Efficient Top-k Subscription Matching for Location-Aware Publish/Subscribe

The dissemination of messages to a vast number of mobile users has raised a lot of attention. This issue is inherent in emerging applications, such as location-based targeted advertising, selective information disseminating, and ride sharing. In this paper, we examine how to support location-based message dissemination in an effective and efficient manner. Our main idea is to develop a location-aware version of the Pub/Sub model, which was designed for message dissemination. While a lot of studies have successfully used this model to match the interest of subscriptions (e.g., the properties of potential customers) and events (e.g., information of casual users), the issues of incorporating the location information of subscribers and publishers have not been well addressed. We propose to model subscriptions and events by boolean expressions and location data. This allows complex information to be specified. However, since the number of publishers and subscribers can be enormous, the time cost for matching subscriptions and events can be prohibitive. To address this problem, we have developed the R-I-tree. This data structure is an integration of the R-tree and the dynamic interval-tree. Together with our novel pruning strategy on R-I-tree, our solution can effectively and efficiently return the top-k subscriptions with respect to an event. We have performed extensive evaluations to verify our approach.