The traditional vertical decomposition methods in relational database normalization fail to prevent common data anomalies. Although a database may be highly normalized, the quality of the data stored in this database may still deteriorate because of potential data anomalies. In this paper, we first discuss why practitioners need to further improve their databases after they apply the traditional normalization methods, because of the existence of functional entanglement, a phenomenon we defined. We outline two methods for identifying functional entanglements in a normalized database as the first step toward data quality improvement. We then analyze several practical methods for preventing common data anomalies by eliminating and restricting the effects of functional entanglements. The goal of this paper is to reveal shortcomings of the traditional database normalization methods with respect to the prevention of common data anomalies, and offer practitioners useful techniques for improving data quality.
Improving data quality in relational databases
By Tennyson Chen, Martin Meyer, Nanthini Ganapathi, Shuangquan Liu, Jonathan Cirella.
May 2011 Open Access Peer Reviewed
© 2021 RTI International. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Chen, T., Meyer, M., Ganapathi, N., Liu, S., & Cirella, J. (2011). Improving data quality in relational databases: Overcoming functional entanglements. RTI Press. RTI Press Occasional Paper No. OP-0004-1105 https://doi.org/10.3768/rtipress.2011.op.0004.1105