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.
|Title of host publication||4th IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2013, Singapore, April 16-19, 2013|
|Editors||Barbara Hammer, Zhi-Hua Zhou, Lipo Wang, Nitesh Chawla|
|Place of Publication||Singapore|
|Pages||135 - 142|
|Publication status||Published - 1 May 2013|