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Conference Papers Year : 2019

Augmenting Analytic Datasets Using Natural and Aggregate-based Schema Complements

Eric Simon
  • Function : Author
  • PersonId : 1130318
Bernd Amann
Stéphane Gançarski

Abstract

The production of analytic datasets is a significant big data trend and has gone well beyond the scope of traditional IT-governed dataset development. Analytic datasets are now created by data scientists and data analysts using big data frameworks and agile data preparation tools. However, despite the profusion of available datasets, it remains quite difficult for a data analyst to start from a dataset at hand and customize it with additional attributes coming from other existing datasets. This article describes a model and algorithms that exploit automatically extracted and user-defined semantic relationships for extending analytic datasets with new atomic or aggregated attribute values. Our framework is implemented as a REST service in the SAP HANA and includes a careful analysis and practical solutions for several complex data quality issues.
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Dates and versions

hal-03981998 , version 1 (10-02-2023)

Identifiers

  • HAL Id : hal-03981998 , version 1

Cite

Rutian Liu, Eric Simon, Bernd Amann, Stéphane Gançarski. Augmenting Analytic Datasets Using Natural and Aggregate-based Schema Complements. Bases de Données Avancées (BDA 2019), Oct 2019, Lyon, France. ⟨hal-03981998⟩
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