Affiliations: Process and Data Science (PADS), RWTH Aachen University, Germany. [email protected]
Correspondence:
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Address for correspondence: Process and Data Science (PADS), RWTH Aachen University, Germany
Abstract: A process model is lucent if no two reachable states are enabling the same set of activities. An event log is translucent if each event carries information about the set of activities enabled when the event occurred (normally one only sees the activity performed). Both lucency and translucency focus on the set of enabled activities and are therefore related. Surprisingly, these notions have not been investigated before. This paper aims to (1) characterize process models that are lucent, (2) provide a discovery approach to learn process models from translucent event logs, and (3) relate lucency and translucency. Lucency is defined both in terms of automata and Petri nets. A marked Petri net is lucent if there are no two different reachable markings enabling the same set of transitions, i.e., states are fully characterized by the transitions they enable. We will also provide a novel process discovery technique starting from a translucent event log. It turns out that information about the set of activities is extremely valuable for process discovery. We will provide sufficient conditions to ensure that the discovered model is lucent and show that a translucent event log sampled from a lucent process model can be used to rediscover the original model. We anticipate new analysis techniques exploiting lucency. Moreover, as shown in this paper, translucent event logs provide valuable information that can be exploited by a new breed to process mining techniques.
Keywords: Process mining, Petri nets, lucent process models, translucent event logs
