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.

There are numerous automated strategies in the context of decision making, each with their own strengths and weaknesses. However, the general utility of a decision support systems relates to its expressive capabilities regarding human-like reasoning. In the ongoing effort to bolster decision support, this work aims to enrich fuzzy integrals with the power to model partial absorption operators. Starting from mathematical definitions, it is shown that it is necessary to redefine specific weights of internal sets in fuzzy measures from constants to functions in order to model this behavior. An implementation is proposed for representing the conjunctive partial absorption and the disjunctive partial absorption. Their results are carefully analysed and it is shown the made alterations do not violate the monotonic nature of fuzzy measures.