The authors wish to thank Patricia Everaert (Ghent University) and two anonymous reviewers of the 2 nd Int’l Workshop on Conceptual Modeling Quality (IWCMQ’03) for their useful comments and suggestions on previous versions of this paper. D/2004/7012/05

The representation of geospatial phenomena in databases is one of the key issues in applications like geo-marketing, environmental modeling, transportation planning and geology. For these geospatial applications, there is a need for abstract concepts that would bridge the conceptual gap between the real world and its spatio-temporal representation in the computer systems. To capture the semantics related to space and time in a conceptual schema, we propose an annotation-based approach that allows a database designer to focus first on the non-temporal and non-spatial aspects of the application, and subsequently augment the schema with spatio-temporal annotations. In this paper, we apply our annotation-based approach to the Unifying Semantic Model (USM) to propose the Spatio-Temporal Unifying Semantic Model (ST USM). ST USM is an upward-compatible, snapshot reducible, annotation-based spatio-temporal conceptual model that can comprehensively capture the semantics related to space and time without adding any new spatio-temporal constructs. We provide the formal semantics of ST USM via a mapping to conventional USM and constraints, from which the logical schema can be derived. We illustrate practical aspects related to our spatiotemporal design methodology with a hydrogeologic application at the United States Geological Survey (USGS) and a web-based design tool, thereby demonstrating simplicity and comprehensiveness to spatio-temporal conceptual modeling. 1

This paper presents a tentative model of user beliefs and attitudes towards conceptual models applying the ideas of Theory of Reasoned Action (Fishbein and Ajzen, 1975) in a conceptual modeling context. We focus on users perceptions of conceptual model quality and investigate the relations between perceived semantic quality and perceived pragmatic quality measures. Given the growing awareness among researchers and practitioners about the importance of high-quality conceptual modeling, it is surprisingly that there are no formal measures to assess the perceived semantic quality of conceptual schemas. Therefore we address this need and present a robust and validated multi-item measurement instrument to evaluate the perceived semantic quality of conceptual schemas. We followed a rigorous empirical development process to ensure the validity and reliability of the proposed measurement instrument. Once this was accomplished, it was possible to empirically test the proposed user beliefs and attitudes model. The conducted experiment confirmed the proposed relations of the user beliefs and attitudes model. It was shown that perceived semantic quality had a direct effect on beliefs as perceived usefulness and perceived ease of use and also indirectly affects the attitudes of conceptual model users.- 1-

One of the most serious limitations of the Entity Relationship (ER) Model in practice is its inability to cope with complexity. Once data models exceed a certain threshold size, they become difficult to understand (end user’s viewpoint) and also to document and maintain (analyst’s viewpoint). A number of approaches have been proposed in the literature to address this problem, but so far there has been no systematic empirical research into the effectiveness of these methods. This paper describes an action research study in which a method for representing large ER models was tested in a large application development project in one of Australia’s largest commercial organisations. The research was successful in achieving both practical and research outcomes�it resulted in change of data modelling practices in the organisation, and the method was refined significantly as a result of experiences in practice. However a major problem experienced in this study was that the size of the project imposed constraints on the evolution of the method. Because of the number of people involved, it was difficult to make changes to the method “on the fly ” and to experiment with variations of the method, as is customary

This paper describes a laboratory experiment which evaluates the effectiveness of different representation methods for end user understanding of large data models. Data model understanding is evaluated in terms of: � � Comprehension performance: the ability to answer questions about the data model � � Verification performance: the ability to identify discrepancies between the data model and a set of user requirements in textual form. This is the first empirical comparison of large data model representation techniques that has been conducted in over two decades of research in this area. The results suggest that there are significant complexity effects on end user understanding of data models. By reducing a data model to “chunks” of manageable size, both comprehension and verification performance can be significantly improved. This finding has implications for other graphical notations used in IS development.

Abstract. In UML class diagrams, a many-to-many relationship with attributes can be represented by an association class or by a connecting object class. It is unclear which modeling construct is preferred in particular modeling scenarios. Because of lack of theory, this paper investigates the issue empirically. An experiment was conducted that tested the effect of representational form chosen on the performance of model users at cardinality interpretation tasks. It was shown that, controlling for cardinality knowledge, business users can better interpret the information that a UML class diagram conveys about a many-to-many relationship with attributes if this relationship is represented as an association class. The implication for ‘best practices ’ in UML modeling is that modelers should refrain from objectifying such relationships if the goal is an effective communication of domain semantics to users that are not modeling experts. 1 1.

Abstract. This paper presents a laboratory experiment that evaluates the REA approach for modelling enterprise-wide accounting information systems. REA is a pattern-driven conceptual modelling approach that is based on the Resource-Event-Agent semantic model of a company’s accountability infrastructure. Using a between-subjects experiment with business students we investigated whether Entity-Relationship (ER) diagrams that show a REA pattern occurrence are better understood than informationally equivalent ER diagrams that do not show a REA pattern occurrence. The results of our experiment indicate that students develop a more accurate understanding of the business processes and policies modelled when they recognize the REA structure of accounting information in ER diagrams. Students also perceive such diagrams to be easier to use when performing model comprehension and validation tasks. The experiment further showed that the number of information systems courses taken by students has a significant effect on task performance. The number of accounting courses completed before the experiment did not affect the understanding of business process models. The paper concludes by discussing some implications for Accounting Information Systems

The authors wish to thank Prof. Patrick Van Kenhove (Ghent University) for his useful comments. D/2004/7012/43 Construction and Pre-Test of a Semantic Expressiveness Measure for Conceptual Models Abstract. One quality attributed to McCarthy’s Resources-Events-Agents (REA) accounting model is semantic expressiveness. Compared to accounting models without a semantic orientation like the Debit-Credit-Account (DCA) model, the REA model is claimed to better represent the economic phenomena underlying an accounting system. The alleged benefits of this increased semantic expressiveness include easier integration with representations of non-accounting information and better user understanding of accounting systems. Dunn and Grabski (2000) have empirically compared the semantic expressiveness of accounting systems based on the REA and DCA models. They showed that accounting students perceived the REA system as more semantically expressive. Furthermore, they demonstrated that higher perceived semantic expressiveness (PSE) leads to higher accuracy in accounting information retrieval tasks. To measure the PSE of a system they used a single 7-point Likert scale assertion, which was applied after performing an information retrieval task. As ideas for future research they

Abstract—Time provides context for all our experiences, cognition, and coordinated collective action. Prior research in linguistics, artificial intelligence and temporal databases suggests the need to differentiate between temporal facts with goal-related semantics (i.e., telic) from those are intrinsically devoid of culmination (i.e., atelic). To differentiate between telic and atelic data semantics in conceptual database design, we propose an annotation-based temporal conceptual model that generalizes the semantics of a conventional conceptual model. Our temporal conceptual design approach involves: 1) capturing “what ” semantics using a conventional conceptual model; 2) employing annotations to differentiate between telic and atelic data semantics that help capture “when ” semantics; 3) specifying temporal constraints, specifically non-sequenced semantics, in the temporal data dictionary as metadata. Our proposed approach provides a mechanism to represent telic/atelic temporal semantics using temporal annotations. We also show how these semantics can be formally defined using constructs of the conventional conceptual model and axioms in first-order logic. Via what we refer to as the “semantics of composition, ” i.e., semantics implied by the interaction of annotations, we illustrate the logical consequences of representing telic/atelic data semantics during temporal conceptual design. Index Terms—temporal database, conceptual modeling, data semantics, temporal conceptual model, database design. 1