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An Efficient Location-Aware Top-k Subscription Matching for Publish/Subscribe with Boolean Expressions

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Database Systems for Advanced Applications (DASFAA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9643))

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Abstract

Location-aware publish/subscribe (pub/sub) has attracted a lot of attentions with the booming of mobile Internet technologies and the rising popularity of smart-phones. Subscribers subscribe their interests with their locations as subscriptions, and publishers publish geo-information as events. Many state-of-art applications with a massive amount of geo-information, such as location-aware targeted advertising systems, face this situation. Existing related work mainly focuses on unstructured geo-textual information. However, many online-to-offline applications have enormous geo-information with different structured descriptions. To handle such structured information, a new type of location-aware pub/sub approach is needed. In this paper, we handle these subscriptions using boolean expressions. Since the number of publishers and subscribers can be enormous, it is extremely important to improve the matching effectiveness and efficiency of top-k query processing. In this paper, we develop a novel solution named RR\(^t\)-trees. RR\(^t\)-trees integrates \(R^t\)-tree and a predicate index structure together to return top-k best matched subscriptions from a great number of events. Our experimental results on synthetic and real-world datasets show that RR\(^t\)-trees achieve better performance than baseline methods.

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Notes

  1. 1.

    http://www.Groupon.com.

  2. 2.

    The attribute with the least appearing frequency in the whole dataset becomes the pivot attribute.

  3. 3.

    The number of distinct attributes in a whole dataset.

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Acknowledgment

This work was partially supported by Chinese NSFC project (61472263, 61402312, 61402311), and the US National Science Foundation (IIS-1115417).

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Authors and Affiliations

  1. School of Computer Science and Technology, Soochow University, Suzhou, China

    Hanhan Jiang, Pengpeng Zhao, Jiajie Xu, An Liu, Jian Wu & Zhiming Cui

  2. Collaborative Innovation Center of Novel Software Technology and Industrialization, Suzhou, 215006, China

    Pengpeng Zhao, Jiajie Xu, An Liu, Jian Wu & Zhiming Cui

  3. Computer Science Department, University of Central Arkansas, Conway, USA

    Victor S. Sheng

Authors
  1. Hanhan Jiang
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  2. Pengpeng Zhao
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  3. Victor S. Sheng
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  4. Jiajie Xu
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  5. An Liu
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  6. Jian Wu
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  7. Zhiming Cui
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Corresponding author

Correspondence to Pengpeng Zhao .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology , Atlanta, Georgia, USA

    Shamkant B. Navathe

  2. University of Texas at Dallas , Richardson, Texas, USA

    Weili Wu

  3. University of Minnesota , Minneapolis, Minnesota, USA

    Shashi Shekhar

  4. Renmin University , Beijing, China

    Xiaoyong Du

  5. Fudan University , Shanghai, China

    Sean X. Wang

  6. Rutgers, The State University of New Jer , New Brunswick, New Jersey, USA

    Hui Xiong

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Jiang, H. et al. (2016). An Efficient Location-Aware Top-k Subscription Matching for Publish/Subscribe with Boolean Expressions. In: Navathe, S., Wu, W., Shekhar, S., Du, X., Wang, S., Xiong, H. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9643. Springer, Cham. https://doi.org/10.1007/978-3-319-32049-6_21

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  • DOI: https://doi.org/10.1007/978-3-319-32049-6_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32048-9

  • Online ISBN: 978-3-319-32049-6

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