Incorporating Negative Information in Process Discovery
The discovery of a formal process model from event logs describing real process executions is a challenging problem that has been studied from several angles. Most of the contributions consider the extraction of a model as a semi-supervised problem where only positive information is available. In this paper we present a fresh look at process discovery where also negative information can be taken into account. This feature may be crucial for deriving process models which are not only simple, fitting and precise, but also good on generalizing the right behavior underlying an event log. The technique is based on numerical abstract domains and Satisfiability Modulo Theories (SMT), and can be combined with any process discovery technique. As an example, we show in detail how to supervise a recent technique that uses numerical abstract domains. Experiments performed in our prototype implementation show the effectiveness of the techniques and the ability to improve the results produced by selected discovery techniques.
KeywordsPositive Information Negative Information Convex Polyhedron Business Process Management Inductive Logic Programming
Unable to display preview. Download preview PDF.
- 1.van der Aalst, W.M.P.: Process Mining - Discovery, Conformance and Enhancement of Business Processes. Springer (2011)Google Scholar
- 3.Weijters, A.J.M.M., Ribeiro, J.T.S.: Flexible heuristics miner (FHM). In: CIDM, pp. 310–317 (2011)Google Scholar
- 6.Lamma, E., Mello, P., Riguzzi, F., Storari, S.: Applying inductive logic programming to process mining. Inductive Logic Programming, 132–146 (2008)Google Scholar
- 9.Rockafellar, R.T.: Convex Analysis. Princeton University Press (1970)Google Scholar
- 12.Fukuda, K., Picozzi, S., Avis, D.: On canonical representations of convex polyhedra. In: Proc. of the First International Congress of Mathematical Software, pp. 350–360 (2002)Google Scholar