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

49 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


Dive into the research topics of 'Location-Aware Top-k Term Publish/Subscribe'. Together they form a unique fingerprint.

Cite this