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

Claudio Di Ciccio, Massimo Mecella

Publication: Chapter in book/Conference proceedingContribution to conference proceedings


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.
Original languageEnglish
Title of host publication4th 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 PublicationSingapore
Pages135 - 142
Publication statusPublished - 1 May 2013

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