Maintaining boolean Top-K spatial temporal results in publish-subscribe systems

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Conference Proceeding
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, 10837 LNCS pp. 147 - 160
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© Springer International Publishing AG, part of Springer Nature 2018. Nowadays many devices and applications in social networks and location-based services are producing, storing and using description, location and occurrence time of objects. Given a massive number of boolean top-k spatial-temporal queries and the spatial-textual message streams, in this paper we study the problem of continuously updating top-k messages with the highest ranks, each of which contains all the requested keywords when rank of a message is calculated by its location and freshness. Decreasing the ranks of existing top-k results over time and producing new incoming messages, cause continuously computing and maintaining the best results. To the best of our knowledge, there is no prior work that can exactly solve this problem. We propose two indexing and matching methods, then conduct an experimental evaluation to show the impact of parameters and analyse the models.
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