SKPS: Towards efficient processing of spatial-keyword publish/subscribe system

Publication Type:
Conference Proceeding
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, 9877 LNCS pp. 444 - 447
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© Springer International Publishing AG 2016. With the popularity of geo-equipped devices and location based services, spatial-keyword publish/subscribe has emerged as a very important framework to disseminate real-time messages (e.g., geo-tagged e-coupon) to registered subscriptions (e.g., users interested in nearby promotions). While there are several work focusing on improving the efficiency of spatial-keyword publish/subscribe, their techniques fail to consider both the spatial and keyword distributions in a fine manner, thus lacking of scalability when coping with massive subscriptions. In this demonstration, we propose SKPS, a centralized in-memory spatial-keyword publish/ subscribe system, which exploits fully the spatial and keyword distributions of subscription workload during indexing construction, and employs an efficient message matching algorithm to disseminate each incoming message to relevant subscriptions in a real-time manner. We present a prototype of SKPS which provides users with a web-based interface to explore the message dissemination in publish/subscribe system.
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