Efficient Filtering Algorithms for Location-Aware Publish/Subscribe

Location-based services have been widely adopted in many systems. Existing works employ a pull model or user-initiated model, where a user issues a query to a server which replies with location-aware answers. To provide users with instant replies, a push model or server-initiated model is becoming an inevitable computing model in the next-generation location-based services. In the push model, subscribers register spatio-textual subscriptions to capture their interests, and publishers post spatio-textual messages. This calls for a high-performance location-aware publish/subscribe system to deliver publishers’ messages to relevant subscribers. The research challenges that arise in designing a location-aware publish/subscribe system is studied. An R-tree based index is proposed by integrating textual descriptions into R-tree nodes. Devised efficient filtering algorithms and effective pruning techniques to achieve high performance. This method can support both conjunctive queries and ranking queries. Support dynamic updates efficiently. commodity computer

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