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Location-Based Top-k Term Querying over Sliding Window

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Web Information Systems Engineering – WISE 2017 (WISE 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10569))

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Abstract

In part due to the proliferation of GPS-equipped mobile devices, massive svolumes of geo-tagged streaming text messages are becoming available on social media. It is of great interest to discover most frequent nearby terms from such tremendous stream data. In this paper, we present novel indexing, updating, and query processing techniques that are capable of discovering top-k locally popular nearby terms over a sliding window. Specifically, given a query location and a set of geo-tagged messages within a sliding window, we study the problem of searching for the top-k terms by considering both the term frequency and the proximities between the messages containing the term and the query location. We develop a novel and efficient mechanism to solve the problem, including a quad-tree based indexing structure, indexing update technique, and a best-first based searching algorithm. An empirical study is conducted to show that our proposed techniques are efficient and fit for users’ requirements through varying a number of parameters.

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Acknowledgement

This work was supported by the NSFC (U1636210, 61373156, 91438121 and 61672351), the National Basic Research Program (973 Program, No. 2015CB352403), the National Key Research and Development Program of China (2016YFB0700502), the Scientific Innovation Act of STCSM (15JC1402400) and the Microsoft Research Asia.

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Authors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Ying Xu, Bin Yao & Fang Li

  2. Hong Kong Baptist University, Hong Kong, China

    Lisi Chen

  3. King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

    Shuo Shang

  4. Xiamen University of Technology, Xiamen, China

    Shunzhi Zhu

  5. Soochow University, Soochow, China

    Kai Zheng

Authors
  1. Ying Xu
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  2. Lisi Chen
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  3. Bin Yao
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  4. Shuo Shang
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  5. Shunzhi Zhu
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  6. Kai Zheng
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  7. Fang Li
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Corresponding authors

Correspondence to Bin Yao or Shuo Shang .

Editor information

Editors and Affiliations

  1. University of Sydney, Darlington, NSW, Australia

    Athman Bouguettaya

  2. Zhejiang University, Hangzhou, China

    Yunjun Gao

  3. Institute of Computing for Physics and Technology, Protvino, Russia

    Andrey Klimenko

  4. Nanyang Technological University, Singapore, Singapore

    Lu Chen

  5. King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

    Xiangliang Zhang

  6. Institute of Computing for Physics and Technology, Protvino, Russia

    Fedor Dzerzhinskiy

  7. Shanghai Jiao Tong University, Minhang Qu, China

    Weijia Jia

  8. Institute of Computing for Physics and Technology, Protvino, Russia

    Stanislav V. Klimenko

  9. City University of Hong Kong, Kowloon, Hong Kong

    Qing Li

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Xu, Y. et al. (2017). Location-Based Top-k Term Querying over Sliding Window. In: Bouguettaya, A., et al. Web Information Systems Engineering – WISE 2017. WISE 2017. Lecture Notes in Computer Science(), vol 10569. Springer, Cham. https://doi.org/10.1007/978-3-319-68783-4_21

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  • DOI: https://doi.org/10.1007/978-3-319-68783-4_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68782-7

  • Online ISBN: 978-3-319-68783-4

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