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作者:

Zhong, Han (Zhong, Han.) | Chen, Jianhui (Chen, Jianhui.) | Kotake, Taihei (Kotake, Taihei.) | Han, Jian (Han, Jian.) | Zhong, Ning (Zhong, Ning.) | Huang, Zhisheng (Huang, Zhisheng.)

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EI Scopus

摘要:

Integrating brain big data is an important issue of the systematic Brain Informatics study. Provenances provide a practical approach to realize the information-level data integration. However, the existing neuroimaging provenances focus on describing experimental conditions and analytical processes, and cannot meet the requirement of integrating brain big data. This paper puts forward a provenance model of brain data, in which model elements are identified and defined by extending the Open Provenance Model. A case study is also described to demonstrate significance and usefulness of the proposed model. Such a provenance model facilitates more accurate modeling of brain data, including data creation and data processing for integrating various primitive brain data, brain data related information during the systematic Brain Informatics study. © Springer International Publishing 2013.

关键词:

Big data Data integration Medical informatics Neuroimaging

作者机构:

  • [ 1 ] [Zhong, Han]International WIC Institute, Beijing University of Technology, Beijing 100024, China
  • [ 2 ] [Chen, Jianhui]Department of Computing, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
  • [ 3 ] [Kotake, Taihei]Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi-City 371-0816, Japan
  • [ 4 ] [Han, Jian]International WIC Institute, Beijing University of Technology, Beijing 100024, China
  • [ 5 ] [Zhong, Ning]International WIC Institute, Beijing University of Technology, Beijing 100024, China
  • [ 6 ] [Zhong, Ning]Beijing Key Laboratory of MRI and Brain Informatics, Beijing, China
  • [ 7 ] [Zhong, Ning]Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi-City 371-0816, Japan
  • [ 8 ] [Huang, Zhisheng]Knowledge Representation and Reasoning Group, Vrije University Amsterdam, 1081 HV Amsterdam, Netherlands

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ISSN: 0302-9743

年份: 2013

卷: 8211 LNAI

页码: 439-449

语种: 英文

被引次数:

WoS核心集被引频次: 0

SCOPUS被引频次: 6

ESI高被引论文在榜: 0 展开所有

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