A method for assessing parameter impact on control-flow discovery algorithms

Given an event log L, a control-flow discovery algorithm f, and a quality metric m, this paper faces the following problem: what are the parameters in f that mostly influence its application in terms of m when applied to L? This paper proposes a method to solve this problem, based on sensitivity analysis, a theory which has been successfully applied in other areas. Clearly, a satisfactory solution to this problem will be crucial to bridge the gap between process discovery algorithms and final users. Additionally, recommendation techniques and meta-techniques like determining the representational bias of an algorithm may benefit from solutions to the problem considered in this paper. The method has been evaluated over a set of logs and the flexible heuristic miner, and the preliminary results witness the applicability of the general framework described in this paper.Peer ReviewedPostprint (author's final draft

Automatic determination of parameters' values for Heuristics Miner++

The choice of parameters' values for noise-tolerant Process Mining algorithms is not trivial, especially for users that are not expert in Process Mining. Exhaustive exploration of all possible set of values is not feasible, since several parameters are real-valued. Selecting the \u201cright\u201d values, however, is important, since otherwise the control-flow network returned by the mining can be quite far from the correct one. Here we face this problem for a specific Process Mining algorithm, i.e. Heuristics Miner++. We recognize that the domain of real-valued parameters can be actually partitioned into a finite number of equivalence classes and we suggest exploring the parameters space by a local search strategy driven by a Minimum Description Length principle. We believe that the proposed approach is sufficiently general to be used for other Process Mining algorithms. Experimental results on a set of randomly generated process models show promising results