Collaboration Petri Nets: Verification, Equivalence, and Discovery (Extended Version)
CoRR(2024)
摘要
Process modeling and discovery techniques aim to construct sound and valid
process models for different types of processes, i.e., process orchestrations
and collaboration processes. Orchestrations represent behavior of cases within
one process. Collaboration processes represent behavior of collaborating cases
within multiple process orchestrations that interact via collaboration concepts
such as organizations, agents, objects, and services. The heterogeneity of
collaboration concepts and types such as message exchange and resource sharing
has led to different representations and discovery techniques for collaboration
process models, but a standard model class is lacking. We propose collaboration
Petri nets (cPN) to achieve comparability between techniques, to enable
approach and property transfer, and to build a standardized collaboration
mining pipeline similar to process mining. For cPN, we require desirable
modeling power, decision power, modeling convenience, and relations to existing
model classes. We show the representation of collaboration types, structural
characterization as workflow nets, automatic verification of soundness,
bisimulation equivalence to existing model classes, and application in a
general discovery framework. As empirical evidence to discover cPN, we conduct
a comparative evaluation between three discovery techniques on a set of
existing collaboration event logs.
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