Abstract: For storage and retrieval of spatial data it is necessary to use a special structure for mul- tidimensional or complex data. In the literature it has been proposed various indexing structures, each with their specific characteristics and behaviors. The R-Tree is a hierar- chical tree, similar to B-Tree tree, which groups co-lococated objects, using surrounding rectangles, called MBR (minimum bounding rectangle) also known as rectangle surroun- ding. The implementation of these structures internally has a division algorithm called split, which has the function to assemble the tree structure, leaving the co-located objects together into groups (node) and maintain balance of tree. The performing split in node is a critical process for the performance of a multidimensional structure, because the split help determine the final shape will be an R-Tree overall goal structure of this study is to evaluate a set of existing splits algorithms, applying the spatial line type data, to identify where each algorithm split to build more balanced trees more fill node up less disk space, so that you can make more efficient data searches.

Keywords: R-Tree Split; Multidimensional Data Structures; R-Tree.

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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: Willian Garcias de Assunção. Evaluation of R-Tree Split Methods for Spatial Datasets of Line Type. Monograph. Bacharelado em Ciências da Computação. Universidade Federal de Goiás, Regional Jataí. Jataí, GO, Brasil. 2016. 70p.

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