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 is 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 has been made for extending existing frameworks for the processing of spatial data. In this context, SpatialHadoop is an extension of Apache Hadoop, which includes a native support for spatial data, in terms of spatial data types, operations and indexes. In particular, its provides five different variants of spatial join 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 algorithm 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 analyze the characteristics of these algorithms and to define a cost model for them which is based on some dataset characteristics (i.e., selectivity or spatial properties). The main goal of the proposed cost model is to rank the spatial join implementations by defining a partial order among them using a dominance relation. This cost model has been extensively tested w.r.t. a set of synthetic datasets in order to prove its effectiveness
