Mathilde Boltenhagen received the Best Process Mining PhD Dissertation Award 2022 during the Fourth International Conference on Process Mining (ICPM 2022) for her thesis entitled "Process Instance Clustering based on Conformance Checking Artefacts".
The Best Process Mining PhD Dissertation Award distinguishes theses that contributed to advancing the state of the art in the foundations, engineering, and on-field application of process mining techniques. In this context, the term "process mining" has to be understood in a broad sense: using event data produced during the execution of business, software, or system processes, in order to extract fact-based knowledge and insights on such processes and manage future processes in innovative ways.
Mathilde's thesis contributes to several aspects of conformance checking. It provides a framework and tools to partition log-traces of a process into clusters that use partial-order traces as centroids to take concurrency into account. In her work, Mathilde refined the notion of anti-alignments as a measure for the precision of process model, and she implemented the first efficient approximation of anti-alignments.
For her PhD programme, Mathilde was jointly supervised by Thomas Chatain (Laboratoire Méthodes Formelles, ENS Paris-Saclay) and Josep Carmona (Universitat Politècnica de Catalunya).
Today, Mathilde works as a DevOps engineer at Arkhn, a French startup that builds a data architecture to leverage health data for medical research.