A quality perspective in data resource management is critical. Because
users have different criteria for determining the quality of data, we
propose tagging data at the cell level with quality indicators, which
are objective characteristics of the data and its manufacturing proce
ss. Based on these indicators, the user may assess the data's quality
for the intended application. This paper investigates how such quality
indicators may be specified, stored, retrieved, and processed. We pro
pose an attribute-based data model, query algebra, and integrity rules
that facilitate cell-level tagging as well as the processing of appli
cation data that is augmented with quality indicators. An ER-based dat
a quality requirements analysis methodology is proposed for specificat
ion of the kinds of quality indicator to be modeled.