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Volume 25 Issue 5
August  2010
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Yi-Fei Chen, Xiao-Lin Qin, Liang Liu. Uncertain Distance-Based Range Queries over Uncertain Moving Objects[J]. Journal of Computer Science and Technology, 2010, 25(5): 982-998. DOI: 10.1007/s11390-010-1078-3
Citation: Yi-Fei Chen, Xiao-Lin Qin, Liang Liu. Uncertain Distance-Based Range Queries over Uncertain Moving Objects[J]. Journal of Computer Science and Technology, 2010, 25(5): 982-998. DOI: 10.1007/s11390-010-1078-3
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Uncertain Distance-Based Range Queries over Uncertain Moving Objects

  • 1. College of Information Science and Technology, Nanjing University of Aeronautics and Astronautics Nanjing 210016, China;
    2. College of Information and Control, Nanjing University of Information Science and Technology, Nanjing 210044, China

Funds: This work is supported by the National High Technology Research and Development 863 Program of China under Grant No. 2007AA01Z404, and the Program of Jiangsu Province under Grant No.BE2008135.
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  • Author Bio:

    Yi-Fei Chen received her B.S. degree in information engineering and M.S. degree in system analysis and integration from Nanjing University of Information Science and Technology in 2002 and 2005 respectively. She is currently a Ph.D. candidate in computer application at Nanjing University of Aeronautics and Astronautics. Her current research interests include spatio-temporal databases, moving objects databases. She is a member of CCF.

    Xiao-Lin Qin received his M.S. degree in computer application technology from Nanjing University of Aeronautics and Astronautics (NUAA). He is now a professor and Ph.D. supervisor of the College of Information Science and Technology, NUAA. His main research interests include spatio-temporal databases, secure database, data management and disaster tolerance in distributed environment and information security. He is a senior member of CCF.

    Liang Liu received his B.S. degree in software engineering from Northwestern Polytechnical University in 2005. He is currently a Ph.D. candidate in computer application at Nanjing University of Aeronautics and Astronautics. His current research interests include data management in sensor network. He is a student member of CCF.

  • Received Date: December 01, 2009
  • Revised Date: July 31, 2010
  • Published Date: August 31, 2010
    Abstract
  • Distance-based range search is crucial in many real applications. In particular, given a database and a query issuer, a distance-based range search retrieves all the objects in the database whose distances from the query issuer are less than or equal to a given threshold. Often, due to the accuracy of positioning devices, updating protocols or characteristics of applications (for example, location privacy protection), data obtained from real world are imprecise or uncertain. Therefore, existing approaches over exact databases cannot be directly applied to the uncertain scenario. In this paper, we redefine the distance-based range query in the context of uncertain databases, namely the probabilistic uncertain distance-based range (PUDR) queries, which obtain objects with confidence guarantees. We categorize the topological relationships between uncertain objects and uncertain search ranges into six cases and present the probability evaluation in each case. It is verified by experiments that our approach outperform Monte-Carlo method utilized in most existing work in precision and time cost for uniform uncertainty distribution. This approach approximates the probabilities of objects following other practical uncertainty distribution, such as Gaussian distribution with acceptable errors. Since the retrieval of a PUDR query requires accessing all the objects in the databases, which is quite costly, we propose spatial pruning and probabilistic pruning techniques to reduce the search space. Two metrics, false positive rate and false negative rate are introduced to measure the qualities of query results. An extensive empirical study has been conducted to demonstrate the efficiency and effectiveness of our proposed algorithms under various experimental settings.
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