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Grid and Cloud Database Management provides an overview of grid/cloud database management. The text builds a foundation by covering basic concepts, and then moves on to standards, real use cases, existing projects, etc.

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Since the 1990s Grid Computing has emerged as a paradigm for accessing and managing distributed, heterogeneous and geographically spread resources, promising that we will be able to access computer power as easily as we can access the electric power grid. Later on, Cloud Computing brought the promise of providing easy and inexpensive access to remote hardware and storage resources. Exploiting pay-per-use models and virtualization for resource provisioning, cloud computing has been rapidly accepted and used by researchers, scientists and industries.

In this volume, contributions from internationally recognized experts describe the latest findings on challenging topics related to grid and cloud database management. By exploring current and future developments, they provide a thorough understanding of the principles and techniques involved in these fields. The presented topics are well balanced and complementary, and they range from well-known research projects and real case studies to standards and specifications, and non-functional aspects such as security, performance and scalability. Following an initial introduction by the editors, the contributions are organized into four sections: Open Standards and Specifications, Research Efforts in Grid Database Management, Cloud Data Management, and Scientific Case Studies.

With this presentation, the book serves mostly researchers and graduate students, both as an introduction to and as a technical reference for grid and cloud database management. The detailed descriptions of research prototypes dealing with spatiotemporal or genomic data will also be useful for application engineers in these fields.

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Open Standards and Specifications.- 1. Open Standards for Service-based Database Access and Integration.- 2. Open Cloud Computing Interface in Data Management-related Setups.- Research Efforts on Grid Database Management.- 3. The GRelC Project from 2001 to 2011, Ten Years Working on Grid-DBMSs.- 4. Distributed Data Management with OGSA-DAI.- 5. The DASCOSA-DB Grid Database System.- Cloud Data Management.- 6. Access Control and Trustiness for Resource Management in Cloud Databases.- 7. Dirty Data Management in Cloud Database.- 8. Virtualization and Column-oriented Database Systems.- 9. Scientific Computation and Data Management using Microsoft Windows Azure.- 10. The Cloud Miner Moving Data Mining into Computational Clouds.- 11. Provenance Support for Data-Intensive Scientific Workflows.- 12. Managing Data-Intensive Workloads in a Cloud.- Scientific Case Studies.- 13. Managing and Analysing Genomic Data using HPC and Clouds.- 14. Grid Technologies for Satellite Data Processing and Management within International Disaster Monitoring Projects.- 15. Transparent Data Cube for Spatio-temporal Data Mining and Visualization.- 16. Distributed Storage of Large Scale Multidimensional Electroencephalogram Data using Hadoop and HBase.

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