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

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. This article is categorized under: Application Areas > Business and Industry Application Areas > Industry Specific Applications

Big Data and IoT Opportunities for Small and Medium-Sized Enterprises (SMEs)

“…Notably, the SMEs at large are unaware of the potential residing within the business datasets, in consequence, creating doubt on the BDA capabilities to derive the expected advantages from it. Although there are general guidelines for the SMEs to refer to, there are little or nil exemplary research case studies of successful BDA implementation in the SMEs context (Mohd Selamat et al, 2018). The current BDA case studies collected in the EU context does not generally correspond with the SMEs' needs as the use cases are in relation to the larger enterprises.…”

“…SME'S DETERRENT FACTORS AGAINST BIG DATA ANALYTICS (BDA) Based on several key research, it is evident that the SME group are skeptic towards new technological adoption -especially so, in the area of BDA application (Mohd Selamat, Prakoonwit, Sahandi, Khan, & Ramachandran, 2018). According to Coleman (2016), there are several key resistant factors towards BDA application.…”

Enhanced CRISP-DM model for SME coach operator

“…Therefore there are no evidential challenges available based on existing research. Nonetheless, the issues to be considered when applying DM in the private transportation domain are based on extensive studies carried out on the application of DM in the transportation sector by the larger enterprise and in the SMEs context [9]. It was uncovered that the CRISP-DM methodology is widely used by both the large enterprise in the transportation domain and the SME group as a whole.…”

“…Despite the evidential validation that SMEs can reap up to 6% productive through data analytics adoption; the SMEs are still however reluctant it in approach [8,7]. Through an extensive conducted it was uncovered that the hindrance factors of data analytics adoption are in the area of data management, knowledge management and data management [9]. Data Mining (DM) is cru-cial for SMEs, especially so, in the private transport domain as the organisation can inherently extract and process its dataset to uncover new insights to facilitate better decision for the business [10,11].…”

“…Data Mining (DM) is cru-cial for SMEs, especially so, in the private transport domain as the organisation can inherently extract and process its dataset to uncover new insights to facilitate better decision for the business [10,11]. In an extensive study of DM application by the large transportation enterprise and SMEs context at large, it was uncovered that the CRISP-DM methodology is the most popular applied DM model by the industry [9]. The other two leading DM methodology includes the Knowledge Discovery in Database (KDD) and 'Sample, Explore, Modify, Model and Assess' (SEMMA) models.…”