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

25 Scopus citations


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
Number of pages16
ISBN (Print)9783319687827
StatePublished - Oct 4 2017

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was supported by the NSFC (U1636210, 61373156, 91438121 and 61672351), the National Basic Research Program (973 Program, No. 2015CB352403), the National Key Research and Development Program of China (2016YFB0700502), the Scientific Innovation Act of STCSM (15JC1402400) and the Microsoft Research Asia.


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