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Erschienen in:
Pragmatics Annotated Coloured Petri Nets for Protocol Software Generation and Verification Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

A Method for Assessing Parameter Impact on Control-Flow Discovery Algorithms

verfasst von : Joel Ribeiro, Josep Carmona

Erschienen in: Transactions on Petri Nets and Other Models of Concurrency XI

Verlag: Springer Berlin Heidelberg

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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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Vorheriges Kapitel Self-tracking Reloaded: Applying Process Mining to Personalized Health Care from Labeled Sensor Data
Nächstes Kapitel Negotiations and Petri Nets
Fußnoten
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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Metadaten
Titel
A Method for Assessing Parameter Impact on Control-Flow Discovery Algorithms
verfasst von
Joel Ribeiro
Josep Carmona
Copyright-Jahr
2016
Verlag
Springer Berlin Heidelberg
Buch
Transactions on Petri Nets and Other Models of Concurrency XI

Print ISBN: 978-3-662-53400-7

Electronic ISBN: 978-3-662-53401-4

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-662-53401-4_9

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