[论文笔记] Methodologies for Data Quality Assessment and Improvement (ACM Comput.Surv, 2009) (2)
本篇博文主要对DMQ(S3.7)的分类进行了研读。数据库
1. 这个章节提出了一种DQM的分类法（以下图）3d
由上图可见，该分类法的分类标准是对assessment & improvement阶段的支持，以及是否解决technique(quality dimensions) & economic方面的问题。orm
2. 从历史发展的角度来看，数据质量维度的关注点与ICT(Information & Communication Technology)的发展是相关的。
(1) 五六十年代时，monolithic系统（一般只包含单个数据库和单个主机），DQ主要关注accuracy, consistency, completeness, time-related dimensions. 数据质量管理面临的最主要问题是：error localization, correction in data sources, record linkage between new data sources and pre-existing data bases.
(2) network-based阶段. DMQ开始关注新的质量维度：compeleteness of the data source, currency of data, consistency of new data sources compared to the enterprise database.
(3) 随着Web的出现，DMQ须要解决新的质量维度：accessibility, reputation
3. audit methodologies比complete、operational methodologies在assessmetn阶段都要更准确。
4. 四种分类的简介blog
- audit methodologies: 主要是关于assessment阶段
- operational methodologeis: 关注点在于评估阶段识别issues以便在改进阶段取得好的效果。主要贡献：(1) 一组相关维度的识别，以便改进; (2) 描述了一些改进这些维度的易懂的方法
- completeness methodologies: 适用于大型DQ项目
- economic methodologies: 做为其余DQM的补充
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- 1. [论文笔记] Methodologies for Data Quality Assessment and Improvement (ACM Comput.Surv, 2009) (1)
- 2. [论文解读] Callisto: Entropy based test generation and data quality assessment for Machine Learning ...
- 3. Perceptual Quality Assessment of Smartphone Photography(论文笔记)
- 4. Image Quality Assessment,IQA and Video Quality Assessment,VQA
- 5. [论文解读] Beyond Code Coverage – an Approach for Test Suite Assessment and Improvement
- 6. 论文笔记《Blind Quality Assessment Based on Pseudo-Reference Image》(2)
- 7. RankIQA:Learning from Rankings for No-reference Image Quality Assessment论文阅读笔记
- 8. [阅读笔记]Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and C
- 9. 图像质量评估 (IQA) 论文笔记： Convolutional Neural Networks for No-Reference Image Quality Assessment
- 10. 论文笔记《Blind Quality Assessment Based on Pseudo-Reference Image》(1)