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A systematic classification of database solutions for data mining to support tasks in supply chains

In: Data Science and Innovation in Supply Chain Management: How Data Transforms the Value Chain. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 29

Author

Listed:
  • Hunker, Joachim
  • Scheidler, Anne Antonia
  • Rabe, Markus

Abstract

Purpose: Our research shows that considering well suited NoSQL databases is beneficial for logistics tasks. For answering tasks we rely on the widespread methods of Data Mining. We stress that using relational databases as basis for Data Mining tools cannot cope with the growing amount of data and that using NoSQL databases can be an important step to address these issues. Methodology: This paper discusses Data Mining in the context of Supply Chain Management tasks in logistics and its requirements on databases. The paper demonstrates that using NoSQL databases as basis for Data Mining process models in logistics is a very promising approach. The research is based on a case study, whose core element is the analysis of different well established studies. Findings: The paper presents results which show that Data Mining tools widely support NoSQL databases through available interfaces. Findings are presented in a comparison table which considers dimensions such as Data Mining tools and supported NoSQL databases. To show practical feasibility, a Data Mining tool is used on data of a Supply Chain stored in a NoSQL database. Originality: The novelty of this paper emerges from addressing issues that have so far been insufficiently analyzed in the scientific discussion. The modular structure of the addressed research method ensures scientific traceability. Breaking down tasks and their requirements on databases in the field of Data Mining is a first step towards meeting trends like Big Data and their challenges.

Suggested Citation

  • Hunker, Joachim & Scheidler, Anne Antonia & Rabe, Markus, 2020. "A systematic classification of database solutions for data mining to support tasks in supply chains," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Data Science and Innovation in Supply Chain Management: How Data Transforms the Value Chain. Proceedings of the Hamburg International Conference of Lo, volume 29, pages 395-425, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    • Handle: RePEc:zbw:hiclch:228928
    DOI: 10.15480/882.3121
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    Citations

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    Cited by:

    1. Hochkamp, Florian & Rabe, Markus, 2022. "Outlier detection in data mining: Exclusion of errors or loss of information?," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Jahn, Carlos & Blecker, Thorsten & Ringle, Christian M. (ed.), Changing Tides: The New Role of Resilience and Sustainability in Logistics and Supply Chain Management – Innovative Approaches for the Shift to a New , volume 33, pages 91-117, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.

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