Conceptual Constraints for Data Quality in Data Lakes

A data lake is a loosely-structured collection of data at scale built for analysis purposes that is initially fed with almost no requirement of data quality. This approach aims at eliminating any effort before the actual exploitation of data, but the problem is only delayed since robust and defensible data analysis can only be performed after very complex data preparation activities. In this paper, we address this problem by proposing a novel and general approach to data curation in data lakes based on: (i) the specification of integrity constraints over a conceptual representation of the data lake and (ii) the automatic translation and enforcement of such constraints over the actual data. We discuss the advantages of this idea and the challenges behind its implementation.