Abstract: Spatial data processing has grown in size since the creation of information retrieval equipment, such as GPS (Global Position System), smartphones, drones, and satellites. With this spatial data, new information can be acquired. An example of spatial data processing is a spatial query, which finds in two or more datasets correlated information. Processing a spatial query can be quite complex because of the amount of data involved and the computational systems that perform it have not evolved in the needed proportion, to meet the processing demand. Thus, the parallel execution of spatial queries in distributed systems is frequently indicated in the literature. The partitioning of spatial data in the distributed system is a point that directly influences the efficiency of spatial query processing. One technique that has been widely employed is the use of multidimensional histograms for the partitioning of data in the distributed system. This research presents an analysis of the partitioning of data in a distributed system using different types of spatial histograms. The tests are based on running different spatial queries using grid and minskew histograms. The executions were carried out in a cluster with 16 identical machines and under the same conditions of use. The tests showed that despite all the preprocessing characteristics of the minskew histogram, the grid histogram presented greater efficiency in the partitioning of the data in the cluster.

Keywords: Multidimensional histograms; Cluster; Spatial Data; Distributed processing.

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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: Leandro Cesar Pita. Avaliação do uso de Histogramas Espaciais para Particionamento de Dados em Sistemas Distribuídos. Monograph. Bacharelado em Ciências da Computação. Universidade Federal de Goiás, Regional Jataí. Jataí, GO, Brasil. 2017. 51p.

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