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Beyond descriptive taxonomies in data analytics: a systematic evaluation approach for data-driven method pipelines

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  • Patrick Zschech

    (Friedrich-Alexander-Universität Erlangen-Nürnberg)

Abstract

Taxonomies can serve as a valuable tool to capture dimensions and characteristics of data analytics solutions in a structured manner and thus create transparency about different design options of the technical solution space. However, previous taxonomic approaches often remain at a purely descriptive level without leveraging morphological structures to investigate the mechanisms between different combinatorial options given in data analytics pipelines. To this end, we propose a taxonomic evaluation approach to evaluate and construct the technical core of analytical information systems more systematically. Specifically, we present a rough guidance model consisting of four steps, which we subsequently instantiate with two application scenarios from the fields of industrial maintenance and predictive business process monitoring. In this way, we demonstrate how taxonomic frameworks can guide the creation of structured evaluation studies to consider the construction and assessment of data analytics pipelines in a multi-perspective and holistic manner. Our approach is sufficiently generic to be applied to various domains, scenarios, and decision support tasks.

Suggested Citation

  • Patrick Zschech, 2023. "Beyond descriptive taxonomies in data analytics: a systematic evaluation approach for data-driven method pipelines," Information Systems and e-Business Management, Springer, vol. 21(1), pages 193-227, March.
    • Handle: RePEc:spr:infsem:v:21:y:2023:i:1:d:10.1007_s10257-022-00577-0
    DOI: 10.1007/s10257-022-00577-0
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    References listed on IDEAS

    as
    1. Patrick Zschech & Richard Horn & Daniel Höschele & Christian Janiesch & Kai Heinrich, 2020. "Intelligent User Assistance for Automated Data Mining Method Selection," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 62(3), pages 227-247, June.
    2. Christian Janiesch & Patrick Zschech & Kai Heinrich, 2021. "Machine learning and deep learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 685-695, September.
    3. Robert C Nickerson & Upkar Varshney & Jan Muntermann, 2013. "A method for taxonomy development and its application in information systems," European Journal of Information Systems, Taylor & Francis Journals, vol. 22(3), pages 336-359, May.
    4. Frederik Wolf & Jens Brunk & Jörg Becker, 2021. "A Framework of Business Process Monitoring and Prediction Techniques," Lecture Notes in Information Systems and Organization, in: Frederik Ahlemann & Reinhard Schütte & Stefan Stieglitz (ed.), Innovation Through Information Systems, pages 714-724, Springer.
    5. Alexandros Bousdekis & Babis Magoutas & Dimitris Apostolou & Gregoris Mentzas, 2018. "Review, analysis and synthesis of prognostic-based decision support methods for condition based maintenance," Journal of Intelligent Manufacturing, Springer, vol. 29(6), pages 1303-1316, August.
    6. Frederik Möller & Hendrik Haße & Can Azkan & Hendrik Valk & Boris Otto, 2021. "Design of Goal-Oriented Artifacts from Morphological Taxonomies: Progression from Descriptive to Prescriptive Design Knowledge," Lecture Notes in Information Systems and Organization, in: Frederik Ahlemann & Reinhard Schütte & Stefan Stieglitz (ed.), Innovation Through Information Systems, pages 523-538, Springer.
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