A Novel RI-tree Based top-k subscription matching for location-aware Publish/Subscribe systems

Computer Engineering

The dissemination of messages to an enormous range of mobile users has raised lots of attention. This issue is inherent in rising applications, like location-based targeted advertising, information diffusive, and ride sharing. During this project, we have to examine a way to support location-based message dissemination in economically good and efficient manner. Our main plan is to develop a location-aware version of the Pub/Sub model that was designed for message dissemination. Whereas lots of studies have with success used this model to match the interest of subscriptions (e.g., the properties of potential customers) and events (e.g., data of casual users), the problems of incorporating the placement data of subscribers and publishers haven't been well addressed. We have propose to model subscriptions and events by Boolean expressions and site knowledge this permits complicated data to be given. However, since the amount of publishers and subscribers is huge, the time price for matching subscriptions and events is prohibitory. To deal with this drawback, we've got developed the RI-tree. This organization is an integration of the R-tree and also the dynamic interval-tree. Beside our novel pruning strategy on RI-tree, our resolution will effectively and with efficiency come back the top-k subscriptions with relevancy a happening. Compared to the prevailing works, the RI-tree projected during this project has 2 characteristic options. First, it permits users to specify their interests with Boolean expressions, that is additional communicative than keywords. Second, it focuses on the top-k semantics that is often utilized in several rising applications (e.g., location-based targeted advertising).

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