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.
Sandro Fiore received his Ph.D. degree in Computer Engineering from the Istituto Superiore Universitario di Formazione Interdisciplinare (ISUFI) at the University of Lecce in 2004. His research activities focus on high-performance computing, distributed computing and grid computing, and in particular on distributed data management. Since 2001 he has been the Principal Investigator of the Grid Relational Catalog (GRelC) project (www.grelc.unile.it). In 2006 he joined the Scientific Computing and Operations Division of the Euro-Mediterranean Center for Climate Change (CMCC), leading the Data Management team. Sandro is a member of the ACM, he is an author or coauthor of more than 50 papers in refereed books, journals and proceedings on parallel and grid computing, and he holds a patent on advanced data management.
Giovanni Aloisio is Full Professor of Information Processing Systems at the Engineering Faculty of the University of Salento, Lecce, Italy. His research interests are in the areas of high-performance computing, grid computing and cloud computing. He was a cofounder of the European Grid Forum (Egrid), which merged into the Global Grid Forum (GGF) and is now the Open Grid Forum (OGF). He founded the SPACI (Southern Partnership for Advanced Computational Infrastructures) Consortium, and is now the Director of the Scientific Computing and Operations Division of the Euro-Mediterranean Center for Climate Change (CMCC). Giovanni is the author of more than 100 papers in refereed journals on parallel and grid computing.
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.
