Cost estimation of spatial join in spatialhadoop

Abstract

Spatial join is an important operation in geo-spatial applications, since it is frequently used for performing data analysis involving geographical information. Many efforts have been done in the past decades in order to provide efficient algorithms for spatial join and this becomes particularly important as the amount of spatial data to be processed increases. In recent years, the MapReduce approach has become a de-facto standard for processing large amount of data (big-data) and some attempts have been made for extending existing frameworks for the processing of spatial data. In this context, several different MapReduce implementations of spatial join have been defined which mainly differ in the use of a spatial index and in the way this index is built and used. In general, none of these algorithms can be considered better than the others, but the choice might depend on the characteristics of the involved datasets. The aim of this work is to deeply analyse them and define a cost model for ranking them based on the characteristics of the dataset at hand (i.e., selectivity or spatial properties). This cost model has been extensively tested w.r.t. a set of synthetic datasets in order to prove its effectiveness.