Joerg Evermann

Associate Professor of Information Systems

Welcome to the site of Dr. Joerg Evermann, Associate Professor of Information Systems with the Faculty of Business Administration at Memorial University of Newfoundland.

Brief Bio

Dr. Evermann received his PhD in Information Systems from the University of British Columbia. Prior to being a faculty member at Memorial University, Dr. Evermann was a lecturer in Information Systems with the School of Information Management at the University of Wellington, New Zeland. Dr. Evermann's interests are in business process management, statistical research methods, and information integration. Dr. Evermann has published his research in more than 70 peer-reviewed publications. His work has appeared in high-quality journals, such as IEEE Transactions on Software Engineering, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Services Computing, Journal of Business Research, Organizational Research Methods, Structural Equation Modeling, Journal of the AIS, Information systems, and Information Systems Journal. Dr. Evermann has presented his work at international conferences, such as ICIS, AMCIS, CAiSE, ER, among others.


Dr. Evermann lives with his wife and his 7-year-old son in St. John's, Canada.

Contact Me

Latest New and Publications

  • Lukyanenko, R., Samuel, B.M., Evermann, J., and Parsons, J.: Toward Artifact Sampling in IS Design Research. WITS Workshop on Information Technology and Systems at the 2016 International Conference on Information Systems (ICIS). (accepted October 15, 2016).
  • Evermann, J., Rehse, J.-R., and Fettke, P.: Process Discovery from Event Stream Data in the Cloud - A Scalable, Distributed Implementation of the Flexible Heuristics Miner on the Amazon Kinesis Cloud Infrastructure. CloudBPM Workshop on Business Process Monitoring and Performance Analysis in the Cloud at the 8th IEEE International Conference on Cloud Computing Technologies and Science (CloudCom 2016) . (accepted September 21, 2016).
  • Evermann, J., Rehse, J.-R., and Fettke, P.: A Deep Learning Approach for Predicting Process Behaviour at Runtime. PRAISE International Workshop on Runtime Analysis of Process-Aware Information Systems at the 14th International Conference on Business Process Management. (accepted July 4, 2016).

All Publications and Downloads


I have always been interested in information technology, especially as it relates to business.

I graduated from the University of Münster, Germany, in 1998 with an undergraduate degree in Information Systems. After a brief stint with KPMG Consulting, where I focused on the SAP enterprise system, I pursued graduate studies at the University of British Columbia, finishing in 2003 with a PhD in information system. After four years as a lecturer at Victoria University of Wellington, I returned to Canada in 2007 to take a position in Information Systems at Memorial University.



My current interests focus on business process management.

I am particularly interested in using event logs from business process management systems to identify the underlying processes and to manage these processes at runtime. Because of the size of such event logs, I am turning to Big Data and Cloud Computing techniques, as well as modern Deep Learning tools.


Current Projects

I am doing interesting work on Process Management in the Big Data and Data Analytics area.

Process discovery is based event logs from servers, or API call logs from enterprise systems. This data is rapidly increasing in volume, and increasingly distributed, for example due to load-balancing across different nodes. I am investigating how scalable, distributed computing techniques such as Map-Reduce can best be applied to process discovery, and process mining in general. Related to this is my interest in Deep Learning. Recent advances in neural network architectures and GPU computing have made process prediction from large event logs feasible.