A Meta-Analysis on Relationship Modeling Accuracy: Comparing Relational and Semantic Models

Semantic data modeling, such as entity-relationship (ER) modeling and extended/enhanced entity-relationship (EER) modeling, has emerged as an alternative to relational data modeling. The majority of research in data modeling suggests that the use of semantic data models leads to better performance. However the findings are not conclusive and sometimes inconsistent. In this research, we investigate modeling relationship correctness in relational and semantic models. The meta-analysis carried out in this research is an attempt to alleviate inconsistent results in previous studies

UML Class Diagram or Entity Relationship Diagram : An Object Relational Impedance Mismatch

It is now nearly 30 years since Peter Chen’s watershed paper “The Entity-Relationship Model –towards a Unified View of Data”. [1] The entity relationship model and variations and extensions to ithave been taught in colleges and universities for many years. In his original paper Peter Chen looked at converting his new ER model to the then existing data structure diagrams for the Network model. In recent years there has been a tendency to use a Unified Modelling Language (UML) class diagram forconceptual modeling for relational databases, and several popular course text books use UMLnotation to some degree [2] [3]. However Object and Relational technology are based on different paradigms. In the paper we argue that the UML class diagram is more of a logical model (implementation specific). ER Diagrams on theother hand, are at a conceptual level of database design dealing with the main items and their relationships and not with implementation specific detail. UML focuses on OOAD (Object Oriented Analysis and Design) and is navigational and program dependent whereas the relational model is set based and exhibits data independence. The ER model provides a well-established set of mapping rules for mapping to a relational model. In this paper we look specifically at the areas which can cause problems for the novice databasedesigner due to this conceptual mismatch of two different paradigms. Firstly, transferring the mapping of a weak entity from an Entity Relationship model to UML and secondly the representation of structural constraints between objects. We look at the mixture of notations which students mistakenly use when modeling. This is often the result of different notations being used on different courses throughout their degree. Several of the popular text books at the moment use either a variation of ER,UML, or both for teaching database modeling. At the moment if a student picks up a text book they could be faced with either; one of the many ER variations, UML, UML and a variation of ER both covered separately, or UML and ER merged together. We regard this problem as a conceptual impedance mismatch. This problem is documented in [21] who have produced a catalogue of impedance mismatch problems between object-relational and relational paradigms. We regard the problems of using UML class diagrams for relational database design as a conceptual impedance mismatch as the Entity Relationship model does not have the structures in the model to deal with Object Oriented concepts Keywords: EERD, UML Class Diagram, Relational Database Design, Structural Constraints, relational and object database impedance mismatch. The ER model was originally put forward by Chen [1] and subsequently extensions have been added to add further semantics to the original model; mainly the concepts of specialisation, generalisation and aggregation. In this paper we refer to an Entity-Relationship model (ER) as the basic model and an extended or enhanced entity-relationship model (EER) as a model which includes the extra concepts. The ER and EER models are also often used to aid communication between the designer and the user at the requirements analysis stage. In this paper when we use the term “conceptual model” we mean a model that is not implementation specific.ISBN: 978-84-616-3847-5 3594Peer reviewe

Using the Data Modeling Worksheet to Improve Novice Data Modeler Performance

This research reports on use and evaluation of the data modeling worksheet as a pedagogical tool for improving a student\u27s ability to learn the extended entity-relationship data modeling methodology. A laboratory experiment using a modified posttest only, control group design compared the performance of two student subject groups. One group used the data modeling worksheet as an integral component of their instruction on database design. A second control group did not use the worksheet, but that group received comparable training in every other respect. Subjects were tasked to develop a data model that represented a textual description of a data modeling problem. The data analysis used a one-way ANOVA to evaluate eight hypotheses, each representing a facet of the data modeling methodology. The results indicate that the data modeling worksheet significantly improved student learning with regard to their ability to identify entities, entity identifier attributes, and ternary relationships

A DSL for EER Data Model Specification

In this paper we present a domain specific language (DSL) for Extended Entity-Relationship (EER) data model approach, named EERDSL. EERDSL is a part of our Multi-Paradigm Information System Modeling Tool (MIST) that provides EER database schema specification at the conceptual level and its transformation into a relational data model, or a class model. EERDSL modeling concepts are specified by Ecore, one of the commonly used approaches to create meta-models. In the paper we present both textual and graphical notations of EERDSL. Since only few modeling constraints may be described at the level of abstract syntax, we use Object Constraint Language (OCL) to specify complex validation rules for EER models

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

The Effect of Abstraction of Constructs in Data Models on Modeling Performance: An Exploratory Empirical Study

Several data models exist at the conceptual level, the most popular being the Extended Entity Relationship Model (EERM). However, the EERM may be difficult to use by end-users, when specifying their data requirements, and is typically used by systems analysts as a precursor to mapping the data to the relational model, in normalized form. A possible reason why the EERM may be difficult to use by end-users or novice data modelers is that the constructs in the EERM may be abstract with regards to the actual usage of the data. In this research in progress, we present an exploratory empirical study that investigates the effects of abstraction of concepts on novice analysts’ data modeling performance

Tailoring temporal description logics for reasoning over temporal conceptual models

Temporal data models have been used to describe how data can evolve in the context of temporal databases. Both the Extended Entity-Relationship (EER) model and the Unified Modelling Language (UML) have been temporally extended to design temporal databases. To automatically check quality properties of conceptual schemas various encoding to Description Logics (DLs) have been proposed in the literature. On the other hand, reasoning on temporally extended DLs turn out to be too complex for effective reasoning ranging from 2ExpTime up to undecidable languages. We propose here to temporalize the ‘light-weight’ DL-Lite logics obtaining nice computational results while still being able to represent various constraints of temporal conceptual models. In particular, we consider temporal extensions of DL-Lite^N_bool, which was shown to be adequate for capturing non-temporal conceptual models without relationship inclusion, and its fragment DL-Lite^N_core with most primitive concept inclusions, which are nevertheless enough to represent almost all types of atemporal constraints (apart from covering)