Title

Approximate spatio-temporal top-k publish/subscribe

RIS ID

127105

Publication Details

Chen, L. & Shang, S. (2018). Approximate spatio-temporal top-k publish/subscribe. World Wide Web, Online first 1-23.

Abstract

2018 Springer Science+Business Media, LLC, part of Springer Nature 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.

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Link to publisher version (DOI)

http://dx.doi.org/10.1007/s11280-018-0564-3