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    Location-Based Top-k Term Querying over Sliding Window

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    wise201787.pdf
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    Description:
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    Type
    Book Chapter
    Authors
    Xu, Ying
    Chen, Lisi
    Yao, Bin
    Shang, Shuo
    Zhu, Shunzhi
    Zheng, Kai
    Li, Fang
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2017-10-04
    Online Publication Date
    2017-10-04
    Print Publication Date
    2017
    Permanent link to this record
    http://hdl.handle.net/10754/625945
    
    Metadata
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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.
    Citation
    Xu Y, Chen L, Yao B, Shang S, Zhu S, et al. (2017) Location-Based Top-k Term Querying over Sliding Window. Web Information Systems Engineering – WISE 2017: 299–314. Available: http://dx.doi.org/10.1007/978-3-319-68783-4_21.
    Sponsors
    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.
    Publisher
    Springer Nature
    Journal
    Web Information Systems Engineering – WISE 2017
    Conference/Event name
    18th International Conference on Web Information Systems Engineering, WISE 2017
    DOI
    10.1007/978-3-319-68783-4_21
    Additional Links
    https://link.springer.com/chapter/10.1007%2F978-3-319-68783-4_21
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-319-68783-4_21
    Scopus Count
    Collections
    Book Chapters; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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