Guide to e-Science

This guidebook on e-science presents real-world examples of practices and applications, demonstrating how a range of computational technologies and tools can be employed to build essential infrastructures supporting next-generation scientific research. Each chapter provides introductory material on core concepts and principles, as well as descriptions and discussions of relevant e-science methodologies, architectures, tools, systems, services and frameworks. Features: includes contributions from an international selection of preeminent e-science experts and practitioners; discusses use of mainstream grid computing and peer-to-peer grid technology for open research and resource sharing in scientific research; presents varied methods for data management in data-intensive research; investigates issues of e-infrastructure interoperability, security, trust and privacy for collaborative research; examines workflow technology for the automation of scientific processes; describes applications of e-science.

Includes contributions from an international selection of preeminent e-science experts and practitioners Examines how e-science techniques can be used to facilitate open research and resource sharing, data-intensive research, collaborative research, and scientific workflows Describes applications of e-science, highlighting systems used in the fields of biometrics, clinical medicine, and ecology

Texte du rabat

The way in which scientific research is carried out is undergoing a series of radical changes, worldwide, as a result of the digital revolution. However, this Science 2.0 requires a comprehensive supporting cyber-infrastructure.

This essential guidebook on e-science presents real-world examples of practices and applications, demonstrating how a range of computational technologies and tools can be employed to build essential infrastructures supporting next-generation scientific research. Each chapter provides introductory material on core concepts and principles, as well as descriptions and discussions of relevant e-science methodologies, architectures, tools, systems, services and frameworks. The guide's explanations and context present a broad spectrum of different e-science system requirements.

Topics and features:

• Includes contributions from an international selection of preeminent e-science experts and practitioners
• Discusses use of mainstream grid computing and peer-to-peer grid technology for open research and resource sharing in scientific research
• Presents varied methods for data management in data-intensive research
• Investigates issues of e-infrastructure interoperability, security, trust and privacy for collaborative research
• Examines workflow technology for the automation of scientific processes, and that ensures the research can be reusable, reproducible and repeatable
• Describes applications of e-science, highlighting systems used in the fields of biometrics, clinical medicine, and ecology This highly practical text/reference is a must-have, must-use resource for both IT professionals and academic researchers. Graduate students will also benefit from the experiences and viewpoints shared by the authors on this important subject, as well as the instructional nature of this guidebook.

Dr. Xiaoyu Yang is a research engineer in theSchool of Electronics and Computer Science at the University of Southampton, UK. Dr. Lizhe Wang is a research scientist in the Pervasive Technology Institute at Indiana University, Bloomington, IN, USA. Dr. Wei Jie is a lecturer in the School of Computing at Thames Valley University, London, UK.

Contenu

Part I: Sharing and Open Research.- Implementing a Grid / Cloud e-Science Infrastructure for Hydrological Sciences.- The German Grid Initiative.- Democratizing Resource-Intensive e-Science Through Peer-to-Peer Grid Computing.- Peer4Peer: E-science Communities for Overlay Network and Grid Computing Research.- Part II: Data-Intensive e-Science.- A Multi-Disciplinary, Model-Driven, Distributed Science Data System Architecture.- An Integrated Ontology Management and Data Sharing Framework for Large-Scale Cyberinfrastructure.- Part III: Collaborative Research.- An e-Science Cyberinfrastructure for Solar-enabled Water Production and Recycling.- e-Science Infrastructure Interoperability Guide.- Trustworthy Distributed Systems Through Integrity-Reporting.- An Intrusion Diagnosis Perspective on Cloud Computing.- Part IV: Research Automation, Reusability, Reproducibility and Repeatability.- Conventional Workflow Technology for Scientific Simulation.- Facilitating E-Science Discovery Using Scientific Workflows on the Grid.- Concepts and Algorithms of Mapping Grid-Based Workflows to Resources Within an SLA Context.- Orchestrating e-Science with the Workflow Paradigm.- Part V: e-Science: Easy Science.- Face Recognition using Global and Local Salient Features.- OGSA-Based SOA for Collaborative Cancer Research.- e-Science.