Semantical Constraints for Database Models

1994 ◽  
pp. 287-327
Author(s):  
B. Thalheim
Keyword(s):  
Database Models

Authorization in multilevel database models

1979 ◽  
Vol 4 (2) ◽  
pp. 155-161 ◽  
Author(s):  
C. Wood ◽  
R.C. Summers ◽  
E.B. Fernandez
Keyword(s):  
Database Models

A Constraint Based Fuzzy Object Oriented Database Model

2011 ◽  
pp. 1-45 ◽  
Author(s):  
Guy de Tre ◽  
Rita de Caluwe
Keyword(s):  
Type System ◽  
Object Oriented ◽  
Building Blocks ◽  
Constraint System ◽  
Database Model ◽  
Database Models ◽  
Data Definition ◽  
Definition Of ◽  
Object Oriented Database ◽  
Adequate Data

The objective of this chapter is to define a fuzzy object-oriented formal database model that allows us to model and manipulate information in a (true to nature) natural way. Not all the elements (data) that occur in the real world are fully known or defined in a perfect way. Classical database models only allow the manipulation of accurately defined data in an adequate way. The presented model was built upon an object-oriented type system and an elaborated constraint system, which, respectively, support the definitions of types and constraints. Types and constraints are the basic building blocks of object schemes, which, in turn, are used for defining database schemes. Finally, the definition of the database model was obtained by providing adequate data definition operators and data manipulation operators. Novelties in the approach are the incorporation of generalized constraints and of extended possibilistic truth values, which allow for a better representation of data(base) semantics.

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.

New Trends in Databases to NonSQL Databases

2021 ◽  
pp. 791-799
Author(s):  
Antonio Sarasa-Cabezuelo
Keyword(s):  
Big Data ◽  
Information Processing ◽  
Real Time ◽  
Relational Databases ◽  
Data Models ◽  
Relational Model ◽  
Alternative Mechanism ◽  
New Models ◽  
Database Models

The appearance of the “big data” phenomenon has meant a change in the storage and information processing needs. This new context is characterized by 1) enormous amounts of information are available in heterogeneous formats and types, 2) information must be processed almost in real time, and 3) data models evolve periodically. Relational databases have limitations to respond to these needs in an optimal way. For these reasons, some companies such as Google or Amazon decided to create new database models (different from the relational model) that solve the needs raised in the context of big data without the limitations of relational databases. These new models are the origin of the so-called NonSQL databases. Currently, NonSQL databases have been constituted as an alternative mechanism to the relational model and its use is widely extended. The main objective of this chapter is to introduce the NonSQL databases.

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.

Aspects of Intelligence in an "SP" Database System

2009 ◽  
pp. 725-754
Author(s):  
J. Gerard Wolff
Keyword(s):  
Pattern Recognition ◽  
Information Retrieval ◽  
Natural Language ◽  
Unsupervised Learning ◽  
Probabilistic Reasoning ◽  
Database System ◽  
Theory Of Computing ◽  
New Knowledge ◽  
Database Models

This chapter describes some of the kinds of “intelligence” that may be exhibited by an intelligent database system based on the SP theory of computing and cognition. The chapter complements an earlier paper on the SP theory as the basis for an intelligent database system (Wolff, forthcoming b) but it does not depend on a reading of that earlier paper. The chapter introduces the SP theory and its main attractions as the basis for an intelligent database system: that it uses a simple but versatile format for diverse kinds of knowledge, that it integrates and simplifies a range of AI functions, and that it supports established database models when that is required. Then with examples and discussion, the chapter illustrates aspects of “intelligence” in the system: pattern recognition and information retrieval, several forms of probabilistic reasoning, the analysis and production of natural language, and the unsupervised learning of new knowledge.

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.

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