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数据质量评估和验证方法 数据质量评估和验证方法
Wen-Chang Tseng1, Kuan-Wen Chen1, Chien-Yeh Hsu2
1National Health Research Institutes-The National Institute of Cancer Research.
Studies in health technology and informatics
|August 8, 2025
概括
随着数据量不断增加,数据质量至关重要. 本研究确定了16个多方面的数据质量评估维度,将定性和定量指标结合起来进行全面评估.
科学领域:
- 数据科学数据科学数据科学
- 信息管理 信息管理
背景情况:
- 数据的指数增长需要强大的数据质量评估.
- 确保数据完整性对于可靠的研究和决策至关重要.
研究的目的:
- 确定和定义多方面的数据质量评估的关键维度.
- 为评估各种应用中的数据质量提供一个全面的框架.
主要方法:
- 对现有数据质量框架的文献审查.
- 分析台湾的实际数据质量挑战和经验.
主要成果:
- 确定16个不同的数据质量维度.
- 分类为定性指标 (例如,货币,相关性,安全性,互操作性) 和定量指标 (例如,完整性,可信性,合规性).
结论:
- 为了有效评估数据质量,必须采用多方面的方法.
- 拟议的16个维度为各种数据质量需求提供了一个全面的框架.
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