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.

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.

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.

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.

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.

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.

Modeling and Indexing Spatiotemporal Trajectory Data in Non-Relational Databases

2016 ◽  
pp. 133-162 ◽  
Author(s):  
Berkay Aydin ◽  
Vijay Akkineni ◽  
Rafal A Angryk
Keyword(s):  
Data Storage ◽  
Relational Databases ◽  
Large Scale ◽  
Database Systems ◽  
Distributed Database ◽  
Data Models ◽  
Trajectory Data ◽  
Nosql Databases ◽  
Distributed Database Systems ◽  
Partitioned Data

With the ever-growing nature of spatiotemporal data, it is inevitable to use non-relational and distributed database systems for storing massive spatiotemporal datasets. In this chapter, the important aspects of non-relational (NoSQL) databases for storing large-scale spatiotemporal trajectory data are investigated. Mainly, two data storage schemata are proposed for storing trajectories, which are called traditional and partitioned data models. Additionally spatiotemporal and non-spatiotemporal indexing structures are designed for efficiently retrieving data under different usage scenarios. The results of the experiments exhibit the advantages of utilizing data models and indexing structures for various query types.

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