Analytic Processing in Data Lakes: A Semantic Query-Driven Discovery Approach

Data integration and discovery are open issues in Data Lakes potentially storing hundreds of data sources. The present paper addresses these issues targeting multidimensional data sources, that is sources containing atomic or derived measures aggregated along a number of dimensions, typically derived from raw data for analytical and reporting purposes. Combining semantic models of metadata with existing data-driven techniques, the paper proposes an approach for the discovery of mappings between source metadata and concepts in a reference knowledge graph, enabling the definition of reasoning-based techniques to discover, integrate, and rank data sources relevant to a given analytical query. The efficiency and effectiveness of the approach is discussed by means of experiments on real-world scenarios.