Big data analytics-A review of data-mining models for small and medium enterprises in the transportation sector <s> Introduction <s> With the rapid increase of modern economical and technical development, the Intelligent Transportation System (ITS) becomes more and more important and essential for a country. Practice shows that relying only on the construction of transport infrastructure and expansion does not fundamentally solve the existing transportation problems and sometimes it even makes such problems more severe. So every country is actively exploring ITS technology to solve traffic problems. But due to the different situations of the fund investment, current technology merit and the various traffic problems for each country, the development level of ITS and research areas are distinct. This paper focuses on the comparison and analysis of international ITS research and integrates the ITS technologies to design an integration model. We regard the traffic problem as not only a problem for individual countries, but also a global topic. Countries should improve the technology communication, update and enhance ITS techniques. <s> BIB001 </s> Big data analytics-A review of data-mining models for small and medium enterprises in the transportation sector <s> Introduction <s> For the last two decades, intelligent transportation systems (ITS) have emerged as an efficient way of improving the performance of transportation systems, enhancing travel security, and providing more choices to travelers. A significant change in ITS in recent years is that much more data are collected from a variety of sources and can be processed into various forms for different stakeholders. The availability of a large amount of data can potentially lead to a revolution in ITS development, changing an ITS from a conventional technology-driven system into a more powerful multifunctional data-driven intelligent transportation system (D2ITS) : a system that is vision, multisource, and learning algorithm driven to optimize its performance. Furthermore, D2ITS is trending to become a privacy-aware people-centric more intelligent system. In this paper, we provide a survey on the development of D2ITS, discussing the functionality of its key components and some deployment issues associated with D2ITS Future research directions for the development of D2ITS is also presented. <s> BIB002 </s> Big data analytics-A review of data-mining models for small and medium enterprises in the transportation sector <s> Introduction <s> Understanding the flow and interrelated nature of institutions and business entities' processes and exchanges helps researchers develop and apply big data analytics techniques more effectively from an ecosystem-based perspective (rather than individual transactions and components). <s> BIB003 </s> Big data analytics-A review of data-mining models for small and medium enterprises in the transportation sector <s> Introduction <s> When Google bought Nest, a maker of digital thermostats, for $3.2 billion just a few months ago, it was a clear indication that digital transformation and connection are spreading across even the most traditional industrial segments and creating a staggering array of business opportunities and threats. The digitization of tasks and processes has become essential to competition. General Electric, for example, was at risk of losing many of its top customers to nontraditional competitors--IBM and SAP on the one hand, big data start-ups on the other--offering data-intensive, analytics-based services that could connect to any industrial device. So GE launched a multibillion-dollar initiative focused on what it calls the industrial internet: adding digital sensors to its machines; connecting them to a common, cloud-based software platform; investing in software development capabilities; building advanced analytics capabilities; and embracing crowd-based product development. With all this, GE is evolving its business model. Now, for example, revenue from its jet engines is tied to reduced downtime and miles flown over the course of a year. After just three years, GE is generating more than $1.5 billion in incremental income with digitally enabled, outcomes based business models. The company expects that number to double in 2014 and again in 2015. INSETS: WHAT MAKES DIGITAL TECHNOLOGY TRANSFORMATIONAL?;Idea in Brief;WHY NEST MATTERS;WHAT SMARTWOOL LEARNED FROM ITS DIGITAL CUSTOMERS <s> BIB004 </s> Big data analytics-A review of data-mining models for small and medium enterprises in the transportation sector <s> Introduction <s> This short article reflects upon the task of informing policymaking and investment decisions in times of deep uncertainty, in the face of problems that are more 'wicked' than 'tame' and in the context of ever present biases. <s> BIB005
Intelligent Transportation Systems (ITS) utilise advanced technologies and systems to provide efficient and safe transportation services while minimising the operational cost and environmental impacts BIB001 . The ITS evolution has seen a dramatic development in the last two decades -whereby, from the 1970s to 1980s the primary area of` development was concentrated in curbing Traffic Congestion 2 . From the 1980s to 1990s, the building of Intelligent Infrastructure and Vehicles was the core focus of development  . With the advancement of technology in the 21st Century, data is increasingly collected every hour, every minute and every second causing a data explosion era. The International Data Corporation (IDC) forecast that the volume of data is expected to grow up to 50 Zettabytes globally (equivalent to fifty billion Terabytes) by the year 2020 BIB003 . These can revolutionise the development of ITS, by shaping a traditional technology-driven system BIB002 into a more robust ITS ecosystem BIB003 . The influx of data can only become an asset to the organisation if they are implicitly intelligible to translate useful knowledge for small and medium enterprise (SMEs) organisations BIB004  . As shared by Lyons in a thought-provoking editorial piece in Transport Reviews -in a progressive environment load with robustness, interconnectedness, it is yet 'uncertain'. Therefore, there is a crucial need to evaluate the relevancy of the transport analysis purposes BIB005 . In a conclusive report by European Commission, the key economic drivers of growth in the European continent is the SMEs -contributed 3.9 trillion euros to the economy in 2015  . This is twice as much in comparison to the large enterprises  . The transportation and the storage enterprise made up of 5% of the 22.3 million of the non-financial business economy in 2012  . SMEs can further reap two to three times growth rate through the exploitation of advanced technologies (such as social media, big data, cloud computing and mobile). Eurostat identified that less than 7% of the European SMEs have employed data analytics in it's their business; making a need for digital transformation as a high priority for the EU 11 in this data explosion era. In an in-depth report by IDC on IDC European Vertical Market Survey 2012, it was ascertained that only 3% (an estimation of 1,500 out of 50,000 organisations) of the SMEs in the transportation and storage sector have deployed data analytics in their business  . The transport and storage SMEs have been relentlessly labelled as laggards in the adoption of Big Data technologies. SMEs can become 5-6% more productive through the utilisation of data analytics in the business -as evident in the larger transport companies 11 . Despite the momentous potential benefits of utilisation of big data analytics, the transport and storage SMEs are still dawdling in their adoption efforts. In 2012, the adoption rate of big data analytics of SMEs in the UK stood at 0.2% compared to the large enterprise, with an uptake of 25%  . This is indeed an alarming figure, as the fast adoption rate by the large enterprise may eventually implicate SMEs to become irrelevant and absolute. Therefore, there is an urgent need for SMEs to begin exploring the implementation of big data analytic and data mining (DM). To ensure the relevance of this study, articles published with the last 10 years were only included. The selections of the literature were divided into three categories. The first category includes papers relating to big data analytics for SMEs. The second and third category encompasses papers relating to DM models in the transportation sector from the SMEs and the large enterprises subsequently. The full text of each article was screened in order to validate the relevancy and applicability of the articles. Upon screening, only suitable articles were included for this study. This paper presents the outcome of a critical study of the big data analytics literature, in respect of data mining models for SMEs in the transportation sector in particular. This information was extracted from online databases such as ACM Digital Library, Science Direct, Springer, EBSCOhost EJS, Semantic Scholar, Google Scholar (search engine) and IGI Global. The research aims to provide researchers, transportation business leaders and policy makers' eminent findings of the big data analytics research studies. It is anticipated that this paper will magnify the emergence of big data technologies aiding SME's understanding and capitalisation to facilitate and spur business growth. In the next section, the paper will provide background information on the big data analytics challenges and problems faced by SMEs. The third and fourth sections will cover the DM application case studies in both the transportation sector and SMEs context. The fifth section will present a comparative study on the most commonly used DM models. This is then followed by a discussion on the model's strengths and deficiencies. Lastly, the paper will conclude with several key points and a discourse of future research work to be undertaken.