research-article

Spatial scene similarity assessment on Hadoop

Authors:

Yuwei WangAuthors Info & Claims

HPDGIS '10: Proceedings of the ACM SIGSPATIAL International Workshop on High Performance and Distributed Geographic Information Systems

Pages 39 - 42

https://doi.org/10.1145/1869692.1869700

Published: 02 November 2010 Publication History

Abstract

Spatial Scene Similarity Assessment (SSSA) is an essential problem in spatial analysis, spatial query, and map generalization, etc. In SSSA, spatial scene similarity needs to be compared between query spatial scene and each candidate spatial scene. The computational complexity of spatial scene comparison often cannot be resolved by sequential computing model. In this paper, we analyze the computational complexity of SSSA and develop a parallel processing method and associated algorithms for SSSA based on Hadoop. The COOT (Cell Object Overlay Times) is proposed as a data locality strategy. The experiment results demonstrate that MapReduce on Hadoop significantly improve SSSA in computing performance and data processing capability.

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Cited By

Wan LHuang ZPeng X(2016)An Effective NoSQL-Based Vector Map Tile Management ApproachISPRS International Journal of Geo-Information10.3390/ijgi51102155:11(215)Online publication date: 12-Nov-2016
https://doi.org/10.3390/ijgi5110215
Chen Z(2013)Adaptive Ant Colony Algorithm Researching in Cloud Computing Routing Resource SchedulingThe 19th International Conference on Industrial Engineering and Engineering Management10.1007/978-3-642-38391-5_11(101-108)Online publication date: 14-Jun-2013
https://doi.org/10.1007/978-3-642-38391-5_11

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Published In

cover image ACM Conferences

HPDGIS '10: Proceedings of the ACM SIGSPATIAL International Workshop on High Performance and Distributed Geographic Information Systems

November 2010

47 pages

ISBN:9781450304320

DOI:10.1145/1869692

General Chairs:
Shaowen Wang
University of Illinois at Urbana-Champaign
,
Nancy Wilkins-Diehr
University of California at San Diego
,
Program Chairs:
Xuan Shi
Georgia Institute of Technology
,
Ranga Raju Vatsavai
Oak Ridge National Laboratory
,
Jianting Zhang
City College of the City University of New York

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 November 2010

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GIS '10

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SIGSPATIAL

GIS '10: 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2, 2010

California, San Jose

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Cited By

Wan LHuang ZPeng X(2016)An Effective NoSQL-Based Vector Map Tile Management ApproachISPRS International Journal of Geo-Information10.3390/ijgi51102155:11(215)Online publication date: 12-Nov-2016
https://doi.org/10.3390/ijgi5110215
Chen Z(2013)Adaptive Ant Colony Algorithm Researching in Cloud Computing Routing Resource SchedulingThe 19th International Conference on Industrial Engineering and Engineering Management10.1007/978-3-642-38391-5_11(101-108)Online publication date: 14-Jun-2013
https://doi.org/10.1007/978-3-642-38391-5_11

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