000 | 03424nam a22005055i 4500 | ||
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001 | 978-3-642-29931-5 | ||
003 | DE-He213 | ||
005 | 20200420221259.0 | ||
007 | cr nn 008mamaa | ||
008 | 120803s2013 gw | s |||| 0|eng d | ||
020 |
_a9783642299315 _9978-3-642-29931-5 |
||
024 | 7 |
_a10.1007/978-3-642-29931-5 _2doi |
|
050 | 4 | _aTA1-2040 | |
072 | 7 |
_aTBC _2bicssc |
|
072 | 7 |
_aTEC000000 _2bisacsh |
|
082 | 0 | 4 |
_a620 _223 |
245 | 1 | 0 |
_aData Provenance and Data Management in eScience _h[electronic resource] / _cedited by Qing Liu, Quan Bai, Stephen Giugni, Darrell Williamson, John Taylor. |
264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2013. |
|
300 |
_aXII, 184 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
||
337 |
_acomputer _bc _2rdamedia |
||
338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
||
490 | 1 |
_aStudies in Computational Intelligence, _x1860-949X ; _v426 |
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505 | 0 | _aProvenance 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. | |
520 | _aeScience 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. | ||
650 | 0 | _aEngineering. | |
650 | 0 | _aArtificial intelligence. | |
650 | 1 | 4 | _aEngineering. |
650 | 2 | 4 | _aEngineering, general. |
650 | 2 | 4 | _aArtificial Intelligence (incl. Robotics). |
700 | 1 |
_aLiu, Qing. _eeditor. |
|
700 | 1 |
_aBai, Quan. _eeditor. |
|
700 | 1 |
_aGiugni, Stephen. _eeditor. |
|
700 | 1 |
_aWilliamson, Darrell. _eeditor. |
|
700 | 1 |
_aTaylor, John. _eeditor. |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783642299308 |
830 | 0 |
_aStudies in Computational Intelligence, _x1860-949X ; _v426 |
|
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-642-29931-5 |
912 | _aZDB-2-ENG | ||
942 | _cEBK | ||
999 |
_c53067 _d53067 |