An Enhanced Partitioning Approach in SpatialHadoop for Handling Big Spatial Data

Abstract

Abstract SpatialHadoop could handle spatial data operations in a low partitioning execution time compared to the traditional Hadoop. However, developing an efficient and an accurate partitioning algorithm is still a research field opened to many researchers. Confidently, this paper proposes a Minimum Boundary Rectangle-aware Priority R-Tree (MBR-aware PR-Tree) as an enhanced partitioning algorithm applicable at SpatialHadoop. Compared to state-of-art partitioning algorithms, our proposed algorithm outperforms them in terms of query execution time, file size, number of partitions, indexing time, and number of returned objects. The experimental results show superiority of our algorithm which have been confirmed for both spatial range query and k-nearest-neighbour query through evaluating the performance in different scenarios using a real dataset.