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26-04-2018

Approximate spatio-temporal top-k publish/subscribe

Authors: Lisi Chen, Shuo Shang

Published in: World Wide Web | Issue 5/2019

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Abstract

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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Title: Approximate spatio-temporal top-k publish/subscribe
Authors: Lisi Chen
Shuo Shang
Publication date: 26-04-2018
Publisher: Springer US
Published in: World Wide Web / Issue 5/2019
Print ISSN: 1386-145X
Electronic ISSN: 1573-1413
DOI: https://doi.org/10.1007/s11280-018-0564-3

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