Starten Sie Ihre Suche...


Durch die Nutzung unserer Webseite erklären Sie sich damit einverstanden, dass wir Cookies verwenden. Weitere Informationen

Comprehensible Predictive Models for Business Processes

Management Information Systems Quarterly (MISQ). Bd. 40. H. 4. Minneapolis. 2016 S. 1009 - 1034

Erscheinungsjahr: 2016

ISBN/ISSN: 0276-7783

Publikationstyp: Zeitschriftenaufsatz

Sprache: Englisch

GeprüftBibliothek

Inhaltszusammenfassung


Predictive modeling approaches in business process management provide a way to streamline operational business processes. For instance, they can warn decision makers about undesirable events that are likely to happen in the future, giving the decision maker an opportunity to intervene. The topic is gaining momentum in process mining, a field of research that has traditionally developed tools to discover business process models from data sets of past process behavior. Predictive modeling techn...Predictive modeling approaches in business process management provide a way to streamline operational business processes. For instance, they can warn decision makers about undesirable events that are likely to happen in the future, giving the decision maker an opportunity to intervene. The topic is gaining momentum in process mining, a field of research that has traditionally developed tools to discover business process models from data sets of past process behavior. Predictive modeling techniques are built on top of process-discovery algorithms. As these algorithms describe business process behavior using models of formal languages (e.g., Petri nets), strong language biases are necessary in order to generate models with the limited amounts of data included in the data set. Naturally, corresponding predictive modeling techniques reflect these biases. Based on theory from grammatical inference, a field of research that is concerned with inducing language models, we design a new predictive modeling technique based on weaker biases. Fitting a probabilistic model to a data set of past behavior makes it possible to predict how currently running process instances will behave in the future. To clarify how this technique works and to facilitate its adoption, we also design a way to visualize the probabilistic models. We assess the effectiveness of the technique in an experimental evaluation with synthetic and real-world data. » weiterlesen» einklappen

  • PROCESS mining
  • PROBABILITY theory
  • MATHEMATICAL models
  • PROCESS optimization
  • PREDICTION models
  • BIG data

Autoren


Dominic, Breuker (Autor)
Martin, Matzner (Autor)
Jörg, Becker (Autor)

Klassifikation


DFG Fachgebiet:
Informatik

DDC Sachgruppe:
Informatik

Verknüpfte Personen