Artificial Intelligence in Automation

Provenance Analytics: Technologien zur Interpretation von Herkunft, Ursache und Quellen in komplexen, datengetriebenen und vernetzten Anwendungen

Prof. Dr. rer. nat. Oliver Niggemann
01.10.2016 bis 30.09.2019

Technologies for Interpretation of Provenance, Cause and Source in Complex, Data Driven and Connected Application


Data Analytics in the age of Big Data is combined with a wide range of intelligent technologies and therefore has been becoming more and more complex.  Although the success of data analytics is impressive, the trust of users in the results of the data analysis should be fostered that is nowadays generally questionable. Provenance plays a key role in building such trust with user through presenting analysis results to user in a comprehensible manner.



There exist already systems, frameworks and initial proposals of standards for modelling, representing and generating provenance information. However, these are both for users and developers often not practical and comprehensive, so that the further development of an “actionable provenance” is necessary. Since the concrete provenance technologies are mostly domain-specific, different applications will be covered in this project, of which only few or no aspects of provenance were considered. Especially, the provenance technology for:


  • Data analysis in industry 4.0 environment with focus on application of diagnosis,
  • 3D digitization in the area of monument conservator and archaeology,
  • Analysis of message flow, detection of reuse and forensics as well as
  • Social semantic web of things with focus on exploration of relationships
This project is promoted by:
Bundesministerium für Bildung und Forschung (BMBF)
Sponsors: Das Deutsche Zentrum für Luft- und Raumfahrt e.V. (DLR)
Funding Code: 03PSIPT5B
Stakeholders / Contacts: Dipl.-Math. Natalia Moriz, Dr.-Ing. Peng Li
Dr.-Ing. Andreas Bunte, Dr.-Ing. Peng Li, Prof. Dr. rer. nat. Oliver Niggemann
Mapping Data Sets to Concepts Using Machine Learning and a Knowledge Based Approach
In: International Conference on Agents and Artificial Intelligence (ICAART), Jan 2018
Baotong Chen, Jiafu Wan, Lei Shu, Dr.-Ing. Peng Li, Mithun Mukherjee, Boxing Yin
Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges
Dr.-Ing. Peng Li, Prof. Dr. rer. nat. Oliver Niggemann
A Data Provenance based Architecture to Enhance the Reliability of Data Analysis for Industry 4.0
In: 23th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Sep 2018
Dr.-Ing. Peng Li, Prof. Dr. rer. nat. Oliver Niggemann, Barbara Hammer
On the Identification of Decision Boundaries for Anomaly Detection in CPPS
In: 20th IEEE International Conference on Industrial Technology (ICIT 2019), Feb 2019
Dr.-Ing. Peng Li, Prof. Dr. rer. nat. Oliver Niggemann
Non-convex hull based anomaly detection in CPPS
Dr.-Ing. Peng Li, Prof. Dr. rer. nat. Oliver Niggemann
A Non-Convex Archetypal Analysis for One-class Classification based Anomaly Detection in Cyber-Physical Systems
In: Transactions on Industrial Informatics, Jul 2020
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