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Intelligent User Assistance for Automated Data Mining Method Selection

Author

Listed:
  • Patrick Zschech

    (TU Dresden)

  • Richard Horn

    (TU Dresden)

  • Daniel Höschele

    (TU Dresden)

  • Christian Janiesch

    (TU Dresden)

  • Kai Heinrich

    (TU Dresden)

Abstract

In any data science and analytics project, the task of mapping a domain-specific problem to an adequate set of data mining methods by experts of the field is a crucial step. However, these experts are not always available and data mining novices may be required to perform the task. While there are several research efforts for automated method selection as a means of support, only a few approaches consider the particularities of problems expressed in the natural and domain-specific language of the novice. The study proposes the design of an intelligent assistance system that takes problem descriptions articulated in natural language as an input and offers advice regarding the most suitable class of data mining methods. Following a design science research approach, the paper (i) outlines the problem setting with an exemplary scenario from industrial practice, (ii) derives design requirements, (iii) develops design principles and proposes design features, (iv) develops and implements the IT artifact using several methods such as embeddings, keyword extractions, topic models, and text classifiers, (v) demonstrates and evaluates the implemented prototype based on different classification pipelines, and (vi) discusses the results’ practical and theoretical contributions. The best performing classification pipelines show high accuracies when applied to validation data and are capable of creating a suitable mapping that exceeds the performance of joint novice assessments and simpler means of text mining. The research provides a promising foundation for further enhancements, either as a stand-alone intelligent assistance system or as an add-on to already existing data science and analytics platforms.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:binfse:v:62:y:2020:i:3:d:10.1007_s12599-020-00642-3
    DOI: 10.1007/s12599-020-00642-3
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    References listed on IDEAS

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    1. Alexander Maedche & Stefan Morana & Silvia Schacht & Dirk Werth & Julian Krumeich, 2016. "Advanced User Assistance Systems," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 58(5), pages 367-370, October.
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    Cited by:

    1. Lukas-Valentin Herm & Theresa Steinbach & Jonas Wanner & Christian Janiesch, 2022. "A nascent design theory for explainable intelligent systems," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2185-2205, December.
    2. 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.
    3. Stefan Morana & Jella Pfeiffer & Marc T. P. Adam, 2020. "User Assistance for Intelligent Systems," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 62(3), pages 189-192, June.

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