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.

A Semantic Data Lake Model for Analytic Query-Driven Discovery / Diamantini, Claudia; Potena, Domenico; Storti, Emanuele. - (2021), pp. 183-186. (Intervento presentato al convegno 23rd International Conference on Information Integration and Web Intelligence (iiWAS2021)) [10.1145/3487664.3487783].

A Semantic Data Lake Model for Analytic Query-Driven Discovery

Claudia Diamantini;Domenico Potena;Emanuele Storti
2021-01-01

Abstract

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.
2021
ACM International Conference Proceeding Series
978-1-4503-9556-4
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/292095
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