Object-Role Modeling: Principles and Benefits

Object-Role Modeling: Principles and Benefits

Terry Halpin (LogicBlox, Australia and INTI Education Group, Malaysia)
Copyright: © 2010 |Pages: 25
DOI: 10.4018/jismd.2010092302


Object-Role Modeling (ORM) is an approach for modeling and querying information at the conceptual level, and for transforming ORM models and queries to or from other representations. Unlike attribute-based approaches such as Entity-Relationship (ER) modeling and class modeling within the Unified Modeling Language (UML), ORM is fact-oriented, where all facts and rules are modeled in terms of natural sentences easily understood and validated by nontechnical business users. ORM’s modeling procedure facilitates validation by verbalization and population with concrete examples. ORM’s graphical notation is far more expressive than that of ER diagrams or UML class diagrams, and its attribute-free nature makes it more stable and adaptable to changing business requirements. This article explains the fundamentals of ORM, illustrates some of its advantages as a data modeling approach, and outlines some recent research to extend ORM, with special attention to mappings to deductive databases.
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Object-Role Modeling (ORM) is a conceptual approach for modeling, querying, and transforming information. Here “conceptual” is used in the same sense as in the seminal report on conceptual schemas published nearly 30 years ago by the International Standards Organization (van Griethuysen, 1982). A conceptual model is a fully detailed description of the universe of discourse (UoD)—the business domain that we wish to discourse or talk about—cast in language that is readily understood by users of the business domain. We use the term “model” to include both the schema (structure of the UoD) and the state-dependent population of instances that instantiate the schema.

Modeling approaches include languages for expressing constructs, and procedures for creating those constructs. ORM includes a graphical and textual language for specifying models, a textual language for formulating queries, as well as procedures for constructing ORM models, transforming them into alternative ORM models, and mapping to/from other kinds of models, including attribute-based models such as Entity-Relationship (ER) models (Chen, 1976), class models in the Unified Modeling Language (UML) (OMG, 2009), and eXtensible Markup Language (XML) documents. Current research is also under way to add procedures for mapping ORM models to logic-based languages such as Datalog and the Web Ontology Language (OWL).

ORM is a prime exemplar of fact-oriented modeling, a family of modeling approaches that enable one to model information in terms of the underlying facts of interest, where facts and rules may be verbalized in language that is easily understood. Unlike ER modeling and UML class diagrams, fact-oriented models are attribute-free, treating all facts as relationships (unary, binary, ternary etc.). For example, instead of using the attributes Employee.isSmoker and Employee.hiredate, fact-oriented models use the fact types Employee smokes and Employee was hired on Date.

ORM evolved from another fact-oriented approach known as Natural Information Analyis Method (NIAM) (Wintraeken, 1990), which originated in Europe in the 1970s with the work of the main pioneers Eckhard Falkenberg, Sjir Nijssen, and Robert Meersman, and was first fully formalized in the late 1980s by the author (Halpin, 1989). Another formalization based on the LISA-D language was provided by ter Hofstede et al. (1993). Fact-oriented modeling includes other closely related approaches, such as Fully-Communication Oriented Information Modeling (FCO-IM) (Bakema & Zwart, 2000), and the Semantics of Business Vocabulary and Business Rules (SBVR) approach (Bollen 2008; OMG, 2008). Recently, the original NIAM was modified to become Cognition-enhanced NIAM (CogNIAM) (Nijssen & Lemmens, 2008), and ORM was extended to second generation ORM (ORM 2) (Halpin, 2005).

Fact-oriented modeling has been used productively in industry for over three decades, with many case studies attesting to its practical benefits for information systems engineering in a variety of areas, including service oriented architecture (Piprani, Wang, & He, 2008), data warehousing (Hansen & dela Cruz, 2006), data quality (Piprani & Ernst, 2008), and decision support (Piersen & dela Cruz, 2005). Apart from its use in model driven engineering, fact-orientation is well suited for requirements analysis (Evans, 2005; Piprani et al., 2008) as well as business rules and regulations modeling. The growing use of fact-oriented languages to capture business rules was given further impetus by the OMG’s recent adoption of SBVR.

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