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Implementing scalable structured machine learning for big data in the SAKE project

: Bin, Simon; Westphal, Patrick; Lehmann, Jens; Ngonga Ngomo, Axel-Cyrille

DOI: 10.1109/BigData.2017.8258073

	Institute of Electrical and Electronics Engineers -IEEE-: IEEE International Conference on Big Data 2017 : 11-14 December 2017, Boston, Mass., USA Piscataway, NJ: IEEE, 2017 ISBN: 978-1-5386-2715-0 ISBN: 978-1-5386-2714-3 ISBN: 978-1-5386-2716-7 pp.1400-1407
	International Conference on Big Data (BigData) <2017, Boston/Mass.>

	English
	Conference Paper
	Fraunhofer IAIS ()

Abstract
Exploration and analysis of large amounts of machine generated data requires innovative approaches. We propose a combination of Semantic Web and Machine Learning to facilitate the analysis. First, data is collected and converted to RDF according to a schema in the Web Ontology Language OWL. Several components can continue working with the data, to interlink, label, augment, or classify. The size of the data poses new challenges to existing solutions, which we solve in this contribution by transitioning from in-memory to database.

: http://publica.fraunhofer.de/documents/N-481437.html