EnTrust: Engineering Trustworthy Data-Intensive Systems


Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to gain knowledge from and insight into data that exists in a variety of formats. Based on the results obtained, many important decisions are made that affect individuals or society as a whole: Diagnoses, therapies, credit decisions, spatial planning, etc. On the other hand, Data Science is characterized by the iterative and empirical-heuristic approach by means of which knowledge is extracted and decisions are derived. What typically falls short is a systematic, engineering-oriented approach that allows statements about the quality of the data analysis. For Data Science and resulting data-intensive software, there is a lack of an appropriate inventory of methods, processes, algorithms, and systems that contribute to correctness. In particular, it is also not straightforward to describe "correct behavior" of a data-intensive system, because the result is not predetermined and should only be obtained through the data analysis process. The goal of this profile area is to explore methodological approaches and formal tools that support the engineering development of correct - or at least trustworthy - data-intensive software.

Fundings & Partners

Funded by
RLP Forschungsinitiativerlp.de/en/home/