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

Selamat, S. A. M., Prakoonwit, S., Sahandi, R., Khan, W. and Ramachandran, M., 2018. Big data analytics — A review of data-mining models for small and medium enterprises in the transportation sector. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8 (3), e1238.

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DOI: 10.1002/widm.1238

Abstract

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.

Item Type:Article
ISSN:1942-4795
Additional Information:This is the peer reviewed version of the following article: Mohd Selamat SA, Prakoonwit S, Sahandi R, Khan W, Ramachandran M. Big data analytics—A review of data-mining models for small and medium enterprises in the transportation sector. WIREs Data Mining Knowl Discov. 2018; e1238, which has been published in final form at https://doi.org/10.1002/widm.1238. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
Uncontrolled Keywords:data mining; knowledge discovery database; CRISP-DM; SEMMA; SMEs; transportation; big data
Group:Faculty of Science & Technology
ID Code:30415
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:26 Feb 2018 12:03
Last Modified:01 May 2018 09:59

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