Abstract
Various indexing methods of spatial data have come out after rigorous efforts put by many researchers for fast processing of spatial queries. Parallelizing spatial index building and query processing have become very popular for improving efficiency. The MapReduce framework provides a modern way of parallel processing. A MapReduce-based works for spatial queries consider the existing traditional spatial indexing for building spatial indexes in parallel. The majority of the spatial indexes implemented in MapReduce use R-Tree and its variants. Therefore, R-Tree and its variantbased traditional spatial indexes are thoroughly surveyed in the paper. The objective is to search for still less explored spatial indexing approaches, having the potential for parallelism in MapReduce. The review work also provides a detailed survey of MapReduce-based spatial query processing approaches - hierarchical indexed and packed key-value storage based spatial dataset. Both approaches use different data partitioning strategies for distributing data among cluster nodes and managing the partitioned dataset through different indexing. Finally, a number of parameters are selected for comparison and analysis of all the existing approaches in the literature.
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