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An efficient approach for continuous density queries

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Abstract

In location-based services, a density query returns the regions with high concentrations of moving objects (MOs). The use of density queries can help users identify crowded regions so as to avoid congestion. Most of the existing methods try very hard to improve the accuracy of query results, but ignore query efficiency. However, response time is also an important concern in query processing and may have an impact on user experience. In order to address this issue, we present a new definition of continuous density queries. Our approach for processing continuous density queries is based on the new notion of a safe interval, using which the states of both dense and sparse regions are dynamically maintained. Two indexing structures are also used to index candidate regions for accelerating query processing and improving the quality of results. The efficiency and accuracy of our approach are shown through an experimental comparison with snapshot density queries.

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Authors and Affiliations

  1. School of Information, Renmin University of China, Beijing, 100872, China

    Jie Wen, Xiaofeng Meng & Xing Hao

  2. Department of Computer Science, Hong Kong Baptist University, Hong Kong, China

    Jianliang Xu

Authors
  1. Jie Wen
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  2. Xiaofeng Meng
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  3. Xing Hao
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  4. Jianliang Xu
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Corresponding author

Correspondence to Jie Wen.

Additional information

Jie Wen received her BE from the School of Information, Renmin University of China in 2010. She is now an MS student at Renmin University of China. Her main research interests include continuous density query and privacy-preserving query processing in cloud computing.

Xiaofeng Meng received his PhD in computer science from the Institute of Computing Technology, Chinese Academy of Sciences. He is a professor and PhD supervisor at Renmin University of China. His research interests include cloud data management, web data management, native XML databases, flash-based databases, and privacy-preservation.

Xing Hao received her MS from the School of Information, Renmin University of China in 2010. Her main research interests focus on mobile data management and privacy preservation with respect to continuous queries in location-based services.

Jianliang Xu received his PhD in computer science from Hong Kong University of Science and Technology in 2002. He is an associate professor and PhD supervisor at Hong Kong Baptist University. His research interests include data management, mobile and pervasive computing, and distributed and networked systems.

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Wen, J., Meng, X., Hao, X. et al. An efficient approach for continuous density queries. Front. Comput. Sci. 6, 581–595 (2012). https://doi.org/10.1007/s11704-012-1120-4

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  • DOI: https://doi.org/10.1007/s11704-012-1120-4

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