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

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


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 (US)
JournalVLDB Journal
StatePublished - Mar 9 2020

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was supported by the National Natural Science Foundation of China (61932004, 61922054, 61872235, 61729202, 61832017, U1636210), the National Key Research and Development Program of China (2018YFC1504504, 2016YFB0700502), and Hong Kong RGC Grant 12201018.


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