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Regular Sparsity in OLAP System

In: Emerging Themes in Information Systems and Organization Studies

Author

Listed:
  • Kalinka Kaloyanova

    (St. Kliment Ohridski University of Sofia)

  • Ina Naydenova

    (St. Kliment Ohridski University of Sofia)

Abstract

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.

Suggested Citation

  • Kalinka Kaloyanova & Ina Naydenova, 2011. "Regular Sparsity in OLAP System," Springer Books, in: Andrea Carugati & Cecilia Rossignoli (ed.), Emerging Themes in Information Systems and Organization Studies, pages 115-123, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2739-2_9
    DOI: 10.1007/978-3-7908-2739-2_9
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