Top-k term publish/subscribe for geo-textual data streams

Lisi Chen, Shuo Shang*, Christian S. Jensen, Jianliang Xu, Panos Kalnis, Bin Yao, Ling Shao

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

34 Citations (Scopus)


Massive amounts of data that contain spatial, textual, and temporal information are being generated at a rapid pace. With streams of such data, which includes check-ins and geo-tagged tweets, available, users may be interested in being kept up-to-date on which terms are popular in the streams in a particular region of space. To enable this functionality, we aim at efficiently processing two types of general top-k term subscriptions over streams of spatio-temporal documents: region-based top-k spatial-temporal term (RST) subscriptions and similarity-based top-k spatio-temporal term (SST) subscriptions. RST subscriptions continuously maintain the top-k most popular trending terms within a user-defined region. SST subscriptions free users from defining a region and maintain top-k locally popular terms based on a ranking function that combines term frequency, term recency, and term proximity. To solve the problem, we propose solutions that are capable of supporting real-life location-based publish/subscribe applications that process large numbers of SST and RST subscriptions over a realistic stream of spatio-temporal documents. The performance of our proposed solutions is studied in extensive experiments using two spatio-temporal datasets.

Original languageEnglish
Pages (from-to)1101-1128
Number of pages28
JournalVLDB Journal
Issue number5
Early online date9 Mar 2020
Publication statusPublished - Sept 2020

Scopus Subject Areas

  • Information Systems
  • Hardware and Architecture

User-Defined Keywords

  • Keyword
  • Publish
  • Spatio-temporal
  • Stream
  • Subscribe


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