In ACM Distributed Event-based Systems (DEBS), pages 14-25, July 2010.
Acceptance rate: 25% .
A new publish/subscribe capability is presented: the ability to predict the likelihood that a subscription will be matched at some point in the future. Composite subscriptions consisting of temporal and logical operators are efficiently represented by a set of finite state machines and rules. The algorithm trains a Markov model to an application's event workload, and predicts the probability that a given subscription will match within a window in the future event stream. Evaluations demonstrate that the memory and processing costs of the algorithm scales well with the number of subscriptions, and the prediction precision is high, especially when the workload characteristics do not change rapidly. A comparison with a hand-crafted Markov model using real data traces shows that the algorithm consumes much less memory and processing power, and still delivers prediction precision that approaches the hand-crafted model's. This is especially impressive since the algorithms lack any of the domain expertise embedded in the hand-crafted model.
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