Abstract: Selectivity estimation is an important metric for choosing efficient spatial database execution plans. Working with such a metric requires that spatial objects be represented by approximations. One of the most commonly used techniques is MBRs. However, for some spatial objects, such lines, the MBR generates a high error rate in the selectivity estimation. Although some works proposed the decomposition of the object as a relevant method for dealing with errors in query estimates, fewer studies investigated the impact of decomposition on line-type objects, i.e., objects that tend to generate more errors when simplified by MBRs. In this work, we performed an exhaustive evaluation considering the decomposition of objects into two, three and four components. We noticed that decomposition of complex spatial objects does not significantly interfere in the reduction of selectivity estimation errors for grid histograms.

Keywords: Spatial Data; Grid Histogram; MBR.

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Disclaimer: Although the student carefully wrote the original abstract, and it was revised and improved, English is not him or the advisor' mother language. The original work is written in Portuguese.

Citation: Isabella de Freitas Nunes. Selectivity Estimate Accuracy Validation in Grid Histograms for Decomposed Line Spatial Objects. Monograph. Bacharelado em Ciências da Computação. Universidade Federal de Goiás, Regional Jataí. Jataí, GO, Brasil. 2018. 58p.

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