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    The Sun ™

    Decision Warehouse ™

    The Sun Decision Warehouse is a comprehensiveprogram designed to help you make the most of yourinformation assets.

    Sun's Ultra ™ Enterprise ™ Servers, along with the

    Solaris ™ operating environment, provide a state-of-the-art platform on which to build your data warehouse.And they allow you to invest as you grow, withoutcostly technology barriers.

    To build the right solution, you need to assemble a

    team of experts. That's why Sun has established closerelationships with industry leading ISVs, VARs, andcommercial systems integrators worldwide. Thesepremier alliances ensure that Sun servers are the idealplatforms on which to run all the leading businessapplications and that the best resources are available

    to you whether you're implementing a data mart, or alarge scale data warehouse.

    And because we know that a solution is successful onlyif it meets your needs, Sun has established the SunDecision Warehouse Sales Support Center and other

    joint competency centers worldwide, so you can modeland test your solution in a real world environment.

    The ultimate success of your enterprise may dependon the reliability and availability of your data warehouse.Sun is committed to providing world-class, innovativeservice and support programs, customized to fit your

    business requirements.

    To find out how your enterprise can benefit from theSun Decision Warehouse program visit our Web Site:

    http://www.sun.com/products-n-solutions/solutions/nba/decision.html

    © 1997 Sun Microsystems, Inc. All Rights Reserved. Sun, Sun Microsystems, theSun Logo, Ultra, Ultra Enterprise, and Solaris and The Network Is The Computerare trademarks or registered trademarks of Sun Microsystems, Inc. in the UnitedStates of America and other countries.

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    Access Path —The path chosen by a database man-agement system to retrieve the requested data.

    Active Data Warehouse —A data warehouse thatsearches for trends and patterns in evaluational data.It is an active process that searches an evaluationaldatabase for trends, patterns, and exceptions.

    Active Metadata Warehouse —A metadata ware-

    house that is automatically updated when new dataenter the data resource.

    Ad-Hoc Query— Any query that cannot be deter-mined prior to the moment the query is issued. A querythat consists of dynamically constructed SQL, which isusually constructed by desktop-resident query tools.

    Ad-Hoc Query Tool— An end-user tool that acceptsan English-like or point-and-click request for data andconstructs an ad-hoc query to retrieve the desired result.

    Administrative Data— In a data warehouse, thedata that helps a warehouse administrator manage thewarehouse. Examples of administrative data are userprofiles and order history data.

    Aggregate Data— Data that is the result of applyinga process to combine data elements. Data that is takencollectively or in summary form.

    Aggregation —Used in the broad sense to mean aggre-gating data horizontally, vertically, and chronologically.

    Architecture —A definition and preliminary designwhich describes the components of a solution and theirinteractions. An architecture is the blueprint by whichimplementers construct a solution which meets theusers’ needs.

    Atomic Data —Data elements that represent the low-est level of detail. For example, in a daily sales report,the individual items sold would be atomic data, whilerollups such as invoice and summary totals from invoicesare aggregate data.

    Data Warehousing Technology Glossary • 3

    Visit ATG’s Web Site

    to read, download, and print

    all the Technology Guides

    in this series.

    w w w .techguide.com

    “T he significant problems we face cannot be solvedby the same level of thinking that created them.”

    Albert Einstein

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    Client/ Server Processing —A form of cooperativeprocessing in which the end-user interaction is througha programmable workstation (desktop) that must executesome part of the application logic over and above dis-play formatting and terminal emulation.

    Coarse Granularity —See highly summarized data.

    Collection —A set of data that resulted from a DBMS

    query.Collection Time —The time data were actuallycollected about the event.

    Combined Data —A concatenation of individualfacts.

    Common Data Architecture —A formal, compre-hensive data architecture that provides a commoncontext within which an integrated data resource isdeveloped so that it adequately supports the businessinformation demand.

    Common Data Model —A comprehensive modelthat represents the universe of data available to anorganization that has been identified and defined withinthe common data architecture. It represents the objectsand events in the real world that are of interest to theorganization, is subject-oriented, and includes allperspectives of the real world.

    Common Data Modeling —The process of develop-ing a model of the integrated data resource within acommon data architecture. The process facilitates theintegration of existing data and increases the awarenessand understanding of those data. It is a process to planthe distribution of data based on business needs and thephysical operating environment.

    Common Data Modeling Method —A methodthat combines forward data modeling, reverse datamodeling, and vertical data modeling. The methodprovides an easy way to move between unnormalized

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    Cardinality —The number of data occurrencesallowed on either side of a data relation. In the com-mon data architecture, cardinality is documented withdata integrity, not with the data structure.

    Central Warehouse —A database created from oper-ational extracts that adheres to a single, consistent,enterprise data model to ensure consistency of decision-support data across the corporation. A style of comput-ing where all the information systems are located andmanaged from a single physical location.

    Centralized Data Warehouse —A data warehouseimplementation in which a single warehouse serves theneed of several business units simultaneously with a sin-gle data model which spans the needs of the multiplebusiness divisions.

    Chained Data Replication —The replication of nonofficial data to another nonofficial data. If data arereplicated from nonofficial data, they are considered

    duplicated data, not replicated data.Change Data Capture —The process of capturingchanges made to a production data source. Changedata capture is typically performed by reading thesource DBMS log. It consolidates units of work, ensuresdata is synchronized with the original source, andreduces data volume in a data warehousing environment.

    Classic Data Warehouse Development —Theprocess of building an enterprise business model, creat-ing a system data model, defining and designing a data

    warehouse architecture, constructing the physical data-base, and lastly, populating the warehouses database.

    Client/ Server —A distributed technology approachwhere the processing is divided by function. The serverperforms shared functions—managing communications,providing database services, etc. The client performsindividual user functions—providing customized inter-faces, performing screen to screen navigation, offeringhelp functions, etc.

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    Consumer Profile —Identification of an individual,group, or application and a profile of the data theyrequest and use: the kinds of warehouse data, physicalrelational tables needed, and the required location andfrequency of the data (when, where, and in what form itis to be delivered).

    Critical Success Factors —K ey areas of activity inwhich favorable results are necessary for a company toreach its goal.

    Crosstab —A process or function that combines and/orsummarizes data from one or more sources into a conciseformat for analysis or reporting.

    Data —Items representing facts, text, graphics, bit-mapped images sound, analog or digital live-videosegments. Data is the raw material of a system suppliedby data producers and is used by information consumersto create information.

    Data Access —The process of entering a database tostore or retrieve data.

    Data Access Tools —An end-user oriented tool thatallows users to build SQL queries by pointing and click-ing on a list of tables and fields in the data warehouse.

    Data Accuracy —The component of data integritythat deals with how well data stored in the data resourcerepresent the real world. It includes a definition of thecurrent data accuracy and the adjustment in dataaccuracy to meet the business needs.

    Data Administration —The processes and proceduresby which the integrity and currency of the data in thewarehouse are maintained.

    Data Aggregation —A type of data derivation wherea data value is derived from the aggregation of two ormore contributing data characteristics in different dataoccurrences within the same data subject.

    business transactions and a denormalized database andbetween the real world and detailed data resourcedesign within the common data architecture.

    Common Data Structure —T he structure of datawithin the common data architecture that provides afull understanding of all the disparate data structuresand multiple perspectives of the real world represented

    by those data structures.Common Metadata —Metadata developed withinthe common data architecture to provide all the detailnecessary to thoroughly understand the data resourceand how it can be improved to meet the business infor-mation demand.

    Comprehensive Data Definition —A formal datadefinition that provides a complete, meaningful, easilyread, readily understood definition explaining the con-tent and meaning of data.

    Conceptual Schema —A schema that represents acommon structure of data that is the common denomi-nator between the internal schema and external schema.

    Connectivity —The ability of a device to connect toanother. This includes not only the physical issues asso-ciated with the buses, connector topologies and othersuch matters, but also the support of the protocolsrequired to pass data successfully over the physicalconnection.

    Consistent Data Quality —The state of a data

    resource where the quality of existing data is thorough-ly understood and the desired quality of the dataresource is known. It is a state where disparate dataquality is known, and the existing data quality is beingadjusted to the level desired to meet the current andfuture business information demand.

    Consumer —An individual, group, or application thataccesses data/ information in a data warehouse.

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    oped for the Warehouse. For example, a product num-ber may be held as a numeric field in one system whilea second system appends an alpha suffix to the numberfor reporting purposes.

    Data Cluster —A temporary group of data subjectsfor a specific purpose. It can be any useful combinationof data subjects for any specific purpose that cannot bemet by any of the other categorical levels.

    Data Collection Frequency —The frequency atwhich data are collected from the world.

    Data Completeness —An indication of whetheror not all the data necessary to meet the current andfuture business information demand are available in thedata resource. It deals with determining the data need-ed to meet the business information demand and ensur-ing those data are captured and maintained in the dataresource so they are available when needed.

    Data Compression —Mathematical techniques usedto reduce the amount of storage required for certaindata.

    Data Concurrency —The situation where the repli-cated data values at are synchronized with the corre-sponding data values at the official data source. Whenthe data values at the official data source are updated,the replicated data values must also be updated so theyare consistent with the official data source.

    Data Conversion —The process of changing datafrom one physical environment to another. This processmakes any changes necessary to move data from oneelectronic medium or database product to another.

    Data Denormalization —The process of developingthe internal schema from the conceptual schema.

    Data Derivation —The process of creating a datavalue from one or more contribution data valuesthrough a data derivation algorithm.

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    Data Architecture —(1) The science and method of designing and constructing an integrated data resourcethat is business-driven, based on real world objects andevents as perceived by the organization, and imple-mented into appropriate operating environments. Theoverall structure of a data resource that provides a con-sistent foundation across organizational boundaries toprovide easily identifiable, readily available, high-qualitydata to support the business information demand.(2) The component of the data resource framework thatcontains all activities, and the products of those activi-ties, related to the identification, naming, definition,structuring, quality, and documentation of the dataresource for an organization.

    Data Attribute —Represents a data characteristicvariation that is used in a logical data model.

    Data Attribute Group —Represents the use of adata characteristic group in a logical data model.

    Data Cardinality —Cardinality is a property of data elements which indicates the number of allowableentries in that element. For example, a data elementsuch as “gender” only allows two entries: “Male” or“Female” Data elements which have few allowableentries are said to possess “Low Cardinality”. Those,such as “age” or “income”, for which many allowableentries are possible, are said to have “High Cardinality”.

    Data Characteristic —An individual characteristicthat describes a data subject. It is developed, directlythrough measurement or indirectly through derivation,from a feature of an object or event. Each data subjectis described by a set of data characteristics.

    Data Cleansing —The process of manipulating thedata extracted from operational systems so as to make itusable by the data warehouse. When loading data fromexisting operational systems, it is likely that few if anyof the operational systems will have data to present in aformat which is compatible with the data model devel-

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    Data Exploration —The process of routinelysearching evaluational data for patterns, trends, andexceptions. Data exploration usually starts with anincomplete definition of the search criteria and anunknown volume of data. As patterns, trends, andexceptions are discovered, the search criteria are refinedand the volume of data may be changed.

    Data Explosion —A term given to express theincrease in stored data when using MultiDimensionalDatabase Systems. The amount of data stored in thesesystems is often a multiple of the size of the raw dataentered into the systems from the existing operationaldatabases. Hence, the data undergoes an “Explosion”to several times (or many times) its original size.

    Data Extract —Data which normally resides on anoperational system and which is removed from thatsystem for loading into a data warehouse.

    Data Extraction Software —Software that reads oneor more sources of data and creates a new image of thedata.

    Data File —A representation of a data entity from thelogical data model that is implemented with a physicaldata model. It is a physical file of data that exists in adatabase management system, as a computer file out-side a database management system, or as a manual fileoutside a computer that represents a data entity.

    Data Flow Diagram —A diagram that shows thenormal flow of data between services as well as the flowof data between data stores and services.

    Data Fragmentation —An unorderly process of placing data at various data sites. It is not done withinthe common data architecture, is not well-managed ordocumented, and results in unknown, undocumented,redundant data.

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    Data Dictionary —A database about data and data-base structures. A catalog of all data elements, contain-ing their names, structures, and information about theirusage. A central location for metadata. Normally, datadictionaries are designed to store a limited set of avail-able metadata, concentrating on the information relat-ing to the data elements, databases, files, and programsof implemented systems.

    Data Dimension —A representation of a single set of objects or events in the real world.

    Data Dissemination —The process of getting datafrom the data resource to a client, within or without theorganization, through appropriate application andtelecommunication networks. Data are disseminatedthrough client/ server applications, electronic mail, andtraditional business applications.

    Data Distribution —The placement and mainte-nance of replicated data at one or more data sites on amainframe computer or across a telecommunicationsnetwork. This part of developing and maintaining anintegrated data resource that ensures data are properlymanaged when distributed across many different datasites. Data distribution is one type of data deployment,which is the transfer of data to data sites.

    Data Duplication —A term used to identify data thatare captured, processed, or stored redundantly. It resultsin unknown, uncontrolled, and unmanaged data redun-dancy. It is not orderly and creates additional disparate

    data.Data Element —The most elementary unit of datathat can be identified and described in a dictionary orrepository which cannot be subdivided.

    Data Entity —Represents a data subject from thecommon data model that is used in the logical datamodel.

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    Data Generalization —The process of creatingsuccessive layers of summary data in an evaluationaldatabase. It is a process of zooming out to get abroader view of a problem, trend or situation. It isalso known as rolling-up data.

    Data Harvesting —See data mining.

    Data Integrity —The formal definition of compre-

    hensive rules and the consistent application of thoserules to assure high integrity data. It consists of tech-niques to determine how well data are maintained inthe data resource and to ensure that the data resourcecontains data that have high integrity. Data integrityincludes techniques for data value integrity, datastructure integrity, data retention integrity, and dataderivation integrity.

    Data Integrity Rule —A statement that defines theactual data values or coded data values that are allowed.

    Data Integrity Value —An actual data value or acoded data value that is allowed.

    Data Key —a set of one or more data characteristicsthat have a special meaning and use in addition todescribing a feature or trait of a data subject. Data keysare important for uniquely identifying data occurrencesin each data subject and for navigating through the dataresource.

    Data Layer —A separate and distinct set of relatedspatial data that are stored and maintained in a spatial

    database. It represents a particular theme or topic of interest in the real world and is equivalent to a datasubject.

    Data Layer Exclusion —The portion of a data layerextent for which data are not captured and stored. It isthe reverse of a data layer coverage.

    Data Loading —The process of populating the datawarehouse. Data loading is provided by DBMS-specific

    load processes, DBMS insert processes, and indepen-dent fastload processes.

    Data Management —Controlling, protecting, andfacilitating access to data in order to provide informationconsumers with timely access to the data they need. Thefunctions provided by a database management system.

    Data Management Software —Software that

    converts data into a unified format by taking deriveddata to create new fields,merging files, summarizingand filtering data; the process of reading data fromoperational systems. Data Management Software isalso known as data extraction software.

    Data Mapping —The process of assigning a sourcedata element to a target data element.

    Data Mart —A subset of the data resource, usuallyoriented to a specific purpose or major data subject,that may be distributed to support business needs. The

    concept of a data mart can apply to any data whetherthey are operational data, evaluational data, spatialdata, or metadata.

    Data Mining — (1) The process of utilizing the resultsof data exploration to adjust or enhance business strate-gies. It builds on the patterns, trends, and exceptionsfound through data exploration to support the business.It is also known as data harvesting. (2) A techniqueusing software tools geared for the user who typicallydoes not know exactly what he’s searching for, but islooking for particular patterns or trends. Data mining isthe process of sifting through large amounts of data toproduce data content relationships. This is also knownas data surfing.

    Data Model —A logical map that represents theinherent properties of the data independent of soft-ware, hardware, or machine performance considera-tions. The model shows data elements grouped intorecords, as well as the association around those records.

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    Data Modeling —A method used to define and ana-lyze data requirements needed to support the businessfunctions of an enterprise. These data requirements arerecorded as a conceptual data model with associateddata definitions. Data modeling defines the relationshipsbetween data elements and structures.

    Data Naming Convention —A convention estab-lished to resolve problems with Traditional data names.Many of these conventions are in use today, such as theOf Language, entity—attribute—class, role—type—class, prime—descriptor—class, entity—adjective—class, entity—attribute—class word, entity—descrip-tion—class, entity keyword—minor keyword—type key-word, and entity keyword—descriptor—domain.

    Data Normalization —A process to develop theconceptual schema from the external schema.

    Data Optimization —A process that prepares thelogical schema from the data view schema. It is thecounterpart of data deoptimization.

    Data Partitioning —(1) The formal process of deter-mining which data subjects, data occurrence groups,and data characteristics are needed at each data site.It is an orderly process for allocating data to data sitesthat is done within the same common data architecture.(2) The process of logically and/ or physically partition-ing data into segments that are more easily maintainedor accessed. Current RDBMS systems provide this kindof distribution functionality. Partitioning of data aids in

    performance and utility processing.Data Pivot —A process of rotating the view of data.

    Data Propagation —T he distribution of data fromone or more source data warehouses to one or morelocal access databases, according to propagation rules.

    Data Quality —Indicates how well data in the dataresource meet the business information demand. Data

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    quality includes data integrity, data accuracy, and datacompleteness.

    Data Quality Activity —An activity in the dataarchitecture component that ensures the maintenanceof high-quality data in an integrated data resource.

    Data Quality Process —Documents and improvesdata quality by using both the deductive and inductive

    techniques. It is a systematic process of examining thedata resource to determine its level of data quality andensuring that the data quality is adjusted to the levelnecessary to support the business information demand.

    Data Redistribution —The process of moving datareplicates from one data site to another to meet businessneeds. It is a process that constantly balances data needs,data volumes, data usage, and the physical operatingenvironment.

    Data Refining —A process that refines disparate data

    within a common context to increase the awareness andunderstanding of the data, remove data variability andredundancy, and develop an integrated data resource.Disparate data are the raw material and an integrateddata resource is the final product.

    Data Refreshing —The process of updating activedata replicates based on a regular, known schedule. Thefrequency and timing of data refreshing must be estab-lished to match business needs and must be known byclients.

    Data Replicate —A set of data copied from a data siteand placed at another data site during data replication.A set of data characteristics from a single data subjector data occurrence group that is copied from the officialdata source and placed at another data site. Data repli-cates are not the same as redundant data.

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    Data Replication —(1) A formal process of creatingexact copies of a set of data from the data site contain-ing the official data source and placing those data in atother data sites. (2) Data Replication: The process of copying a portion of a database from one environmentto another and keeping the subsequent copies of thedata in sync with the original source. Changes made tothe original source are propagated to the copies of thedata in other environments.Data Repository —A logical (and sometimes physical)partitioning of data where multiple databases whichapply to specific applications or sets of applicationsreside. For example, several databases (revenues,expenses) which support financial applications (A/ R,A/ P) could reside in a single financial Data Repository.

    Data Resource —A component of informationtechnology infrastructure that represents all the dataavailable to an organization, whether they are automat-

    ed or nonautomated.Data Restructuring —The process to restructurethe source data to the target data during data transfor-mation.

    Data Retention Integrity —A subset of data integritythat specifies criteria for preventing the loss of criticaldata through updates or deletion. It considers the futurevalue of data to determine what data should be retainedand how they should be retained. It looks to the futureto determine the unknown or hidden usefulness of the

    data.Data Scheme —A diagrammatic representation of the structure of data. It represents any set of data thatis being captured, manipulated, stored, retrieved,transmitted, or displayed.

    Data Schema Concept —A concept that provides astructure or framework for managing the integrateddata resource.

    Data Scrubbing —The process of filtering, merging,decoding, and translating source data to create validateddata for the data warehouse.

    Data Source —A specific data site where data arestored and can be obtained. Any source of data froma specific organization, such as a data base or data file.A data source may include nonautomated data, but itdoes not include unpublished documents containingdata.

    Data Store —A place where data is stored;data at rest.A generic term that includes databases and flat files.

    Data Structure —A representation of the arrange-ment, relationship, and contents of data subjects, dataentities, and data files in the common data architecture.It includes all logical and physical data within the com-mon data architecture.

    Data Structure Component —A component of themetadata warehouse that contains the structure of datawithin the common data architecture.Data Structure Integrity —A subset of data integritythat specifies the integrity for data relations.

    Data Summarization —The process of summarizingprimitive evaluational data or derived evaluational datato create more generalized derived evaluational data.

    Data Surfing —See Data Mining.

    Data Synchronization —The process of identifyingactive data replicates and ensuring that data concurrencyis maintained. Also known as data version synchroniza-tion or data version concurrency because all replicateddata values are consistent with the same version as theofficial data.

    Data Thesaurus —A component of the metadatawarehouse that contains a set of data name synonymsto help people locate the particular data they need. Itprovides a reference between similar names or businessterms and the common data names.

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    attributes. It deals with understanding patterns, trends,and relationships in historical data, and providing visualinformation to the decision maker.

    Data Warehouse —(1) A subject oriented, integrated,time-variant, non-volatile collection of data in supportof management’s decision making process. A repositoryof consistent historical data can that can be easilyaccessed and manipulated for decision support. (2) Animplementation of an informational database used tostore sharable data sourced from an operational data-base-of-record. It is typically a subject database thatallows users to tap into a company’s vast store of opera-tional data to track and respond to business trends andfacilitate forecasting and planning efforts.

    Data Warehouse Architecture —An integrated setof products that enable the extraction and transforma-tion of operational data to be loaded into a database forend-user analysis and reporting.

    Data Warehouse Engines —Relational databases(RDBMS) and Multi-dimensional databases (MDBMS).Data warehouse engines require strong query capabilities,fast load mechanisms, and large storage requirements.

    Data Warehouse Infrastructure —A combinationof technologies and the interaction of technologies thatsupport a data warehousing environment.

    Data Warehouse Management Tools —Softwarethat extracts and transforms data from operationalsystems and loads it into the data warehouse.

    Database —A collection of data which are logicallyrelated.

    Database Management Systems —A set of software modules which are used to manipulate andmanage (create, read, update, and delete) data elementswithin one or more databases.

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    Data Tracking —The process of tracking data fromtheir data origin to their current data site.

    Data Transfer —The process of moving data fromone environment to another environment. An environ-ment may be an application system or operating envi-ronment. See Data Transport.

    Data Transformation —(1) The formal process

    of transforming data in the data resource within acommon data architecture. It includes transforming dis-parate data to an integrated data resource, transformingdata within the integrated data resource, and transform-ing disparate data. It includes transforming operational,historical, and evaluational data within a common dataarchitecture. (2) Creating “information” from data.

    This includes decoding production data and mergingof records from multiple DBMS formats. It is alsoknown as data scrubbing or data cleansing.

    Data Type —The form of a data value, such as date,number, string, floating point, packed, and doubleprecision.

    Data Value —The individual facts and figurescontained in data characteristics, data characteristicvariations, data attributes, and data items.

    Data Value Integrity —A subset of data integritythat specifies the allowable values for each data charac-teristic and each relation between data characteristicswithin the common data architecture. Data valueintegrity is specified as data integrity values or data

    integrity rules.Data Version Concurrency —See data synchro-nization.

    Data Version Synchronization —See data synchro-nization.

    Data Visualization —The process of creating andpresenting a chart from a set of data based on a set of

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    Derived Data —Data that is the result of a computa-tional step applied to reference or event data. Deriveddata is the result either of relating two or more elementsof a single transaction (such as an aggregation), or of relating one or more elements of a transaction to anexternal algorithm or rule.

    Disparate Data —Data that are essentially not alike,or are distinctly different in kind, quality, or character.

    They are unequal and cannot be readily integrated toadequately meet the business information demand.Disparate data are heterogeneous data.

    Disparate Databases —Databases or databasemanagement systems that are not electronically oroperationally compatible. Disparate databases areknown as heterogeneous databases.

    Disparate Metadata Cycle —A self-perpetuatingcycle where disparate metadata are being producedfaster than ever before.

    Disparate Operational Data —The current-valueoperational data that support daily business transactions.

    They are the disparate data, including both tabular andnontabular data, that most organizations currently useto support their daily business operations.

    Distributed Database —A collection of multiple,logically related databases that is provided to data sites.

    Distributed Database Management System —A software product that manages and maintains thedistributed database and makes it transparent to clients.

    Data flow freely over any network or combination of networks by using one or more network protocols.

    Distributed Data Set —A data set from one datasubject or data occurrence group that is distributed.

    Drill Down —A method of exploring detailed datathat was used in creating a summary level of data. Drilldown levels depend on the granularity of the data inthe data warehouse.

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    Database Schema —The logical and physical defini-tion of a database structure.

    DBA —Database Administrator.

    Decentralized Database —A centralized databasethat has been partitioned according to a business orend-user defined subject area. Typically ownership isalso moved to the owners of the subject area.

    Decentralized Warehouse —A remote data sourcethat users can query/ access via a central gateway thatprovides a logical view of corporate data in terms thatusers can understand. The gateway parses and distrib-utes queries in real time to remote data sources andreturns result sets back to users.

    Decision Support —A set of software applicationsintended to allow users to search vast stores of informa-tion for specific reports which are critical for makingmanagement decisions.

    Decision Support Processing —See on-line analyti-cal processing.

    Decision Support Systems —Systems which allowdecision makers in organizations to access data relevantto the decisions they are required to make.

    Demographic —A term derived from demos meaningpopulation and graphein meaning to write or describe.Literally it means describing populations.

    Demographic Data —Any data that locate, identify,or describe populations. Demographic data can be relat-ed to the Earth the same as geographic data.

    Denormalized Data —Data that have been throughdata denormalization. The data in the physical schemaand internal schema.

    Denormalized Data Store —A data store that doesnot comply to one or more of several normal forms. SeeNormalization.

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    Enterprise Storage —A shared central repository forinformation, connected to disparate computer systems,that provides common management, protection andinformation sharing capabilities.

    Enterprise System Connection Architecture(ESCON) —An IBM mainframe channel architecturecommonly used to attach storage devices.

    Entity Relation Diagram —A data relation diagramthat represents the arrangement and relationship of data entities for the logical data structure. It is alsoknown as an E-R diagram.

    Entity Relationship Diagramming —A processthat visually identifies the relationships between dataelements.

    Entity Structure Chart —A chart that shows theexistence and structure of data attributes and dataentities in the common data structure. It directly sup-

    ports the entity relation diagram to provide a completerepresentation of the logical data structure.

    Event —A happening in the real world.

    Event Frequency —The frequency at which an eventoccurs or an object changes in the real world.

    Executive Information Systems (EIS) —Toolsprogrammed to provide canned reports or briefingbooks to top-level executives. They offer strong report-ing and drill-down capabilities. Today these tools allowad-hoc querying against a multi-dimensional database,and most offer analytical applications along functionallines such as sales or financial analysis.

    Existing Data Quality Criteria —The criteriadocumenting the data quality that currently exists in thedata resource.

    External Schema —A schema representing thestructure of data used by applications.

    Data Warehousing Technology Glossary • 2 5

    Drilling Down —The process of viewing data inmore detail.

    DSS —See Decision Support System.

    Dual Data Partitioning —The situation where bothdata occurrence partitioning and data characteristicpartitioning are done on the same data subject. Dualdata partitioning is common in most data distribution.

    Dynamic Data Distribution —The situation wheredistributed data need to be continually evaluated andadjusted to meet the business information demand inan optimum manner.

    Dynamic Queries —Dynamically constructed SQLthat is usually constructed by desktop-resident querytools. Queries that are not pre-processed and areprepared and executed at run time.

    EIS —See Executive Information System.

    End User Data —Data formatted for end-user queryprocessing;data created by end users; data provided bya data warehouse.

    Enterprise —A complete business consisting of func-tions, divisions, or other components used to accomplishspecific objectives and defined goals.

    Enterprise Data —Data that is defined for use acrossa corporate environment.

    Enterprise Data Model —A blueprint for all of the data used by all departments in the enterprise. An

    Enterprise Data Model has resolved all of the potentialinconsistencies and parochial interpretations of the dataused and presents a consistent and commonly under-stood and accepted view and definition of the enterprisedata.

    Enterprise Data Warehouse —An Enterprise datawarehouse is a Centralized Warehouse which servicesthe entire enterprise.

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    Functional Data Warehouse —A warehouse thatdraws data from nearby operational systems. Each func-tional warehouse serves a distinct and separate group(such as a division), functional area (such as manufac-turing), geographic unit, or product marketing group.

    Gateway —A software product that allows SQL-basedapplications to access relational and non-relational datasources.

    Generic Data Architecture —A standard architec-ture for a specific purpose, such as purchase orders orstudent registration. It is an attempt to get organizationsto do similar business functions in a similar manner.

    Hash —Data allocated in an algorithmically random-ized fashion in an attempt to evenly distribute data andsmooth access patterns.

    Highly Summarized Data —Evaluational data thatare summarized by removing many data characteristics

    from the primary key of the data focus. Highly summa-rized data have coarse granularity.

    Historical Database —A database that provides anhistorical perspective on the data.

    Householding —A methodology of consolidatingnames and addresses.

    Information —(1) A collection of data that is relevantto on or more recipients at a point in time. It must bemeaningful and useful to the recipient at a specific timefor a specific purpose. Information is data in context,data that have meaning, relevance, and purpose.(2) Data that has been processed in such a way that itcan increase the knowledge of the person who receivesit. Information is the output, or “finished goods,” of information systems. Information is also what individualsstart with before it is fed into a Data Capture transactionprocessing system.

    Data Warehousing Technology Glossary • 2 7

    Extract —A set of data which resides normally on theoperational systems which is uploaded into the datawarehouse.

    Extract Date —The date data was extracted.

    Extract Frequency —The latency of data extracts,such as daily versus weekly, monthly, quarterly, etc.

    The frequency that data extracts are needed in the

    data warehouse is determined by the shortest frequencyrequested through an order, or by the frequencyrequired to maintain consistency of the other associateddata types in the source data warehouse.

    Extract Specification —The standard expectationsof a particular source data warehouse for data extractsfrom the operational database system-of-record. Asystem-of-record uses an extract specification to retrievea snapshot of shared data, and formats the data in theway specified for updating the data in the source datawarehouse. An extract specification also contains

    extract frequency rules for use by the Data Accessenvironment.

    Fact Table —See data focus.

    Fastload — A technology that typically replaces aspecific DBMS loadfunction. A fastload technologyobtains significantly faster load times by preprocessingdata and bypassing data integrity checks and logging.

    Filters — Saved sets of chosen criteria that specify asubset of information in a data warehouse.

    Foredata —A new term developed from fore, meaningbeforehand, up front, at or near the front. Foredata areall data about the objects and events, including bothpraedata and paradata. Foredata are the upfront datathat an organization sees about objects and events.

    Four-Schema Concept —A concept that resolvesproblems with the three-schema concept. It includes aphysical schema, a logical schema, a data view schema,and a business schema.

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    Knowledge —Information that is retained with anunderstanding about the significance of that information.It is knowing something gained by experience, study,familiarity, association, awareness, or comprehension.

    Legacy Data —Another term for disparate databecause they support legacy systems.

    Lightly Summarized Data —Evaluational data

    that are summarized by removing one, or a few, datacharacteristic from the primary key of the data focus.Lightly summarized data have fine granularity.

    Limited Primary Key —A primary key whoseuniqueness is limited to a subset of the data occurrenceswithin the scope of the common data architecture.

    Local Area Network (LAN) —(1) A network coveringa relatively small geographic area (usually not largerthan a floor or small building). Compared to WANs,LANs are usually characterized by relatively high data

    rates. (2) Network permitting transmission and commu-nication between hardware devices, usually in onebuilding or complex.

    Logical Data Model —(1) A data model that repre-sents the normalized design of data needed to supportan information system. Data are drawn from the com-mon data model and normalized to support the designof a specific information system. (2) Actual implementa-tion of a conceptual module in a database. It may takemultiple logical data models to implement one concep-tual data model.

    Manageability —The collective processes of storageconfiguration, optimization and administration includ-ing backup and recovery and business continuance.

    Metadata —(1) Traditionally, metadata were dataabout the data. In the common data architecture meta-data are all data describing the foredata, including meta-praedata and the meta-paradata. They are data that

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    Information Consumer —A person or softwareservice that uses data to create information.

    Information Needs Analysis —T he identificationand analysis of the needs for information required tosatisfy a particular business driver.

    Information Systems —A component of informationtechnology infrastructure that represents the implemen-

    tation of business activities, using the data resource, andresiding on the platform resource.

    Integration —Used here in the broad sense to meanthe transformation of disparate data into an integrateddata resource.

    Integrated Historical Data —Integrated operationaldata that have either been archived as individual datavalues or as full records from integrated operationaldata or are disparate operational or disparate historicaldata that have been transformed to the integrated data

    resource.Integrated Operational Data —Subject-oriented,integrated, time-current, volatile collection of data thatsupport an organization’s daily business activities. Theyare also know as operational data stores.

    Intelligent Agent —A software routine that waits inthe background and performs an action when a speci-fied event occurs. For example, agents could transmit asummary file on the first day of the month or monitorincoming data and alert the user when certain transac-

    tions have arrived.Intuitive Data Warehouse —A data warehouse thattracks the analysis performed on evaluational data, addsor suggests the addition of permanent data subjectsor queries based on frequency of use, and deletes orsuggests the deletion of summary data subjects andqueries based on a lack of use.

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    Multi-Dimensional Analysis —InformationalAnalysis on data which takes into account many differ-ent relationships, each of which represents a dimension.For example, a retail analyst may want to understandthe relationships among sales by region, by quarter, bydemographic distribution (income, education level,gender), by product.Multi-dimensional analysis willyield results for these complex relationships.

    Multi-Dimensional Database — (1) A databasewhich has been constructed with the multiple dimensionspre-filled in hyper dimensional “cubes” of data ratherthan the traditional two dimensional tables of RelationalDatabases. (2) A database concept designed for decisionsupport systems in which related data is stored in multi-dimensional “hypercubes”. This data organizationallows for sophisticated and complex queries and canprovide superior performance in certain cases overtraditional relational structures.

    Multiple Dimension Analysis —See multipledimension processing.

    Multiple Dimension Processing —On-lineanalytical processing for decision support that uses acombination of single dimension and multiple dimen-sion data subjects. It is also referred to as static dataanalysis because the data values do not change. It isalso known as multiple dimensional analysis.

    Multi-dimensional Database (MDBS andMDBMS) —A powerful database that lets users analyze

    large amounts of data.An MDBS captures and presentsdata as arrays that can be arranged in multiple dimen-sions.

    Network-Driven Data Distribution —The situationwhere the existence of a data site on a telecommunica-tions network drives the distribution of data to the datasite to support a business need.

    Data Warehousing Technology Glossary • 3 1

    come after or behind the foredata and support theforedata. (2) Metadata is data about data. Examples of metadata include data element descriptions, data typedescriptions, attribute/ property descriptions, range/domain descriptions, and process/ method descriptions.

    The repository environment encompasses all corporatemetadata resources: database catalogs, data dictionaries,and navigation services. Metadata includes things likethe name, length, valid values, and description of a dataelement. Metadata is stored in a data dictionary andrepository. It insulates the data warehouse from changesin the schema of operational systems.

    Metadata Synchronization —The process of consolidating, relating, and synchronizing data elementswith the same or similar meaning from different systems.Metadata synchronization joins these differing elementstogether in the data warehouse to allow for easier access.

    Metadata Warehouse —A database that contains

    the common metadata and client-friendly search routinesto help people fully understand and utilize the dataresource. It contains common metadata about the dataresource in a single organization or an integrated dataresource that crosses multiple disciplines and multiple

    jurisdictions. It contains a history of the data resource,what the data initially represented, and what theyrepresent now.

    Methodology —A system of principles, practices, andprocedures applied to a specific branch of knowledge.

    Middleware —A communications layer that allowsapplications to interact across hardware and networkenvironments.

    Mini Marts —A small subset of a data warehouseused by a small number of users. A mini mart is a veryfocused slice of a larger data warehouse.

    Massive Parallel Processing ( MPP) —The“shared nothing” approach of parallel computing.

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    Normalization —The process of reducing a complexdata structure into its simplest, most stable structure. Ingeneral, the process entails the removal of redundantattributes, keys, and relationships from a conceptualdata model.

    Normalized Data —Data that have been throughdata normalization. The data in the data view schemaand the external schema.

    ODBC —Open Database Connectivity. A standard fordatabase access co-opted by Microsoft from the SQLAccess Group consortium.

    OLAP —See On-Line Analytical Processing.

    OLTP —See On-Line Transaction Processing.

    Operational Database —The database-of-record,consisting of system-specific reference data and eventdata belonging to a transaction-update system. It mayalso contain system control data such as indicators,flags, and counters. The operational database is thesource of data for the data warehouse. It containsdetailed data used to run the day-to-day operations of the business. The data continually changes as updatesare made, and reflect the current value of the lasttransaction.

    Operational Data Store (ODS) —An ODS is anintegrated database of operational data. Its sourcesinclude legacy systems and it contains current or nearterm data. An ODS may contain 30 to 60 days of

    information, while a data warehouse typically containsyears of data.

    On-Line Analytical Processing —Processing thatsupports the analysis of business trends and projections.It is also known as decision support processing and OLAP

    On-Line Transaction Processing —Processing thatsupports the daily business operations.Also know asoperational processing and OLTP.

    Operational Data —Data used in the operationalprocessing of business transactions that support day-to-day business operations. They are detailed, largelyprimitive data necessary to keep the organizationoperating.

    Operational Data Stores —Data which is kept tosupport Operational Applications. This class of data isusually transaction oriented.

    Operational Metadata —Metadata about opera-tional data.

    Optimized Data —Data that have been throughdata optimization. The data in the logical schema andconceptual schema.

    Parallelism —The ability to perform functions inparallel.

    Physical Data Model —A data model that representsthe denormalized physical implementation of data that

    support an information system. The logical data modelis denormalized to a physical data model according tospecific criteria that do not compromise the logical datamodel but allow the database to operate efficiently in aspecific operating environment.

    Population —See Data Loading and Data Replication.

    Primary Data Source —The first data site where theoriginal data are stored after their origination.

    Primary Key —A set of one or more data character-istics whose value uniquely identifies each data occur-

    rence in a data subject. A primary key is also known asa unique identifier.

    Propagated Data —Data that is transferred from adata source to one or more target environments accord-ing to propagation rules. Data propagation is normallybased on transaction logic.

    Query —A (usually) complex SELECT statement fordecision support. See Ad-Hoc Query.

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    Reference Data —Business data that has a consistentmeaning and definition and is used for reference andvalidation (Process, Person, Vendor, and Customer, forexample). Reference data is fundamental to the opera-tion of the business. The data is used for transactionvalidation by the data capture environment, decisionsupport systems, and for representation of business rules.Its source for distribution and use is a data warehouse.

    Refining —A process that removes impurities fromcrude or impure material to form useful products, suchas refining crude oil.

    Replicated Data —Data that is copied from a datasource to one or more target environments based onreplication rules. Replicated data can consist of fulltables or rectangular extracts.

    Replication —The process of keeping a copy of data,either through shadowing or caching.

    Repository —A location, physical or logical, wheredatabases supporting similar classes of applications arestored.

    Return on Investment —A financial measure used toquantify the desirability of promoting a particular effort.Return on Investment compares the benefits returnedto the enterprise against the cost required to implementit. It is usually expressed as a ratio.

    Reverse Data Denormalization —A process thattransforms the physical schema to the distribution

    schema. It undoes the database to its distributed schemaor to the logical schema if there is no data distribution.

    Reverse Data Modeling —Movement from thephysical schema to the distribution schema to the logi-cal schema to the data view schema to the businessschema.

    Roll Up Queries —Queries that summarize data at alevel higher than the previous level of detail.

    Data Warehousing Technology Glossary • 3 5

    Query Response Times —The time it takes for thewarehouse engine to process a complex query across alarge volume of data and return the results to therequester.

    Query Tools —Software that allows a user to createand direct specific questions to a database. These toolsprovide the means for pulling the desired informationfrom a database. They are typically SQL-based toolsand allow a user to define data in end-user language.

    R Tree —A balanced tree indexing technique appliedto databases of geometric objects, used to find shapes ina graphical file structure.

    RDBMS —Relational Database Management System.

    Redundancy —The storage of multiple copies of identical data.

    Redundancy Control —Management of a distributeddata environment to limit excessive copying, update,and transmission costs associated with multiple copiesof the same data. Data replication is a strategy forredundancy control with the intention to improveperformance.

    Redundant Data —The situation where the samedata characteristic exists at two or more data sites.Redundant data are created, stored, and maintainedindependent of each other and are often unknown tothe organization.

    Redundant Data Diagram —A diagram that speci-fies the data characteristics representing the official datasource and the redundant data characteristics thatshould be maintained from that official data source.

    Redundant Data Integrity —T he process of identi-fying, documenting, and maintaining redundant datauntil the redundancy can be eliminated or reduced toa manageable level.

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    data enhance the value of the hard data and supportmore informed decisions.

    Spatial Data —A type of nontabular data with aspatial component that allows them to be preciselylocated on some base, such as the Earth. Sometimesreferred to as aspatial data. Includes both geospatialdata and structospatial data.

    Spatial Data Hierarchy —A data subject hierarchyfor defining spatial data themes and subthemes thatincludes both current and future spatial data needs.

    SQL (Structured Query Language) —A structuredquery language for accessing relational, ODBC, DRDA,or non-relational compliant database systems.

    SQL Query Tool —An end-user tool that acceptsSQL to be processed against one or more relationaldatabases.

    Standard Query —A stored procedure of a recently

    executed query. Technically, a standard query may bestored on the desktop as “canned” SQL and passed asdynamic SQL to the server database to execute. This isundesirable unless the stored query is seldom executed.

    Star Schema —A database design characterized by itssimplicity, allowing users to navigate through the dataeasily, and its rapid response time. Unlike traditionalrelational schemas, normalization is not a goal of starschema design. Star schemas are usually divided intofact tables and dimensional tables, where the dimen-sional tables supply supporting information, such as the

    demographics of the buyers who made up the entries inthe primary fact table.

    Static Query —A stored, parameterized procedure,optimized for access to a particular data warehouse.

    Storage Array —A collection of disks (usually a disksubsystem) combined with array management softwarethat controls the disks and presents them as one ormore virtual (logical) disks.

    Rolling-Up Data —See data generalization.

    Scalability —(1) The ability to scale to support largeror smaller volumes of data and more or less users.

    The ability to increase or decrease size or capability incost-effective increments with minimal impact on theunit cost of business and the procurement of additionalservices. (2) The ability of a system to accommodateincreases in demand by upgrading and/ or expandingexisting components, as opposed to meeting thoseincreased demands by implementing a new system.

    Schema —(1) A diagrammatic representation of thestructure or framework of something. (2) The logicaland physical definition of data elements, physicalcharacteristics and inter-relationships.

    Secondary Data Site —A data site where data maybe moved after their origination.

    Secondary Data Source —Any data site where data

    acquired from another data site are stored withoutalteration or modification. The data for a secondarydata source may come from a primary data source oranother secondary data source. If data are altered ormodified in any way at a data site, that data sitebecomes a primary data source for those new data.

    Server —A service that provides standard functions forclients in response to standard messages from clients.Note: A commonly used definition of server alsorefers to the physical computer from which servicesare provided.

    Small Computer System Interface (SCSI) —A standard for connecting peripheral devices to com-puters. The standard defines a physical connectionscheme as well as a set of protocols for controlling theflow of data across the interface.

    Soft Data —The opinions, comments, explanations,observations, and evaluations about the business. Soft

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    Transaction —The occurrence of two or more relatedactions which result in a transfer from one party toanother, or from each party to the other. Transactionsinclude order entry, point of sale exchanges, tellermachines, etc.

    Update —One of the four actions associated with data:CRUD—Create, Read, Update, and Delete. Notallowed in a data warehouse because a data warehousedepends on a time series of data.

    Versioning —The ability for a single definition to main-tain information about multiple physical instantiations.

    Vertical Data Aggregation —The summarization of data to higher levels of generalization.

    Vertical Data Layer Aggregation —The combina-tion of two or more data layers to form a moreenhanced data layer. Aggregation may occur with eithergeospatial data layers or structospatial data layers.

    Vertical Data Modeling —The process of movingthrough the logical schema, tactical schema, and strategicschema. Transforming a general schema to a moredetailed schema is a specialization process, and trans-forming a detailed schema to a more general schemais a generalization process.

    Warehouse Business Directory —Provides businessprofessionals access to the data warehouse by browsinga catalog of contents.

    Warehouse Technical Directory —Defines and

    manages an information life cycle, a definition of ware-house construction, change management, impact analy-sis, distribution, and operation of a warehouse.

    Data Warehousing Technology Glossary • 3 9

    Striping —A RAID technique which breaks up blocksof data into parallel “stripes” which are stored ondifferent disks in order to enhance performance.

    Summarization Tables —These tables are createdalong commonly used access dimensions to speed queryperformance, although the redundancies increase theamount of data in the warehouse. See Aggregate Data.

    Systems Architecture —One of the four layers of theinformation systems architecture. The systems architec-ture represents the definitions and inter-relationshipsbetween applications and the product architecture.

    Table Partitioning —The process of identifying thetables needed at each data site.

    Tactical Data Warehouse Development —Theprocess of selecting a portion of an enterprise andimplementing a data warehouse. The process includesconstructing a data model for the area, determining the

    data warehouse architecture, constructing the physicalmodel, and populating the warehouse database. It alsoincludes creating or buying the applications to accessthe data warehouse, prototyping the tactical warehouses(access definitions, views, etc.), and incorporating end-user feedback.

    Target Database —The database in which data willbe loaded or inserted.

    Temporal —Related to, concerned with, or limited bytime. It is derived from tempus, meaning time.

    Temporal Data —Any data that represent a pointin time or a time interval. They are data with a timecomponent.

    Temporal Database —A database that has the capa-bility to store temporal data and manage data based onthose temporal data. It can recreate data values for pastor future dates based on the temporal data values. Thesedatabases are also known as time-relational databases.

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    How can you be successful with your datawarehouse?

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    w w w .techguide.w w te hguide o

    This Technology Guide is one

    of a series of guides, published

    by ATG, designed to put complex

    data warehousing concepts into

    practical and understandable terms.

    Each guide provides objective,

    non-biased information to assist in

    the internal education, evaluation

    and decision making process.

    This Technology Guide, as well

    as the other Data Warehousing

    Technology Guides in the series,

    are available on ATG‘s Web Site.

    Produced and Published by