Spatio-temporal top-k term search over sliding window

Authors

Lisi Chen, University of WollongongFollow
Shuo Shang, King Abdulla University of Science and Technology
Bin Yao, Shanghai Jiao Tong University
Kai Zheng, University of Electronic Science and Technology of China

RIS ID

128611

Publication Details

Chen, L., Shang, S., Yao, B. & Zheng, K. (2018). Spatio-temporal top-k term search over sliding window. World Wide Web, 1-18.

Abstract

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