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

Learning from AI : new trends in database technology

Recently some researchers in the areas of database data modelling and knowledge representations in artificial intelligence have recognized that they share many common goals. In this survey paper we show the relationship between database and artificial intelligence research. We show that there has been a tendency for data models to incorporate more modelling techniques developed for knowledge representations in artificial intelligence as the desire to incorporate more application oriented semantics, user friendliness, and flexibility has increased. Increasing the semantics of the representation is the key to capturing the "reality" of the database environment, increasing user friendliness, and facilitating the support of multiple, possibly conflicting, user views of the information contained in a database

Implementing imperfect information in fuzzy databases

Information in real-world applications is often vague, imprecise and uncertain. Ignoring the inherent imperfect nature of real-world will undoubtedly introduce some deformation of human perception of real-world and may eliminate several substantial information, which may be very useful in several data-intensive applications. In database context, several fuzzy database models have been proposed. In these works, fuzziness is introduced at different levels. Common to all these proposals is the support of fuzziness at the attribute level. This paper proposes first a rich set of data types devoted to model the different kinds of imperfect information. The paper then proposes a formal approach to implement these data types. The proposed approach was implemented within a relational object database model but it is generic enough to be incorporated into other database models.ou

A Logical Design Methodology for Relational Databases Using the Extended ER Model

https://deepblue.lib.umich.edu/bitstream/2027.42/154152/1/39015099114723.pd

The StarView intelligent query mechanism

The StarView interface is being developed to facilitate the retrieval of scientific and engineering data produced by the Hubble Space Telescope. While predefined screens in the interface can be used to specify many common requests, ad hoc requests require a dynamic query formulation capability. Unfortunately, logical level knowledge is too sparse to support this capability. In particular, essential formulation knowledge is lost when the domain of interest is mapped to a set of database relation schemas. Thus, a system known as QUICK has been developed that uses conceptual design knowledge to facilitate query formulation. By heuristically determining strongly associated objects at the conceptual level, QUICK is able to formulate semantically reasonable queries in response to high-level requests that specify only attributes of interest. Moreover, by exploiting constraint knowledge in the conceptual design, QUICK assures that queries are formulated quickly and will execute efficiently

Integrating intelligent methodological and tutoring assistance in a CASE platform: The PANDORA experience

Database Design discipline involves so different aspects as conceptual and logical modelling knowledge or domain understanding. That implies a great effort to carry out the real world abstraction task and represent it through a data model. CASE tools emerge in order to automating the database development process. These platforms try to help to the database designer in different database design phases. Nevertheless, this tools are frequently mere diagrammers and do not carry completely out the design methodology that they are supposed to support; furthermore, they do not offer intelligent methodological advice to novice designers. This paper introduces the PANDORA tool (acronym of Platform for Database Development and Learning via Internet) that is being developed in a research project which tries to mitigate some of the deficiencies observed in several CASE tools, defining methods and techniques for database development which are useful for students and practitioners. Specifically, this work is focused on two PANDORA components: Conceptual Modelling and Learning Support subsystems