Detecting Quality Problems in Research Data: A Model-Driven Approach

Abstract

The quality of research data is essential for scientific progress. A major challenge in data quality assurance is the localisation of quality problems that are inherent to data. Based on the observation of a dynamic shift in the database technologies employed, we present a model-driven approach to analyse the quality of research data. It allows a data engineer to formulate anti-patterns that are generic concerning the database format and technology. A domain expert chooses a pattern that has been adapted to a specific database technology and concretises it for a domain-specific database format. The resulting concrete pattern is used by a data analyst to locate quality problems in the database. As a proof of concept, we implemented tool support that realises this approach for XML databases. We evaluated our approach concerning expressiveness and performance. The original paper has been published at the International Conference on Model Driven Engineering Languages and Systems 2020.