Studies on the Discovery of Declarative Control Flows from Error-prone Data

The declarative modeling of workflows has been introduced to cope with flexibility in processes. Its rationale is based on the idea of stating some basic rules (named constraints), tying the execution of some activities to the enabling, requiring or disabling of other activities. What is not explicitly prohibited by such constraints is implicitly considered legal, w.r.t. the specification of the process. Declarative models for workflows are based on a taxonomy of constraint templates. Constraints are thus instances of constraint templates, applied to specific activities. Many algorithms for the automated discovery of declarative workflows associate to each constraint a support. The support is a statistical measure assessing to what extent a constraint was respected during the enactment(s) of the process. In current state-of-the-art literature, constraints having a support below a user-defined threshold are considered not valid for the process. Thresholds are useful for filtering out guesses based on possible misleading events, reported in logs either because of errors in the execution, unlikely process deviations, or wrong recordings in logs. The latter circumstance can be considered extremely relevant when logs are not written down directly by machines reporting their work, but extracted from other source of information. Here, we present an insight on the actual capacity of filtering constraints by modifying the threshold for support, on the basis of real data. Then, taking a cue from the results performed on such analysis, we consider the trend of support when controlled errors are injected into the log, w.r.t. individual constraint templates. Through these tests, we demonstrate by experiment that each constraint template reveal to be less or more robust to different kinds of error, according to its nature.