Class Notes: Power of Text-mining in BPM
Blog: BPTrends - Class Notes
Jan vom Brocke, with his colleagues, Stefan Debortoli and Oliver Mueller, investigate text-mining’s potential to support BPM capabilities. Their results show that text-mining offers significant potential for building BPM capabilities in both exploitation and exploration. They identify the considerable potential of applying text-mining in BPM and conclude with a call for more discussion and contributions to this promising new lens through which to build BPM capabilities.
Oliver Müller is an Associate Professor at the IT University of Copenhagen. He holds a BSc and MSc in Information Systems and a PhD in Business Economics from the University of Münster, Germany. The goal of Oliver’s research is to help organizations and individuals to create value through (big) data and analytics. At this, he particularly focuses on extracting knowledge from large amounts of unstructured text data, from both the Internet and enterprise-internal sources. His research has been published in the European Journal of Information Systems, Journal of the Association for Information Systems, Communications of the Association for Information Systems, IEEE Transactions on Engineering Management, and others.
Stefan Debortoli is an associated researcher at the Institute of Information Systems at the University of Liechtenstein. He holds a BSc and MSc in Information Systems and a PhD in Business Economics from the University of Liechtenstein. His doctoral studies focused on applying big data analytics as a new strategy of inquiry in Information Systems Research. In the field of big data analytics, he focused on applying text-mining techniques for research purposes. His work has been published in the European Journal of Information Systems, Communications of the Association for Information Systems, and Business & Information Systems Engineering.