数据质量度量与监控工具综述
A Survey of Data Quality Measurement and Monitoring Tools
摘要：
高质量的数据是可解释和可信赖的数据分析的关键，也是有意义的数据驱动决策的基础。在实际场景中，数据质量通常与数据预处理、分析和清理相关联，以便执行数据集成或数据分析等后续任务。然而，从科学的角度来看，已经发表了大量关于测量的研究（即，数据质量问题的检测）以及不同的普遍适用的数据质量维度和度量。在这项工作中，我们通过调查当前数据质量工具的功能范围，缩小了数据质量测量研究与实际实现之间的差距。通过系统搜索，我们确定了667个专用于“数据质量”的软件工具，其中我们评估了三个功能领域的13个工具：（1）数据分析，（2）数据质量度量指标，（3）持续的数据质量监控。我们根据预先定义的排除标准选择了评估工具，以确保它们独立于领域，提供研究功能，并且可自由评估或作为试验。本调查旨在全面概述最先进的数据质量工具，并揭示其功能增强的潜力。此外，结果允许一个关键的讨论概念，这是广泛接受的研究，但几乎没有实现在任何工具观察，例如，普遍适用的数据质量指标。
英文摘要：
High-quality data is key to interpretable and trustworthy data analytics and the basis for meaningful data-driven decisions. In practical scenarios, data quality is typically associated with data preprocessing, profiling, and cleansing for subsequent tasks like data integration or data analytics. However, from a scientific perspective, a lot of research has been published about the measurement (i.e., the detection) of data quality issues and different generally applicable data quality dimensions and metrics have been discussed. In this work, we close the gap between research into data quality measurement and practical implementations by investigating the functional scope of current data quality tools. With a systematic search, we identified 667 software tools dedicated to "data quality", from which we evaluated 13 tools with respect to three functionality areas: (1) data profiling, (2) data quality measurement in terms of metrics, and (3) continuous data quality monitoring. We selected the evaluated tools with regard to pre-defined exclusion criteria to ensure that they are domain-independent, provide the investigated functions, and are evaluable freely or as trial. This survey aims at a comprehensive overview on state-of-the-art data quality tools and reveals potential for their functional enhancement. Additionally, the results allow a critical discussion on concepts, which are widely accepted in research, but hardly implemented in any tool observed, for example, generally applicable data quality metrics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
