World Wide Web

, Volume 22, Issue 5, pp 1953–1970 | Cite as

Spatio-temporal top-k term search over sliding window

  • Lisi Chen
  • Shuo ShangEmail author
  • Bin Yao
  • Kai Zheng
Part of the following topical collections:
  1. Special Issue on Web Information Systems Engineering 2017


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.


Top-k Term Spatial Temporal 


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of WollongongWollongongAustralia
  2. 2.King Abdullah University of Science and TechnologyThuwalSaudi Arabia
  3. 3.Shanghai Jiao Tong UniversityShanghaiChina
  4. 4.University of Electronic Science and Technology of ChinaChengduChina

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