Multiple Uses of Frequent Episodes in Temporal Process Modeling

This dissertation investigates algorithmic techniques for temporal process discovery in many domains. Many different formalisms have been proposed for modeling temporal processes such as motifs, dynamic Bayesian networks and partial orders, but the direct inference of such models from data has been computationally intensive or even intractable. In this work, we propose the mining of frequent episodes as a bridge to inferring more formal models of temporal processes. This enables us to combine the advantages of frequent episode mining, which conducts level wise search over constrained spaces, with the formal basis of process representations, such as probabilistic graphical models and partial orders. We also investigate the mining of frequent episodes in infinite data streams which further expands their applicability into many modern data mining contexts. To demonstrate the usefulness of our methods, we apply them in different problem contexts such as: sensor networks in data centers, multi-neuronal spike train analysis in neuroscience, and electronic medical records in medical informatics.