Conceptual Database Design Research Articles

Descriptive specification of database object behaviour

Traditional database design methodologies are not appropriate for the specific requirements of object-oriented database systems and new database application areas. Apart from semantic complications arising from object-oriented database structures with complex objects, arbitrary data types as attribute domains, or generalization hierarchies, specification and semantics of dynamic database behaviour has to be of main interest for typical object-oriented applications, too. We propose the use of a temporal logic as a specification language for dynamic object behaviour and point out the formal semantics of such database dynamics specifications. A layered conceptual database design methodology is presented together with a discussion on design support techniques for behaviour specifications. Finally, implementation aspects are treated.

Representation of object-oriented data models

Database design tools have been introduced as a means of reducing the expertise required of the designer, to free him or her from the implementation details and to aid in managing the complexity of the design task. However, one of the common shortcomings of database design tools is that they are closely tied to specific data models, thus limiting the applicability of their underlaying concepts and restricting the reusability of the design software. SeaWeed, a component of the Data Model Compiler (DMC) project, proposes a solution by automatically generating database design software from data model specifications. In this paradigm, a data model specification model, or metamodel of data models, is introduced for specifying particular object-oriented data models. Using the knowledge in the specification, conceptual database design software is automatically generated for the given data model. This paper presents a metamodel for object-oriented data models and offers a technique for expressing the semantics of an object-oriented data model in terms of the metaobject types, which consist of attributes, operations, constraints and graphical representations.

Database organization strategies for spatial decision support systems

Abstract The selection of a data model is an important step in designing a spatial decision support system (SDSS) because the database is the foundation of the system, and other system components must draw upon the database to perform analysis and display functions. The diversity of representations that must be maintained to support these functions often results in a complex database. One way to simplify the implementation of a complex database is to exploit features of a data model embedded in database management software. A methodology for SDSS conceptual database design was developed using the Entity-Category-Relationship approach. To accommodate this conceptual structure in database management software, we reviewed available data models and concluded that the hybrid extended network model is appropriate for use in an SDSS context. To illustrate this methodology, we designed a logical database structure for a locational analysis SDSS.

From enterprise analysis to conceptual database design in production systems

In this paper, main features of the M ∗ methodology for organization analysis and information system design are presented. The importance of integrating enterprise analysis and modelling and database design is stressed. M ∗ is structured into three major phases: Enterprise Modelling and Analysis, database Conceptual Design, and database Implementation Design. In this paper, the first two phases will be discussed and explained by means of case study concerning a flexible manufacturing system.

Enterprise analysis and data base design with [formula omitted]: A case study

M ∗ is a methodology developed at the National Research Council of Canada for modelling and analysis of information system and database design of production environments. M ∗ is structured into three methodological phases: Enterprise Modelling and Analysis, Database Conceptual Design, and Implementation Design. In this paper, the first and second phase of the methodology and their application principles are presented by means of a case study about a discrete manufacturing plant.

Design of engineering databases: A case for more varied semantic modelling concepts

Interest in database support for engineering applications is rapidly growing. In this paper we concentrate on conceptual database design and address the question of what a semantic model should look like, that meets the needs of engineering applications and is sufficiently formal to support validation, optimization, analysis, as well as transformation to an implementation schema. We present several case studies of engineering databases in order to determine major modelling requirements, and compare these to modelling concepts from the data base and knowledge representation fields. We demonstrate that the main issue is not adding further concepts, but to integrate the existing ones in a selective and precise fashion. We suggest to do so by tailoring the semantic model, starting from a set of base concepts and extending these. An initial model and an extensibility mechanism allowing an explicit and declarative definition of higher-order abstractions are presented. This is demonstrated by specifying some simple concepts such as generalization and a more complex time concept for image sequence evaluation.

Information system analysis and conceptual database design in production environments with M ∗

A methodology, called M ∗, for information system analysis and database design of production environments is presented in detail. The originality of M ∗ is to recognize the importance of integrating factory analysis methods for data and user requirements definition with logical database design methods for database specification. M ∗ is a pragmatic, complete, manual methodology involving three major phases: Enterprise Modelling and Analysis which is the phase devoted to accurate analysis of the structure of the enterprise and precise definition of the scope of the future database application; Conceptual Design which is the phase concerned with data modelling of both static and dynamic properties of the enterprise; and Implementation Design which is the phase dealing with adaptation of the conceptual schema to the data model specifications of the physical database system used for application development and tuning performances of the physical system. The methodology is explained and illustrated by means of an example related to a Flexible Manufacturing System for mechanical parts.

An alternative approach to the conceptual database design using fragments of nat

An alternative approach to the conceptual database design using fragments of nat

Entity-relationship behavior modeling in conceptual schema design

A method for behavior analysis and modeling of entities in conceptual database design based on the entity-relationship approach is discussed. For this purpose, the notion of instantiation of entities is introduced. Behavior of entities, which is defined as a set of transactions and states that result from transactions, is analyzed and modeled using the notion of instantiation and the diagrammatic technique of Petri nets. A formalization of behavior descriptions, together with the notion of transaction normalization, is proposed.

A computer-aided methodology for conceptual data-base design

In this paper we describe an integrated system to aid data-base conceptual design. In this system, incremental enrichments of the conceptual description of user requirements both with a top-down and a bottom-up discipline are allowed. Such enrichments involve both data and transactions. Data enrichments can be expressed by a disciplined set of design primitives. For every primitive any inconsistencies arising are described and a set of scenarios to eliminate them is suggested. Transaction enrichments are expressed in terms of declarations which define these transactions at various conceptual refinement levels. Such declarations can be compared to the partial conceptual schema obtained by data enrichments and possible incompleteness and inconsistency situations arising are described. Future developments of the research program are also outlined.