Big data analytics-A review of data-mining models for small and medium enterprises in the transportation sector <s> Strengths and Limitations of Models in the SMEs Context <s> For Aircraft Launch and Recovery Equipment (ALRE), the goal is to get planes in the air and ensure they land safely. Consequently, a high operational availability (Ao) is crucial to ALRE operations. In order to ensure high Ao, it is crucial that the amount of maintenance, both corrective and preventative, is kept to a minimum. Historically, improvements have been reactive in nature to satisfy the Fleet's needs of the moment and are never implemented across the Fleet. One approach to improving maintenance practices is to use historical data in combination with data mining to determine where and how maintenance procedures can be changed or enhanced. For example, if a maintenance manual says to remove three electronics boxes based on a built-in test (BIT) code, but historically, the data shows that removing and replacing two of the boxes never fix the problem, then the maintainer can be directed to first remove and replace the box which the data suggests is the most-likely cause of failure. This type of improvement is where data mining can be used to enhance or modify maintenance procedures. The Integrated Support Environment (ISE) team and the Integrated Diagnostics and Automated Test Systems (IDATS) team of NAVAIR Lakehurst are jointly investigating the use of data mining as an important tool to enhance ALRE systems and to potentially decrease preventive maintenance on-board Navy vessels, thereby reducing the total cost of ownership. The authors' approach is to use maintenance actions, system performance data, and supply information to draw a clear picture of the failures, diagnoses and repair actions for specific components of ALRE systems. The authors are using a commercial off-the-shelf (COTS) data mining suite, called SPSS Clementine, alongside custom software tools to detect the meaningful, yet hidden, patterns within the mountain of data associated with ALRE systems. SPSS Clementine is one of the data mining industry's premier tools, allowing rapid development of models for data mining. Additionally, ALRE subject matter experts (SMEs) were consulted to ensure the validity of the teams' findings. The combination of modern data mining practices and expert knowledge of ALRE systems will be leveraged to improve the maintenance performed at the O-level and to possibly understand why the failure happened in the first place. This paper will describe the forthcoming investigation exemplifying how the data warehouse holding various sources of data about ALRE systems will be utilized to improve the education of maintainers and to enhance maintenance practices, to understand the cause of component failures, as well as provide solutions to diagnose these failures. Utilizing the knowledge and expertise of database systems and data mining which the ISE team provides, combined with SME knowledge, non-trivial solutions to ALRE maintenance practices shall be uncovered to improve the maintenance environment on-ship <s> BIB001 </s> Big data analytics-A review of data-mining models for small and medium enterprises in the transportation sector <s> Strengths and Limitations of Models 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> BIB002
Following on from the model's comparison, the strengths and limitations of each model will be discussed in this section. This will be in the context of SMEs in the transportation sector. Table 5 shows strengths and limitations of each model according to the case studies listed. As Table 5 shows CRISP-DM. The consistent list of strengths of CRISP-DM illustrates that the model is applicable to the industry, addressing business objectives and issues, providing structured approaches and processes, as well as having the flexibility to use any DM tool. This holistically means that CRISP-DM is practically applicable to the business environment. The compiled list of limitations, on the other hand, is as follows: 1. Long and arduous process with detail steps to be undertaken in each process 2. Requires DM knowledge 3. Explicit need for detailed DM requirements 4. Challenge faced in deriving how and when the selection of data is necessary or irrelevant 5. The late selection of DM technique affects the data format compatibility causing to return to the data analysis stage 6. Inadequate knowledge on domain expert's terminology Based on the CRISP-DM limitations perhaps the outcome of the case studies is suggesting that the user is experiencing an exhaustive process when applying the model in view of the detail steps that each phase contain. Further, with insufficient knowledge on DM, the user may face challenges in grasping the CRISP-DM concept and mode of application. For instance, in the study by Cheung and Li, the limitation encountered during the identification of the necessary or irrelevant data for analysis  . In the study by Ibukun et al., the issue faced was the inadequacy knowledge of DM's terminologies BIB002 . The technical aspect as encountered by Young et al. was the delay in the selection of the DM technique BIB001 . When the selected DM techniques do not correspond with the selected data format, incompatibility will occur, causing the user to return again to the data analysis stage. The strengths of the second most applied model, KDD were accredited to its highly interactive process, containing feedback loops in each process. Like CRISP-DM, the KDD model is endorsed for being industrially applicable in the business environment. The limitations of KDD on the contrary, share similar aspects to that of CRISP-DM. Whereby, the incompatibility of tool and data sets would require the KDD users to return to the KDD process again. Lastly, the strengths of the least applied model, SEMMA are attributed to the availability of its DM robust user support. Its drawback is in the case of the model's highly technical centric process. This may pose a great challenge if an organisation has assigned a user who is not technically equipped in the SEMMA domain. In addition, in comparison to CRISP-DM and KDD, the SEMMA model falls short on the knowledge application phase. 