Spatio-temporal top-k term search over sliding window

Lisi chen, Shuo Shang, Bin Yao, Kai Zheng

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

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.
Original languageEnglish (US)
Pages (from-to)1953-1970
Number of pages18
JournalWorld Wide Web
Volume22
Issue number5
DOIs
StatePublished - Jun 18 2018

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