Big data analytics-A review of data-mining models for small and medium enterprises in the transportation sector <s> Data Mining in the SMEs Context <s> Abstract Credit risk assessment for secured loans is an important operation in banking systems to ensure the lenders pay the loans on schedule and to classify the bank as a well performing bank due to regulation. This paper aims to identify factors which are necessary for a rural bank (Bank Perkreditan Rakyat) to assess credit application. By aiming on the reduction of number of non-performing loans, current decision criteria on credit risk assessment are evaluated. Subsequently, a decision tree model is proposed by applying data mining methodology. The credit risk assessment model is applied to PT BPR X in Bali that had 1082 lenders (11.99%) who had non-performing loans and were identified as bad loan cases. This made PT BPR X was categorized as a poorly performing bank. Data mining is used to suggest a decision tree model for credit assessment as it can indicate whether the request of lenders can be classified as performing or non-performing loans risk. Using C 5.0 methodology, a new decision tree model is generated. This model suggests that new criteria in analyzing the loan application. The evaluation results show that if this model is applied, PT BPR X can reduce non-performing loans to less than 5% and the bank can be classified as a well performing bank. <s> BIB001 </s> Big data analytics-A review of data-mining models for small and medium enterprises in the transportation sector <s> Data Mining in the SMEs Context <s> One of the biggest problems of SMEs is their tendencies to financial distress because of insufficient finance background. In this study, an early warning system (EWS) model based on data mining for financial risk detection is presented. CHAID algorithm has been used for development of the EWS. Developed EWS can be served like a tailor made financial advisor in decision making process of the firms with its automated nature to the ones who have inadequate financial background. Besides, an application of the model implemented which covered 7853 SMEs based on Turkish Central Bank (TCB) 2007 data. By using EWS model, 31 risk profiles, 15 risk indicators, 2 early warning signals, and 4 financial road maps has been determined for financial risk mitigation. <s> BIB002 </s> Big data analytics-A review of data-mining models for small and medium enterprises in the transportation sector <s> Data Mining in the SMEs Context <s> In this paper, we focus on the identification of the experience required for solving IT problems in small to medium size enterprises. Our goal is to utilize information retrieval and data mining techniques to automatically extract information from public forums, mailing lists, and FAQs in order to automatically generate a knowledge base for dynamic system administration support. To this end, we explore two similarity-distance measures and five clustering algorithms on three different datasetsto evaluate their performances. During the evaluations, CES+ algorithm gives promising results in terms of automatically extracting the most similar past experiences (problems /solutions) to a given fault. <s> BIB003 </s> Big data analytics-A review of data-mining models for small and medium enterprises in the transportation sector <s> Data Mining in the SMEs Context <s> Presently, in addition to the lack of funds experienced by SMEs in Nigeria, the main challenges experienced by SMEs (Small and Medium Enterprises) in Nigeria has to do with them not being able to understand and the apply marketing concept. Also there are a lot of resources being wasted in marketing campaign which does not target anybody in particular. This study is therefore focused on finding the target market of SMEs using the K-means clustering technique in combination with sentiment analysis, also known as opinion mining. The study also aims at recommending the best medium to market these SMEs as discovered from the output of the analysis. The result of this research will give positive direction to improving the profit of small and medium business through target marketing. Also, this study is not trivial because it will help to reduce marketing cost through target marketing. The study also helps to discover the opinion of the populace on the small and medium business and the medium through which they can be targeted. This discovery will definitely form solid foundation for further marketing action and improve their profit. Keywords : Data Mining, K-means Clustering, Target Marketing, Sentiment Analysis, SME, Opinion Mining, <s> BIB004 </s> Big data analytics-A review of data-mining models for small and medium enterprises in the transportation sector <s> Data Mining in the SMEs Context <s> This paper examines how data mining, an aspect of analytical science, can be applied to assist a Small to Medium Enterprise (SME) industry using unsupervised learning techniques, association rules and time-series analysis. Whilst recent developments have meant it is now possible for SME to compile large amounts of commercial data, this information is rarely utilised effectively. The study builds on a number of standard data mining techniques to produce a tailored set of analyses that provide maximum benefit to the company. Self-Organising Maps were utilised to visualise the core characteristics of the firm's customers. The study outlines a new technique to determine associations between customer variables using the arules package available within RStudios. Finally, time-series forecasting was conducted highlighting the seasonal variations and trends for potential growth in the coming year. <s> BIB005
Having understood that there is a minimal study on SMEs in the transportation sector, this section aims to encapsulate the use of DM in the SMEs context across various industries. In all, ten recent case studies have been tabulated, as seen in Table 3 . The case studies reflect the application of DM in the food & beverage, tourism, Information Technology (IT), financial, aviation, trading and manufacturing industries. From the ten case studies, DM is primarily used for prediction and improvement of the decision-making process. For instance, under the finance industry, Mandala et al.'s research are involved in assessing the credit risk of loan lenders BIB001 . On the other hand, Koyuncugil et al. in their research, employ DM to predict to detect financial risks for SMEs BIB002 . In respect of IT, SMEs are also utilising DM for various usage. In a research conducted by Bozdogand and Zincir-Heywood, DM is used to facilitate the SME's in collecting its public resources automatically to create a knowledge base to support it's IT management BIB003 . In another study conducted by Ibukun et al., the objective for applying DM is to identify customer segmentation in order to carry out target marketing BIB004 . A recent study by Packianather et al. for SMEs in the manufacturing industry, they applied DM to generate unique and new knowledge for forecasting and strategic decision-making BIB005 . The case studies for application of DM by SMEs as depicted in Table  3 insinuate that DM can be applied across many industries. The CRISP-DM is a widely used model. Our in-depth review, (see Tables 2 and 3) , exhibits that CRISP-DM is the most frequently used DM model. Out of the ten case studies considered, six studies utilised the CRISP-DM model, three used the KDD model and one adopted the SEMMA model. Another synonymous result reflects that the SMEs are in most cases using their historical structured data for DM applications -as reflected in Table 2 . Details of the elements and functions of the most frequently used DM models will be discussed in the next section.