Optimizing Intelligent Reduction Techniques for Big Data

Abstract

Working with big volume of data collected through many applications in multiple storage locations is both challenging and rewarding. Extracting valuable information from data means to combine qualitative and quantitative analysis techniques. One of the main promises of analytics is data reduction with the primary function to support decision-making. The motivation of this chapter comes from the new age of applications (social media, smart cities, cyber-infrastructures, environment monitoring and control, healthcare, etc.), which produce big data and many new mechanisms for data creation rather than a new mechanism for data storage. The goal of this chapter is to analyze existing techniques for data reduction, at scale to facilitate Big Data processing optimization and understanding. The chapter will cover the following subjects: data manipulation, analytics and Big Data reduction techniques considering descriptive analytics, predictive analytics and prescriptive analytics. The CyberWater case study will be presented by referring to: optimization process, monitoring, analysis and control of natural resources, especially water resources to preserve the water quality.