Big data analytics-A review of data-mining models for small and medium enterprises in the transportation sector <s> The Evolution of Data Mining <s> Big Data is a new term used to identify datasets that we can not manage with current methodologies or data mining software tools due to their large size and complexity. Big Data mining is the capability of extracting useful information from these large datasets or streams of data. New mining techniques are necessary due to the volume, variability, and velocity, of such data. The Big Data challenge is becoming one of the most exciting opportunities for the years to come. We present in this issue, a broad overview of the topic, its current status, controversy, and a forecast to the future. We introduce four articles, written by influential scientists in the field, covering the most interesting and state-of-the-art topics on Big Data mining. <s> BIB001 </s> Big data analytics-A review of data-mining models for small and medium enterprises in the transportation sector <s> The Evolution of Data Mining <s> Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution. <s> BIB002
Data mining (DM) refers to the science of identifying valuable and unique information from a substantial size of data sets or databases. The mining procedures involve intensive computing of data analysis BIB002 . The core goal of DM is processing a large amount of data to generate new knowledge BIB001 . DM serves two primary goals -(1) uncover new insights and (2) generate predictions  . DM is a process for Knowledge Discovery in Database 36 30 . The term KDD refers to a set of broad processes used to discover valuable knowledge from a set of data collection  . The emergence of KDD was sparked with the rising establishment of big databases in the varying number of organisations in the early 90s  . This created a paradigm shift for the need to develop data mining algorithms with the capabilities to unearth gainful insights from the big volume of data that are residing in companies' databases. Figure 1 depicts the overall evolution of the data mining process models with KDD as its foundation and CRISP-DM as the core focal point of the evolution. This section will only discuss the three most applied models, KDD, SEMMA and CRISP-DM in depth. A critical comparison of these models has been made which is discussed in the next section. 