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Towards a Cross-Company Data and Model Platform for SMEs

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Business Information Systems Workshops (BIS 2019)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 373))

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Abstract

The global volume of data is increasing. As a result, companies are increasingly concerned with using the available data and generating added value from it. The development of data products is necessary to obtain information from data and to integrate it into decision making processes. One possibility is the application of artificial intelligence. However, large companies such as Google or Facebook benefit most from this technology. SMEs in particular are falling by the wayside and are confronted with many challenges. The cross-company platform presented in this article represents an approach to enable even smaller companies to access artificial intelligence and to support data management in machine learning projects.

PhD supervisor Prof. Dr. Jorge Marx Gómez, University of Oldenburg, Department Very Large Business Application, the PhD thesis will be written in the research cooperation POINT (project members: University of Oldenburg and abat AG).

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Notes

  1. 1.

    https://trends.google.com/trends/explore?date=all&q=Big%20Data.

  2. 2.

    https://www.tensorflow.org/.

  3. 3.

    https://keras.io/.

  4. 4.

    https://scikit-learn.org/stable/.

  5. 5.

    https://www.opalproject.org/.

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Authors and Affiliations

  1. Department Very Large Business Applications, University of Oldenburg, 26129, Oldenburg, Germany

    René Kessler

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  1. René Kessler
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Correspondence to René Kessler .

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Editors and Affiliations

  1. Poznań University of Economics and Business, Poznan, Poland

    Witold Abramowicz

  2. University of Seville, Seville, Spain

    Rafael Corchuelo

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Kessler, R. (2019). Towards a Cross-Company Data and Model Platform for SMEs. In: Abramowicz, W., Corchuelo, R. (eds) Business Information Systems Workshops. BIS 2019. Lecture Notes in Business Information Processing, vol 373. Springer, Cham. https://doi.org/10.1007/978-3-030-36691-9_55

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  • DOI: https://doi.org/10.1007/978-3-030-36691-9_55

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