METHOD FOR SYNTHESIS OF CONCEPTUAL DATA MODELS BASED ON PROCESS DOMAIN MODELS

2020 ◽  
pp. 80-87
Author(s):  
С.И. Рябухин
Keyword(s):  
Data Modeling ◽  
Database Design ◽  
Data Models ◽  
Process Models ◽  
Domain Models ◽  
Conceptual Data

Процессные модели предметной области широко применяются при проектировании баз данных, а именно в ходе концептуального моделирования данных. Предлагается решение проблемы неоднозначности преобразования процессных доменных моделей типа SADT в концептуальные модели данных. Domain process models are widely used in database design, namely in conceptual data modeling. The solution of the problem of ambiguity of transformation of process domain models of the SADT type into conceptual data models is proposed.

Essential, Mandatory, and Shared Parts in Conceptual Data Models

2010 ◽  
pp. 17-52 ◽  
Author(s):  
Alessandro Artale ◽  
C. Maria Keet
Keyword(s):  
Life Cycle ◽  
Formal Semantics ◽  
Data Modeling ◽  
Data Models ◽  
Modeling Language ◽  
The Novel ◽  
Conceptual Data ◽  
Temporal Modality

This chapter focuses on formally representing life cycle semantics of part-whole relations in conceptual data models by utilizing the temporal modality. The authors approach this by resorting to the temporal conceptual data modeling language ERVT and extend it with the novel notion of status relations. This enables a precise axiomatization of the constraints for essential parts and wholes compared to mandatory parts and wholes, as well as introduction of temporally suspended part-whole relations. To facilitate usage in the conceptual stage, a set of closed questions and decision diagram are proposed. The longterm objectives are to ascertain which type of shareability and which lifetime aspects are possible for part-whole relations, investigate the formal semantics for sharability, and how to model these kind of differences in conceptual data models.

Modeling Imprecise and Uncertain Engineering Information in EXPRESS-G and Relational Data Models

2005 ◽  
Author(s):  
Z. M. Ma
Keyword(s):  
Systems Engineering ◽  
Manufacturing Systems ◽  
Data Modeling ◽  
Database Systems ◽  
Data Models ◽  
Information Modeling ◽  
Uncertain Information ◽  
Engineering Applications ◽  
Engineering Information ◽  
Conceptual Data

Computer-based information systems have become the nerve center of current manufacturing systems. Engineering information modeling in databases is thus essential. However, information imprecision and uncertainty extensively arise in engineering design and manufacturing. So contemporary engineering applications have put a requirement on imprecise and uncertain information modeling. Viewed from database systems, engineering information modeling can be identified at two levels: conceptual data modeling and logical database modeling and correspondingly we have conceptual data models and logical database models, respectively. In this paper, we first investigate information imprecision and uncertainty in engineering applications. Then EXPRESS-G, which is a graphical modeling tool of EXPRESS for conceptual data modeling of engineering information, and nested relational databases are extended based on possibility distribution theory, respectively, in order to model imprecise and uncertain engineering information. The formal methods to mapping fuzzy EXPRESS-G schema to fuzzy relational schema are developed.

Fuzzy Information Modeling with the UML

2011 ◽  
pp. 153-176 ◽  
Author(s):  
Zongmin Ma
Keyword(s):  
Knowledge Engineering ◽  
Unified Modeling Language ◽  
Data Modeling ◽  
Data Models ◽  
Information Modeling ◽  
Fuzzy Information ◽  
Unified Modeling ◽  
Management Group ◽  
Conceptual Data ◽  
Design Databases

Computer applications in nontraditional areas have put requirements on conceptual data modeling. Some conceptual data models, being the tool of design databases, were proposed. However, information in real-world applications is often vague or ambiguous. Currently, less research has been done in modeling imprecision and uncertainty in conceptual data models. The UML (Unified Modeling Language) is a set of object-oriented modeling notations and is a standard of the Object Data Management Group (ODMG). It can be applied in many areas of software engineering and knowledge engineering. Increasingly, the UML is being applied to data modeling. In this chapter, different levels of fuzziness are introduced into the class of the UML and the corresponding graphical representations are given. The class diagrams of the UML can hereby model fuzzy information.

Conceptual data modeling of expert systems

IEEE Expert ◽  
1989 ◽  
Vol 4 (1) ◽  
pp. 50-61 ◽  
Author(s):  
J.P. Held ◽  
J.V. Carlis
Keyword(s):  
Expert Systems ◽  
Data Modeling ◽  
Conceptual Data

A METRIC-BASED APPROACH FOR PREDICTING CONCEPTUAL DATA MODELS MAINTAINABILITY

2001 ◽  
Vol 11 (06) ◽  
pp. 703-729 ◽  
Author(s):  
MARIO PIATTINI ◽  
MARCELA GENERO ◽  
LUIS JIMÉNEZ
Keyword(s):  
Structural Complexity ◽  
Data Models ◽  
Major Step ◽  
Development Life Cycle ◽  
Novel Method ◽  
Is Development ◽  
Entity Relationship ◽  
Conceptual Data ◽  
Entity Relationship Diagrams

It is generally accepted in the information system (IS) field that IS quality is highly dependent on the decisions made early in the development life cycle. The construction of conceptual data models is often an important task of this early development. Therefore, improving the quality of conceptual data models will be a major step towards the quality improvement of the IS development. Several quality frameworks for conceptual data models have been proposed, but most of them lack valid quantitative measures in order to evaluate the quality of conceptual data models in an objective way. In this article we will define measures for the structural complexity (internal attribute) of entity relationship diagrams (ERD) and use them for predicting their maintainability (external attribute). We will theoretically validate the proposed metrics following Briand et al.'s framework with the goal of demonstrating the properties that characterise each metric. We will also show how it is possible to predict each of the maintainability sub-characteristics using a prediction model generated using a novel method for induction of fuzzy rules.

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