The Resource Data Provenance and Data Management in eScience, edited by Qing Liu, Quan Bai, Stephen Giugni, Darrell Williamson, John Taylor, (electronic resource)

Data Provenance and Data Management in eScience, edited by Qing Liu, Quan Bai, Stephen Giugni, Darrell Williamson, John Taylor, (electronic resource)

Label
Data Provenance and Data Management in eScience
Title
Data Provenance and Data Management in eScience
Statement of responsibility
edited by Qing Liu, Quan Bai, Stephen Giugni, Darrell Williamson, John Taylor
Contributor
Editor
Editor
Subject
Language
  • eng
  • eng
Summary
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
Member of
Is Subseries of
Dewey number
502.85/57
http://bibfra.me/vocab/relation/httpidlocgovvocabularyrelatorsedt
  • gsirNpEEar8
  • FHl0S-dUvqk
  • mIGoVWuHd7o
  • JOEg_CAP8s4
  • bPc6bVfhSoE
Image bit depth
0
Language note
English
LC call number
TA1-2040
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorName
  • Liu, Qing.
  • Bai, Quan.
  • Giugni, Stephen.
  • Williamson, Darrell.
  • Taylor, John.
Series statement
Studies in Computational Intelligence,
Series volume
426
http://library.link/vocab/subjectName
  • Engineering
  • Artificial intelligence
  • Engineering, general
  • Artificial Intelligence
Label
Data Provenance and Data Management in eScience, edited by Qing Liu, Quan Bai, Stephen Giugni, Darrell Williamson, John Taylor, (electronic resource)
Instantiates
Publication
Note
Bibliographic Level Mode of Issuance: Monograph
Antecedent source
mixed
Bibliography note
Includes bibliographical references and author index
Carrier category
online resource
Carrier category code
  • cr
Color
not applicable
Content category
text
Content type code
  • txt
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
Dimensions
unknown
Edition
1st ed. 2013.
Extent
1 online resource (XII, 184 p.)
File format
multiple file formats
Form of item
online
Isbn
9783642299315
Level of compression
uncompressed
Media category
computer
Media type code
  • c
Other control number
10.1007/978-3-642-29931-5
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
  • (CKB)3390000000030177
  • (SSID)ssj0000745898
  • (PQKBManifestationID)11433384
  • (PQKBTitleCode)TC0000745898
  • (PQKBWorkID)10861479
  • (PQKB)10124519
  • (DE-He213)978-3-642-29931-5
  • (MiAaPQ)EBC3071018
  • (EXLCZ)993390000000030177
Label
Data Provenance and Data Management in eScience, edited by Qing Liu, Quan Bai, Stephen Giugni, Darrell Williamson, John Taylor, (electronic resource)
Publication
Note
Bibliographic Level Mode of Issuance: Monograph
Antecedent source
mixed
Bibliography note
Includes bibliographical references and author index
Carrier category
online resource
Carrier category code
  • cr
Color
not applicable
Content category
text
Content type code
  • txt
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
Dimensions
unknown
Edition
1st ed. 2013.
Extent
1 online resource (XII, 184 p.)
File format
multiple file formats
Form of item
online
Isbn
9783642299315
Level of compression
uncompressed
Media category
computer
Media type code
  • c
Other control number
10.1007/978-3-642-29931-5
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
  • (CKB)3390000000030177
  • (SSID)ssj0000745898
  • (PQKBManifestationID)11433384
  • (PQKBTitleCode)TC0000745898
  • (PQKBWorkID)10861479
  • (PQKB)10124519
  • (DE-He213)978-3-642-29931-5
  • (MiAaPQ)EBC3071018
  • (EXLCZ)993390000000030177

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