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Scientific Data Management: Challenges, Technology, and Deployment (Chapman & Hall/CRC Computational Science)

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Excellent survey of methods and tools to manage massive amounts of scientific data. The chapter "Metadata and Provenance Management" is particularly helpful since this is an area my workgroup has been weak in.


Contents
List of Figures
List of Tables
Acknowledgments
Contributors
Introduction
Part I: Storage Technology and Efficient Storage Access
Chapter 1. Storage Technology
Chapter 2. Parallel Data Storage and Access
Chapter 3. Dynamic Storage Management
Part II: Data Transfer and Scheduling
Chapter 4. Coordination of Access to Large-Scale Datasets in Distributed Environments
Chapter 5. High Throughput Data Movement
Part III: Specialized Retrieval Techniques and Database Systems
Chapter 6. Accelerating Queries on Very Large Datasets
Chapter 7. Emerging Database Systems in Support of Scientific Data
Part IV: Data Analysis, Integration, and Visualization Methods
Chapter 8. Scientific Data Analysis*
Chapter 9. Scientific Data Management Challenges in High-Performance Visual Data Analysis
Chapter 10. Interoperability and Data Integration in the Geosciences
Chapter 11. Analyzing Data Streams in Scientific Applications*
Part V: Scientific Process Management
Chapter 12. Metadata and Provenance Management
Chapter 13. Scientific Process Automation and Workflow Management
Conclusions and Future Outlook
Index