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

• Published in: Concurrency and computation Vol. 31; no. 17
• Main Authors: Singh, Hari, Bawa, Seema
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc 10.09.2019
• Subjects: Algorithms; Analytics; Architecture; Computational grids; Computer networks; Data analysis; Distributed processing; Efficiency; Geographic information systems; grid; Hadoop; MapReduce; Programming environments; Queries; Spatial data; spatial index; spatial query
• ISSN: 1532-0626; 1532-0634
• DOI: 10.1002/cpe.5202

Summary Variegated distributed computing technologies have been used in recent years of revolutionary phase for efficiently and logically planned spatial data analysis. Grid computing and MapReduce technologies have provided a prodigious technological furtherance in the Geographic Information System (GIS) domain. The Grid is known for its high computing and the MapReduce implementation‐Hadoop is known for its data analytics. A lot of research exist to prove that the integration of Grid and MapReduce complements each other. In our earlier work, a novel architecture Integrated Grid and Spatially Indexed MapReduce (IGSIM) was proposed that integrates Grid and SpatialHadoop for fast spatial queries. The R‐Tree and the R∗‐Tree spatial indexes of SpatialHadoop were exploited for fast data accessing in the IGSIM. However, efficiency of spatial queries can be enhanced further by employing a better spatial indexing algorithm. In this paper, a thorough literature survey has been done on the available traditional spatial indexes from the serial programming environment and Hilbert TGS R‐Tree has been selected on the basis of several parameters for its parallel implementation and extending spatial query efficiency work 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.