Bipolar Fuzzy Querying of Temporal Databases

Billiet, Christophe; Pons, José Enrique; Matthé, Tom; Tré, Guy De; Capote, Olga Pons

doi:10.1007/978-3-642-24764-4_6

Cited by 21 publications

(27 citation statements)

References 18 publications

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“…In order to present the results to the user, a crude ranking method is used: for every record r, the sum of Pos Q time (r) and Nec Q time (r) gives an evaluation score e Q time (r) in interval [0,2]. Because necessity cannot exceed 0 unless possibility is 1, this gives a natural ranking score.…”

Section: Ranking and Aggregationmentioning

confidence: 99%

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A Possibilistic Valid-Time Model

Pons

Billiet

Capote

et al. 2012

Communications in Computer and Information Science

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Abstract. Information in databases can be imperfect and this imperfection has several forms and causes. In some cases, a single value should be stored, but it is (partially) unknown. The uncertainty about which value to store leads to the aforementioned imperfection. In temporal databases, uncertainty can arise, concerning which temporal notion needs to be stored. Because in temporal databases, temporal notions influence the consistency with which the database models the reality, this uncertainty has a direct impact on the consistency of the model. To represent this temporal uncertainty, previous works have adapted fuzzy sets with conjunctive interpretation, an approach that might prove misleading. This work presents a model that represents the uncertainty using possibility and necessity measures, which are fuzzy sets with disjunctive interpretations.

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Section: Ranking and Aggregationmentioning

confidence: 99%

“…To allow information systems to cope with these imperfections, there are some approaches that work with probability [7], [21] whereas some other approaches adopt fuzzy sets for the representation of temporal information [2], [8]. The temporal relations studied by Allen were recently fuzzified by several authors [20], [23].…”

Section: Introductionmentioning

confidence: 99%

A Possibilistic Valid-Time Model

Pons

Billiet

Capote

et al. 2012

Communications in Computer and Information Science

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“…Because necessity cannot exceed 0 unless possibility is 1 and P os Q T (r) ∈ [0, 1] and N ec Q T (r) ∈ [0, 1], the sum in the numerator gives a natural ranking score in [0,2]. The function of the denominator is to normalize this score to a value in [0, 1].…”

Section: Allen Relation Constraintsmentioning

confidence: 99%

“…To allow information systems to cope with these and similar imperfections, many approaches adopt fuzzy sets for the representation of temporal information [13], [14], [2], [6]. The temporal relationships studied by Allen were fuzzified by several authors [16], [14], [18].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Possibilistic Valid-Time Queries

Billiet

Pons

Capote

et al. 2012

Communications in Computer and Information Science

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Abstract. A temporal database schema models objects or concepts with time-related or time-variant properties and a derived database contains measurements or descriptions concerning these temporal properties. The modelling of temporal information in a temporal database has an impact on the consistency of the database. Of course, this temporal information can be queried. In some cases, information in the query's temporal demand is (partially) unknown. This paper presents a technique for querying a valid-time relation using, among others, a temporal constraint in which the time indication in the temporal expression contains uncertainty and a method for evaluating such queries. This should allow querying using a (partially) unknown temporal demand.

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“…In [5] [6], an approach that integrates bipolar classifications to determine the degree of satisfaction of records, is proposed. It relies on using both positive and negative imprecise and possibly temporal preferences.…”

Section: Introductionmentioning

confidence: 99%

An Advanced Tool for Managing Fuzzy Complex Temporal Information

Gammoudi

Hadjali

Yaghlane³

2016

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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Many real-life applications need to handle and manage time pieces of information. Allen temporal relations are one of the most used and known formalisms for modelling and handling temporal data. This paper discusses a novel idea to introduce some kind of flexibility in defining such relations between two fuzzy time intervals. The key concept of this approach is a fuzzy tolerance relation conveniently modelled. Tolerant Allen temporal relations are then defined using the dilated and the eroded intervals of the initial fuzzy time intervals. By leveraging some particular fuzzy indices to compare two fuzzy time intervals, this extension of Allen relations isintegrated in the Fuzz-TIME system developed in our previous works.

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Bipolar Fuzzy Querying of Temporal Databases

Cited by 21 publications

References 18 publications

A Possibilistic Valid-Time Model

A Possibilistic Valid-Time Model

Evaluating Possibilistic Valid-Time Queries

An Advanced Tool for Managing Fuzzy Complex Temporal Information

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