Big Data Computing

Due to market forces and technological evolution, Big Data computing is developing at an increasing rate. A wide variety of novel approaches and tools have emerged to tackle the challenges of Big Data, creating both more opportunities and more challenges for students and professionals in the field of data computation and analysis.

Presenting a mix of industry cases and theory, Big Data Computing discusses the technical and practical issues related to Big Data in intelligent information management. Emphasizing the adoption and diffusion of Big Data tools and technologies in industry, the book introduces a broad range of Big Data concepts, tools, and techniques. It covers a wide range of research, and provides comparisons between state-of-the-art approaches.

Comprised of five sections, the book focuses on:

• What Big Data is and why it is important
• Semantic technologies
• Tools and methods
• Business and economic perspectives
• Big Data applications across industries

Table of Contents

Section I: Introduction
Chapter 1. Toward Evolving Knowledge Ecosystems for Big Data Understanding
Chapter 2. Tassonomy and Review of Big Data Solutions Navigation
Chapter 3. Big Data: Challenges and Opportunities

Section II: Semantic Technologies and Big Data
Chapter 5. Linked Data in Enterprise Integration
Chapter 6. Scalable End-User Access to Big Data
Chapter 7. Semantic Data Interoperability: The Key Problem of Big Data

Section III: Big Data Processing
Chapter 8. Big Data Exploration
Chapter 9. Big Data Processing with MapReduce
Chapter 10. Efficient Processing of Stream Data over Persistent Data

Section IV: Big Data and Business
Chapter 11. Economics of Big Data: A Value Perspective on State of the Art and Future Trends
Chapter 12. Advanced Data Analytics for Business

Section V: Big Data Applications
Chapter 13. Big Social Data Analysis
Chapter 14. Real-Time Big Data Processing for Domain Experts: An Application to Smart Buildings
Chapter 15. Big Data Application: Analyzing Real-Time Electric Meter Data
Chapter 16. Scaling of Geographic Space from the Perspective of City and Field Blocks and Using Volunteered Geographic Information
Chapter 17. Big Textual Data Analytics and Knowledge Management