Simple-ML: Towards a Framework for Semantic Data Analytics Workflows

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Gottschalk, S.; Tempelmeier, N.; Kniesel, G.; Iosifidis, V.; Fetahu, B. et al.: Simple-ML: Towards a Framework for Semantic Data Analytics Workflows. In: Acosta, M.; Cudré-Mauroux, P.; Maleshkova, M.; Pellegrini, T.; Sack, H.; Sure-Vetter, Y. (Eds.): Semantic Systems. The Power of AI and Knowledge Graphs. Heidelberg : Springer Verlag, 2019 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; 11702 LNCS), S. 359-366. DOI: https://doi.org/10.1007/978-3-030-33220-4_26

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Sum total of downloads: 83




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Abstract: 
In this paper we present the Simple-ML framework that we develop to support efficient configuration, robustness and reusability of data analytics workflows through the adoption of semantic technologies. We present semantic data models that lay the foundation for the framework development and discuss the data analytics workflows based on these models. Furthermore, we present an example instantiation of the Simple-ML data models for a real-world use case in the mobility domain. © 2019, The Author(s).
License of this version: CC BY 4.0 Unported
Document Type: bookPart
Publishing status: publishedVersion
Issue Date: 2019
Appears in Collections:Forschungszentren

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pos. country downloads
total perc.
1 image of flag of Germany Germany 71 85.54%
2 image of flag of United States United States 5 6.02%
3 image of flag of Russian Federation Russian Federation 3 3.61%
4 image of flag of No geo information available No geo information available 1 1.20%
5 image of flag of Iran, Islamic Republic of Iran, Islamic Republic of 1 1.20%
6 image of flag of France France 1 1.20%
7 image of flag of Belgium Belgium 1 1.20%

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