engleski

Top-k/w publish/subscribe : A publish/subscribe model for continuous top-k processing over data streams

Continuous processing of top-k queries over data streams is a promising technique for alleviating the information overload problem as it distinguishes relevant from irrelevant data stream objects with respect to a given scoring function over time. Thus it enables filtering of irrelevant data objects and delivery of top-k objects relevant to user interests in real-time. We propose a solution for distributed continuous top- k processing based on the publish/subscribe communication paradigm—top-k publish/subscribe over sliding windows (top-k/w publish/subscribe). It identifies k best-ranked objects with respect to a given scoring function over a sliding window of size w, and extends the publish/subscribe communication paradigm by continuous top-k processing algorithms coming from the field of data stream processing. In this paper, we introduce, analyze and evaluate the essential building blocks of distributed top- k/w publish/subscribe systems: First, we present a formal top-k/w publish/subscribe model and compare it to the prevailing Boolean publish/subscribe model. Next, we outline the top-k/w processing tasks performed by publish/subscribe nodes and investigate the properties of supported scoring functions. Furthermore, we explore potential routing strategies for distributed top- k/w publish/subscribe systems. Finally, we experimentally evaluate model properties and provide a comparative study investigating traffic requirements of potential routing strategies.

event-based systems; data stream; sliding-window queries

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