Big Data Analytics

Not many academic papers are available to provide taxonomy for big data analytics techniques. Only one research paper by A. Gani, A. Siddiqa, S. Shamshirband, and F. Hanum was found which offers taxonomy according to big data analytics techniques. The paper developed a categorised indexing techniques taxonomy to provide insight to enable researchers to understand and select a technique as a basis to design an indexing mechanism with reduced time and space consumption for big data and Mobile Cloud Computing. The categories are non-artificial intelligence, artificial intelligence, and collaborative artificial intelligence indexing methods. Furthermore, another study has tried to provide the link between the analytical methods and techniques presented by N.I. Academies focused on big data analytics methods and its associated techniques, including those associated with data preparation and descriptive, predictive, and prescriptive analytics as shown in Table 2. Mathematical calculation and visualisation techniques were under the descriptive and exploratory method, whereas machine learning, linear and non-linear regression, classification, data mining, text analytics, Bayesian methods, and simulation techniques were considered under the predictive method. Stochastic models of uncertainty, mathematical optimization under uncertainty and optimal solutions techniques were listed under prescriptive method.

Table 2. Big data analytical (BDA) methods and their associate techniques.

Table 2 removes the vagueness in understanding the difference between data analytics methods and techniques as it groups the techniques which are suitable for a specific analytical stage. The table groups the techniques according to the three methods proposed in Strengthening Data Science Methods for Department of Defence Personnel and Readiness Missions. Therefore, this section will provide examples of known big data analytics technologies that are implemented in research papers based on the findings represented in Figure 4 Section 3 (the four analytical methods proposed: descriptive, diagnostic, predictive and prescriptive methods). Table 3 lists examples of known technologies for each analytic method mentioned before. Yet, given the breadth of the available techniques, an exhaustive list of techniques is beyond the scope of a single paper. As shown in Table 3, the work presented in Use of Feed-Forward to Improve Students' Engagement and Achievement adopted a descriptive method in the research as it was based on questionnaires and interview techniques to collect the data required, whereas the visualisation technique used was ExcelPro. Similarly, the research work in IoT leak detection system for building hydronic pipes paper as the 4th generation of supervisory control and data acquisition (SCADA) system was adopted as the descriptive method and implemented Web-application (WebForms class library which was hosted in Microsoft Azure servers) as a visualisation technique. The research in Intelligent MANET Routing Optimizer paper used a particle swarm algorithm as a clustering technique for its diagnostic methods and MATLAB software as the visualisation technique. Logistic regression models (statistical models based on traditional mathematical equation) and artificial intelligence (AI) modelling are used as predictive methods alongside genetic algorithms (GA) and neuro-fuzzy techniques used for classification. Excel, and Simulink software were the visualisation techniques. The work in IMAN: An Intelligent MANET routing system provided a good example for prescriptive methods as a system was developed to provide the optimum routing protocol according to the context. OPNET was the visualisation technique used.

Table 3. Analytical methods sampling techniques.

It clearly shows from Table 3 that the authors recognised the effect of the visualisation technique and its existence in all big data analytics methods due to its importance in presenting the information to the individuals in a professional and structured manner. Visualisations could be accomplished by using common tools such as bar charts, box plots, and scatter plots. This will have a great impact on the data analysis and reduce the chance of missing important information. From Table 3. It has been concluded that there is a need for another big data analytics taxonomy which will provide a bigger picture to link between big data analytics methods and techniques.