About projection-selection-join queries addressed to possibilistic relational databases

Bosc, Patrick; Pivert, Olivier

doi:10.1109/tfuzz.2004.836070

Cited by 58 publications

(33 citation statements)

References 16 publications

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“…It makes use of a processing strategy that has been pointed out 13,14 for dealing with general algebraic queries~not for yes/no queries, which are more specific!. Of course, the price to pay for the compliance with property~P1!…”

Section: Strong Representation Systemsmentioning

confidence: 99%

About yes/no queries against possibilistic databases

Bosc

Pivert

2007

Int. J. Intell. Syst.

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This article is concerned with the handling of imprecise information in databases. The need for dealing with imprecise data is more and more acknowledged in order to cope with real data, even if commercial systems are most of the time unable to manage them. Here, the possibilistic setting is taken into consideration because it is less demanding than the probabilistic one. Then, any imprecise piece of information is modeled as a possibility distribution intended for constraining the more or less acceptable values. Such a possibilistic database has a natural interpretation in terms of a set of regular databases, which provides the basic gateway to interpret queries. However, if this approach is sound, it is not realistic, and it is necessary to consider restricted queries for which a calculus grounded on the possibilistic database, that is, where the operators work directly on possibilistic relations, is feasible. Extended yes/no queries are dealt with here, where their general form is: "to what extent is it possible and certain that tuple t~given! belongs to the answer to Q," where Q is an algebraic relational query. A strategy for processing such queries efficiently is proposed under some assumptions as to the operators appearing in Q.

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Section: Strong Representation Systemsmentioning

confidence: 99%

About yes/no queries against possibilistic databases

Bosc

Pivert

2007

Int. J. Intell. Syst.

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“…To deal with uncertain values of database attributes, several models were proposed. The most studied and known models are: probabilistic databases [11,12,1], possibilistic databases [7,8] and evidential databases (based on Dempster-shafer theory) [19,22,23,4,3]. The advantage of the evidential databases model is twofold: (i) it allows modeling both uncertainty and imprecision (due to the lack of information) in data; and (ii) it represents a generalization of both probabilistic and possibilistic models.…”

Section: Introductionmentioning

confidence: 99%

Computing Skyline from Evidential Data

Elmi¹,

Benouaret²,

Hadjali³

et al. 2014

Lecture Notes in Computer Science

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Abstract.The skyline operator is a powerful means in multi-criteria decision-making since it retrieves the most interesting objects according to a set of attributes. On the other hand, uncertainty is inherent in many real applications. One of the most powerful approaches used to model uncertainty is the evidence theory. Databases that manage such type of data are called evidential databases. In this paper, we tackle the problem of skyline analysis on evidential databases. We first introduce a skyline model that is appropriate to the evidential data nature. We then develop an efficient algorithm to compute this kind of skyline. Finally, we present a thorough experimental evaluation of our approach.

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“…In the similarity-based framework, Buckles et al [8] defined the relational algebraic operators and discussed the uniqueness of tuple interpretation. In the possibility-based framework, Bosc et al [3,7] view the possibilistic database as a set of (weighted disjunctive) regular databases (worlds), a nesting mechanism is introduced in the framework to support the expression of results of some operations, and value-based querying especially the projection-selection-join operations are studied in detail. In the extended-possibility-based framework, Chen et al [11] presented fuzzy extensions of the eight relational algebraic operators.…”

Section: Introductionmentioning

confidence: 99%

“…There are two basic lines of research in incorporating fuzziness into the relational databases. The first is to apply fuzzy queries to crisp data [2,4,19], the second is to deal with ill-known data represented by multi-values or fuzzy sets/possibility distributions [3,[6][7][8][11][12][13][14]21,23]. This paper belongs to the second class.…”

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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About projection-selection-join queries addressed to possibilistic relational databases

Cited by 58 publications

References 16 publications

About yes/no queries against possibilistic databases

About yes/no queries against possibilistic databases

Computing Skyline from Evidential Data

Equivalence and transformation of extended algebraic operators in fuzzy relational databases

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