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.

Imprecise and Uncertain Engineering Information Modeling in Databases

2011 ◽  
pp. 190-216
Author(s):  
Z.M. Ma
Keyword(s):  
Systems Engineering ◽  
Manufacturing Systems ◽  
Data Modeling ◽  
Database Systems ◽  
Information Modeling ◽  
Uncertain Information ◽  
Engineering Applications ◽  
Database Models ◽  
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 chapter, we firstly 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 nested relational schema are developed.

Databases Modeling of Engineering Information

2009 ◽  
pp. 338-361
Author(s):  
Z. M. Ma
Keyword(s):  
Data Storage ◽  
Database Systems ◽  
Data Models ◽  
Information Modeling ◽  
Database Modeling ◽  
Business Applications ◽  
Database Models ◽  
Engineering Information ◽  
Computer Based ◽  
Conceptual Data

Information systems have become the nerve center of current computer-based engineering applications, which hereby put the requirements on engineering information modeling. Databases are designed to support data storage, processing, and retrieval activities related to data management, and database systems are the key to implementing engineering information modeling. It should be noted that, however, the current mainstream databases are mainly used for business applications. Some new engineering requirements challenge today’s database technologies and promote their evolvement. Database modeling can be classified into two levels: conceptual data modeling and logical database modeling. In this chapter, we try to identify the requirements for engineering information modeling and then investigate the satisfactions of current database models to these requirements at two levels: conceptual data models and logical database models. In addition, the relationships among the conceptual data models and the logical database models for engineering information modeling are presented in the chapter viewed from database conceptual design.

Databases Modeling of Engineering Information

2011 ◽  
pp. 65-84
Author(s):  
Z.M. Ma
Keyword(s):  
Data Storage ◽  
Database Systems ◽  
Data Models ◽  
Information Modeling ◽  
Database Modeling ◽  
Business Applications ◽  
Database Models ◽  
Engineering Information ◽  
Computer Based ◽  
Conceptual Data

Information systems have become the nerve center of current computer-based engineering applications, which hereby put the requirements on engineering information modeling. Databases are designed to support data storage, processing, and retrieval activities related to data management, and database systems are the key to implementing engineering information modeling. It should be noted that, however, the current mainstream databases are mainly used for business applications. Some new engineering requirements challenge today’s database technologies and promote their evolvement. Database modeling can be classified into two levels: conceptual data modeling and logical database modeling. In this chapter, we try to identify the requirements for engineering information modeling and then investigate the satisfactions of current database models to these requirements at two levels: conceptual data models and logical database models. In addition, the relationships among the conceptual data models and the logical database models for engineering information modeling are presented in the chapter viewed from database conceptual design.

Databases Modeling of Engineering Information

2008 ◽  
pp. 1182-1204
Author(s):  
Z. M. Ma
Keyword(s):  
Data Storage ◽  
Database Systems ◽  
Data Models ◽  
Information Modeling ◽  
Database Modeling ◽  
Business Applications ◽  
Database Models ◽  
Engineering Information ◽  
Computer Based ◽  
Conceptual Data

Information systems have become the nerve center of current computer-based engineering applications, which hereby put the requirements on engineering information modeling. Databases are designed to support data storage, processing, and retrieval activities related to data management, and database systems are the key to implementing engineering information modeling. It should be noted that, however, the current mainstream databases are mainly used for business applications. Some new engineering requirements challenge today’s database technologies and promote their evolvement. Database modeling can be classified into two levels: conceptual data modeling and logical database modeling. In this chapter, we try to identify the requirements for engineering information modeling and then investigate the satisfactions of current database models to these requirements at two levels: conceptual data models and logical database models. In addition, the relationships among the conceptual data models and the logical database models for engineering information modeling are presented in the chapter viewed from database conceptual design.

Databases Modeling of Engineering Information

2011 ◽  
pp. 1-34 ◽  
Author(s):  
Z. M. Ma
Keyword(s):  
Data Storage ◽  
Database Systems ◽  
Data Models ◽  
Information Modeling ◽  
Database Modeling ◽  
Business Applications ◽  
Database Models ◽  
Engineering Information ◽  
Computer Based ◽  
Conceptual Data

Information systems have become the nerve center of current computer-based engineering applications, which hereby put the requirements on engineering information modeling. Databases are designed to support data storage, processing, and retrieval activities related to data management, and database systems are the key to implementing engineering information modeling. It should be noted that, however, the current mainstream databases are mainly used for business applications. Some new engineering requirements challenge today’s database technologies and promote their evolvement. Database modeling can be classified into two levels: conceptual data modeling and logical database modeling. In this chapter, we try to identify the requirements for engineering information modeling and then investigate the satisfactions of current database models to these requirements at two levels: conceptual data models and logical database models. In addition, the relationships among the conceptual data models and the logical database models for engineering information modeling are presented in the chapter viewed from database conceptual design.

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.

Handling Imprecise and Uncertain Engineering Information in IDEF1X and Relational Data Models

2004 ◽  
Author(s):  
Z. M. Ma
Keyword(s):  
Relational Database ◽  
Data Model ◽  
Manufacturing Systems ◽  
Relational Databases ◽  
Manufacturing Industries ◽  
Fuzzy Data ◽  
Uncertain Information ◽  
Database Modeling ◽  
Engineering Information ◽  
Conceptual Data

Database modeling of engineering information is crucial for constructing manufacturing systems because current manufacturing industries are typically information-based enterprises and information systems have become their nervous center. Engineering information can be modeled at two levels: conceptual data model and logical database model. Generally a conceptual data model is designed and then the designed conceptual data model will be transformed into the chosen logical database schema. Imprecise and uncertain information, however, is generally involved in many engineering activities and imprecise and uncertain engineering information are represented by fuzzy sets. Nowadays relational databases are still the most useful database product and IDEF1X is most useful for logical database design of relational databases in engineering. So in this paper, we focus on fuzzy data modeling in IDEF1X and relational databases. The formal approaches to mapping fuzzy IDEF1X models to fuzzy relational database schemes are hereby developed.

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.

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