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Visual Online Analytical Processing (OLAP)

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Encyclopedia of Database Systems

Synonyms

Interactive visual exploration of multidimensional data; Visual multidimensional analysis

Definition

An umbrella term encompassing a new generation of online analytical processing (OLAP) end user tools for interactive ad hoc exploration of large multidimensional data volumes. Visual OLAP provides a comprehensive framework of advanced visualization techniques for representing the retrieved data set along with a powerful navigation and interaction scheme for specifying, refining, and manipulating the subset of interest. The concept emerged from the convergence of business intelligence (BI) techniques and the achievements in the areas of information visualization and visual analytics. Traditional OLAP frontends, designed primarily to support routine reporting and analysis, use visualization merely for expressive presentation of the data. In the visual OLAP approach, however, visualization plays the key role as the method of interactive query-driven analysis. Comprehensive...

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Recommended Reading

  1. Keim DA, Krigel H-P. VisDB: database exploration using multidimensional visualization. IEEE Comput Graph Appl. 1994;14(5):40–9.

    Article  Google Scholar 

  2. Eick SG. Visualizing multi-dimensional data. ACM SIGGRAPH Comput Graph. 2000;34(1):61–7.

    Article  MathSciNet  Google Scholar 

  3. Lee H-Y, Ong H-L. A new visualisation technique for knowledge discovery in OLAP. In: Advances in Knowledge Discovery and Data Mining, 1st Pacific-Asia Conference; 1997, p. 279–86.

    Google Scholar 

  4. Sifer M. A visual interface technique for exploring OLAP data with coordinated dimension hierarchies. In: Proceedings of the International Conference on Information and Knowledge Management; 2003, p. 532–35.

    Google Scholar 

  5. Hanrahan P, Stolte C, Mackinlay J. Visual analysis for everyone: understanding data exploration and visualization. Tableau Software Inc., 2007. White Paper, http://www.tableausoftware.com/docs/Tableau_Whitepaper.pdf

  6. Stolte C, Tang D, Hanrahan P. Polaris: a system for query, analysis, and visualization of multidimensional relational databases. IEEE Trans Visual Comput Graph. 2002;8(1):52–65.

    Article  Google Scholar 

  7. Middelfart M, Pedersen TB. The meta-morphing model used in TARGIT BI suite. In: Proceedings of the ER Workshops; 2011, p. 364–70.

    Chapter  Google Scholar 

  8. Russom P. Trends in data visualization software for business users. DM Review, May 2000.

    Google Scholar 

  9. Mansmann S, Scholl MH. Extending visual OLAP for handling irregular dimensional hierarchies. In: Proceedings of the 8th International Conference Data Warehousing and Knowledge Discovery; 2006. p.~95–105.

    Chapter  Google Scholar 

  10. Mansmann S, Scholl MH. Exploring OLAP aggregates with hierarchical visualization techniques. In: Proceedings of the 2007 ACM Symposium on Applied Computing; 2007, p. 1067–73.

    Google Scholar 

  11. Maniatis A, Vassiliadis P, Skiadopoulos S, Vassiliou Y, Mavrogonatos G, Michalarias I. A presentation model & non-traditional visualization for OLAP. Int J Data Warehouse Min. 2005;1(1):1–36.

    Article  Google Scholar 

  12. Techapichetvanich K, Datta A. Interactive visualization for OLAP, Part III. In: Proceedings of the International Conference on Computational Science and Its Applications; 2005. p. 206–14.

    Google Scholar 

  13. Tegarden DP. Business information visualization. Comm AIS. 1999;1(1):Article 4.

    Google Scholar 

  14. Golfarelli M, Graziani S, Rizzi S. Shrink: an OLAP operation for balancing precision and size of pivot tables. Data Knowl Eng. 2014;93(Sept):19–41.

    Article  Google Scholar 

  15. Cuzzocrea A, Saccà D, Serafino P. Semantics-aware advanced OLAP visualization of multi-dimensional data cubes. Int J Data Warehouse Min. 2007;3(4):1–30.

    Article  Google Scholar 

  16. Bellatreche L, Giacometti A, Marcel P, Mouloudi H, Laurent DA. Personalization framework for OLAP queries. In: Proceedings of the ACM 8th International Workshop on Data Warehousing and OLAP; 2005. p. 9–18.

    Google Scholar 

  17. Rosling H, Rönnlund AR, Rosling O. New software brings statistics beyond the eye. In: Proceedings of the Organisation for Economic Co-operation and Development; 2006. p. 522–30.

    Google Scholar 

  18. Rivest S, Bédard Y, Marchand P. Toward better support for spatial decision making: defining the characteristics of spatial On-Line Analytical Processing (SOLAP). Geomatica. 2001;55(4):539–55.

    Google Scholar 

  19. Leonardi L, Orlando S, Raffaetà R, Roncato A, Silvestri C, Andrienko G, Andrienko N. A general framework for trajectory data warehousing and visual OLAP. GeoInformatica. 2014;18(2):273–312.

    Article  Google Scholar 

  20. Keim DA Exploring big data using visual analytics. In: Proceedings of the Workshops on Extending Database Technology/Database Theory; 2014, p. 160.

    Google Scholar 

  21. Shneiderman B. Extreme visualization: squeezing a billion records into a million pixels. In: Proceedings of the International Conference on Management of Data; 2008, p. 3–12.

    Google Scholar 

  22. Liu Z, Jiang B, Heer J. imMens: real-time visual querying of big data. Computer Graphics Forum. 32(3–4):421–30.

    Article  Google Scholar 

  23. Bikakis N, Sellis T. Exploration and visualization in the web of big linked data: a survey of the state of the art. arXiv preprint arXiv:1601.08059; 2016.

    Google Scholar 

  24. Stoltec C, Tang D, Hanrahan P. Multiscale visualization using data cubes. IEEE Trans Visual Comput Graph. 2003;9(2):176–87.

    Article  Google Scholar 

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Correspondence to Marc H. Scholl .

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Scholl, M.H., Mansmann, S., Golfarelli, M., Rizzi, S. (2018). Visual Online Analytical Processing (OLAP). In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_447

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