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作者:

Chen, Jianhui (Chen, Jianhui.) | Zhong, Ning (Zhong, Ning.) | Feng, Jianhua (Feng, Jianhua.)

收录:

Scopus SCIE

摘要:

Aiming at the unstructured brain data and data-driven research process, provenances have become an important component of brain and health big data rather than the accessory of raw experimental data in the systematic Brain Informatics (BI) study. However, the existing file-based or transaction-database-based provenance queries cannot effectively support quickly understanding data and generating decisions or suppositions in the systematic BI study, which need multi-aspect and multi-granularity provenance information and a process of incremental modification. Inspired by studies on the data warehouse and online analytical processing (OLAP) technology, this paper proposes a BI provenance warehouse. The provenance cube and basic OLAP operations are defined. A complete Data-Brain-based development approach is also designed. Such a BI provenance warehouse represents a radically new way for developing the brain big data center, which regards raw experimental data, provenances and domain ontologies as different levels of brain big data for data sharing and mining.

关键词:

online analytical processing provenances data warehouse provenance cube Data-brain

作者机构:

  • [ 1 ] [Chen, Jianhui]Beijing Univ Technol, Int WIC Inst, Beijing 100124, Peoples R China
  • [ 2 ] [Zhong, Ning]Beijing Univ Technol, Int WIC Inst, Beijing 100124, Peoples R China
  • [ 3 ] [Zhong, Ning]Maebashi Inst Technol, Dept Life Sci & Informat, Maebashi, Gunma 3710816, Japan
  • [ 4 ] [Feng, Jianhua]Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China

通讯作者信息:

  • 钟宁

    [Zhong, Ning]Beijing Univ Technol, Int WIC Inst, Beijing 100124, Peoples R China;;[Zhong, Ning]Maebashi Inst Technol, Dept Life Sci & Informat, Maebashi, Gunma 3710816, Japan

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来源 :

INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING

ISSN: 0219-6220

年份: 2017

期: 6

卷: 16

页码: 1581-1609

4 . 9 0 0

JCR@2022

ESI学科: COMPUTER SCIENCE;

ESI高被引阀值:175

中科院分区:3

被引次数:

WoS核心集被引频次: 2

SCOPUS被引频次: 2

ESI高被引论文在榜: 0 展开所有

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