Abstract
Big data analytics refers to a set of advanced technologies, which are designed to efficiently operate and maintain data that are not only big, but also high in variety and velocity. This paper analyses these emerging big data technologies and presents a comparison of the selected big data analytics platforms through the whole data life. The main aim is then to propose and demonstrate the use of an AHP model for the big data analytics platform selection, which may be used by businesses, public sector institutions as well as citizens to solve multiple criteria decision-making problems. It would help them to discover patterns, relationships and useful information in their big data, make sense of them and to take responsive action.
Highlights
The period of business decision-making processes based on simple reports generated from filtered, preselected and structured data is coming to an end
The main aim is to propose and demonstrate the use of an Analytic Hierarchy Process (AHP) model for the big data analytics platform selection, which may be used by businesses, public sector institutions as well as citizens to solve multiple criteria decision-making problems
The main aim of this paper is to propose an Analytic Hierarchy Process (AHP) model for the big data analytics platform selection based on the three defined use cases
Summary
The period of business decision-making processes based on simple reports generated from filtered, preselected and structured data is coming to an end. The emphasis is no longer placed on counting and comparing key performance indicators, but rather on finding a statistically significant connection between these indicators and related datasets. These have much less structure, or more complex structure, than the traditional models. Challenges vary for different applications as they have differing requirements of consistency, usability, flexibility, As a result, the main aim of this paper is to propose an Analytic Hierarchy Process (AHP) model for the big data analytics platform selection based on the three defined use cases. Some of the various big data analytics platforms are discussed in detail and their applications and opportunities provided in several big data life cycle phases are portrayed
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