Location-Aware Top-k Term Publish/Subscribe

Lisi chen, Shuo Shang, Zhiwei Zhang, Xin Cao, Christian S. Jensen, Panos Kalnis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

50 Scopus citations


Massive amount of data that contain spatial, textual, and temporal information are being generated at a high scale. These spatio-Temporal documents cover a wide range of topics in local area. Users are interested in receiving local popular terms from spatio-Temporal documents published with a specified region. We consider the Top-k Spatial-Temporal Term (ST2) Subscription. Given an ST2 subscription, we continuously maintain up-To-date top-k most popular terms over a stream of spatio-Temporal documents. The ST2 subscription takes into account both frequency and recency of a term generated from spatio-Temporal document streams in evaluating its popularity. We propose an efficient solution to process a large number of ST2 subscriptions over a stream of spatio-Temporal documents. The performance of processing ST2 subscriptions is studied in extensive experiments based on two real spatio-Temporal datasets.
Original languageEnglish (US)
Title of host publication2018 IEEE 34th International Conference on Data Engineering (ICDE)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages12
ISBN (Print)9781538655207
StatePublished - Oct 25 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was supported by the grant of the Hong Kong Research Grants Council, Hong Kong SAR, China, No. 12258116 and the Nation Nature Science Foundation of China, China, No. 61602395.


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