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BigFlow: 科学数据跨中心协同分析服务系统.

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Title: BigFlow: 科学数据跨中心协同分析服务系统.
Alternate Title: BigFlow: A service system for cross-center collaborative analysis of scientific data.
Authors: 朱小杰1,2 xjzhu@cnic.cn, 程振京1 zjcheng@cnic.cn, 王华进1 wanghj@cnic.cn, 杨 刚1, 田 尧1, 樊东卫3, 米琳莹3, 梁兆基1,2
Source: Computer Engineering & Science / Jisuanji Gongcheng yu Kexue. Apr2025, Vol. 47 Issue 4, p706-717. 12p.
Subjects: Astronomical catalogs, Heterogeneous computing, Watersheds, Big data, Trust
Abstract (English): The integration of big data technology and scientific data has spawned numerous new paradigms for scientific research and brought about a widespread need for cross-center collaborative analysis of scientific data. However, such analysis faces significant technical challenges, including inefficient cross-center data transfer, difficulties in cross-framework heterogeneous computing, and low efficiency in cross-center job scheduling, while also requiring trustworthiness throughout the analysis process. To address these technological challenges, a scientific data cross-center collaborative analysis service system called BigFlow has been developed. The system's cross-center collaborative analysis capabilities have been tested and validated based on scenarios such as large-scale astronomical star catalog cross-matching and the identification of check dam locations in the Yellow River basin. [ABSTRACT FROM AUTHOR]
Abstract (Chinese): 大数据技术与科学数据的融合催生了诸多科学研究的新范式, 也带来了对科学数据进行跨中 心协同分析的广泛需求。科学数据跨中心协同分析面临跨中心数据流转不畅、跨框架异构计算困难和跨 中心作业调度效率不高等技术挑战, 同时要确保分析过程的可信性。为应对这些技术挑战, 研制了科学数 据跨中心协同分析服务系统 BigFlow, 该系统采用跨中心分布式架构, 配备跨框架工作流执行引擎, 实现 了工作流跨域的可信执行。基于大规模天文星表交叉证认及黄河流域淤地坝位置识别等应用场景, 对系 统的跨中心协同分析能力进行了测试与验证。 [ABSTRACT FROM AUTHOR]
Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: BigFlow: 科学数据跨中心协同分析服务系统.
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  Data: BigFlow: A service system for cross-center collaborative analysis of scientific data.
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  Data: <searchLink fieldCode="DE" term="%22Astronomical+catalogs%22">Astronomical catalogs</searchLink><br /><searchLink fieldCode="DE" term="%22Heterogeneous+computing%22">Heterogeneous computing</searchLink><br /><searchLink fieldCode="DE" term="%22Watersheds%22">Watersheds</searchLink><br /><searchLink fieldCode="DE" term="%22Big+data%22">Big data</searchLink><br /><searchLink fieldCode="DE" term="%22Trust%22">Trust</searchLink>
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  Data: The integration of big data technology and scientific data has spawned numerous new paradigms for scientific research and brought about a widespread need for cross-center collaborative analysis of scientific data. However, such analysis faces significant technical challenges, including inefficient cross-center data transfer, difficulties in cross-framework heterogeneous computing, and low efficiency in cross-center job scheduling, while also requiring trustworthiness throughout the analysis process. To address these technological challenges, a scientific data cross-center collaborative analysis service system called BigFlow has been developed. The system's cross-center collaborative analysis capabilities have been tested and validated based on scenarios such as large-scale astronomical star catalog cross-matching and the identification of check dam locations in the Yellow River basin. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label: Abstract (Chinese)
  Group: Ab
  Data: 大数据技术与科学数据的融合催生了诸多科学研究的新范式, 也带来了对科学数据进行跨中 心协同分析的广泛需求。科学数据跨中心协同分析面临跨中心数据流转不畅、跨框架异构计算困难和跨 中心作业调度效率不高等技术挑战, 同时要确保分析过程的可信性。为应对这些技术挑战, 研制了科学数 据跨中心协同分析服务系统 BigFlow, 该系统采用跨中心分布式架构, 配备跨框架工作流执行引擎, 实现 了工作流跨域的可信执行。基于大规模天文星表交叉证认及黄河流域淤地坝位置识别等应用场景, 对系 统的跨中心协同分析能力进行了测试与验证。 [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.3969/j.issn.1007-130X.2025.04.014
    Languages:
      – Code: chi
        Text: Chinese
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        PageCount: 12
        StartPage: 706
    Subjects:
      – SubjectFull: Astronomical catalogs
        Type: general
      – SubjectFull: Heterogeneous computing
        Type: general
      – SubjectFull: Watersheds
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      – SubjectFull: Big data
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      – SubjectFull: Trust
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              Text: Apr2025
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