An entity-relationship approach to the modelling of vagueness in databases

Vandenberghe, Ria; Caluwe, Rita M. M. De

doi:10.1007/3-540-54659-6_112

Cited by 6 publications

(2 citation statements)

References 5 publications

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“…Thus, two streams of research in data modeling have emerged, namely, fuzzy extensions to the ER models [24,26,31], which lead to fuzzy ER/EER models allowing Table 2 The closeness relation c Performance imprecise and uncertain concepts to be represented and manipulated at a conceptual level, and fuzzy extensions to the RDB model [1,4,8,14,19,21,23], which allow imprecise attribute values and linguistic constraints to be combined into the classical context. Since the early 1980s, the research on fuzzy relational databases has centered on the problems of how to represent fuzziness in database models, and how to formulate queries in linguistic terms.…”

Section: Introductionmentioning

confidence: 99%

Equivalence and transformation of extended algebraic operators in fuzzy relational databases

Tang

2006

Fuzzy Sets and Systems

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Section: Introductionmentioning

confidence: 99%

Equivalence and transformation of extended algebraic operators in fuzzy relational databases

Tang

2006

Fuzzy Sets and Systems

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“…Advanced semantic data models, like the Fuzzy Entity/Relationship Model, [6][7][8] can be considered as the beginning of the study of fuzziness in object-oriented models.…”

Section: Introductionmentioning

confidence: 99%

Development of applications with fuzzy objects in modern programming platforms

Berzal

Marín

Pons

et al. 2005

Int. J. Intell. Syst.

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Most of the applications that are currently being developed use object-oriented programming technology, as is the case of those applications built with the Java or C# languages. Data management has not kept out of this trend, and object-oriented and object-relational database management systems have arisen as a result. Soft-computing applications need to manage imperfect data and Fuzzy Sets Theory has proven to be a good choice for accomplishing the task of imperfect data management. In this article we present a framework that allows the programmers of soft-computing applications to deal with fuzzy objects in a transparent and intuitive way. This framework can be used to develop an object-oriented code in those systems that conform with current hip object-oriented languages, so that imperfect information can be managed.

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