Discovering Metric Temporal Business Constraints from Event Logs

Abstract

Process discovery aims at building process models using information retrieved from logs. Process characteristics play a significant role in the selection of a suitable process modeling language for describing process discovery results. Business processes characterized by high variability, in which participants have a lot of autonomy and flexibility in executing the process, are difficult to be described with procedural process modeling languages,since they explicitly represent in a model every possible path. Declarative languages, like Declare, alleviate this issue by defining a set of constraints between activities that must not be violated during the process execution instead of describing what to do step by step. Recently, several process discovery techniques have been proposed for extracting a set of Declare constraints from a log. However, no one of these techniques allows the user to exploit the time perspective often available in a log to discover “time-aware” Declare constraints. Timed Declare has already previously been introduced to monitor metric temporal constraints at runtime. In this paper, we use this semantics for discovering a set of Timed Declare constraints from an event log. We have implemented the proposed approach as a plug-in of the process mining tool ProM. We have validated the approach by using our plug-in to mine two real-life event logs.