ManuKnowVis:如何支持不同用户群体对知识库进行上下文化和利用
IEEE transactions on visualization and computer graphics
|June 19, 2023
概括
ManuKnowVis弥合了制造业知识提供商和消费者之间的差距. 该工具通过实现知识共享和提高电动汽车电池生产效率来增强数据驱动分析.
科学领域:
- 制造过程分析 制造过程分析
- 数据可视化 数据可视化
- 知识管理知识管理
背景情况:
- 在制造数据分析方面,领域专家和数据科学家之间存在差异.
- 知识提供者缺乏数据分析技能,而消费者缺乏领域专业知识.
- 弥合这一差距对于制造业有效的数据驱动决策至关重要.
研究的目的:
- 介绍一下ManuKnowVis,该工具旨在将电动汽车电池模块制造中的多个知识存储库中的数据置于上下文中.
- 为了应对在连续制造过程中将领域知识与数据驱动分析整合在一起的挑战.
- 通过连接知识提供者和消费者,促进制造知识的创造和完成.
主要方法:
- 一个多利益相关者设计研究,涉及ManuKnowVis的代开发,其中包括来自汽车公司的知识提供商和消费者.
- 开发一个多重链接视图工具,使供应商能够描述和连接制造实体.
- 利用消费者的增强数据,以提高理解和有效的数据分析.
主要成果:
- ManuKnowVis成功地弥合了制造业知识提供者和消费者之间的差距.
- 该工具使提供商能够有效地将其域名知识外部化.
- 消费者可以使用增强的数据更有效地进行数据驱动的分析.
结论:
- ManuKnowVis对从制造数据进行数据驱动分析的成功有重大影响.
- 该工具促进了知识的外部化,并提高了不同利益相关者群体的数据分析效率.
- 这种方法增强了对制造环境中复杂领域问题的理解.
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