Towards New Data Quality Rules for Modeling Data Change

Data is not static, and attribute value changes often trigger changes in another set of attributes. Traditional methods for analyzing data changes often treat these changes in isolation, failing to consider the broader context in which they occur. This lack of contextual awareness limits the ability to capture relationships between attributes or interpret their significance, especially when distinguishing between normal variations and potential anomalies. In this paper, we discuss the importance of context-awareness and the need to identify normal change behaviour. To achieve this, we introduce a new data quality rule, called change rule, capable of capturing changes in both antecedent and consequent attributes within ordered tuples of a relational instance.