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Top-k/w publish/subscribe: finding k most relevant publications in sliding time window w

11 years 3 months ago
Top-k/w publish/subscribe: finding k most relevant publications in sliding time window w
Existing content-based publish/subscribe systems are designed assuming that all matching publications are equally relevant to a subscription. As we cannot know in advance the distribution of publication content, the following two unwanted situations are highly possible: a subscriber either receives too many or only few publications. In this paper we present a new publish/subscribe model which is based on the sliding window computation model. Our model assumes that publications have different relevance to a subscription. In the model, a subscriber receives k most relevant publications published within a time window w, where k and w are parameters defined per each subscription. As a consequence, the arrival rate of incoming relevant publications per subscription is predefined, and does not depend on the publication rate. Since all relevant objects (i.e. publications in our case) cannot be kept in main memory, existing solutions immediately discard less relevant objects, and store only a...
Kresimir Pripuzic, Ivana Podnar Zarko, Karl Aberer
Added 19 Oct 2010
Updated 19 Oct 2010
Type Conference
Year 2008
Where DEBS
Authors Kresimir Pripuzic, Ivana Podnar Zarko, Karl Aberer
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