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Data Provenance and Data Management in eScience

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 d...

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Corporate Author: SpringerLink (Online service)
Other Authors: Liu, Qing. (Editor, http://id.loc.gov/vocabulary/relators/edt), Bai, Quan. (Editor, http://id.loc.gov/vocabulary/relators/edt), Giugni, Stephen. (Editor, http://id.loc.gov/vocabulary/relators/edt), Williamson, Darrell. (Editor, http://id.loc.gov/vocabulary/relators/edt), Taylor, John. (Editor, http://id.loc.gov/vocabulary/relators/edt)
Language:English
Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.
Edition:1st ed. 2013.
Series:Studies in Computational Intelligence, 426
Subjects:
Engineering.
Artificial intelligence.
Engineering, general.
Artificial Intelligence.
Online Access:https://doi.org/10.1007/978-3-642-29931-5
Table of Contents:
  • Provenance Model for Randomized Controlled Trials
  • Evaluating Workflow Trust Using Hidden Markov Modeling and Provenance Data
  • Unmanaged Workflows: Their Provenance and Use
  • Sketching Distributed Data Provenance
  • A Mobile Cloud with Trusted Data Provenance Services for Bioinformatics Research
  • Data Provenance and Management in Radio Astronomy: A Stream Computing Approach
  • Using Provenance to Support Good Laboratory Practice in Grid Environments.

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