Mining Insights from Weakly-Structured Event Data.

This thesis focuses on process mining on event data where such a normative specification is absent and, as a result, the event data is less structured. The thesis puts special emphasis on one application domain that fits this description: the analysis of smart home data where sequences of daily activities are recorded. In this thesis we propose a set of techniques to analyze such data, which can be grouped into two categories of techniques. The first category of methods focuses on preprocessing event logs in order to enable process discovery techniques to extract insights into unstructured event data. In this category we have developed the following techniques: - An unsupervised approach to refine event labels based on the time at which the event took place, allowing for example to distinguish recorded eating events into breakfast, lunch, and dinner. - An approach to detect and filter from event logs so-called chaotic activities, which are activities that cause process discovery methods to overgeneralize. - A supervised approach to abstract low-level events into more high-level events, where we show that there exist situations where process discovery approaches overgeneralize on the low-level event data but are able to find precise models on the high-level event data. The second category focuses on mining local process models, i.e., collections of process model patterns that each describe some frequent pattern, in contrast to the single global process model that is obtained with existing process discovery techniques. Several techniques are introduced in the area of local process model mining, including a basic method, fast but approximate heuristic methods, and constraint-based techniques.