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The widespread use of GPS-enabled smartphones along with the popularity ofmicro-blogging and social networking applications, e.g., Twitter and Facebook,has resulted in the generation of huge streams of geo-tagged textual data. Manyapplications require real-time processing of these streams. For example,location-based e-coupon and ad-targeting systems enable advertisers to registermillions of ads to millions of users. The number of users is typically veryhigh and they are continuously moving, and the ads change frequently as well.Hence sending the right ad to the matching users is very challenging. Existingstreaming systems are either centralized or are not spatial-keyword aware, andcannot efficiently support the processing of rapidly arriving spatial-keyworddata streams. This paper presents Tornado, a distributed spatial-keyword streamprocessing system. Tornado features routing units to fairly distribute theworkload, and furthermore, co-locate the data objects and the correspondingqueries at the same processing units. The routing units use the Augmented-Grid,a novel structure that is equipped with an efficient search algorithm fordistributing the data objects and queries. Tornado uses evaluators to processthe data objects against the queries. The routing units minimize the redundantcommunication by not sending data updates for processing when these updates donot match any query. By applying dynamically evaluated cost formulae thatcontinuously represent the processing overhead at each evaluator, Tornado isadaptive to changes in the workload. Extensive experimental evaluation usingspatio-textual range queries over real Twitter data indicates that Tornadooutperforms the non-spatio-textually aware approaches by up to two orders ofmagnitude in terms of the overall system throughput.

Adaptive Processing of Spatial-Keyword Data Over a Distributed Streaming Cluster