Fuzzy And Uncertain Object-Oriented Databases

Caluwe, Rita De

doi:10.1142/3311

Cited by 59 publications

(17 citation statements)

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“…24 The second approach directly uses possibility distributions for attribute values and applies fuzzy pattern matching to model the evaluation of flexible queries. 2,3,5,7,9,25 In the approaches based on the similarity measure, named similarity based models, the evaluation of flexible queries requires the computation of a measure of similarity or proximity of the actual attribute values with respect to the soft query constraints~conditions! on that attribute values.…”

Section: Modeling Preferences In Fuzzy Databasesmentioning

confidence: 99%

A flexible approach to evaluating soft conditions with unequal preferences in fuzzy databases

Bordogna

Pasi

2007

Int. J. Intell. Syst.

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A flexible model for evaluating soft query with unequal preferences in fuzzy databases is proposed. We assume that conditions with unequal preferences have an exclusive meaning like in the request "find a holiday accommodation such that big apartments are preferred to high rating hotels." In this case it is assumed that the aggregator of the soft conditions is an implicit OR. Conversely, conditions with unequal importance have an inclusive meaning, like in the query "find a house to rent that is cheap~most important!, big~important!, new~fairly important!." In this case the implicit aggregator is an AND. What we propose in this article is to model preferences as modifiers of the semantics of the evaluation function of the conditions. Because the soft conditions are aggregated by an OR, the more a soft condition is preferred, the more its evaluation function tolerates a greater undersatisfaction of the soft condition. The proposed approach is formalized by considering two alternative semantics of the evaluation function: the first semantics defines the evaluation function by means of a generalized fuzzy inclusion measure, and the second one as a generalized similarity measure. These functions are parameterized so that their modification is simply achieved by tuning the functions' parameters.

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Section: Modeling Preferences In Fuzzy Databasesmentioning

confidence: 99%

A flexible approach to evaluating soft conditions with unequal preferences in fuzzy databases

Bordogna

Pasi

2007

Int. J. Intell. Syst.

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“…In particular, fuzzy information has been extensively investigated in the context of the relational model (Petry 1996;Chen 1999). Recent efforts have extended these results to object-oriented databases by introducing the related notions of classes, generalization/specialization, and inheritance (de Caluwe 1998;Ma 2005a). In addition, fuzzy data modeling has been investigated in the context of the conceptual data models such as ER (Zvieli and Chen 1986), EER (Chen and Kerre 1998) and UML (Ma and Yan 2007;Ma et al 2011;Teshnehlab 2007, 2009;Sicilia et al 2002).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Sets and Fuzzy Database Models

Zhang

2013

Fuzzy XML Data Management

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Information imprecision and uncertainty exist in many real-world applications, and for this reason fuzzy data modeling has been extensively investigated in various database models. In particular, Zadeh's fuzzy set theory has been identified as a successful technique for modeling imprecise and uncertain information in various database models. This has resulted in numerous contributions, mainly with respect to the popular fuzzy conceptual data models (fuzzy ER/ EER model, fuzzy UML data model, and etc.) and fuzzy logical database models (fuzzy relational database model, fuzzy object-oriented database model, and etc.). Also, it is shown that fuzzy set theory is very useful in Web-based business intelligence. Therefore, topics related to the modeling of fuzzy data are considered very interesting in XML since it is the current standard data representation and exchange format over the Web. In particular, to manage fuzzy XML data, it is necessary to integrate fuzzy XML and various fuzzy databases, and various fuzzy database models (fuzzy relational database model and fuzzy object-oriented database model) need to be used for mapping to and from the fuzzy XML models. Therefore, in this chapter, we mainly introduce several fuzzy database models, including fuzzy UML data model, fuzzy relational database model, and fuzzy object-oriented database model. Before that, we briefly introduce some notions of fuzzy set theory. IntroductionInformation is often imprecise and uncertain in many real-world applications, and many sources can contribute to the imprecision and uncertainty of data or information. Therefore, it has been pointed out that we need to learn how to manage data that is imprecise or uncertain (Dalvi and Suciu 2007).Unfortunately, the classical data management techniques such as databases and XML as introduced in Chap. 1 often suffer from their incapability of representing and manipulating imprecise and uncertain data information. On this basis, since

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“…Once 'null' values are admitted into a database, it is necessary to define the impact of transformations and modifications in the presence of 'nulls'. On the other hand, techniques for the handling of imperfect information have been studied by the 'fuzzy' database community [3,17,9,16] and research on such techniques has been recognized as a challenge for the near future [15].…”

Section: Introductionmentioning

confidence: 99%

Null Values Revisited in Prospect of Data Integration

Tré¹,

Caluwe²,

Prade³

2004

Semantics of a Networked World. Semantics for Grid Databases

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Abstract.A considerable part of the information available in the real world is inherently imperfect and/or incomplete. Traditionally, the most commonly adopted modelling approaches for dealing with imperfect and missing information are based on the use of default values and of null values. However, in order to deal with imperfect information in a more efficient way, a database system needs some more advanced modelling facilities that better reflect the semantics of the data. Such facilities become even more necessary if data stemming from different data sources must be integrated in a distributed, federated database system. In this paper, the concept of a 'null' value has been revisited. A semantically richer definition, based on possibility theory, is proposed together with a description of the accompanying many-valued logic. Additionally, the potentials of the new approach with respect to the integration of data in multi-database environments or grid database services are illustrated.

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Fuzzy And Uncertain Object-Oriented Databases

Cited by 59 publications

References 0 publications

A flexible approach to evaluating soft conditions with unequal preferences in fuzzy databases

A flexible approach to evaluating soft conditions with unequal preferences in fuzzy databases

Fuzzy Sets and Fuzzy Database Models

Null Values Revisited in Prospect of Data Integration

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