Olap Cube

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OLAP cube are derived from the dimension tables. m Ti Products e 2 Hierarchy Cities The elements of a dimension can be organized as a hierarchy,[4] a set of parent-child relationships, typically where a parent member summarizes its children. Parent elements can further be aggregated as the children of another parent.[5] For example May 2005’s parent is Second Quarter 2005 which is in turn the child of Year 2005. Similarly cities are the children of regions; products roll into product groups and individual expense items into types of expenditure. An example of an OLAP cube An OLAP cube is an array of data understood in terms 3 Operations of its 0 or more dimensions. OLAP is an acronym for online analytical processing.[1] OLAP is a computer- Conceiving data as a cube with hierarchical dimensions based technique for analyzing business data in the search leads to conceptually straightforward operations to facilifor business intelligence.[2] tate analysis. Aligning the data content with a familiar visualization enhances analyst learning and productivity.[5] The user-initiated process of navigating by calling for 1 Terminology page displays interactively, through the specification of slices via rotations and drill down/up is sometimes called A cube can be considered a multi-dimensional general- “slice and dice”. Common operations include slice and ization of a two- or three-dimensional spreadsheet. For dice, drill down, roll up, and pivot. example, a company might wish to summarize financial data by product, by time-period, and by city to compare actual and budget expenses. Product, time, city and scenario (actual and budget) are the data’s dimensions.[3] Cube is a shortcut for multidimensional dataset, given that data can have an arbitrary number of dimensions. The term hypercube is sometimes used, especially for data with more than three dimensions. OLAP slicing Slicer is a term for a dimension which is held constant for all cells so that multidimensional information Slice is the act of picking a rectangular subset of a cube by can be shown in a two dimensional physical space of a choosing a single value for one of its dimensions, creatspreadsheet or pivot table. ing a new cube with one fewer dimension.[5] The picture Each cell of the cube holds a number that represents some shows a slicing operation: The sales figures of all sales measure of the business, such as sales, profits, expenses, regions and all product categories of the company in the budget and forecast. year 2004 are “sliced” out of the data cube. OLAP data is typically stored in a star schema or snowflake schema in a relational data warehouse or in a special-purpose data management system. Measures are derived from the records in the fact table and dimensions Dice: The dice operation produces a subcube by allowing the analyst to pick specific values of multiple dimensions.[6] The picture shows a dicing operation: The new cube shows the sales figures of a limited number of 1 2 5 SEE ALSO 4 Mathematical definition In database theory, an OLAP cube is[8] an abstract representation of a projection of an RDBMS relation. Given a relation of order N, consider a projection that subtends X, Y, and Z as the key and W as the residual attribute. Characterizing this as a function, f : (X,Y,Z) → W, OLAP dicing the attributes X, Y, and Z correspond to the axes of the cube, while the W value into which each ( X, Y, Z ) triple product categories, the time and region dimensions cover maps corresponds to the data element that populates each the same range as before. cell of the cube. Insofar as two-dimensional output devices cannot readily characterize three dimensions, it is more practical to project “slices” of the data cube (we say project in the classic vector analytic sense of dimensional reduction, not in the SQL sense, although the two are conceptually similar), g : (X,Y) → W OLAP Drill-up and drill-down Drill Down/Up allows the user to navigate among levels of data ranging from the most summarized (up) to the most detailed (down).[5] The picture shows a drill-down operation: The analyst moves from the summary category “Outdoor-Schutzausrüstung” to see the sales figures for the individual products. which may suppress a primary key, but still have some semantic significance, perhaps a slice of the triadic functional representation for a given Z value of interest. The motivation[8] behind OLAP displays harks back to the cross-tabbed report paradigm of 1980s DBMS. The resulting spreadsheet-style display, where values of X populate row $1; values of Y populate column $A; and Roll-up: A roll-up involves summarizing the data along a values of g : ( X, Y ) → W populate the individual cells dimension. The summarization rule might be computing “southeast of” $B2, so to speak, $B2 itself included. totals along a hierarchy or applying a set of formulas such as “profit = sales - expenses”.[5] 5 See also • Comparison of OLAP Servers • Business intelligence • Data mining • Data Mining Extensions OLAP pivoting • Data warehouse • Data mart Pivot allows an analyst to rotate the cube in space to see its various faces. For example, cities could be arranged vertically and products horizontally while viewing data for a particular quarter. Pivoting could replace products with time periods to see data across time for a single product.[5][7] • Fast Analysis of Shared Multidimensional Information The picture shows a pivoting operation: The whole cube is rotated, giving another perspective on the data. • Online analytical processing (OLAP) • Pivot Table • Multidimensional Expressions (MDX) • XML for Analysis 3 6 References [1] “Just What Are Cubes Anyway? (A Painless Introduction to OLAP Technology)". Msdn.microsoft.com. Retrieved 2012-07-25. [2] Deepak Pareek (2007). Business Intelligence for Telecommunications. CRC Press. pp. 294 pp. ISBN 0-84938792-2. Retrieved 2008-03-18. [3] “Cybertec releases OLAP cubes for PostgreSQL”. PostgreSQL. 2006-10-02. Retrieved 2008-03-05. [4] “Oracle9i Data Warehousing Guide hierarchy”. Lorentz Center. Retrieved 2008-03-05. [5] “OLAP and OLAP Server Definitions”. Council. 1995. Retrieved 2008-03-18. The OLAP [6] “Glossary of Data Mining Terms”. University of Alberta. 1999. Retrieved 2008-03-17. [7] “Computer Encyclopedia: multidimensional views”. Answers.com. Retrieved 2008-03-05. [8] Gray, Jim; Bosworth, Adam; Layman, Andrew; Priahesh, Hamid (1995-11-18). “Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals”. Proc. 12th International Conference on Data Engineering. IEEE. pp. 152–159. Retrieved 2008-11-09. 7 External links • Daniel Lemire (December 2007). “Data Warehousing and OLAP - A Research-Oriented Bibliography”. Retrieved 2008-03-05. • The RDF Data Cube Vocabulary 4 8 TEXT AND IMAGE SOURCES, CONTRIBUTORS, AND LICENSES 8 Text and image sources, contributors, and licenses 8.1 Text • OLAP cube Source: http://en.wikipedia.org/wiki/OLAP%20cube?oldid=623296069 Contributors: Michael Hardy, Charles Matthews, Urantian, SEWilco, Rohan Jayasekera, Jmabel, Goethean, Lysy, Sepreece, Vasile, Asqueella, S.K., Fenice, Aranel, John Vandenberg, .:Ajvol:., Hooperbloob, Cma, Brycen, Kgrr, Gyrae, FluffyPanda, MauriceKA, DePiep, Lockley, Jmcc150, Yorrose, EngineerScotty, Cvanhasselt, Crasshopper, Wizzard, Flagboy, Modify, SmackBot, Eskimbot, Ohnoitsjamie, Thumperward, Emurphy42, Sspecter, T-borg, Wikiolap, Soumyasch, Robofish, Dll99, David Tristram, Quaeler, Bertport, OS2Warp, SqlPac, CmdrObot, Cydebot, Labreuer, LeoHeska, Astazi, Thijs!bot, Hazmat2, CharlesHoffman, Lfstevens, Paulrho, Magioladitis, AndyReid, Animum, EagleFan, J.delanoy, McSly, Plasticup, Jrolston, Qseep, VolkovBot, Technopat, Mihirgokani007, Andy Dingley, SieBot, SouthLake, Hiddenfromview, ClueBot, Niceguyedc, PasabaPorAqui, Yx7557, Addbot, Yobot, Ningauble, AnomieBOT, Materialscientist, Julianhyde, Osiris.toth138, NinjaCross, Crysb, RjwilmsiBot, Igor Yalovecky, Carmichael, 28bot, ClueBot NG, Helpful Pixie Bot, Infopedian, Anubhab91, Krystof1000, BattyBot, Monkbot and Anonymous: 103 8.2 Images • File:Edit-clear.svg Source: http://upload.wikimedia.org/wikipedia/en/f/f2/Edit-clear.svg License: ? Contributors: The Tango! Desktop Project. Original artist: The people from the Tango! project. And according to the meta-data in the file, specifically: “Andreas Nilsson, and Jakub Steiner (although minimally).” • File:Internet_map_1024.jpg Source: http://upload.wikimedia.org/wikipedia/commons/d/d2/Internet_map_1024.jpg License: CC-BY2.5 Contributors: Originally from the English Wikipedia; description page is/was here. Original artist: The Opte Project • File:Merge-arrow.svg Source: http://upload.wikimedia.org/wikipedia/commons/a/aa/Merge-arrow.svg License: Public domain Contributors: ? Original artist: ? • File:OLAP_Cube.svg Source: http://upload.wikimedia.org/wikipedia/commons/5/52/OLAP_Cube.svg License: CC-BY-SA-3.0 OLAP_Cube.png Original artist: OLAP_Cube.png: Konrad Roeder • File:OLAP_dicing.png Source: http://upload.wikimedia.org/wikipedia/commons/d/d0/OLAP_dicing.png License: CC-BY-SA-3.0 Contributors: Own work Original artist: Infopedian • File:OLAP_drill_up&down.png Source: http://upload.wikimedia.org/wikipedia/commons/4/46/OLAP_drill_up%26down.png License: GFDL Contributors: own illustration Original artist: Infopedian • File:OLAP_pivoting.png Source: http://upload.wikimedia.org/wikipedia/commons/c/cb/OLAP_pivoting.png License: CC-BY-SA-3.0 Contributors: Own work Original artist: Infopedian • File:OLAP_slicing.png Source: http://upload.wikimedia.org/wikipedia/commons/f/ff/OLAP_slicing.png License: CC-BY-SA-3.0 Contributors: Own work Original artist: Infopedian • File:Question_book-new.svg Source: http://upload.wikimedia.org/wikipedia/en/9/99/Question_book-new.svg License: ? 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