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WIREs Data Mining Knowl Discov
Impact Factor: 2.111

Big data analytics—A review of data‐mining models for small and medium enterprises in the transportation sector

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The need for small and medium enterprises (SMEs) to adopt data analytics has reached a critical point, given the surge of data implied by the advancement of technology. Despite data mining (DM) being widely used in the transportation sector, it is staggering to note that there are minimal research case studies being done on the application of DM by SMEs, specifically in the transportation sector. From the extensive review conducted, the three most common DM models used by large enterprises in the transportation sector are identified, namely “Knowledge Discovery in Database,” “Sample, Explore, Modify, Model and Assess” (SEMMA), and “CRoss Industry Standard Process for Data Mining” (CRISP‐DM). The same finding was revealed in the SMEs' context across the various industries. It was also uncovered that among the three models, CRISP‐DM had been widely applied commercially. However, despite CRISP‐DM being the de facto DM model in practice, a study carried out to assess the strengths and weakness of the models reveals that they have several limitations with respect to SMEs. This paper concludes that there is a critical need for a novel model to be developed in order to cater to the SMEs' prerequisite, especially so in the transportation sector context.

This article is categorized under:

  • Application Areas > Business and Industry
  • Application Areas > Industry Specific Applications
Evolution of data‐mining methodologies. The figure represents the evolution of data‐mining methodologies from the 1990s to 2000s. Source: Mariscal et al. ()
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CRISP‐DM methodology. The figure represents the overall CRISP‐DM methodology process. Adapted from Chapman et al. ()
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Sample, Explore, Modify, Model, Assess methodology. The figure represents the overall SEMMA methodology process. Source: Mariscal et al. ()
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Knowledge Discovery in Database. The figure represents the overall KDD methodology process. Source: Fayyad et al. ()
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Application Areas > Industry Specific Applications

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