Abstract
Given a 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 face 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 two different miners: the inductive miner and the flexible heuristic miner, and the experimental results witness the applicability of the general framework described in this paper.
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Notes
- 1.
This paper is an improved and extended version of [10].
- 2.
\(\mathcal {P}(X)\) denotes the powerset of some set X.
- 3.
Depending on the context, we will consider P either as a parameter list \([p_i,...,p_k]\) or its concrete instantiation \([v_i,...,v_k]\).
- 4.
OAT stands for One (factor) At a Time.
- 5.
A point in the parameter space is the result of assigning specific values to the parameters in the parameter list P: \(p_1\)=\(v_1\),..., \(p_k\)=\(v_k\).
- 6.
The Node Arc Degree measure consists of the average of incoming and outgoing arcs of every node of the process model.
- 7.
For computing \(EE_i\), \(\alpha _i - \beta _i\) is considered to be 1 when the parameter is changed from a disabled to an enabled state, or the other way around (e.g., the last parameter in Table 4).
- 8.
All possible parameter settings the control-flow algorithm allows.
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Acknowledgments
This work as been partially supported by funds from the Spanish Ministry for Economy and Competitiveness (MINECO) and the European Union (FEDER funds) under grant COMMAS (ref. TIN2013-46181-C2-1-R).
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Ribeiro, J., Carmona, J. (2016). A Method for Assessing Parameter Impact on Control-Flow Discovery Algorithms. In: Koutny, M., Desel, J., Kleijn, J. (eds) Transactions on Petri Nets and Other Models of Concurrency XI. Lecture Notes in Computer Science(), vol 9930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-53401-4_9
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