Abstract
Selectivity estimation is an activity performed during a query optimization process. Selectivity parameter lets estimate the query result size before the query is really executed. This allows to obtain the best query execution plan. For complex queries (where selection condition is based on many attributes) an accurate selectivity estimation requires a multidimensional distribution of attributes values. But often, attribute value independence assumption and usage of only 1-dimensional distributions give a sufficient accuracy of selectivity approximation. The paper describes the method of selectivity estimation for queries with a complex selection condition based on a sum of independent attributes or sub-independent ones. The proposed method operates on 1-dimensional Fourier Transforms of marginal distributions of attributes that are involved in the selection condition.
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Augustyn, D.R. (2014). The Method of Query Selectivity Estimation for Selection Conditions Based on Sum of Sub-Independent Attributes. In: Gruca, D., Czachórski, T., Kozielski, S. (eds) Man-Machine Interactions 3. Advances in Intelligent Systems and Computing, vol 242. Springer, Cham. https://doi.org/10.1007/978-3-319-02309-0_65
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DOI: https://doi.org/10.1007/978-3-319-02309-0_65
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02308-3
Online ISBN: 978-3-319-02309-0
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