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Handbook of Research on Big Data Storage and Visualization Techniques

by Richard S. Segall

• Length: 917 pages
• Edition: 1
• Language: English
• Publisher: IGI Global
• Publication Date: 2022-01-05

The digital age has presented an exponential growth in the amount of data available to individuals looking to draw conclusions based on given or collected information across industries. Challenges associated with the analysis, security, sharing, storage, and visualization of large and complex data sets continue to plague data scientists and analysts alike as traditional data processing applications struggle to adequately manage big data.

The Handbook of Research on Big Data Storage and Visualization Techniques is a critical scholarly resource that explores big data analytics and technologies and their role in developing a broad understanding of issues pertaining to the use of big data in multidisciplinary fields. Featuring coverage on a broad range of topics, such as architecture patterns, programing systems, and computational energy, this publication is geared towards professionals, researchers, and students seeking current research and application topics on the subject.

Table of Contents

Section 1: Introduction to Big Data and Storage Systems
Chapter 1: Overview of Big Data and Its Visualization
Chapter 2: Overview of Big-Data-Intensive Storage and Its Technologies

Section 2: Big Data Technologies and Architectural Patterns
Chapter 3: Database Systems for Big Data Storage and Retrieval
Chapter 4: Hadoop Framework for Handling Big Data Needs
Chapter 5: Role of Open Source Software in Big Data Storage

Section 3: Big Data in Clouds, Clusters, and Grids
Chapter 6: Big Data Tools for Computing on Clouds and Grids
Chapter 7: A Review of Security Challenges in Cloud Storage of Big Data
Chapter 8: Architecture for Big Data Storage in Different Cloud Deployment Models

Section 4: Big Data Processing for Storage and Visualization
Chapter 9: Programming and Pre-Processing Systems for Big Data Storage and Visualization
Chapter 10: High Performance Storage for Big Data Analytics and Visualization
Chapter 11: Big Data in Massive Parallel Processing
Chapter 12: Distributed Streaming Big Data Analytics for Internet of Things (IoT)

Section 5: Applications of Big Data Storage
Chapter 13: Scalable Data Warehouse Architecture
Chapter 14: Resource Provisioning and Scheduling of Big Data Processing Jobs