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Spatio-temporal top-k term search over sliding window

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Abstract

In part due to the proliferation of GPS-equipped mobile devices, massive volumes 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 most frequent 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 term frequency, spatial proximity, and term freshness. 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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Authors and Affiliations

  1. University of Wollongong, Wollongong, Australia

    Lisi Chen

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

    Shuo Shang

  3. Shanghai Jiao Tong University, Shanghai, China

    Bin Yao

  4. University of Electronic Science and Technology of China, Chengdu, China

    Kai Zheng

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  1. Lisi Chen
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  2. Shuo Shang
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  3. Bin Yao
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  4. Kai Zheng
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Corresponding author

Correspondence to Shuo Shang.

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This article belongs to the Topical Collection: Special Issue on Web Information Systems Engineering 2017

Guest Editors: Lu Chen and Yunjun Gao

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Chen, L., Shang, S., Yao, B. et al. Spatio-temporal top-k term search over sliding window. World Wide Web 22, 1953–1970 (2019). https://doi.org/10.1007/s11280-018-0606-x

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