A framework for systematic database de-normalization

Abstract

It is currently the norm that relational database designs should be based on a normalized logical data model. The primary objective of this design technique is data integrity and database extendibility. The Third Normal Form is regarded by academicians and practitioners alike to be point at which the database design is most efficient. Unfortunately, even this lower level normalization form has a major drawback with regards to query evaluation. Information retrievals from the database can result in large number of joins which degrades query performance. So you need to sometimes break theoretical rules for real world performance gains. Most existing conceptual level RDBMS data models provide a set of constructs that only describes "what data is used" and does not capture "how the data is being used". The question of "how data is used" gets embedded in the implementation level details. As a result, every application built on the existing database extracts the same or similar data in different ways. If the functional use of the data is also captured, common query evaluation techniques can be formulated and optimized at the design phase, without affecting the normalized database structure constructed at the Conceptual Design phase. This paper looks at denormalization as an effort to improve the performance in data retrievals made from the database without compromising data integrity. A study on a hierarchical database table shows the performance gain - with respect to response time – using a denormalization technique.