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On the Complexity of the Reduction of Multidimensional Data Models

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Abstract

In this paper, decomposition methods for multidimensional data hypercubes of OLAP systems are investigated. Criteria for reducing the computational complexity of the decomposition methods are presented and comparisons are made with the traditional solutions of multidimensional data analysis problems. Examples illustrating the application of these criteria to investigating the dynamics of computational complexity changes for specific types of reduction problems are considered.

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Authors and Affiliations

  1. Institute for System Analysis, Computer Science and Control Federal Research Center, Russian Academy of Sciences, Moscow, 119333, Russia

    A. A. Akhrem, V. Z. Rakhmankulov & K. V. Yuzhanin

Authors
  1. A. A. Akhrem
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  2. V. Z. Rakhmankulov
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  3. K. V. Yuzhanin
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Corresponding author

Correspondence to V. Z. Rakhmankulov.

Additional information

Original Russian Text © A.A. Akhrem, V.Z. Rakhmankulov, K.V. Yuzhanin, 2016, published in Iskusstvennyi Intellekt i Prinyatie Reshenii, 2016, No. 4, pp. 79–85.

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Akhrem, A.A., Rakhmankulov, V.Z. & Yuzhanin, K.V. On the Complexity of the Reduction of Multidimensional Data Models. Sci. Tech. Inf. Proc. 44, 406–411 (2017). https://doi.org/10.3103/S0147688217060028

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  • DOI: https://doi.org/10.3103/S0147688217060028

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