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Auflistung nach Schlagwort "Process discovery"

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  • Zeitschriftenartikel
    Generating Artificial Data for Empirical Analysis of Control-flow Discovery Algorithms
    (Business & Information Systems Engineering: Vol. 61, No. 6, 2019) Jouck, Toon; Depaire, Benoît
    Within the process mining domain, research on comparing control-flow (CF) discovery techniques has gained importance. A crucial building block of empirical analysis of CF discovery techniques is obtaining the appropriate evaluation data. Currently, there is no answer to the question of how to collect such evaluation data. The paper introduces a methodology for generating artificial event data (GED) and an implementation called the Process Tree and Log Generator. The GED methodology and its implementation provide users with full control over the characteristics of the generated event data and an integration within the ProM framework. Unlike existing approaches, there is no tradeoff between including long-term dependencies and soundness of the process. The contributions of the paper provide a solution for a necessary step in the empirical analysis of CF discovery algorithms.
  • Zeitschriftenartikel
    Guided Interaction Exploration and Performance Analysis in Artifact-Centric Process Models
    (Business & Information Systems Engineering: Vol. 61, No. 6, 2019) Eck, Maikel L.; Sidorova, Natalia; Aalst, Wil M. P.
    Artifact-centric process models aim to describe complex processes as a collection of interacting artifacts. Recent development in process mining allow for the discovery of such models. However, the focus is often on the representation of the individual artifacts rather than their interactions. Based on event data, composite state machines representing artifact-centric processes can be discovered automatically. Moreover, the study provides ways of visualising and quantifying interactions among different artifacts. For example, strongly correlated behaviours in different artifacts can be highlighted. Interesting correlations can be subsequently analysed to identify potential causes of process performance issues. The study provides a strategy to explore the interactions and performance differences in this context. The approach has been fully implemented as a ProM plug-in; the CSM Miner provides an interactive artifact-centric process discovery tool focussing on interactions. The approach has been evaluated using real life data, to show that the guided exploration of artifact interactions can successfully identify process performance issues.
  • Zeitschriftenartikel
    Towards Confirmatory Process Discovery: Making Assertions About the Underlying System
    (Business & Information Systems Engineering: Vol. 61, No. 6, 2019) Janssenswillen, Gert; Depaire, Benoît
    The focus in the field of process mining, and process discovery in particular, has thus far been on exploring and describing event data by the means of models. Since the obtained models are often directly based on a sample of event data, the question whether they also apply to the real process typically remains unanswered. As the underlying process is unknown in real life, there is a need for unbiased estimators to assess the system-quality of a discovered model, and subsequently make assertions about the process. In this paper, an experiment is described and discussed to analyze whether existing fitness, precision and generalization metrics can be used as unbiased estimators of system fitness and system precision. The results show that important biases exist, which makes it currently nearly impossible to objectively measure the ability of a model to represent the system.