A Two-Step Fast Algorithm for the Automated Discovery of Declarative Workflows

Declarative approaches are particularly suitable for modeling highly flexible processes. They especially apply to artful processes, i.e., rapid informal processes that are typically carried out by those people whose work is mental rather than physical (managers, professors, researchers, engineers, etc.), the so called ``knowledge workers''. This paper describes MINERful++, a two-step algorithm for an efficient discovery of constraints that constitute declarative workflow models. As a first step, a knowledge base is built, with information about temporal statistics gathered from execution traces. Then, the statistical support of constraints is computed, by querying that knowledge base. MINERful++ is fast, modular, independent of the specific formalism adopted for representing constraints, based on a probabilistic approach and capable of eliminating the redundancy of subsumed constraints.