention defines the policies of persistent data and records management for meeting legal and business data archival requirements; although sometimes interchangeable, not to be confused with the Data Protection Act 1998.The different data retention policies weigh legal and privacy concerns against economics and need-to-know concerns to determine the retention time, archival rules, data formats, and the permissible means of storage, access, and encryption.In the field of telecommunications, data retention generally refers to the storage of call detail records (CDRs) of telephony and internet traffic and transaction data (IPDRs) by governments and commercial organisations. In the case of government data retention, the data that is stored is usually of telephone calls made and received, emails sent and received, and websites visited. Location data is also collected.The primary objective in government data retention is traffic analysis and mass surveillance. By analysing the retained data, governments can identify the locations of individuals, an individual's associates and the members of a group such as political opponents. These activities may or may not be lawful, depending on the constitutions and laws of each country. In many jurisdictions access to these databases may be made by a government with little or no judicial oversight.In the case of commercial data retention, the data retained will usually be on transactions and web sites visited.Data retention also covers data collected by other means (e.g., by Automatic number-plate recognition systems) and held by government and commercial organisations.

A data model (or datamodel) is an abstract model that organizes elements of data and standardizes how they relate to one another and to properties of the real world entities. For instance, a data model may specify that the data element representing a car be composed of a number of other elements which, in turn, represent the color and size of the car and define its owner.The term data model is used in two distinct but closely related senses.

Data level parallelism

Data network identification code

Data intensive apps

Data logger

Data over signalling

Data recording

Data intensive task

A data processing system is a combination of machines, people, and processes that for a set of inputs produces a defined set of outputs. The inputs and outputs are interpreted as data, facts, information, ... depending on the interpreter's relation to the system.

Data masking or data obfuscation is the process of hiding original data with random characters or data.The main reason for applying masking to a data field is to protect data that is classified as personal identifiable data, personal sensitive data or commercially sensitive data, however the data must remain usable for the purposes of undertaking valid test cycles. It must also look real and appear consistent.

Data publication

Data processing (disambiguation)

Data striping

Data link escape

Data service unit

Data protection

Data length code

A data service unit, sometimes called a digital service unit, is a piece of telecommunications circuit terminating equipment that transforms digital data between telephone company lines and local equipment. The device converts bipolar digital signals coming ultimately from a digital circuit and directly from a Channel service unit (CSU), into a format (e.g. RS- 530) compatible with the piece of data terminal equipment (DTE) (e.g. a router) to which the data is sent.

Data shadows refer to the information that a person leaves behind unintentionally while taking part in daily activities such as checking their e-mails, scrolling through social media or even by using their debit or credit card. The generated information has the potential to create a vastly detailed record of an individual's daily trails, which includes the individual's thoughts and interests, whom they communicate with, information about the organizations with which they work or interact with and so forth. This information can be dispersed to a dozen organizations and servers depending on their use. Along with Individuals, the activities of institutions and organizations are also tracked. Data Shadows are closely linked with data footprints, which are defined as the data that has been left behind by the individual themselves through various activities such as online activities, communication information, and transactions. In a chapter for the book Geography and Technology, researcher Matthew Zook and his co-authors note that data shadows have come as a result of people becoming "digital individuals" and that these shadows are continually evolving and changing. They are used to model and predict political opinions, and make inferences about a person's political values or susceptibility to advertising.

Data island

Data packet

Data log

Data storing

Daugavgrīva Castle

Data matrix (barcode)

Data import

Data system is a term used to refer to an organized collection of symbols and processes that may be used to operate on such symbols. Any organised collection of symbols and symbol-manipulating operations can be considered a data system. Hence, human-speech analysed at the level of phonemes can be considered a data system as can the Incan artefact of the khipu and an image stored as pixels.

Data storage tag

Data pointer

Data remanence is the residual representation of digital data that remains even after attempts have been made to remove or erase the data. This residue may result from data being left intact by a nominal file deletion operation, by reformatting of storage media that does not remove data previously written to the media, or through physical properties of the storage media that allow previously written data to be recovered.

Data origin authenticity

Data retention defines the policies of persistent data and records management for meeting legal and business data archival requirements; although sometimes interchangeable, not to be confused with the Data Protection Act 1998.The different data retention policies weigh legal and privacy concerns against economics and need-to-know concerns to determine the retention time, archival rules, data formats, and the permissible means of storage, access, and encryption.In the field of telecommunications, data retention generally refers to the storage of call detail records (CDRs) of telephony and internet traffic and transaction data (IPDRs) by governments and commercial organisations.

Data retrieval means obtaining data from a database management system such as ODBMS. In this case, it is considered that data is represented in a structured way, and there is no ambiguity in data.In order to retrieve the desired data the user present a set of criteria by a query. Then the Database Management System (DBMS), software for managing databases, selects the demanded data from the database.

Data merge (disambiguation)

Data inconsistency

Data Sovereignty is the idea that data are subject to the laws and governance structures within the nation it is collected. The concept of data sovereignty is closely linked with data security, cloud computing and technological sovereignty. Unlike technological sovereignty, which is vaguely defined and can be used as an umbrella term in policymaking, data sovereignty is specifically concerned with questions surrounding the data itself.

A data link connection identifier (DLCI) is a Frame Relay 10-bit-wide link-local virtual circuit identifier used to assign frames to a specific PVC or SVC. Frame Relay networks use DLCIs to statistically multiplex frames. DLCIs are preloaded into each switch and act as road signs to the traveling frames.The standard allows the existence of 1024 DLCIs. DLCI 0 is reserved for the ANSI/q993a LMI standard—only numbers 16 to 976 are usable for end-user equipment. DLCI 1023 is reserved for Cisco LMI, however, numbers 16 to 1007 are usable.In summary, if using Cisco LMI, numbers from 16 to 1007 are available for end-user equipment. The rest are reserved for various management purposes. DLCI are Layer 2 Addresses that are locally significant. No two devices have the same DLCI mapped to its interface in one frame relay cloud.

Data refuge

Data stream management system

Data strobe encoding (or D/S encoding) is an encoding scheme for transmitting data in digital circuits. It uses two signal lines (e.g. wires in a cable or traces on a printed circuit board), Data and Strobe. These have the property that either Data or Strobe changes its logical value in one clock cycle, but never both. More precisely data is transmitted as-is and strobe changes its state if and only if data stays constant between two data bits. This allows for easy clock recovery with a good jitter tolerance by XORing the two signal line values.There is an equivalent way to specify the relationship between Data and Strobe. For even-numbered Data bits, Strobe is the opposite of Data. For odd-numbered Data bits, Strobe is the same as Data. From this definition it is more obvious that the XOR of Data and Strobe will yield a clock signal. Also, it specifies the simplest means of generating the Strobe signal for a given Data stream.Data strobe encoding originated in IEEE 1355 Standard and is used on the signal lines in SpaceWire and the IEEE 1394 (also known as FireWire 400) system.Gray code is another code that always changes one logical value, but never more than one.

Data rush

Data stream mining

Data proliferation

Data loss prevention software detects potential data breaches/data ex-filtration transmissions and prevents them by monitoring, detecting and blocking sensitive data while in-use (endpoint actions), in-motion (network traffic), and at-rest (data storage).The terms "data loss" and "data leak" are related and are often used interchangeably.

Data rescue is a name for both a movement among scientists, researchers and others and the processes they use to preserve primarily government-hosted data sets, often scientific in nature, to ward off their removal from publicly available websites. While the concept of preserving federal data existed before, it gained new impetus with the election in 2016 of U.S.

Data journalism

Data integrity is the maintenance of, and the assurance of the accuracy and consistency of, data over its entire life-cycle, and is a critical aspect to the design, implementation and usage of any system which stores, processes, or retrieves data. The term is broad in scope and may have widely different meanings depending on the specific context – even under the same general umbrella of computing. It is at times used as a proxy term for data quality, while data validation is a pre-requisite for data integrity. Data integrity is the opposite of data corruption. The overall intent of any data integrity technique is the same: ensure data is recorded exactly as intended (such as a database correctly rejecting mutually exclusive possibilities,) and upon later retrieval, ensure the data is the same as it was when it was originally recorded. In short, data integrity aims to prevent unintentional changes to information. Data integrity is not to be confused with data security, the discipline of protecting data from unauthorized parties.Any unintended changes to data as the result of a storage, retrieval or processing operation, including malicious intent, unexpected hardware failure, and human error, is failure of data integrity. If the changes are the result of unauthorized access, it may also be a failure of data security. Depending on the data involved this could manifest itself as benign as a single pixel in an image appearing a different color than was originally recorded, to the loss of vacation pictures or a business-critical database, to even catastrophic loss of human life in a life-critical system.

Data infrastructure

Data source (disambiguation)

Data matrix (computer)

Data movement

Data jam

Data loss is an error condition in information systems in which information is destroyed by failures or neglect in storage, transmission, or processing. Information systems implement backup and disaster recovery equipment and processes to prevent data loss or restore lost data.Data loss is distinguished from data unavailability, which may arise from a network outage. Although the two have substantially similar consequences for users, data unavailability is temporary, while data loss may be permanent. Data loss is also distinct from data breach, an incident where data falls into the wrong hands, although the term data loss has been used in those incidents.

Data mine

Data link connector (automotive)

The Datuk Patinggi Haji Abdul Rahman Bridge or Jambatan Datuk Patinggi Haji Abdul Rahman and New Satok Bridge is a major bridge crossing Sarawak River in Kuching city, Sarawak, Malaysia. Near the bridge is the site of the former Satok Bridge.

A data logger (also datalogger or data recorder) is an electronic device that records data over time or in relation to location either with a built in instrument or sensor or via external instruments and sensors. Increasingly, but not entirely, they are based on a digital processor (or computer). They generally are small, battery powered, portable, and equipped with a microprocessor, internal memory for data storage, and sensors. Some data loggers interface with a personal computer, and use software to activate the data logger and view and analyze the collected data, while others have a local interface device (keypad, LCD) and can be used as a stand-alone device.Data loggers vary between general purpose types for a range of measurement applications to very specific devices for measuring in one environment or application type only. It is common for general purpose types to be programmable; however, many remain as static machines with only a limited number or no changeable parameters. Electronic data loggers have replaced chart recorders in many applications.One of the primary benefits of using data loggers is the ability to automatically collect data on a 24-hour basis. Upon activation, data loggers are typically deployed and left unattended to measure and record information for the duration of the monitoring period. This allows for a comprehensive, accurate picture of the environmental conditions being monitored, such as air temperature and relative humidity.The cost of data loggers has been declining over the years as technology improves and costs are reduced. Simple single channel data loggers cost as little as $25. More complicated loggers may costs hundreds or thousands of dollars.

In software engineering, the data mapper pattern is an architectural pattern. It was named by Martin Fowler in his 2003 book Patterns of Enterprise Application Architecture.

Data set (IBM mainframe)

Data relay satellite

Data protection API

Data modelling

Data rooms are spaces used for housing data, usually of a secure or privileged nature. They can be physical data rooms, virtual data rooms, or data centers. They are used for a variety of purposes, including data storage, document exchange, file sharing, financial transactions, legal transactions, and more.In mergers and acquisitions, the traditional data room will literally be a physically secure continually monitored room, normally in the vendor’s offices (or those of their lawyers), which the bidders and their advisers will visit in order to inspect and report on the various documents and other data made available. Often only one bidder at a time will be allowed to enter and if new documents or new versions of documents are required these will have to be brought in by courier as hardcopy. Teams involved in large due diligence processes will typically have to be flown in from many regions or countries and remain available throughout the process. Such teams often comprise a number of experts in different fields and so the overall cost of keeping such groups on call near to the data room is often extremely high. Combating the significant cost of physical datarooms is the virtual data room, which provides for the secure, online dissemination of confidential information.A virtual data room (VDR) is essentially a website with limited controlled access (using a secure log-on supplied by the vendor/authority which can be disabled at any time by the vendor/authority if a bidder withdraws) to which the bidders and their advisers are given access. Much of the information released will be confidential and restrictions should be applied to the viewers' ability to release this to third parties by forwarding, copying or printing. Digital rights management is sometimes applied to control information.Detailed auditing must be provided for legal reasons so that a record is kept of who has seen which version of each document.Data rooms are commonly used by legal, accounting, investment banking and private equity companies performing mergers and acquisitions, fundraising, insolvency, corporate restructuring, and joint ventures including bio-technology and tender processes.

A data mule is a vehicle that physically carries a computer with storage between remote locations to effectively create a data communication link. A data mule is a special case of a sneakernet, where the data is automatically loaded and unloaded when the data mule arrives at its terminal locations. Disruption Tolerant Networking (DTN) can use data mules to exchange data among computers that do not have access to the TCP/IP-based Internet.Data mules have been used to offer internet connectivity to remote villages. Computers with a disk and wifi link are attached to buses on a bus route between villages. As a bus stops at the village to pick up passengers and cargo, the DTN router on the bus communicates with a DTN router in the bus station over Wi-Fi. Email is down-loaded to the village and up-loaded for transport to the Internet or to other villages along the bus route.Data mules are a cost-effective mechanism for rural connectivity because they use inexpensive commodity hardware, can be quickly installed, and can be piggy backed on existing transportation infrastructure.Despite potentially long delays for receiving data, surprisingly large bandwidths can be achieved. For example, delivering a 1TB disk once per day has an effective bandwidth of 100Mbit/s.The term data mule was originally coined as the acronym Data MULE. (Mobile Ubiquitous LAN Extension)

Data storage disk

Data leak prevention

Data science notebook

Data marts builder

Data smoothing

Data loss prevention software

Data rate

Date notation by country

Data normalisation

Data loss is an error condition in information systems in which information is destroyed by failures or neglect in storage, transmission, or processing. Information systems implement backup and disaster recovery equipment and processes to prevent data loss or restore lost data.Data loss is distinguished from data unavailability, which may arise from a network outage.

A data library, data archive, or data repository is a collection of numeric and/or geospatial data sets for secondary use in research. A data library is normally part of a larger institution (academic, corporate, scientific, medical, governmental, etc.) established for research data archiving and to serve the data users of that organisation. The data library tends to house local data collections and provides access to them through various means (CD-/DVD-ROMs or central server for download). A data library may also maintain subscriptions to licensed data resources for its users to access. Whether a data library is also considered a data archive may depend on the extent of unique holdings in the collection, whether long-term preservation services are offered, and whether it serves a broader community (as national data archives do). Most public data libraries are listed in the Registry of Research Data Repositories.

Data storage technology

Data presentation architecture

Data laundering

Data store

Data journalism is a journalism specialty reflecting the increased role that numerical data is used in the production and distribution of information in the digital era. It reflects the increased interaction between content producers (journalist) and several other fields such as design, computer science and statistics.

A data mart is the access layer of the data warehouse environment that is used to get data out to the users. The data mart is a subset of the data warehouse and is usually oriented to a specific business line or team. Whereas data warehouses have an enterprise-wide depth, the information in data marts pertains to a single department. In some deployments, each department or business unit is considered the owner of its data mart including all the hardware, software and data. This enables each department to isolate the use, manipulation and development of their data. In other deployments where conformed dimensions are used, this business unit ownership will not hold true for shared dimensions like customer, product, etc.Organizations build data warehouses and data marts because the information in the database is not organized in a way that makes it readily accessible, requiring queries that are too complicated or resource-consuming.While transactional databases are designed to be updated, data warehouses or marts are read only. Data warehouses are designed to access large groups of related records. Data marts improve end-user response time by allowing users to have access to the specific type of data they need to view most often by providing the data in a way that supports the collective view of a group of users.A data mart is basically a condensed and more focused version of a data warehouse that reflects the regulations and process specifications of each business unit within an organization. Each data mart is dedicated to a specific business function or region. This subset of data may span across many or all of an enterprise’s functional subject areas. It is common for multiple data marts to be used in order to serve the needs of each individual business unit (different data marts can be used to obtain specific information for various enterprise departments, such as accounting, marketing, sales, etc.).The related term spreadmart is a derogatory label describing the situation that occurs when one or more business analysts develop a system of linked spreadsheets to perform a business analysis, then grow it to a size and degree of complexity that makes it nearly impossible to maintain.

In computing, a data source name (DSN, sometimes known as a database source name, though "data sources" can comprise other repositories apart from databases) is a string that has an associated data structure used to describe a connection to a data source. Most commonly used in connection with ODBC, DSNs also exist for JDBC and for other data access mechanisms. The term often overlaps with "connection string". Most systems do not make a distinction between DSNs or connection strings and the term can often be used interchangeably.DSN attributes may include, but are not limited to:the name of the data sourcethe location of the data sourcethe name of a database driver which can access the data sourcea user ID for data access (if required)a user password for data access (if required)The system administrator of a client machine generally creates a separate DSN for each relevant data source.Standardizing DSNs offers a level of indirection; various applications (for example: Apache/PHP and IIS/ASP) can take advantage of this in accessing shared data sources.

Data link (disambiguation)

Data integrator

Data serialization language

Data parallel

In computing and data management, data mapping is the process of creating data element mappings between two distinct data models.

A data island is a data store, such as on a PDA or other computing device, that has non-existent or limited external connectivity. This limits the ability of the user to synchronize with or copy the data to other devices. Though new data can be added to the system, the ability to move that data elsewhere is impractical or impossible. Data islands, in general, contain a very huge set of data relative to its small physical space that it occupies.The connectivity here does not necessarily imply a hardware interface. For example, it may be a result of poorly written system interface software.A data island is a subset of entities that are connected to each other via relationships, but that are independent of other entities within the same data store.

Data mover

Data race

Data mungling

Data rates

Data shadows refer to the information that a person leaves behind unintentionally while taking part in daily activities such as checking their e-mails, scrolling through social media or even by using their debit or credit card.

Data page boiling points of the elements

Daugavgrīva Abbey

Data system

Data stream

Data structure persistence

Data over Signalling

Data loss prevention

Data loggers

Data processing inequality

Data sheets

Data sampling

Data space

Data protection (privacy) laws in Russia are a rapidly developing branch in Russian legislation that have mostly been enacted in the 2005 and 2006. The Russian Federal Law on Personal Data (No. 152-FZ), implemented on July 27, 2006, constitutes the backbone of Russian privacy laws and requires data operators to take "all the necessary organizational and technical measures required for protecting personal data against unlawful or accidental access". Russia's Federal Service for Supervision of Communications, Information Technology and Mass Media is the government agency tasked with overseeing compliance.

Data rescue

Data journalist

Data spill

Data reduction

The United States Navy occupational rating of data processing technician (abbreviated as DP) was a designation given by the Bureau of Naval Personnel (BUPERS) to enlisted members who satisfactorily completed initial data processing technician "A" school training.This navy rating originated as the machine accountant (MA) rating in large number of navy rating changes implemented in 1948. The rating name and designation was changed to data processing technician (DP) in 1967.

Data matrix (multivariate statistics)

Data sahib

Data source names

Data resource management