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With the prevalence of social media and GPS-enabled devices, a massive amountof geo-textual data has been generated in a stream fashion, leading to avariety of applications such as location-based recommendation and informationdissemination. In this paper, we investigate a novel real-time top-k monitoringproblem over sliding window of streaming data; that is, we continuouslymaintain the top-k most relevant geo-textual messages (e.g., geo-tagged tweets)for a large number of spatial-keyword subscriptions (e.g., registered usersinterested in local events) simultaneously. To provide the most recentinformation under controllable memory cost, sliding window model is employed onthe streaming geo-textual data. To the best of our knowledge, this is the firstwork to study top-k spatial-keyword publish/subscribe over sliding window. Anovel centralized system, called Skype (Topk Spatial-keywordPublish/Subscribe), is proposed in this paper. In Skype, to continuouslymaintain top-k results for massive subscriptions, we devise a novel indexingstructure upon subscriptions such that each incoming message can be immediatelydelivered on its arrival. To reduce the expensive top-k re-evaluation costtriggered by message expiration, we develop a novel cost-based k-skybandtechnique to reduce the number of re-evaluations in a cost-effective way.Extensive experiments verify the great efficiency and effectiveness of ourproposed techniques. Furthermore, to support better scalability and higherthroughput, we propose a distributed version of Skype, namely, DSkype, on topof Storm, which is a popular distributed stream processing system. With thehelp of fine-tuned subscription/message distribution mechanisms, DSkype canachieve orders of magnitude speed-up than its centralized version.

Top-k Spatial-keyword Publish/Subscribe Over Sliding Window