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Using Copula and Quantiles Evolution in Prediction of Multidimensional Distributions for Better Query Selectivity Estimation

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Man-Machine Interactions 6 (ICMMI 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1061 ))

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Abstract

In query optimization theory a selectivity parameter is used by cost query optimizer for early estimating the size of data that satisfies a query condition. It requires some representation of distribution of attribute values. There are many approximate representations of m–d distribution where the copula-based is new one. This approach gives a possibility to take into account the fact of a varying m–d distribution by predicting both a copula and 1–d marginal distributions. In this paper we propose the method of forecasting trajectories of either copula parameters and marginals’ quantiles using time series prediction models. This method is mainly designated for predicting outdated distribution representation what may improve accuracy of selectivity estimation based on such representation. It also may be used for predicting a varying query workload to forecast important regions of data domain. Having detected such regions we may improve there the resolution of distribution representation.

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Notes

  1. 1.

    Estimate AR and ARMA Models – Matlab and Simulink (2019) https://www.mathworks.com/help/ident/ug/estimating-ar-and-arma-models.html.

  2. 2.

    Matlab Sample Data Sets (2016) https://www.mathworks.com/help/stats/_bq9uxn4.html.

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Acknowledgements

This work was supported by the Statutory Research funds of Institute of Informatics, Silesian University of Technology (grant No BK/204/RAU2/2019).

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Authors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Akademicka 16, Gliwice, Poland

    Dariusz Rafal Augustyn

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  1. Dariusz Rafal Augustyn
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Correspondence to Dariusz Rafal Augustyn .

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Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Aleksandra Gruca

  2. Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Gliwice, Poland

    Tadeusz Czachórski

  3. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Sebastian Deorowicz

  4. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Katarzyna Harężlak

  5. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Agnieszka Piotrowska

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Augustyn, D.R. (2020). Using Copula and Quantiles Evolution in Prediction of Multidimensional Distributions for Better Query Selectivity Estimation. In: Gruca, A., Czachórski, T., Deorowicz, S., Harężlak, K., Piotrowska, A. (eds) Man-Machine Interactions 6. ICMMI 2019. Advances in Intelligent Systems and Computing, vol 1061 . Springer, Cham. https://doi.org/10.1007/978-3-030-31964-9_20

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