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A Data Provenance based Architecture to Enhance the Reliability of Data Analysis for Industry 4.0

 
: Li, P.; Niggemann, O.

:

Institute of Electrical and Electronics Engineers -IEEE-; IEEE Industrial Electronics Society -IES-:
IEEE 23rd International Conference on Emerging Technologies and Factory Automation, ETFA 2018. Proceedings : Politecnico di Torino, Torino, Italy, 04-07 September 2018
Piscataway, NJ: IEEE, 2018
ISBN: 978-1-5386-7108-5
ISBN: 978-1-5386-7107-8
ISBN: 978-1-5386-7109-2
pp.1375-1382
International Conference on Emerging Technologies and Factory Automation (ETFA) <23, 2018, Torino>
English
Conference Paper
Fraunhofer IOSB ()

Abstract
Integrating data analysis into workflows is a recent tendency in manufacturing sectors. According to the vision of Industry 4.0, data analysis can be automatically performed at any point of workflows if needed. In distributed and complex manufacturing systems, checking the integrity of data analysis processes is becoming more and more challenging and the dependency between (intermediate) analysis results is no more easy to understand for users involved in workflows. Therefore, a mechanism is desired, which is able to assist users in tracking and verifying distributed data analysis processes. In this paper, we extend the concept “data provenance” in the manufacturing domain to acquire information about the data origin and data changes. Furthermore, an architecture is proposed to manage provenance of process data, in which the data provenance is considered as annotation of process data. Different use cases are also given to show how data provenance can have impact on understanding and verifying data analysis processes in the manufacturing domain.

: http://publica.fraunhofer.de/documents/N-581590.html