A survey of object-relational transformation patterns for high-performance UML-based applications

Abstract

We outline a methodology for automatic and efficient object-relational mapping (ORM) in the context of model-driven development (MDD) of high-performance information systems specified with executable UML models. Although there are various approaches to performance tuning, we focus here on the persistence layer the relational database. The relational data model is usually designed following the well-known normal forms. However, a fully normalized relational model often does not provide sufficient performance, and improper relational model design can easily lead to a slow and unusable relational database for particular operations. Our ORM approach is intended to exploit smart optimization techniques from the relational paradigm that abandon normalization and its positive effects, and trade them off for better performance. Our ORM approach hence combines the classical denormalization transformations, based on reducing or eliminating expensive database operations by the model restructuring, but applies them to a non-redundant conceptual UML model. In this paper, we also present the first step towards this goal: a catalogue of ORM transformation patterns.