TABLE OF CONTENTS In the previous two chapters, we have presented models to represent data quality requirements. At the conceptual level, Storey and Wang [ [4]] propose a Quality Entity-Relationship (QER) model that incorporates data quality requirements. At the logical level, the Polygen Model [ [8], [9]] and the Attribute-Based Model [ [6]] extend the relational model to capture data quality attributes and data quality indicators.
In this chapter, we present a knowledge-based model that derives an overall data quality value from local relationships among quality parameters. [5] Because the model provides an overall measure of data quality, information consumers can thus use it for data quality judgment. This model was first proposed in 1991 by Jang and Wang [ [2]]. Although inconclusive, this effort represented a first attempt at automating data quality judgment. It can provide a basis for implementing data quality in environments where the quality of data must be judged automatically by the system. The model was later extended in 1995 by Wang, Reddy, and Kon [ [6]].
[4]Storey, V. C. and R. Y. Wang. "An Analysis of Quality Requirements in Database Design," in Proceedings of the 1998 Conference on Information Quality. Massachusetts Institute of Technology: pp. 64 87, 1998.
[8]Wang, Y. R. and S. E. Madnick. "A Polygen Model for Heterogeneous Database Systems: The Source Tagging Perspective," in Proceedings of the 16th International Conference on Very Large Data bases (VLDB). Brisbane, Australia: pp. 519 538, 1990.
[9]Wang, Y.
