- Full Description
Data and knowledge play a key role in both current and future GRIDs. The issues concerning representation, discovery, and integration of data and knowledge in dynamic distributed environments can be addressed by exploiting features offered by GRID Technologies. Current research activities are leveraging the GRID for the provision of generic- and domain-specific solutions and services for data management and knowledge discovery. Knowledge and Data Management in GRIDs is the third volume of the CoreGRID series and brings together scientific contributions by researchers and scientists working on storage, data, and knowledge management in GRID and Peer-to-Peer systems. This volume presents the latest GRID solutions and research results in key areas of knowledge and data management such as distributed storage management, GRID databases, Semantic GRID and GRID-aware data mining. Knowledge and Data Management in GRIDs is designed for a professional audience, composed of researchers and practitioners in industry. This book is also suitable for graduate-level students in computer science.
- Table of Contents
Table of Contents
- Foreword by Thierry Priol.
- Contributing Authors.
- Grid Data Management.
- Accessing Data In Grids Using OGSA
- Service Choreography For Data Integration On The Grid.
- Accessing Web Databases Using OGSA
- DAI In DBWorld.
- Failure Recovery Alternatives In Grid Based Distributed Query Processing: A Case Study.
- Grid Data Storage.
- Conductor: Support for Autonomous Configuration of Storage Systems.
- Violin: A Framework for Extensible Block
- Level Storage.
- ClusteriX Data Management System (CDMS) – Architecture and Use Cases.
- Semantic Grid.
- Architectural Patterns For The Semantic Grid.
- A Metadata Model For The Discovery And Exploitation Of Scientific Studies.
- Ideas for the Provision of Ontology Access in Grid Environments.
- Semantic Support For Meta
- Scheduling In Grids.
- Semantic Grid Resource Discovery In Atlas.
- Distributed Data Mining.
- Based Services For Distributed Data Mining.
- Mining Frequent Closed Itemsets From Distributed Repositories.
- Distributed Data Mining And Knowledge Management With Networks Of Sensor Arrays.
Please Login to submit errata.No errata are currently published