Editors:
- Recent research on Data Provenance and Data Management for eScience
- How to use advanced semantic and AI techniques to track and manage information which describe the life cycle of data items and products
- Written by leading experts in the field
Part of the book series: Studies in Computational Intelligence (SCI, volume 426)
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Table of contents (7 chapters)
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Front Matter
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Provenance in eScience: Representation and Use
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Front Matter
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Data Provenance and Data Management Systems
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Front Matter
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Back Matter
About this book
eScience allows scientific research to be carried out in highly distributed environments. The complex nature of the interactions in an eScience infrastructure, which often involves a range of instruments, data, models, application, people and computational facilities, suggests there is a need for data provenance and data management (DPDM). The W3C Provenance Working Group defines the provenance of a resource as a “record that describes entities and processes involved in producing and delivering or otherwise influencing that resource”. It has been widely recognised that provenance is a critical issue to enable sharing, trust, authentication and reproducibility of eScience process.
Data Provenance and Data Management in eScience identifies the gaps between DPDM foundations and their practice within eScience domains including clinical trials, bioinformatics and radio astronomy. The book covers important aspects of fundamental research in DPDM including provenance representation and querying. It also explores topics that go beyond the fundamentals including applications. This book is a unique reference for DPDM with broad appeal to anyone interested in the practical issues of DPDM in eScience domains.
Reviews
From the reviews:
“This book, a compilation of independent chapters, reflects the research work of several groups in the field of data provenance and data management for eScience. … the book will be particularly useful for researchers in the area of data provenance, as well as for those in data management in the application domains covered in the book.” (Sergio Ilarri, Computing Reviews, April, 2013)Editors and Affiliations
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, Information and Communications, CSIRO, Hobart, Australia
Qing Liu, Stephen Giugni
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, School of Computing & Mathematical, Auckland University of Technology, Auckland, New Zealand
Quan Bai
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, Information Management and Technology, CSIRO, Acton, Australia
Darrell Williamson
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Mathematics Informatics and Statistics, CSIRO, Acton, Australia
John Taylor
Bibliographic Information
Book Title: Data Provenance and Data Management in eScience
Editors: Qing Liu, Quan Bai, Stephen Giugni, Darrell Williamson, John Taylor
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-642-29931-5
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2013
Hardcover ISBN: 978-3-642-29930-8Published: 04 August 2012
Softcover ISBN: 978-3-642-44158-5Published: 20 September 2014
eBook ISBN: 978-3-642-29931-5Published: 04 August 2012
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XII, 184