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
Introduction: Toward Evolving Knowledge Ecosystems for Big Data Understanding. Tassonomy and Review of Big Data Solutions Navigation. Big Data: Challenges and Opportunities. Semantic Technologies and Big Data: Management of Big Semantic Data. Linked Data in Enterprise Integration. Scalable End-User Access to Big Data. Semantic Data Interoperability: The Key Problem of Big Data. Big Data Processing: Big Data Exploration. Big Data Processing with MapReduce. Efficient Processing of Stream Data over Persistent Data. Big Data and Business: The Economics of Big Data: A Value Perspective on State of the Art and Future Trends. Advanced Data Analytics for Business. Big Data Applications: Big Social Data Analysis. Real-Time Big Data Processing for Domain Experts: An Application to Smart Buildings. Big Data Application: Analyzing Real-Time Electric Meter Data. Scaling of Geographic Space from the Perspective of City and Field Blocks and Using Volunteered Geographic Information. Big Textual Data Analytics and Knowledge Management.