Location-Based Top-k Term Querying over Sliding Window

Ying Xu, Lisi Chen, Bin Yao, Shuo Shang, Shunzhi Zhu, Kai Zheng, Fang Li

Research output: Chapter in Book/Report/Conference proceedingChapter

21 Scopus citations

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.
Original languageEnglish (US)
Title of host publicationWeb Information Systems Engineering – WISE 2017
PublisherSpringer Nature
Pages299-314
Number of pages16
ISBN (Print)9783319687827
DOIs
StatePublished - Oct 4 2017

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