Process discovery is an important area in the field of process mining, where most discovery algorithms focus on process control-flow, giving little attention to the data-flow perspective. As a result, the discovered process models lack information about data dependencies, and process experts need to manually enrich the discovered process models accordingly. This requires deep domain knowledge, is not scalable, and error-prone. To overcome this limitation, this paper proposes an approach that aims to discover the data objects and their behavior by investigating how event attributes are manipulated during process execution. The resulting data objects are used to enhance the discovered process model. The feasibility of the proposed approach is evaluated with two real-life event logs: Road Traffic Fine Management and Hospital Billing.
Dorina Bano, Francesca Zerbato, Barbara Weber, Mathias Weske
8 Dec 2021