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

机译:大数据分析 - 对运输部门中小企业数据采矿模式的综述

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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
机译: >在鉴于技术进步所暗示的数据潜在的情况下,对中小企业(中小企业)采用数据分析达到了一个关键点。尽管数据挖掘(DM)广泛用于运输部门,但它令人惊讶地注意到,在SMES的应用中,在运输部门的应用,DM的应用已经进行了最小的研究案例研究。从进行的广泛审查中,识别出运输部门的大型企业使用的三种最常见的DM模型,即“数据库中的知识发现”,“样本,探索,修改,模型和评估”(SEMMA),以及交叉业数据挖掘标准过程“(CRISP-DM)。在各行业的中小企业的背景下,相同的发现是透露。它还发现,三种模型中,CRISP-DM已被广泛应用于商业上。然而,尽管CRISP-DM在实践中成为事实上的DM模型,但为评估模型的优势和弱点而进行的一项研究表明,他们对中小企业有几个限制。本文得出结论,旨在开发一种新型模型的批判性,以便迎合中小企业的先决条件,特别是在运输部门环境中。 > 本文分类为: 应用领域&商业和行业 应用领域&行业特定应用

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