Abstract
Based on Spark platform, we propose an efficient top-k spatial join query processing algorithm on big spatial data, in which, the whole data space is divided into same-sized cells by using a grid partitioning method. Then spatial objects in two data sets are projected and replicated to these cells by projection and replication operations respectively, meanwhile a filtering operation is used to speed up the processing. After that, an R-tree based local top-k spatial join algorithm is proposed to compute the top-k candidate results in each cell, which extends the traditional R-tree index and combines threshold filtering techniques to reduce the communication and computation costs, therefore speeding up the query processing. Experimental results on synthetic data sets show that the proposed algorithm is significantly better than the existing top-k spatial join query processing algorithms in performance.
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