A COMPARISON OF USER PERFORMANCE BETWEEN THE RELATIONAL AND THE EXTENDED ENTITY RELATIONSHIP MODELS IN THE DISCOVERY PHASE OF DATABASE DESIGN

This paper reports on a laboratory study which compared conceptual data models developed by casual autonomous users using the relational and the extended entity relationship (EER) representation techniques. It was found that the EER model led to better user performance in modeling binary relationships, while the relational model was better in modeling unary relationships. Subjects found it difficult to model ternary relationships using either model, although the performance using the EER model was slightly better. In general, there was evidence that the EER model led to better user performance. Subjects using the EER model were more confident about their solutions and perceived the model as easier to use than their relational counterparts. The study\u27s results raise questions concerning user performance using the relational model for a discovery (conceptual modeling) task

A COMPARISON OF THE DATA AGGREGATION APPROACH WITH THE LOGICAL RELATIONAL DESIGN METHODOLOGY

A laboratory study comparing relational representations developed using the Data Aggregation approach with the Logical Relational Design Methodology (LRDM) was conducted to investigate whether non-expert users could better comprehend and apply either methodology. While no significant differences between user performance were noted, the study did find that subjects following the LRDM produced quality Entity-Relationship (ER) representations, but there was a marked deterioration of the translation to the relational form. The Data Aggregation solutions were generally poor in quality. The study concludes that while non-expert designers can produce acceptable data abstractions using a conceptual modeling methodology (e.g., ER diagrams), problems may arise during conversion to normalized relations (e.g., relational representations)

Comparing the Understandability of Alternative Data Warehouse Schemas: An Empirical Study

An easily understood data warehouse model enables users to better identify and retrieve its data. It also makes it easier for users to suggest changes to its structure and content. Through an exploratory, empirical study, we compared the understandability of the star and traditional relational schemas. The results of our experiment contradict previous findings and show schema type did not lead to significant performance differences for a content identification task. Further, the relational schema actually led to slightly better results for a schema augmentation task. We discuss the implications of these findings for data warehouse design and future research

Towards Conceptual Modelling Interoperability in a Web Tool for Ontology Engineering

The definition of suitable visual paradigms for ontology modelling is still an open issue. Despite obvious differences between the expressiveness of conceptual modelling (CM) languages and ontologies, many proposed tools have been based on UML, EER and ORM. Additionally, all of these tools support only one CM as visual language, reducing even more their modelling capabilities. In previous works, we have presented crowd as a Web architecture for graphical ontology designing in UML and logical reasoning to verify the relevant properties of these models. The aim of this tool is to extend the reasoning capabilities on top of visual representations as much as possible. In this paper, we present an extended crowd architecture and a new prototype focusing on an ontology-driven metamodel to enable different CMs visual languages for ontology modelling. Thus facilitating inter-model assertions across models represented in different languages, converting between modelling languages and reasoning on them. Finally, we detail the new architecture and demonstrate the usage of the prototype with simple examples.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Towards Conceptual Modelling Interoperability in a Web Tool for Ontology Engineering

The definition of suitable visual paradigms for ontology modelling is still an open issue. Despite obvious differences between the expressiveness of conceptual modelling (CM) languages and ontologies, many proposed tools have been based on UML, EER and ORM. Additionally, all of these tools support only one CM as visual language, reducing even more their modelling capabilities. In previous works, we have presented crowd as a Web architecture for graphical ontology designing in UML and logical reasoning to verify the relevant properties of these models. The aim of this tool is to extend the reasoning capabilities on top of visual representations as much as possible. In this paper, we present an extended crowd architecture and a new prototype focusing on an ontology-driven metamodel to enable different CMs visual languages for ontology modelling. Thus facilitating inter-model assertions across models represented in different languages, converting between modelling languages and reasoning on them. Finally, we detail the new architecture and demonstrate the usage of the prototype with simple examples

Towards Conceptual Modelling Interoperability in a Web Tool for Ontology Engineering

The definition of suitable visual paradigms for ontology modelling is still an open issue. Despite obvious differences between the expressiveness of conceptual modelling (CM) languages and ontologies, many proposed tools have been based on UML, EER and ORM. Additionally, all of these tools support only one CM as visual language, reducing even more their modelling capabilities. In previous works, we have presented crowd as a Web architecture for graphical ontology designing in UML and logical reasoning to verify the relevant properties of these models. The aim of this tool is to extend the reasoning capabilities on top of visual representations as much as possible. In this paper, we present an extended crowd architecture and a new prototype focusing on an ontology-driven metamodel to enable different CMs visual languages for ontology modelling. Thus facilitating inter-model assertions across models represented in different languages, converting between modelling languages and reasoning on them. Finally, we detail the new architecture and demonstrate the usage of the prototype with simple examples.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Modeling Relationships Using The Relational And Object-Oriented Data Models

We compare the performance of naive data modelers in modeling association, generalization, and aggregation relationships with the relational and object-oriented data models. We first develop research hypotheses based on the properties of expressiveness, minimality, and unique semantic interpretation to analyze the effectiveness of the two models. We then test our hypotheses in anexperiment with 22 naive modelers. The findings of our study support the notion that, to be effective, a data model should satisfy these three propertie