Regular Sparsity in OLAP System

  • Kalinka Kaloyanova
  • Ina Naydenova


One of the primary challenges of storing multidimensional data is the degree of sparsity that is often encountered. Because the extremely sparse cubes are frequent phenomenon, OLAP engines offer different methods of increasing the performance of sparse cubes, but all of these methods do not take account of the sparsity nature and did not divide the sparsity into any types. Our experience leads us to a following division of the empty areas in the multidimensional cubes: (a) areas that are empty because of the semantics of the business (the semantics enforces lack of value) and (b) areas that are empty by a chance. To formally distinguish these types of sparsity, we introduce a new object (“regular sparsity map”) which provides business analysts with the ability to define rules and place data constraints over the multidimensional cube. In this paper we present our regular sparsity map editor and discuss how it can be used for the purpose of data errors detection and selection of relevant dimension elements.


Empty Cell Rectangular Domain Business Rule Business Intelligence System Travel Insurance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Mitchell, D. (2007). Performance management, eBook, Publisher Chandni Chowk, Delhi: Global Media.Google Scholar
  2. 2.
    Muller, H., and Freytag, J. (2003). Problems, Methods, and Challenges in Comprehensive Data Cleansing. Technical Report HUB-IB-164, Humboldt University Berlin, Germany.Google Scholar
  3. 3.
    Pedersen,T., Christian, S., Curtis,J.,and Dyreson, E. (1999). Extending Practical Pre- Aggregation. On-Line Analytical Processing, VLDB'99,663-674.Google Scholar
  4. 4.
    Pendse, N. (2007) The Problems with OLAP, DM Review Magazine March 2007.Google Scholar
  5. 5.
    Pedersen, T., and Jensen, Ch. (2005). Multidimensional Databases, The Industrial Information Technology Handbook by Richard Zurawski, pages 1-13, CRC Press.Google Scholar
  6. 6.
    Chaudhuri, S., and Dayal, U. (1997). An Overview of Data Warehousing and OLAP Technology. SIGMOD Record 26(1), 65-74.CrossRefGoogle Scholar
  7. 7.
    Potgieter, J. (2003). OLAP Data Scalability. DM Review Magazine, October 2003.
  8. 8.
    Naydenova, I. (2008). Regular Sparsity Map. ISGT’2010, Sofia, BulgariaGoogle Scholar
  9. 9.
    Pendse,N.(2005).Database explosion. Business Application Research Center,
  10. 10.
    Kang, J.,Yong, H., and Masunaga, Y. (2002). Classification of Sparsity Patterns and Performance Evaluation in OLAP System, IEIC Technical Report, ISSN:0913-5685,vol.102, No.209, pp.61- 66, Japan.Google Scholar
  11. 11.
    Naydenova, I., and Kaloyanova, K. (2006). Some Extensions to the Multidimensional Data Model. Proceedings of the IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing, Bulgaria, 63 – 68.Google Scholar
  12. 12.
    Rahm, E., and Hong Hai, Do. (2000). Data Cleaning: Problems and Current Approaches, IEEE Data Engineering Bulletin, 23(4),3-13.Google Scholar
  13. 13.
    Naydenova, I., and Kaloyanova, K. (2007). An Approach Of Non-Additive Measures Compression In Molap Environment, Proceedings of the IADIS Multi Conference on Computer Science and Information Systems Lisbon, Portugal, July 2007, 394-399.Google Scholar
  14. 14.
    Naydenova, I., Kovacheva, Z., and Kaloyanova, K.(2009). A Model of Regular Spasity Map Representation. Scientifical Journal of Ovidius University of Constantza, Romania 17(3), 197–208.Google Scholar

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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Faculty of Mathematics and InformaticsSt. Kliment Ohridski University of SofiaSofiaBulgaria

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