Approximate spatio-temporal top-k publish/subscribe

Lisi Chen, Shuo Shang

Research output: Contribution to journalArticlepeer-review

39 Scopus citations


Location-based publish/subscribe plays a significant role in mobile information disseminations. In this light, we propose and study a novel problem of processing location-based top-k subscriptions over spatio-temporal data streams. We define a new type of approximate location-based top-k subscription, Approximate Temporal Spatial-Keyword Top-k (ATSK) Subscription, that continuously feeds users with relevant spatio-temporal messages by considering textual similarity, spatial proximity, and information freshness. Different from existing location-based top-k subscriptions, Approximate Temporal Spatial-Keyword Top-k (ATSK) Subscription can automatically adjust the triggering condition by taking the triggering score of other subscriptions into account. The group filtering efficacy can be substantially improved by sacrificing the publishing result quality with a bounded guarantee. We conduct extensive experiments on two real datasets to demonstrate the performance of the developed solutions.
Original languageEnglish (US)
Pages (from-to)2153-2175
Number of pages23
JournalWorld Wide Web
Issue number5
StatePublished - Apr 26 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


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