Since the first algorithms for automatically discovering process models from event logs have been proposed in the late 1990ies the problem of obtaining insights into processes by mining from event logs gained growing attention. By now, the field has grown into a maturing discipline and industry has begun adopting process mining in regular operations, supported by several commercial process mining solutions are available on the market.
In the early days of process mining, several algorithms for constructively discovering a process model from an event log were proposed, each algorithm pursuing unique principles for constructing a model. This first generation of process discovery techniques, which includes for instance the alpha-algorithm, paved the ground for process mining as research discipline. As these algorithms were applied in practice, new research challenges showed up, sparking new results in both pre-processing event data and evaluating process models on event logs. In particular the latter deepened the understanding of the challenges in process mining and established a reliable feedback mechanism in process mining in the form of conformance checking. This feedback mechanism enabled researching a second generation of process mining techniques addressing a large variety of problems such as quality guarantees for discovered models, including the data perspective in discovered models, or discovering temporal logic constraints. In particular, the inductive miner family was seen as a new milestone as it provided a systematic way to develop process discovery algorithms with reliable results. Yet again, as these more capable techniques are being applied to the growing and more detailed event data recorded in practice, further unsolved challenges arise.
In the first part of my talk I will draw an arc from the early days of process mining to the current state of the art in process mining – highlighting central techniques and their impact on later developments. In the second part of my talk, I will then turn to what kinds of event data and challenges are being found in practice today, how existing process mining techniques fail to address them, and thus which open challenges and opportunities the process mining field offers also for researchers from other domains.