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Issue No. 04 - April (2015 vol. 27)
ISSN: 1041-4347
pp: 950-963
Minghe Yu , Department of Computer Science, Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing, China
Guoliang Li , Department of Computer Science, Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing, China
Ting Wang , Department of Computer Science, Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing, China
Jianhua Feng , Department of Computer Science, Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing, China
Zhiguo Gong , Department of Computer and Information Science, University of Macau, Macao, China
ABSTRACT
Location-based services have been widely adopted in many systems. Existing works employ a pull model or user-initiated model, where a user issues a query to a server which replies with location-aware answers. To provide users with instant replies, a push model or server-initiated model is becoming an inevitable computing model in the next-generation location-based services. In the push model, subscribers register spatio-textual subscriptions to capture their interests, and publishers post spatio-textual messages. This calls for a high-performance location-aware publish/subscribe system to deliver publishers’ messages to relevant subscribers. In this paper, we address the research challenges that arise in designing a location-aware publish/subscribe system. We propose an R -tree based index by integrating textual descriptions into R- tree nodes. We devise efficient filtering algorithms and effective pruning techniques to achieve high performance. Our method can support both conjunctive queries and ranking queries. We discuss how to support dynamic updates efficiently. Experimental results show our method achieves high performance which can filter 500 messages in a second for 10 million subscriptions on a commodity computer
INDEX TERMS
Subscriptions, Computational modeling, Indexes, Time complexity, Keyword search, Semantics
CITATION

M. Yu, G. Li, T. Wang, J. Feng and Z. Gong, "Efficient Filtering Algorithms for Location-Aware Publish/Subscribe," in IEEE Transactions on Knowledge & Data Engineering, vol. 27, no. 4, pp. 950-963, 2015.
doi:10.1109/TKDE.2014.2349906
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