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An Efficient Location-Aware Publish/Subscribe Index with Boolean Expressions

  • Hanhan Jiang
  • Pengpeng Zhao
  • Victor S. Sheng
  • Guanfeng Liu
  • An Liu
  • Jian Wu
  • Zhiming Cui
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9418)

Abstract

A location-aware publish/subscribe (pub/sub) system is gaining more and more interest in both industry and academia with the rapid progress of mobile Internet and the rising popularity of smart-phones. Nowadays, with the booming of E-commerce, Object-to-Object (OTO) services are gaining more and more popularity, which results in millions of products with different structured descriptions and locations. To meet this requirement, a pub/sub system should handle subscriptions with location-aware boolean expressions to present users’ interests. In this paper, we propose an efficient location-aware pub/sub index for boolean expressions, called RP-trees. RP-trees integrates an R-tree index and a boolean expression index together, can efficiently and simultaneously prune boolean expressions and spatial dimensions. Our experimental results show that RP-trees achieves better performance on both a synthetic dataset and a real-world dataset.

Keywords

Location-aware pub/sub Boolean expression OTO 

Notes

Acknowledgment

This work was partially supported by Chinese NSFC project (61402312, 61170020, 61402311, 61440053), and the US National Science Foundation (IIS-1115417).

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Hanhan Jiang
    • 1
  • Pengpeng Zhao
    • 1
  • Victor S. Sheng
    • 2
  • Guanfeng Liu
    • 1
  • An Liu
    • 1
  • Jian Wu
    • 1
  • Zhiming Cui
    • 1
  1. 1.School of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.Computer Science DepartmentUniversity of Central ArkansasConwayUSA

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