Author List: Bodart, Franois; Patel, Arvind; Sim, Marc; Weber, Ron;
Information Systems Research, 2001, Volume 12, Issue 4, Page 384-405.
An important feature of some conceptual modelling grammars is the features they provide to allow database designers to show real-world things may or may not possess a particular attribute or relationship. In the entity-relationship model, for example, the fact that a thing may not possess an attribute can be represented by using a special symbol to indicate that the attribute is optional. Similarly, the fact that a thing may or may not be involved in a relationship can be represented by showing the minimum cardinality of the relationship as zero. Whether these practices should be followed, however, is a contentious issue. An alternative approach is to eliminate optional attributes and relationships from conceptual schema diagrams by using subtypes that have only mandatory attributes and relationships. In this paper, we first present a theory that led us to predict that optional attributes and relationships should be used in conceptual schema diagrams only when users of the diagrams require a surface-level understanding of the domain being represented by the diagrams. When users require a deep-level understanding, however, optional attributes and relationships should not be used because they undermine users' abilities to grasp important domain semantics. We describe three experiments which we then undertook to test our predictions. The results of the experiments support our predictions.
Keywords: Data Models; Database Design; Entity-Relationship Model; Ontology; Optional Attributes; Optional Relationships; Semantic Data Models; Subtyping; Systems Theory
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#216 0.588 conceptual model modeling object-oriented domain models entities representation understanding diagrams schema semantic attributes represented representing object relationships concepts classes entity-relationship
#130 0.101 online users active paper using increasingly informational user data internet overall little various understanding empirical despite lead cascades help availability
#97 0.088 set approach algorithm optimal used develop results use simulation experiments algorithms demonstrate proposed optimization present analytical distribution selection number existing
#108 0.074 model research data results study using theoretical influence findings theory support implications test collected tested based empirical empirically context paper