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Data Reduction in Multifunction OLAP

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Book cover Advances in Databases and Information Systems (ADBIS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11695))

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Abstract

Multifunction OLAP allows to associate several types of aggregation functions to the same measure: general, dimensional for each analysis axis, hierarchical for each hierarchy and differentiated for each granularity level. These functions are generally non-commutative, so, an execution order between the functions is predefined. Pivot tables and several diagram types (bars, pies, etc.) are used to visualize interactively the result of an OLAP query. Unfortunately, no works investigate readability issues in multifunction OLAP. Therefore, we propose a post-processing method to reduce data size of the multifunction OLAP query result in order to improve the readability. This method aggregates data at higher granularity levels, i.e., doing a Rollup operation. It starts by studying the current query to find the functions that have already been executed. Then, it finds all possible Rollup operations, which respect the execution order and the aggregation constraints, and it calculates its data size. We propose several strategies to select a Rollup that gives a readable diagram and keeps as many details as possible: looking at the data size only, the number of implicated granularity levels and the number or the type of implicated dimensions. Once a Rollup is selected, we find the functions that realize it and we execute them in the right execution order.

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Notes

  1. 1.

    \((p +^{\prec } i)\) returns the parameter at the i-th position relative to p: \((i=1)\) returns the directly upper parameter; \((i<0)\) returns a lower parameter.

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Authors and Affiliations

  1. R&D Umanis, 7-9 rue Paul Vaillant Couturier, 92300, Levallois-Perret, France

    Ali Hassan & Patrice Darmon

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  1. Ali Hassan
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  2. Patrice Darmon
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Correspondence to Ali Hassan .

Editor information

Editors and Affiliations

  1. University of Maribor, Maribor, Slovenia

    Tatjana Welzer

  2. Alpen-Adria Universität Klagenfurt, Klagenfurt, Austria

    Johann Eder

  3. University of Maribor, Maribor, Slovenia

    Vili Podgorelec

  4. University of Maribor, Maribor, Slovenia

    Aida Kamišalić Latifić

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Hassan, A., Darmon, P. (2019). Data Reduction in Multifunction OLAP. In: Welzer, T., Eder, J., Podgorelec, V., Kamišalić Latifić, A. (eds) Advances in Databases and Information Systems. ADBIS 2019. Lecture Notes in Computer Science(), vol 11695. Springer, Cham. https://doi.org/10.1007/978-3-030-28730-6_25

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  • DOI: https://doi.org/10.1007/978-3-030-28730-6_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-28729-0

  • Online ISBN: 978-3-030-28730-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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