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Data Provenance and Data Management in eScience

  • Book
  • © 2013

Overview

Editors:
  1. Qing Liu

    1. , Information and Communications, CSIRO, Hobart, Australia

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  2. Quan Bai

    1. , School of Computing & Mathematical, Auckland University of Technology, Auckland, New Zealand

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  3. Stephen Giugni

    1. , Information and Communications, CSIRO, Hobart, Australia

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  4. Darrell Williamson

    1. , Information Management and Technology, CSIRO, Acton, Australia

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  5. John Taylor

    1. Mathematics Informatics and Statistics, CSIRO, Acton, Australia

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  • 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)

  1. Front Matter

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

    1. Front Matter

      Pages 1-1
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    2. Provenance Model for Randomized Controlled Trials

      • Vasa Curcin, Roxana Danger, Wolfgang Kuchinke, Simon Miles, Adel Taweel, Christian Ohmann
      Pages 3-33

    3. Evaluating Workflow Trust Using Hidden Markov Modeling and Provenance Data

      • Mahsa Naseri, Simone A. Ludwig
      Pages 35-58

    4. Unmanaged Workflows: Their Provenance and Use

      • 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
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    2. Sketching Distributed Data Provenance

      • Tanu Malik, Ashish Gehani, Dawood Tariq, Fareed Zaffar
      Pages 85-107

    3. A Mobile Cloud with Trusted Data Provenance Services for Bioinformatics Research

      • Jinhui Yao, Jingyu Zhang, Shiping Chen, Chen Wang, David Levy, Qing Liu
      Pages 109-128

    4. Data Provenance and Management in Radio Astronomy: A Stream Computing Approach

      • Mahmoud S. Mahmoud, Andrew Ensor, Alain Biem, Bruce Elmegreen, Sergei Gulyaev
      Pages 129-156

    5. Using Provenance to Support Good Laboratory Practice in Grid Environments

      • Miriam Ney, Guy K. Kloss, Andreas Schreiber
      Pages 157-180

  4. Back Matter

    Pages 0--1
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Keywords

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

  • , Information and Communications, CSIRO, Hobart, Australia

    Qing Liu, Stephen Giugni

  • , School of Computing & Mathematical, Auckland University of Technology, Auckland, New Zealand

    Quan Bai

  • , Information Management and Technology, CSIRO, Acton, Australia

    Darrell Williamson

  • 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

  • Topics: Engineering, general, Artificial Intelligence

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