JsonCurer:基于聚合图表的JSON数据质量管理
IEEE transactions on visualization and computer graphics
|April 16, 2024
概括
在JSON中管理数据质量是一项挑战. JsonCurer是一个交互式可视化系统,旨在帮助识别和修复半结构化的JSON数据中的数据质量问题,提高信息可靠性.
科学领域:
- 计算机科学 计算机科学
- 数据管理数据管理
- 信息可视化 信息可视化
背景情况:
- 高质量的数据对于可靠的信息至关重要,但实际数据往往存在质量问题.
- 现有的数据质量研究主要涉及表格数据,忽视了像JSON这样的半结构化格式.
- JSON数据的无模式和等级性质使质量问题的发现和解决变得复杂.
研究的目的:
- 介绍JsonCurer,一个交互式可视化系统,用于管理JSON数据中的数据质量.
- 解决半结构化数据中识别和纠正质量问题的挑战.
- 为数据从业者提供有效的工具,以确保JSON数据集的可靠性.
主要方法:
- 根据从业者采访和对119个JSON文件的分析,开发了数据质量问题的分类.
- 设计了一个模式可视化来呈现结构信息,统计特征和质量问题.
- 采用基于相似性的聚合技术,创建一个简洁的JSON数据树可视化,结合总结可视化和泡集来说明质量问题.
主要成果:
- JsonCurer有效地将JSON数据中的结构信息,统计特征和数据质量问题可视化.
- 该系统使用简洁的树结构,集结节点和泡集来表示复杂的数据和质量问题.
- 在数据分析和MongoDB质量保证方面的案例研究表明了JsonCurer的有效性和可用性.
结论:
- JsonCurer为JSON数据提供了一种新的数据质量管理方法.
- 交互式可视化系统有助于有效发现和纠正数据质量问题.
- JsonCurer提高了从半结构化数据中获得的信息的可靠性,特别是在数据分析和数据库质量保证方面.
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