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Approximate spatio-temporal top-k publish/subscribe

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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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Authors and Affiliations

  1. University of Wollongong, Wollongong, Australia

    Lisi Chen

  2. King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

    Shuo Shang

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  1. Lisi Chen
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  2. Shuo Shang
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Correspondence to Shuo Shang.

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This article belongs to the Topical Collection: Special Issue on Big Data Management and Intelligent Analytics

Guest Editors: Junping Du, Panos Kalnis, Wenling Li, and Shuo Shang

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Chen, L., Shang, S. Approximate spatio-temporal top-k publish/subscribe. World Wide Web 22, 2153–2175 (2019). https://doi.org/10.1007/s11280-018-0564-3

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  • DOI: https://doi.org/10.1007/s11280-018-0564-3

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