Enhanced-Sweep: Communication Cost Efficient Top-K Best Region Search

The best region search (BRS) is one of the major research problems in geospatial data processing applications. The BRS problem objective is to discover the ideal location of a particular size specified rectangle, with a predetermined end goal of maximizing the user-defined scoring function. The exis...

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Published in:Arabian journal for science and engineering (2011) Vol. 48; no. 2; pp. 2121 - 2132
Main Authors: Potluri, Avinash, Bhattu, S. Nagesh, Kumar, N. V. Narendra, Subramanyam, R. B. V.
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2023
Springer Nature B.V
Subjects:
Algorithms
Data processing
Datasets
Engineering
Experimentation
Humanities and Social Sciences
Massive data points
multidisciplinary
Rectangles
Research Article-Computer Engineering and Computer Science
Science
Spatial data
Big data
Best region search
Distributed and parallel processing
MapReduce
ISSN:2193-567X, 1319-8025, 2191-4281
Online Access:Get full text
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Abstract The best region search (BRS) is one of the major research problems in geospatial data processing applications. The BRS problem objective is to discover the ideal location of a particular size specified rectangle, with a predetermined end goal of maximizing the user-defined scoring function. The existing solutions for finding the top- k best regions have focused on designing algorithms for centralized settings. These solutions are not suitable for processing massive datasets. In this paper, we enable a Hadoop MapReduce-based parallel and distributed computation to obtain significant improvement in the performance. In addition to the parallel and distributed setting, we also incorporate early pruning strategies to eliminate the need to process rectangles that are not part of the output to minimize the communication cost involved in computing k -BRS. We later introduced a redistribution strategy over the initially proposed methodology that handles skew inherited from the dataset. Our results are obtained from extensive experimentation, both synthetic and real-world datasets.
AbstractList The best region search (BRS) is one of the major research problems in geospatial data processing applications. The BRS problem objective is to discover the ideal location of a particular size specified rectangle, with a predetermined end goal of maximizing the user-defined scoring function. The existing solutions for finding the top-k best regions have focused on designing algorithms for centralized settings. These solutions are not suitable for processing massive datasets. In this paper, we enable a Hadoop MapReduce-based parallel and distributed computation to obtain significant improvement in the performance. In addition to the parallel and distributed setting, we also incorporate early pruning strategies to eliminate the need to process rectangles that are not part of the output to minimize the communication cost involved in computing k-BRS. We later introduced a redistribution strategy over the initially proposed methodology that handles skew inherited from the dataset. Our results are obtained from extensive experimentation, both synthetic and real-world datasets.
The best region search (BRS) is one of the major research problems in geospatial data processing applications. The BRS problem objective is to discover the ideal location of a particular size specified rectangle, with a predetermined end goal of maximizing the user-defined scoring function. The existing solutions for finding the top- k best regions have focused on designing algorithms for centralized settings. These solutions are not suitable for processing massive datasets. In this paper, we enable a Hadoop MapReduce-based parallel and distributed computation to obtain significant improvement in the performance. In addition to the parallel and distributed setting, we also incorporate early pruning strategies to eliminate the need to process rectangles that are not part of the output to minimize the communication cost involved in computing k -BRS. We later introduced a redistribution strategy over the initially proposed methodology that handles skew inherited from the dataset. Our results are obtained from extensive experimentation, both synthetic and real-world datasets.
Author Potluri, Avinash
Kumar, N. V. Narendra
Subramanyam, R. B. V.
Bhattu, S. Nagesh
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Snippet The best region search (BRS) is one of the major research problems in geospatial data processing applications. The BRS problem objective is to discover the...
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SubjectTerms Algorithms
Data processing
Datasets
Engineering
Experimentation
Humanities and Social Sciences
Massive data points
multidisciplinary
Rectangles
Research Article-Computer Engineering and Computer Science
Science
Spatial data
Title Enhanced-Sweep: Communication Cost Efficient Top-K Best Region Search
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