Description
Book SynopsisBig Data analytics is the complex process of examining big data to uncover information such as correlations, hidden patterns, trends and user and customer preferences, to allow organizations and businesses to make more informed decisions. These methods and technologies have become ubiquitous in all fields of science, engineering, business and management due to the rise of data-driven models as well as data engineering developments using parallel and distributed computational analytics frameworks, data and algorithm parallelization, and GPGPU programming. However, there remain potential issues that need to be addressed to enable big data processing and analytics in real time.
In the first volume of this comprehensive two-volume handbook, the authors present several methodologies to support Big Data analytics including database management, processing frameworks and architectures, data lakes, query optimization strategies, towards real-time data processing, data stream analytics, Fog and Edge computing, and Artificial Intelligence and Big Data.
The second volume is dedicated to a wide range of applications in secure data storage, privacy-preserving, Software Defined Networks (SDN), Internet of Things (IoTs), behaviour analytics, traffic predictions, gender based classification on e-commerce data, recommender systems, Big Data regression with Apache Spark, visual sentiment analysis, wavelet Neural Network via GPU, stock market movement predictions, and financial reporting.
The two-volume work is aimed at providing a unique platform for researchers, engineers, developers, educators and advanced students in the field of Big Data analytics.
Table of Contents
- Chapter 1: The impact of Big Data on databases
- Chapter 2: Big data processing frameworks and architectures: a survey
- Chapter 3: The role of data lake in big data analytics: recent developments and challenges
- Chapter 4: Query optimization strategies for big data
- Chapter 5: Toward real-time data processing: an advanced approach in big data analytics
- Chapter 6: A survey on data stream analytics
- Chapter 7: Architectures of big data analytics: scaling out data mining algorithms using Hadoop-MapReduce and Spark
- Chapter 8: A review of fog and edge computing with big data analytics
- Chapter 9: Fog computing framework for Big Data processing using cluster management in a resource-constraint environment
- Chapter 10: Role of artificial intelligence and big data in accelerating accessibility for persons with disabilities
- Overall conclusions
