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

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

Motivation

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.

Challenges

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