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.
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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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Jiang, H. et al. (2015). An Efficient Location-Aware Publish/Subscribe Index with Boolean Expressions. In: Wang, J., et al. Web Information Systems Engineering – WISE 2015. WISE 2015. Lecture Notes in Computer Science(), vol 9418. Springer, Cham. https://doi.org/10.1007/978-3-319-26190-4_15
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DOI: https://doi.org/10.1007/978-3-319-26190-4_15
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