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

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.

Data Modeling

2014 ◽  
Vol 25 (4) ◽  
pp. 17-37 ◽  
Author(s):  
Hock Chuan Chan ◽  
Chuan-Hoo Tan ◽  
Hock-Hai Teo
Keyword(s):  
Data Modeling ◽  
Clear Distinction ◽  
User Performance ◽  
Experiment Data ◽  
The World ◽  
Database Models ◽  
User Data ◽  
Conceptual Data ◽  
The Relationship

Research on ontology and conceptual data modeling builds on the premise that the world could be represented in terms of concepts (also known as “things”), their properties and their (things') relationships. A persistent effort in the representation is how to distinguish things from their properties, as well as how to denote relationships that relate two things together. Following the tradition of deriving guidelines from ontology studies, this paper proposes a need to make a clear distinction between relationships among things and relationships among things' properties. The central thrust of this paper is in proposing an ontologically guided principle: For the same relationship, working with the relationship among things will lead to better user performance than working with the relationship among things' properties. This principle, called Relationship without Pointers principle, is robustly validated by re-analyzing a set of experiment data on user data modeling performance with three database models. This principle may be applicable to other contexts that study relationships.

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.

Sign in / Sign up

Export Citation Format

Share Document