In this paper, a novel framework for data quality measurement is proposed by adopting a measure-theoretic treatment of the problem. Instead of considering a specific setting in which quality must be assessed, our approach departs more formally from the concept of measurement. The basic assumption of the framework is that the highest possible quality can be described by means of a set of predicates. Quality of data is then measured by evaluating those predicates and by combining their evaluations. This combination is based on a capacity function (i.e., a fuzzy measure) that models for each combination of predicates the capacity with respect to the quality of the data. It is shown that expression of quality on an ordinal scale entails a high degree of interpretation and a compact representation of the measurement function. Within this purely ordinal framework for measurement, it is shown that reasoning about quality beyond the ordinal level naturally originates from the uncertainty about predicate evaluation. It is discussed how the proposed framework is positioned with respect to other approaches with particular attention to aggregation of measurements. The practical usability of the framework is discussed for several well known dimensions of data quality and demonstrated in a use-case study about clinical trials.

中文翻译：

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数据质量的测度理论基础


本文通过采用测量理论处理问题，提出了一种新的数据质量测量框架。我们的方法没有考虑必须评估质量的特定环境，而是更正式地脱离了测量的概念。该框架的基本假设是可以通过一组谓词来描述最高可能的质量。然后通过评估这些谓词并结合它们的评估来衡量数据的质量。该组合基于容量函数（即模糊度量），该函数为每个谓词组合相对于数据质量的容量进行建模。结果表明，序数尺度上的质量表达需要高度的解释和测量函数的紧凑表示。在这个纯粹的序数测量框架内，结果表明，超出序数水平的质量推理自然源于谓词评估的不确定性。讨论了所提出的框架如何相对于其他方法定位，特别关注测量的聚合。该框架的实际可用性针对数据质量的几个众所周知的维度进行了讨论，并在有关临床试验的用例研究中得到了证明。