Location-aware publish/subscribe index with complex boolean expressions

Pengpeng Zhao, Hanhan Jiang, Jiajie Xu, Victor S. Sheng, Guanfeng Liu, An Liu, Jian Wu, Zhiming Cui

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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, OTO (online-to-offline) 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. RP-trees is also extensible to support complex environment such as prefix-matching and subscriptions in format of CNF and DNF. Our experimental results show that RP-trees achieves good performance on a synthetic dataset and two real-world datasets (58 city and ebay).

Original languageEnglish
Pages (from-to)1363-1384
Number of pages22
JournalWorld Wide Web
Volume20
Issue number6
DOIs
StatePublished - Nov 1 2017

Keywords

  • Boolean expressions
  • Location-aware pub/sub
  • OTO

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