Abstract: The estimation of spatial query selectivity using grid spatial histograms is one of the methods proposed in the literature to perform selectivity estimation. In this way, defining the spatial histogram grid to reduce the use of computational resources and the execution time of the queries is an important challenge. In this work, we examine the definition of the number of cells for complex object types such as line and polygon. The avglimit method we propose uses the average width and height of the objects in a grid histogram metadata to generate a more suitable histogram. Our results show that the generated histogram has up to 50% fewer columns and lines compared to the original histogram while maintains the selectivity estimation accuracy. This result induces a reduction in communication between servers when using distributed systems and also a reduction in the computational cost when processing queries.

Keywords: Histogram; Spatial Data; Selectivity Estimation.

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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: Rafael Grisotto e Souza. Proposta de Método de Redução de Grade de Histograma Espacial para Melhoria na Precisão de Estimativas de Consultas. Monograph. Bacharelado em Ciências da Computação. Universidade Federal de Goiás, Regional Jataí. Jataí, GO, Brasil. 2017. 43p.

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