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A Comparison of Data Modeling in UML and ORM
Abstract
The Unified Modeling Language (UML) was adopted by the Object Management Group (OMG) in 1997 as a language for object-oriented (OO) analysis and design. After several minor revisions, a major overhaul resulted in UML version 2.0 (OMG, 2003), and the language is still being refined. Although suitable for object-oriented code design, UML is less suitable for information analysis, since its graphical language provides only weak support for the kinds of business rules found in data-intensive applications, and its textual Object Constraint Language (OCL) is too technical for most business people to understand. Moreover, UML’s graphical language does not lend itself readily to verbalization and multiple instantiation for validating data models with domain experts. These problems can be remedied by using a fact-oriented approach for information analysis, where communication takes place in simple sentences, each sentence type can easily be populated with multiple instances, and attributes are avoided in the base model. At design time, a fact-oriented model can be used to derive a UML class model or a logical database model. Object Role Modeling (ORM), the main exemplar of the fact-oriented approach, originated in Europe in the mid-1970s (Falkenberg, 1976), and has been extensively revised and extended since, along with commercial tool support (e.g., Halpin, Evans, Hallock, & MacLean, 2003). Recently, a major upgrade to the methodology resulted in ORM 2, a second-generation ORM (Halpin 2005). Neumont ORM Architect (NORMA), an open source tool accessible online at http://sourceforge.net/projects/orm, is under development to provide deep support for ORM 2 (Curland & Halpin, 2007). This article provides a concise comparison of the data modeling features within UML and ORM. The next section provides background on both approaches. The following section summarizes the main structural differences between the two approaches, and outlines some benefits of ORM’s factoriented approach. A simple example is then used to highlight the need to supplement UML’s class modeling notation with additional constraints, especially those underpinning natural identification schemes. Future trends are then briefly outlined, and the conclusion motivates the use of both approaches in concert to provide a richer data modeling experience, and provides references for further reading.
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