SEDAR: A Semantic Data Reservoir for Heterogeneous Datasets

ACM International Conference on Information and Knowledge Management (CIKM), pp. 5056–5060

Abstract

Data lakes have emerged as a solution for managing vast and diverse datasets for modern data analytics. To prevent them from becoming ungoverned, semantic data management techniques are crucial, which involve connecting metadata with knowledge graphs, following the principles of Linked Data. This semantic layer enables more expressive data management, integration from various sources and enhances data access utilizing the concepts and relations to semantically enrich the data. Some frameworks have been proposed, but requirements like data versioning, linking of datasets, managing machine learning projects, automated semantic modeling and ontology-based data access are not supported in one uniform system. We demonstrate SEDAR, a comprehensive semantic data lake that includes support for data ingestion, storage, processing, and governance with a special focus on semantic data management. The demo will showcase how the system allows for various ingestion scenarios, metadata enrichment, data source linking, profiling, semantic modeling, data integration and processing inside a machine learning life cycle.

Authors 3

1. Sayed Hoseini

   Hochschule Niederrhein

   Affiliation as printed

   Hochschule Niederrhein, Krefeld, Germany

2. Haron Shaker Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Aachen, Germany

3. Christoph Quix

   Fraunhofer Institute for Applied Information Technology · Hochschule Niederrhein

   Affiliation as printed

   Fraunhofer FIT & Hochschule Niederrhein, St. Augustin & Krefeld, Germany