Using translucent activity relationships frequencies to enhance process discovery

Event logs are the primary source of information in process mining. An event log consists of events, each having three mandatory attributes: a case identifier, an activity, and a timestamp. Translucent event logs add a fourth mandatory attribute: enabled activities. These are activities for which execution was possible besides the executed activity. Information on enabled activities can be accessed in task management software, in tasks executed within a desktop environment, or by utilizing domain knowledge. Information on enabled activities is valuable for process discovery and other process mining tasks. For example, utilizing the information when discovering process models from translucent event logs results in more robust and generalizable process models. In recent work, translucent activity relationships were defined, and their application was shown by extending the inductive mining approach. This work extends the defined relationships by also introducing frequencies. As a result, the Inductive Miner—infrequent can be extended. Hence, it is possible to use these relationships in the presence of noise in the recorded event logs. We introduce three translucent variants for the Inductive Miner—infrequent, each using translucent activity relationships at different points. Moreover, we allow for different graphs when fall-throughs within the Inductive Miner occur. Using artificial and enriched real-life event logs, we show that considering these relationships, more data-specific process models that still have desired generalization capabilities can be discovered.