Classifying big data analytic approaches: A generic architecture

Abstract

The explosion of the huge amount of generated data to be analyzed by several applications, imposes the trend of the moment, the Big Data boom, which in turn causes the existence of a vast landscape of architectural solutions. Non expert users who have to decide which analytical solutions are the most appropriates for their particular constraints and specific requirements in a Big Data context, are today lost, faced with a panoply of disparate and diverse solutions. To support users in this hard selection task, in a previous work, we proposed a generic architecture to classify Big Data Analytical Approaches and a set of criteria of comparison/evaluation. In this paper, we extend our classification architecture to consider more types of Big Data analytic tools and approaches and improve the list of criteria to evaluate them. We classify different existing Big Data analytics solutions according to our proposed generic architecture and qualitatively evaluate them in terms of the criteria of comparison. Additionally, we propose a preliminary design of a decision support system, intended to generate suggestions to users based on such classification and on a qualitative evaluation in terms of previous users experiences, users requirements, nature of the analysis they need, and the set of evaluation criteria.