Bio-Swarm-Pipeline: a light-weight, extensible batch processing system for efficient biomedical data processing
A streamlined scientific workflow system that can track the details of the data processing history is critical for the efficient handling of fundamental routines used in scientific research. In the scientific workflow research community, the information that describes the details of data processing...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2009-10-01
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| Series: | Frontiers in Neuroinformatics |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/neuro.11.035.2009/full |
| _version_ | 1819326761918267392 |
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| author | Xi Cheng Ricardo Pizarro Yunxia Tong Brad Zoltick Qian Luo Daniel R Weinberger Venkata S Mattay |
| author_facet | Xi Cheng Ricardo Pizarro Yunxia Tong Brad Zoltick Qian Luo Daniel R Weinberger Venkata S Mattay |
| author_sort | Xi Cheng |
| collection | DOAJ |
| description | A streamlined scientific workflow system that can track the details of the data processing history is critical for the efficient handling of fundamental routines used in scientific research. In the scientific workflow research community, the information that describes the details of data processing history is referred to as provenance which plays an important role in most of the existing workflow management systems. Despite its importance, however, provenance modeling and management is still a relatively new area in the scientific workflow research community. The proper scope, representation, granularity and implementation of a provenance model can vary from domain to domain and pose a number of challenges for an efficient pipeline design. This paper provides a case study on structured provenance modeling and management problems in the neuroimaging domain by introducing the Bio-Swarm-Pipeline (BSP). This new model, which is evaluated in the paper through real world scenarios, systematically addresses the provenance scope, representation, granularity, and implementation issues related to the neuroimaging domain. Although this model stems from applications in neuroimaging, the system can potentially be adapted to a wide range of bio-medical application scenarios. |
| first_indexed | 2024-12-24T13:00:05Z |
| format | Article |
| id | doaj.art-6c0d20488f364938a566a8c244cc1ab7 |
| institution | Directory Open Access Journal |
| issn | 1662-5196 |
| language | English |
| last_indexed | 2024-12-24T13:00:05Z |
| publishDate | 2009-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroinformatics |
| spelling | doaj.art-6c0d20488f364938a566a8c244cc1ab72022-12-21T16:54:11ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962009-10-01310.3389/neuro.11.035.2009616Bio-Swarm-Pipeline: a light-weight, extensible batch processing system for efficient biomedical data processingXi Cheng0Ricardo Pizarro1Yunxia Tong2Brad Zoltick3Qian Luo4Daniel R Weinberger5Venkata S Mattay6National Institute of Mental Health/National Institutes of HealthNational Institute of Mental Health/National Institutes of HealthNational Institute of Mental Health/National Institutes of HealthNational Institute of Mental Health/National Institutes of HealthNational Institute of Mental Health/National Institutes of HealthNational Institute of Mental Health/National Institutes of HealthNational Institute of Mental Health/National Institutes of HealthA streamlined scientific workflow system that can track the details of the data processing history is critical for the efficient handling of fundamental routines used in scientific research. In the scientific workflow research community, the information that describes the details of data processing history is referred to as provenance which plays an important role in most of the existing workflow management systems. Despite its importance, however, provenance modeling and management is still a relatively new area in the scientific workflow research community. The proper scope, representation, granularity and implementation of a provenance model can vary from domain to domain and pose a number of challenges for an efficient pipeline design. This paper provides a case study on structured provenance modeling and management problems in the neuroimaging domain by introducing the Bio-Swarm-Pipeline (BSP). This new model, which is evaluated in the paper through real world scenarios, systematically addresses the provenance scope, representation, granularity, and implementation issues related to the neuroimaging domain. Although this model stems from applications in neuroimaging, the system can potentially be adapted to a wide range of bio-medical application scenarios.http://journal.frontiersin.org/Journal/10.3389/neuro.11.035.2009/fullNeuroimagingneuroinformaticsprovenancescientific workflowswarm |
| spellingShingle | Xi Cheng Ricardo Pizarro Yunxia Tong Brad Zoltick Qian Luo Daniel R Weinberger Venkata S Mattay Bio-Swarm-Pipeline: a light-weight, extensible batch processing system for efficient biomedical data processing Frontiers in Neuroinformatics Neuroimaging neuroinformatics provenance scientific workflow swarm |
| title | Bio-Swarm-Pipeline: a light-weight, extensible batch processing system for efficient biomedical data processing |
| title_full | Bio-Swarm-Pipeline: a light-weight, extensible batch processing system for efficient biomedical data processing |
| title_fullStr | Bio-Swarm-Pipeline: a light-weight, extensible batch processing system for efficient biomedical data processing |
| title_full_unstemmed | Bio-Swarm-Pipeline: a light-weight, extensible batch processing system for efficient biomedical data processing |
| title_short | Bio-Swarm-Pipeline: a light-weight, extensible batch processing system for efficient biomedical data processing |
| title_sort | bio swarm pipeline a light weight extensible batch processing system for efficient biomedical data processing |
| topic | Neuroimaging neuroinformatics provenance scientific workflow swarm |
| url | http://journal.frontiersin.org/Journal/10.3389/neuro.11.035.2009/full |
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