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    Top-k term publish/subscribe for geo-textual data streams

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    Name:
    Top-k term publishsubscribe for geo-textual data streams.pdf
    Size:
    1.481Mb
    Format:
    PDF
    Description:
    Accepted manuscript
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    Type
    Article
    Authors
    Chen, Lisi
    Shang, Shuo
    Jensen, Christian S.
    Xu, Jianliang
    Kalnis, Panos cc
    Yao, Bin
    Shao, Ling
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    InfoCloud Research Group
    Date
    2020-03-09
    Online Publication Date
    2020-03-09
    Print Publication Date
    2020-09
    Embargo End Date
    2021-03-09
    Submitted Date
    2019-01-19
    Permanent link to this record
    http://hdl.handle.net/10754/662293
    
    Metadata
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    Abstract
    Massive amounts of data that contain spatial, textual, and temporal information are being generated at a rapid pace. With streams of such data, which includes check-ins and geo-tagged tweets, available, users may be interested in being kept up-to-date on which terms are popular in the streams in a particular region of space. To enable this functionality, we aim at efficiently processing two types of general top-k term subscriptions over streams of spatio-temporal documents: region-based top-k spatial-temporal term (RST) subscriptions and similarity-based top-k spatio-temporal term (SST) subscriptions. RST subscriptions continuously maintain the top-k most popular trending terms within a user-defined region. SST subscriptions free users from defining a region and maintain top-k locally popular terms based on a ranking function that combines term frequency, term recency, and term proximity. To solve the problem, we propose solutions that are capable of supporting real-life location-based publish/subscribe applications that process large numbers of SST and RST subscriptions over a realistic stream of spatio-temporal documents. The performance of our proposed solutions is studied in extensive experiments using two spatio-temporal datasets.
    Citation
    Chen, L., Shang, S., Jensen, C. S., Xu, J., Kalnis, P., Yao, B., & Shao, L. (2020). Top-k term publish/subscribe for geo-textual data streams. The VLDB Journal. doi:10.1007/s00778-020-00607-8
    Sponsors
    This work was supported by the National Natural Science Foundation of China (61932004, 61922054, 61872235, 61729202, 61832017, U1636210), the National Key Research and Development Program of China (2018YFC1504504, 2016YFB0700502), and Hong Kong RGC Grant 12201018.
    Publisher
    Springer Nature
    Journal
    The VLDB Journal
    DOI
    10.1007/s00778-020-00607-8
    Additional Links
    http://link.springer.com/10.1007/s00778-020-00607-8
    ae974a485f413a2113503eed53cd6c53
    10.1007/s00778-020-00607-8
    Scopus Count
    Collections
    Articles; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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