Data Provenance and Data Management in eScience

  • Qing Liu
  • Quan Bai
  • Stephen Giugni
  • Darrell Williamson
  • John Taylor

Part of the Studies in Computational Intelligence book series (SCI, volume 426)

Table of contents

  1. Front Matter
    Pages 1-10
  2. Provenance in eScience: Representation and Use

    1. Front Matter
      Pages 1-1
    2. Vasa Curcin, Roxana Danger, Wolfgang Kuchinke, Simon Miles, Adel Taweel, Christian Ohmann
      Pages 3-33
    3. Mehmet S. Aktas, Beth Plale, David Leake, Nirmal K. Mukhi
      Pages 59-81
  3. Data Provenance and Data Management Systems

    1. Front Matter
      Pages 83-83
    2. Tanu Malik, Ashish Gehani, Dawood Tariq, Fareed Zaffar
      Pages 85-107
    3. Jinhui Yao, Jingyu Zhang, Shiping Chen, Chen Wang, David Levy, Qing Liu
      Pages 109-128
    4. Mahmoud S. Mahmoud, Andrew Ensor, Alain Biem, Bruce Elmegreen, Sergei Gulyaev
      Pages 129-156
    5. Miriam Ney, Guy K. Kloss, Andreas Schreiber
      Pages 157-180
  4. Back Matter
    Pages 0--1

About this book

Introduction

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, applications, 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.

Keywords

Computational Intelligence Privacy Security

Editors and affiliations

  • Qing Liu
    • 1
  • Quan Bai
    • 2
  • Stephen Giugni
    • 3
  • Darrell Williamson
    • 4
  • John Taylor
    • 5
  1. 1., Information and CommunicationsCSIROHobartAustralia
  2. 2., School of Computing & MathematicalAuckland University of TechnologyAucklandNew Zealand
  3. 3., Information and CommunicationsCSIROHobartAustralia
  4. 4., Information Management and TechnologyCSIROActonAustralia
  5. 5.Mathematics Informatics and StatisticsCSIROActonAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-29931-5
  • Copyright Information Springer-Verlag Berlin Heidelberg 2013
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-29930-8
  • Online ISBN 978-3-642-29931-5
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • About this book