1 resultado para data movement problem
em Department of Computer Science E-Repository - King's College London, Strand, London
Filtro por publicador
- Aberdeen University (1)
- Academic Research Repository at Institute of Developing Economies (1)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (18)
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- Archive of European Integration (5)
- Aston University Research Archive (87)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (17)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (23)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (39)
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- Bucknell University Digital Commons - Pensilvania - USA (1)
- Bulgarian Digital Mathematics Library at IMI-BAS (21)
- CentAUR: Central Archive University of Reading - UK (83)
- Central European University - Research Support Scheme (1)
- CiencIPCA - Instituto Politécnico do Cávado e do Ave, Portugal (2)
- Cochin University of Science & Technology (CUSAT), India (9)
- Collection Of Biostatistics Research Archive (4)
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- Scottish Institute for Research in Economics (SIRE) (SIRE), United Kingdom (2)
- Universidad de Alicante (13)
- Universidad del Rosario, Colombia (2)
- Universidad Politécnica de Madrid (35)
- Universidade Complutense de Madrid (1)
- Universidade de Lisboa - Repositório Aberto (1)
- Universidade do Minho (15)
- Universidade Federal do Pará (9)
- Universidade Federal do Rio Grande do Norte (UFRN) (5)
- Universidade Metodista de São Paulo (1)
- Universitat de Girona, Spain (13)
- Universitätsbibliothek Kassel, Universität Kassel, Germany (4)
- Université de Lausanne, Switzerland (52)
- Université de Montréal, Canada (4)
- University of Connecticut - USA (3)
- University of Michigan (17)
- University of Queensland eSpace - Australia (42)
- University of Southampton, United Kingdom (4)
- University of Washington (3)
Resumo:
As scientific workflows and the data they operate on, grow in size and complexity, the task of defining how those workflows should execute (which resources to use, where the resources must be in readiness for processing etc.) becomes proportionally more difficult. While "workflow compilers", such as Pegasus, reduce this burden, a further problem arises: since specifying details of execution is now automatic, a workflow's results are harder to interpret, as they are partly due to specifics of execution. By automating steps between the experiment design and its results, we lose the connection between them, hindering interpretation of results. To reconnect the scientific data with the original experiment, we argue that scientists should have access to the full provenance of their data, including not only parameters, inputs and intermediary data, but also the abstract experiment, refined into a concrete execution by the "workflow compiler". In this paper, we describe preliminary work on adapting Pegasus to capture the process of workflow refinement in the PASOA provenance system.