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Uniform access to multiform data lakes using semantic technologies

: Mami, M.N.; Graux, D.; Scerri, S.; Jabeen, H.; Auer, S.; Lehmann, J.


Indrawan-Santiago, M. ; Association for Computing Machinery -ACM-:
iiWAS 2019, 21st International Conference on Information Integration and Web-based Applications & Services. Proceedings : December 2-4, 2019, Munich, Germany
New York: ACM, 2019
ISBN: 978-1-4503-7179-7
International Conference on Information Integration and Web-Based Applications & Services (iiWAS) <21, 2019, Munich>
Fraunhofer IAIS ()

Increasing data volumes have extensively increased application possibilities. However, accessing this data in an ad hoc manner remains an unsolved problem due to the diversity of data management approaches, formats and storage frameworks, resulting in the need to effectively access and process distributed heterogeneous data at scale. For years, Semantic Web techniques have addressed data integration challenges with practical knowledge representation models and ontology-based mappings. Leveraging these techniques, we provide a solution enabling uniform access to large, heterogeneous data sources, without enforcing centralization; thus realizing the vision of a Semantic Data Lake. In this paper, we define the core concepts underlying this vision and the architectural requirements that systems implementing it need to fulfill. Squerall, an example of such a system, is an extensible framework built on top of state-of-the-art Big Data technologies. We focus on Squerall's distributed query execution techniques and strategies, empirically evaluating its performance throughout its various sub-phases.