Authors:
            
                    Nemanja Kojić
                    
                        
                    
                     and
                
                    Dragan Milićev
                    
                        
                    
                    
                
        
        
            Affiliation:
            
                    
                        
                    
                    University of Belgrade, Serbia
                
        
        
        
        
        
             Keyword(s):
            Object-relational Mapping, Relational Databases, Denormalization, UML.
        
        
            
                Related
                    Ontology
                    Subjects/Areas/Topics:
                
                        Languages, Tools and Architectures
                    ; 
                        Model Transformation
                    ; 
                        Model-Driven Architecture
                    ; 
                        Model-Driven Software Development
                    ; 
                        Models
                    ; 
                        Paradigm Trends
                    ; 
                        Software Engineering
                    
            
        
        
            
                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 restructu
                ring, 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.
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