An improved integrated Grid and MapReduce-Hadoop architecture for spatial data: Hilbert TGS R Tree–based IGSIM

摘要

Variegated distributed computing technologies have been used in recent years of revolutionaryphase for efficiently and logically planned spatial data analysis. Grid computing and MapReducetechnologies have provided a prodigious technological furtherance in the Geographic InformationSystem (GIS) domain. The Grid is known for its high computing and the MapReduceimplementation-Hadoop is known for its data analytics. A lot of research exist to prove that theintegration of Grid and MapReduce complements each other. In our earlier work, a novel architectureIntegrated Grid and Spatially Indexed MapReduce (IGSIM) was proposed that integratesGrid and SpatialHadoop for fast spatial queries. The R-Tree and the R∗-Tree spatial indexesof SpatialHadoop were exploited for fast data accessing in the IGSIM. However, efficiency ofspatial queries can be enhanced further by employing a better spatial indexing algorithm. In thispaper, a thorough literature survey has been done on the available traditional spatial indexesfrom the serial programming environment and Hilbert TGS R-Tree has been selected on thebasis of several parameters for its parallel implementation and extending spatial query efficiencywork of the IGSIM. The improved architecture is named as Hilbert TGS R-Tree–based IGSIM.The experimental results demonstrate high efficiency of the proposed work.