W. M. van der Aalst, Process mining: data science in action, 2, Springer, 2016.Google Scholar
Joerg Evermann, Jana-Rebecca Rehse and Peter Fettke, A Deep Learning Approach for Predicting Process Behaviour at Runtime, in: Proceedings of the 1st International Workshop on Runtime Analysis of Process-Aware Information Systems. International Workshop on Runtime Analysis of Process-Aware Information Systems (PRAISE-2016), located at International Conference on Business Process Management, September 18–22, Rio de Janeiro, Brazil (Marlon Dumas and Marcelo Fantinato, eds), Springer, 2016.Google Scholar
Joerg Evermann, Jana-Rebecca Rehse and Peter Fettke, Predicting process behaviour using deep learning, Decision Support Systems 100 (2017), 129–140, Special Issue on Smart Business Process Management.Web of ScienceCrossrefGoogle Scholar
Constantin Houy, Peter Fettke and Peter Loos, Empirical Research in Business Process Management – Analysis of an emerging field of research, Business Process Management Journal (BPMJ) 16 (2010), 619–661.CrossrefGoogle Scholar
Constantin Houy, Peter Fettke, Peter Loos, Wil van der Aalst and John Krogstie, BPM-in-the-Large – Towards a higher level of abstraction in Business Process Management, in: E-Government and E-Services EGES/Global Information Systems Processes GISP 2010. World Computer Congress (WCC-2010), September 20–23, Brisbane, Australia, (Marijn Janssen, Winfried Lamersdorf, Jan Pries-Heje and Michael Rosemann, eds), 334, pp. 237–248, Springer, Berlin, 2010.Google Scholar
Christian Janiesch, Agnes Koschmider, Massimo Mecella, Barbara Weber, Andrea Burattin, Claudio Di Ciccio andet al., The Internet-of-Things Meets Business Process Management: Mutual Benefits and Challenges, CoRR, arXiv:1709.03628 (2017).Google Scholar
Jana-Rebecca Rehse, Peter Fettke and Peter Loos, A graph-theoretic method for the inductive development of reference process models, Software & Systems Modeling 16 (2017), 833–873.Web of ScienceCrossrefGoogle Scholar
August-Wilhelm Scheer, ARIS - Business Process Modeling, 3ed, Springer, Berlin et al., 2000.Google Scholar
Konsta Mikael Sirvio, Intelligent Systems in Maintenance Planning and Management, pp. 221–245, Springer International Publishing, Cham, 2015.Google Scholar
Verband Deutscher Maschinen- und Anlagenbauer (VDMA) - Forum Industrie 4.0: Leitfaden Industrie 4.0. Frankfurt am Main, 2015.
About the article
Jana-Rebecca Rehse is a Researcher at the Institute for Information Systems (IWi) at the German Research Center for Artificial Intelligence (DFKI). She holds a Bachelor and a Master Degree in Information Systems from Saarland University. In 2014, she was a visiting research scholar at Stevens Institute of Technology in Hoboken, NJ. Her research interests include Business Process Management, Reference Modeling, Process Mining, Process Prediction, and Design Science.
Sharam Dadashnia is a Researcher at the Institute for Information Systems (IWi) at the German Research Center for Artificial Intelligence (DFKI). His research interest is primary in the field of business process management, especially in process mining and the application in domains such as Industry 4.0 and the automated testing of software usability. Prior to that, he worked in software development at SAP SE. In addition to his scientific activities, Sharam Dadashnia is managing director of a software development company iSol UG he co-founded.
Prof. Dr Peter Fettke is Professor of Business Informatics at Saarland University and Principal Researcher, Research Fellow and Research Group Leader at the German Research Center for Artificial Intelligence (DFKI). Prof. Fettke and his 30-strong research group focus on the interface between the topics of business process management and artificial intelligence (AI). He is the author of more than 100 publications and belongs to the top 10 most cited scientists at the DFKI.
Published Online: 2018-06-28
Published in Print: 2018-07-01