We present a generative model for representing and reasoning about the relationships

among events in continuous time. We apply the model to the domain of networked

and distributed computing environments where we fit the parameters of the

model from timestamp observations, and then use hypothesis testing to discover dependencies between the events and changes in behavior for monitoring and diagnosis.

After introducing the model, we present an EM algorithm for fitting the parameters

and then present the hypothesis testing approach for both dependence discovery

and change-point detection. We validate the approach for both tasks using real data

from a trace of network events at Microsoft Research Cambridge. Finally, we formalize

the relationship between the proposed model and the noisy-or gate for cases when

time can be discretized.

In: International Conference on Uncertainty in Artificial Intelligence (UAI)

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