Execution analysis of spatial data storage indexing on cloud environment

Abstract

Cloud computing overcome the GIS issues are huge storage, computing and reliability. Cloud computing with SpatialHadoop framework gives high performance in GIS. This paper presents spatial partition, global index and map reduce operations were studied and described in detail. Bloom filter R-tree index in the Map-reduce for providing more efficiency than the existing approaches. The BR-tree index on Map-Reduce is implemented in SpatialHadoop process that reduces intermediate data access time. Global index decreases the number of data accesses for range queries and thus improves efficiency. It is observed through experimental results that the proposed index along cloud environment performs better than existing techniques.