Process Analytics

Process Analytics

Process analytics on raw sensor events

The rise of Internet-of-Things will change the way how process models are captured. While in the past process models have been discovered from documents or interviews, the challenge will be to automatically discover the de-jure process model from raw sensor events. The aim of this research is to develop techniques allowing to discover process models from such data. Framework

 

  • D. Janssen, F. Mannhardt, A. Koschmider: Process Model Discovery from Sensor Event Data (upcoming)
  • A. Koschmider, M. Degeling, M. Weidlich: Privacy in Process Analytics, Informatik Spektrum, Volume 42, Issue 5, October 2019
  • P. Soffer, A. Hinze, A. Koschmider, H. Ziekow, C. Di Ciccio, B. Koldehofe, O. Kopp, H.-A. Jacobsen, J. Sürmeli, W. Song: From event streams to process models and back: Challenges and opportunities. Inf. Syst. 81: 181-200, 2019
  • A. Koschmider, D. Siqueira Vidal Moreira: Change Detection in Event Logs by Clustering. OTM Conferences (1) 2018: 643-660
  • A. Koschmider, F. Mannhardt, T. Heuser: On the Contextualization of Event-Activity Mappings. Business Process Management Workshops 2018: 445-457

Privacy preserving process mining

Process mining allows considerable insight into data, which has the inherent risk that what is disclosed may be private. Also, process mining aims to discover accurate process models from event logs at the expense of disclosure of information that should be protected. In this research, we aim to support process discovery that is still useful while reducing the disclosure of sensitive data.  
  • S. Nuñez von Voigt, S.A. Fahrenkrog-Petersen, D. Janssen, A. Koschmider, F. Tschorsch, F. Mannhardt, O. Landsiedel, M. Weidlich (2020): Quantifying the Re-identification Risk of Event Logs for Process Mining, 32nd International Conference on Advanced Information Systems Engineering (CAiSE 2020), to appear
  • F. Mannhardt, A. Koschmider, N. Baracaldo, M. Weidlich, J. Michael: Privacy-preserving Process Mining: Differential Privacy for Event Logs. In: Business & Information Systems Engineering (BISE), vol. 5, 2019, Springer
  • M. Bauer, S. Fahrenkrog-Petersen, A. Koschmider, F. Mannhardt, H. van der Aa, M. Weidlich (2019) ELPaaS: Event Log Privacy as a Service. BPM (PhD/Demos) 2019: 159-163, Springer.
  • J. Michael, A. Koschmider, F. Mannhardt, N. Baracaldo, B. Rumpe: User-Centered and Privacy-Driven Process Mining System Design for IoT. CAiSE Forum 2019: 194-206

Blockchain and event log extraction

This research addresses the extraction of meaningful events for process mining from a blockchain with the intention to analyze changes in smart contracts and to analyze their conformance. Process mining techniques allow to diagnose (non)conformity in smart contracts by means of common quality measures. The source code of this reseach can be downloaded from here. Blockchain
  • F. Duchmann, A. Koschmider: Validation of Smart Contracts Using Process Mining. ZEUS 2019: 13-16

BPM Patterns and anti-patterns repository

Patterns have been proven to be useful for documenting reusable solutions to common problems. Anti-patterns are solutions that are known to have deficiencies. The aim of this research is to evolve a repository of published bibliography of business process model patterns and anti-patterns and to provide a systematic categorization of these patterns.

  • R. Laue, A. Koschmider, M. Fellmann, A. Schoknecht, A. Vetter: bpmpatterns.org - An Interactive Catalog of Business Process Modeling Patterns Literature. BPM (PhD/Demos) 2019: 179-183
  • M. Fellmann, A. Koschmider, R. Laue, A. Schoknecht, A. Vetter: Business process model patterns: state-of-the-art, research classification and taxonomy", Business Process Management Journal, Vol. 25 No. 5, pp. 972-994, 2019. https://doi.org/10.1108/BPMJ-01-2018-0021
  • A. Koschmider, R. Laue, M. Fellmann: Business Process Model anti-Patterns: a Bibliography and Taxonomy of published Work. ECIS 2019