Big Data Analytics

Analytics methods and techniques have been applied to various phases of the analytical lifecycle, e.g., identification, preparation, aggregation and visualisation, however the application of various analytical techniques to big data has been limited with research having been conducted by only a few over the past 20 years. While limited, such research has been highly effective in proposing different sets and kinds of classification, however a problem that has not been effectively researched is how those techniques are classified and how such an activity can be made systematic via a clear taxonomy that integrates the classification of those analytical methods and its associated techniques. This paper investigates this problem and introduced a novel correlation taxonomy based on the findings presented in the previous sections. The rationale of introducing this taxonomy is offering a better big data analytics topology by examining the existing big data analytics methods, as well as understanding what types of techniques map with the existing methods. The taxonomy also draws a clear relationship between research approach methods and big data analytics methods techniques and represents the correlation between big data analytical methods and techniques based on research approaches. This taxonomy aims to classify big data analytics methods and techniques according to well-known research approach methods. This taxonomy determines the right techniques (and consequently the methods) that will equip new researchers in the field with the right tools. The taxonomy proposed is presented in Figure 5. The new structural model illustrates the relationship among big data analytics techniques and their methods according to quantitative and qualitative approaches. A quantitative approach is a strategy for systematic collection, organisation and interpretation of information, this approach is implemented to generate data to allow researchers to quantify the problem by having data sets. Whereas a qualitative approach aims to gain a degree of understanding for the affecting parameters. Based on these definitions the techniques were classified. In this taxonomy, data gathering techniques were counted as a quantitative research approach as shown in Figure 5, whereas clustering, classification, prediction, summarisation and optimisation techniques were all considered as qualitative research approach.

Figure 5. Big data analytic correlation taxonomy.

BD analytics methods could have a different classification based on the findings from the previous sections which suggest data gathering techniques belong to descriptive analytic methods and clustering is a good example of a diagnostic analytics method whereas classification, prediction and summarisation techniques are all listed under predictive analytics methods, whereas, optimisation techniques are considered as prescriptive analytics methods. In the new proposed classification the descriptive analytics method belongs to the quantitative research approach, whereas diagnostic, predictive and prescriptive analytics methods belong to the qualitative research approach.