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Ontology-based data mining model management for self-service knowledge discovery

Author

Listed:
  • Yan Li

    (Claremont Graduate University)

  • Manoj A. Thomas

    (Virginia Commonwealth University, School of Business)

  • Kweku-Muata Osei-Bryson

    (Virginia Commonwealth University, School of Business)

Abstract

Data mining (DM) models are knowledge-intensive information products that enable knowledge creation and discovery. As large volume of data is generated with high velocity from a variety of sources, there is a pressing need to place DM model selection and self-service knowledge discovery in the hands of the business users. However, existing knowledge discovery and data mining (KDDM) approaches do not sufficiently address key elements of data mining model management (DMMM) such as model sharing, selection and reuse. Furthermore, they are mainly from a knowledge engineer’s perspective, while the business requirements from business users are often lost. To bridge these semantic gaps, we propose an ontology-based DMMM approach for self-service model selection and knowledge discovery. We develop a DM3 ontology to translate the business requirements into model selection criteria and measurements, provide a detailed deployment architecture for its integration within an organization’s KDDM application, and use the example of a student loan company to demonstrate the utility of the DM3.

Suggested Citation

  • Yan Li & Manoj A. Thomas & Kweku-Muata Osei-Bryson, 0. "Ontology-based data mining model management for self-service knowledge discovery," Information Systems Frontiers, Springer, vol. 0, pages 1-19.
    • Handle: RePEc:spr:infosf:v::y::i::d:10.1007_s10796-016-9637-y
    DOI: 10.1007/s10796-016-9637-y
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    References listed on IDEAS

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    1. Waleed A. Muhanna & Roger Alan Pick, 1994. "Meta-Modeling Concepts and Tools for Model Management: A Systems Approach," Management Science, INFORMS, vol. 40(9), pages 1093-1123, September.
    2. Claudia Diamantini & Domenico Potena & Emanuele Storti, 2013. "A virtual mart for knowledge discovery in databases," Information Systems Frontiers, Springer, vol. 15(3), pages 447-463, July.
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    Cited by:

    1. Lawrence Bunnell & Kweku-Muata Osei-Bryson & Victoria Y. Yoon, 0. "RecSys Issues Ontology: A Knowledge Classification of Issues for Recommender Systems Researchers," Information Systems Frontiers, Springer, vol. 0, pages 1-42.
    2. Lawrence Bunnell & Kweku-Muata Osei-Bryson & Victoria Y. Yoon, 2020. "RecSys Issues Ontology: A Knowledge Classification of Issues for Recommender Systems Researchers," Information Systems Frontiers, Springer, vol. 22(6), pages 1377-1418, December.

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