Cloud Analytics and Big Data Computing

Abstract

The goal of Knowledge Discovery in Data (KDD) is to extract information that is not obvious by using careful and detailed analysis and interpretation. To drive decisions and actions, analytics employs KDD, data mining, text mining, statistical and quantitative analysis, explanatory and predictive models, and advanced and interactive visualisation techniques. Cloud computing is a versatile technology that can be used for a variety of purposes. The use of data mining techniques based on Cloud computing will enable users to retrieve meaningful information from virtually integrated data warehouses, lowering infrastructure and storage costs. Data mining can extract useful and potentially useful information from the cloud. Big Data is typically defined by three characteristics known as the 3Vs (Volume, Velocity and Variety). It refers to data that is excessively large, dynamic, and complex. Data are difficult to capture, store, manage, and analyse in this context using traditional data management tools. This paper surveys approaches, environments, and technologies in key areas for Big Data analytics capabilities and discusses how they aid in the development of analytics solutions for Clouds.