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.

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.

scholarly journals Lossless Join Decomposition for Extended Possibility-Based Fuzzy Relational Databases

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Julie Yu-Chih Liu
Keyword(s):  
Relational Database ◽  
Data Model ◽  
Decomposition Method ◽  
Relational Databases ◽  
Data Models ◽  
Functional Dependency ◽  
Fuzzy Data ◽  
Functional Dependencies ◽  
Approximate Equality ◽  
Database Normalization

Functional dependency is the basis of database normalization. Various types of fuzzy functional dependencies have been proposed for fuzzy relational database and applied to the process of database normalization. However, the problem of achieving lossless join decomposition occurs when employing the fuzzy functional dependencies to database normalization in an extended possibility-based fuzzy data models. To resolve the problem, this study defined fuzzy functional dependency based on a notion of approximate equality for extended possibility-based fuzzy relational databases. Examples show that the notion is more applicable than other similarity concept to the research related to the extended possibility-based data model. We provide a decomposition method of using the proposed fuzzy functional dependency for database normalization and prove the lossless join property of the decomposition method.

A Literature Overview of Fuzzy Database Modeling

2008 ◽  
pp. 187-207 ◽  
Author(s):  
Z.. M. Ma
Keyword(s):  
Relational Databases ◽  
Object Oriented ◽  
Data Models ◽  
Relational Model ◽  
Complex Objects ◽  
Database Modeling ◽  
Database Models ◽  
Fuzzy Database ◽  
Conceptual Data ◽  
Object Oriented Databases

Fuzzy set theory has been extensively applied to extend various data models and resulted in numerous contributions, mainly with respect to the popular relational model or to some related form of it. To satisfy the need of modeling complex objects with imprecision and uncertainty, recently many researches have been concentrated on fuzzy semantic (conceptual) and object-oriented data models. This chapter reviews fuzzy database modeling technologies, including fuzzy conceptual data models and database models. Concerning fuzzy database models, fuzzy relational databases, fuzzy nested relational databases, and fuzzy object-oriented databases are discussed, respectively.

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.

A Literature Overview of Fuzzy Database Modeling

2009 ◽  
pp. 105-125 ◽  
Author(s):  
Z.M. Ma
Keyword(s):  
Relational Databases ◽  
Object Oriented ◽  
Data Models ◽  
Relational Model ◽  
Complex Objects ◽  
Database Modeling ◽  
Database Models ◽  
Fuzzy Database ◽  
Conceptual Data ◽  
Object Oriented Databases

Fuzzy set theory has been extensively applied to extend various data models and resulted in numerous contributions, mainly with respect to the popular relational model or to some related form of it. To satisfy the need of modeling complex objects with imprecision and uncertainty, recently many researches have been concentrated on fuzzy semantic (conceptual) and object-oriented data models. This chapter reviews fuzzy database modeling technologies, including fuzzy conceptual data models and database models. Concerning fuzzy database models, fuzzy relational databases, fuzzy nested relational databases, and fuzzy object-oriented databases are discussed, respectively.

Design for Database of Energy Project Management Based on Portfolio

2011 ◽  
Vol 211-212 ◽  
pp. 62-67
Author(s):  
Yun Na Wu ◽  
Jiang Shuai Li ◽  
Jia Li Wang
Keyword(s):  
Project Management ◽  
Portfolio Management ◽  
Data Model ◽  
Management System ◽  
Management Technique ◽  
Project Portfolio ◽  
Project Portfolio Management ◽  
Database Modeling ◽  
Project Management System ◽  
Conceptual Data

With the continuous development of energy projects and the actual needs of the project, project portfolio management technique is known by people more and more. However, current databases of energy project management system are too different. This paper studies actual demand of energy project database, taking portfolio management theory as the basic, and use database modeling technology to build database’s conceptual data model, logical data model and physics data model based on the portfolio of energy project management. These models can be very good instruction of energy database design and construction, and will support energy project portfolio management system design to some guidance.

scholarly journals FORMALIZED REPRESENTATION FOR THE DATA MODEL WITH THE UNIVERSAL BASIS OF RELATIONS

2019 ◽  
pp. 453-460
Author(s):  
Vitalii I. Yesin ◽  
Mikolaj Karpinski ◽  
Maryna V. Yesina ◽  
Vladyslav V. Vilihura
Keyword(s):  
Relational Database ◽  
Data Model ◽  
Relational Databases ◽  
Subject Domain ◽  
Dynamic Changes ◽  
Standard Data ◽  
The Subject ◽  
Logical Design ◽  
The Creation ◽  
Universal Standard

The goal of the article is to develop a universal (standard) data model that allows you to get rid of the need for a costly policy of doing extra work when developing new ones or transforming existing relational databases (RDBs) caused by dynamic changes in the subject domain (SD). The requirements for the developed data model were formulated. In accordance with the formulated requirements, the data model was synthesized. To simplify the process of creating relational database schemas an algorithm for transforming the description of the subject domain into the relations of the universal basis of the developed model was proposed. The scientific novelty of the obtained results is: a data model that, unlike known ones, allows us to simplify the creation of RDB schemas at the stage of logical design of relational databases, under the conditions of dynamic changes in subject domains, due to the introduced universal basis of relations, as a means of describing structures and the presentation of data for various SDs has been developed.

Modeling Fuzzy Information in the IFO and Relational Data Model

2011 ◽  
pp. 273-293
Author(s):  
Z. M. Ma
Keyword(s):  
Fuzzy Set ◽  
Relational Database ◽  
Real World ◽  
Data Model ◽  
Data Models ◽  
Computer Applications ◽  
Relational Data Model ◽  
Relational Database Schema ◽  
Conceptual Data ◽  
Design Databases

Computer applications in non-traditional areas have put requirements on conceptual data modeling. Some conceptual data models, being the tool of design databases, have been 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 and the design of databases with imprecision and uncertainty. In this chapter, a different level of fuzziness based on fuzzy set and possibility distribution theory will be introduced into the IFO data model and the corresponding graphical representations will be given. The IFO data model is then extended to a fuzzy IFO data model, denoted IF2O. In particular, we provide the approach to mapping an IF2O model to a fuzzy relational database schema.

A Literature Overview of Fuzzy Database Modeling

2011 ◽  
pp. 167-196
Author(s):  
Z. M. Ma
Keyword(s):  
Relational Databases ◽  
Object Oriented ◽  
Data Models ◽  
Relational Model ◽  
Complex Objects ◽  
Database Modeling ◽  
Database Models ◽  
Fuzzy Database ◽  
Conceptual Data ◽  
Object Oriented Databases

Fuzzy set theory has been extensively applied to extend various data models and resulted in numerous contributions, mainly with respect to the popular relational model or to some related form of it. To satisfy the need of modeling complex objects with imprecision and uncertainty, recently many researches have been concentrated on fuzzy semantic (conceptual) and object-oriented data models. This chapter reviews fuzzy database modeling technologies, including fuzzy conceptual data models and database models. Concerning fuzzy database models, fuzzy relational databases, fuzzy nested relational databases, and fuzzy object-oriented databases are discussed, respectively.

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