2019•Journal of Testing and EvaluationRequires access

RETRACTED: Spatial Data Indexing and Query Processing in GeoCloud

Karthi Shankar, Prabu Sevugan

Open publisher page 4 citations

Abstract

Abstract Following an investigation undertaken by the publisher, we have determined that this paper was accepted on the basis of a compromised peer review process. We hereby retract the paper. The corresponding author has been notified of the retraction. The retraction statement can be found here: https://doi.org/10.1520/JTE20259998. GeoCloud is essential for spatial data management. This article depicts GeoCloud and SpatialHadoop, both of which are developed for spatial information, indexing, and query processing. It contains traditional spatial indexing that comprises R-tree, Hilbert R-tree, and improved Bloom filter tree. We enhance the query search by utilizing Spatial Join, Range Query, k-nearest neighbor (k-NN), and Max k-NN queries. By doing so, we implement the data structures and query evaluation performance of different spatial datasets in GeoCloud instances with SpatialHadoop. We show that our proposed system is more efficient in terms of data storage and retrieval in GeoCloud.

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What this paper is about

Abstract Following an investigation undertaken by the publisher, we have determined that this paper was accepted on the basis of a compromised peer review process. We hereby retract the paper. The corresponding author has been notified of the retraction. The retraction statement can be found here: https://doi.org/10.1520/JTE20259998. GeoCloud is essential for spatial data management. This article depicts GeoCloud and SpatialHadoop, both of which are developed for spatial information, indexing, and query processing. It contains traditional spatial indexing that comprises R-tree, Hilbert R-tree, and improved Bloom filter tree. We enhance the query search by utilizing Spatial Join, Range Query, k-nearest neighbor (k-NN), and Max k-NN queries. By doing so, we implement the data structures and query evaluation performance of different spatial datasets in GeoCloud instances with SpatialHadoop. We show that our proposed system is more efficient in terms of data storage and retrieval in GeoCloud.

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Available abstract

Abstract Following an investigation undertaken by the publisher, we have determined that this paper was accepted on the basis of a compromised peer review process. We hereby retract the paper. The corresponding author has been notified of the retraction. The retraction statement can be found here: https://doi.org/10.1520/JTE20259998. GeoCloud is essential for spatial data management. This article depicts GeoCloud and SpatialHadoop, both of which are developed for spatial information, indexing, and query processing. It contains traditional spatial indexing that comprises R-tree, Hilbert R-tree, and improved Bloom filter tree. We enhance the query search by utilizing Spatial Join, Range Query, k-nearest neighbor (k-NN), and Max k-NN queries. By doing so, we implement the data structures and query evaluation performance of different spatial datasets in GeoCloud instances with SpatialHadoop. We show that our proposed system is more efficient in terms of data storage and retrieval in GeoCloud.

Key concepts: Spatial query, Search engine indexing, R-tree, Computer science, Spatial database, Query optimization, Spatial analysis, Sargable

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