The Need for a Process Mining Evaluation Framework in Research and Practice

Rozinat, A Anne; Medeiros, AK Ana Karla de; Günther, CW Christian; Weijters, Ajmm Ton; Aalst, Wil M. P. van der

doi:10.1007/978-3-540-78238-4_10

Cited by 69 publications

(51 citation statements)

References 10 publications

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“…The quality criterion precision expresses whether the model does not allow for too much behaviour, generalisation expresses that the model will allow future behaviour that is currently absent in the log. [10] Other model quality criteria exist, for which we refer to [23]. In this paper, we focus on soundness and fitness, as so far no existing discovery algorithm guarantees to return a sound fitting model in finite time.…”

Section: Introductionmentioning

confidence: 99%

Discovering Block-Structured Process Models from Event Logs - A Constructive Approach

Leemans

Fahland

Aalst

2013

Application and Theory of Petri Nets and Concurrency

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399

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Process discovery is the problem of, given a log of observed behaviour, finding a process model that 'best' describes this behaviour. A large variety of process discovery algorithms has been proposed. However, no existing algorithm guarantees to return a fitting model (i.e., able to reproduce all observed behaviour) that is sound (free of deadlocks and other anomalies) in finite time. We present an extensible framework to discover from any given log a set of block-structured process models that are sound and fit the observed behaviour. In addition we characterise the minimal information required in the log to rediscover a particular process model. We then provide a polynomial-time algorithm for discovering a sound, fitting, block-structured model from any given log; we give sufficient conditions on the log for which our algorithm returns a model that is languageequivalent to the process model underlying the log, including unseen behaviour. The technique is implemented in a prototypical tool.

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Section: Introductionmentioning

confidence: 99%

Discovering Block-Structured Process Models from Event Logs - A Constructive Approach

Leemans

Fahland

Aalst

2013

Application and Theory of Petri Nets and Concurrency

Self Cite

399

350

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“…Literature clearly shows that conformance of a model with respect to a log is a multidimensional property. There is consensus on four orthogonal dimensions: fitness, precision, generalization and simplicity [1,3,13]. In this paper, we will focus on the first two: fitness and precision.…”

Section: Conformance Stagementioning

confidence: 99%

Hierarchical Conformance Checking of Process Models Based on Event Logs

Muñoz-Gama

Carmona

Aalst

2013

Application and Theory of Petri Nets and Concurrency

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Abstract. Process mining techniques aim to extract knowledge from event logs. Conformance checking is one of the hard problems in process mining: it aims to diagnose and quantify the mismatch between observed and modeled behavior. Precise conformance checking implies solving complex optimization problems and is therefore computationally challenging for real-life event logs. In this paper a technique to apply hierarchical conformance checking is presented, based on a state-of-the-art algorithm for deriving the subprocesses structure underlying a process model. Hierarchical conformance checking allows us to decompose problems that would otherwise be intractable. Moreover, users can navigate through conformance results and zoom into parts of the model that have a poor conformance. The technique has been implemented as a ProM plugin and an experimental evaluation showing the significance of the approach is provided.

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“…Several authors describe how to evaluate discovered process models [10, 4-6, 8, 7]. For example, in [8] an evaluation framework is defined. The framework provides an extended set of tests to judge the quality of process mining results.…”

Section: Related Workmentioning

confidence: 99%

Definition and Validation of Process Mining Use Cases

Ailenei

Rozinat

Eckert

et al. 2012

Business Process Management Workshops

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Abstract. Process mining is an emerging topic in the BPM marketplace. Recently, several (commercial) software solutions have become available. Due to the lack of an evaluation framework, it is very difficult for potential users to assess the strengths and weaknesses of these process mining tools. As the first step towards such an evaluation framework, we developed a set of process mining use cases and validated these use cases by means of expert interviews and a survey. We present the list of use cases and discuss the insights from our empirical validation. These use cases will then form the basis for a detailed evaluation of current process mining tools on the market.

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The Need for a Process Mining Evaluation Framework in Research and Practice

Cited by 69 publications

References 10 publications

Discovering Block-Structured Process Models from Event Logs - A Constructive Approach

Discovering Block-Structured Process Models from Event Logs - A Constructive Approach

Hierarchical Conformance Checking of Process Models Based on Event Logs

Definition and Validation of Process Mining Use Cases

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