A Semantic Data Lake Model for Analytic Query-Driven Discovery

Data Lake (DL) architectures have recently emerged as an effective solution to the problem of data analytics with big, highly heterogeneous, and quickly changing data sources. However, novel challenges arise too, including how to make sense of disparate raw data and how to identify the sources that satisfy a data need. In the paper, we introduce a semantic model for a Data Lake aimed to support data discovery and integration in data analytics scenarios. By formally modeling indicators of interest, their computation formulas, and dimensions of analysis in a knowledge graph, and by seamlessly mapping them to relevant source metadata, the framework is suited for identifying the sources and the required transformation steps according to the analytical request.