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Data Source Selection Support in the Big Data Integration Process – Towards a Taxonomy

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Innovation Through Information Systems (WI 2021)

Part of the book series: Lecture Notes in Information Systems and Organisation ((LNISO,volume 48))

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Abstract

Selecting data sources is a crucial step in providing a useful information base to support decision-makers. While any data source can represent a potential added value in decision making, it’s integration always implies a representative effort. For decision-makers, data sources must contain relevant information in an appropriate scope. The data scientist must assess whether the integration of the data sources is technically possible and how much effort is required. Therefore, a taxonomy was developed to identify the relevant data sources for the decision-maker and minimize the data integration effort. The taxonomy was developed and evaluated with real data sources and six companies from different industries. The final taxonomy consists of sixteen dimensions that support the data scientist and decision-maker in selecting data sources for the big data integration process. An efficient and effective big data integration process can be carried out with a minimum of data sources to be integrated.

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Notes

  1. 1.

    https://sites.google.com/site/anhaidgroup/projects/magellan.

  2. 2.

    https://www.biggorilla.org/.

  3. 3.

    https://developer.uspto.gov/product/patent-grant-bibliographic-dataxml.

  4. 4.

    https://opencorporates.com/.

  5. 5.

    Powered by Crunchbase: https://data.crunchbase.com/docs/open-data-map.

  6. 6.

    Crunchbase 2013 Snapshot ©, Creative Commons Attribution License [CC-BY], https://data.crunchbase.com/docs/2013-snapshot.

  7. 7.

    https://www.gleif.org/de/lei-data/gleif-concatenated-file/download-the-concatenated-file.

  8. 8.

    https://www.upcitemdb.com/.

  9. 9.

    At the time of access still freely available: https://public.enigma.com/browse/collection/stock-exchanges-company-listings/50a2457d-6407-4581-8f14-5d37a9410fa9.

  10. 10.

    https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset.

  11. 11.

    http://www.naturalearthdata.com/downloads/10m-cultural-vectors/.

  12. 12.

    https://www.kaggle.com/juanumusic/countries-iso-codes.

  13. 13.

    https://susanqq.github.io/UTKFace/.

  14. 14.

    https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/.

  15. 15.

    https://de.appanion.com/startups.

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Authors and Affiliations

  1. Department for Business Informatics (VLBA), Carl Von Ossietzky Universität Oldenburg, Oldenburg, Germany

    Felix Kruse & Jorge Marx Gómez

  2. Volkswagen AG, Corporate Foresight, Wolfsburg, Germany

    Christoph Schröer

Authors
  1. Felix Kruse
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  2. Christoph Schröer
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  3. Jorge Marx Gómez
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Correspondence to Felix Kruse .

Editor information

Editors and Affiliations

  1. Faculty of Business Administration and Economics, University of Duisburg-Essen, Essen, Germany

    Frederik Ahlemann

  2. Faculty of Business Administration and Economics, University of Duisburg-Essen, Essen, Germany

    Reinhard Schütte

  3. Department of Computer Science and Applied Cognitive Science, University of Duisburg-Essen, Duisburg, Germany

    Stefan Stieglitz

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Kruse, F., Schröer, C., Gómez, J.M. (2021). Data Source Selection Support in the Big Data Integration Process – Towards a Taxonomy. In: Ahlemann, F., Schütte, R., Stieglitz, S. (eds) Innovation Through Information Systems. WI 2021. Lecture Notes in Information Systems and Organisation, vol 48. Springer, Cham. https://doi.org/10.1007/978-3-030-86800-0_1

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  • DOI: https://doi.org/10.1007/978-3-030-86800-0_1

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  • Print ISBN: 978-3-030-86799-7

  • Online ISBN: 978-3-030-86800-0

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