2 research outputs found
Preventing Object-centric Discovery of Unsound Process Models for Object Interactions with Loops in Collaborative Systems: Extended Version
Object-centric process discovery (OCPD) constitutes a paradigm shift in
process mining. Instead of assuming a single case notion present in the event
log, OCPD can handle events without a single case notion, but that are instead
related to a collection of objects each having a certain type. The object types
constitute multiple, interacting case notions. The output of OCPD is an
object-centric Petri net, i.e. a Petri net with object-typed places, that
represents the parallel execution of multiple execution flows corresponding to
object types. Similar to classical process discovery, where we aim for
behaviorally sound process models as a result, in OCPD, we aim for soundness of
the resulting object-centric Petri nets. However, the existing OCPD approach
can result in violations of soundness. As we will show, one violation arises
for multiple interacting object types with loops that arise in collaborative
systems. This paper proposes an extended OCPD approach and proves that it does
not suffer from this violation of soundness of the resulting object-centric
Petri nets. We also show how we prevent the OCPD approach from introducing
spurious interactions in the discovered object-centric Petri net. The proposed
framework is prototypically implemented
Predictive Compliance Monitoring in Process-Aware Information Systems: State of the Art, Functionalities, Research Directions
Business process compliance is a key area of business process management and
aims at ensuring that processes obey to compliance constraints such as
regulatory constraints or business rules imposed on them. Process compliance
can be checked during process design time based on verification of process
models and at runtime based on monitoring the compliance states of running
process instances. For existing compliance monitoring approaches it remains
unclear whether and how compliance violations can be predicted, although
predictions are crucial in order to prepare and take countermeasures in time.
This work, hence, analyzes existing literature from compliance and SLA
monitoring as well as predictive process monitoring and provides an updated
framework of compliance monitoring functionalities. For each compliance
monitoring functionality we elicit prediction requirements and analyze their
coverage by existing approaches. Based on this analysis, open challenges and
research directions for predictive compliance and process monitoring are
elaborated