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IDEFlX has provided a formal framework for consistent modeling of the data necessary for the integration of various functional areas in computer integrated manufacturing (CIM). The basic idea has been extensively applied in curren...
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IDEFlX has provided a formal framework for consistent modeling of the data necessary for the integration of various functional areas in computer integrated manufacturing (CIM). The basic idea has been extensively applied in current manufacturing industry. Imprecise and uncertain information, however, is generally involved in many engineering activities. It is especially true for constructing intelligent manufacturing systems. This paper provides extensions to the IDEFlX, which makes it possible to represent fuzzy information.
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In this paper, a new methodology for simulating bootstrap samples of fuzzy numbers is proposed. Unlike the classical bootstrap, it allows enriching a resampling scheme with values from outside the initial sample. Although a second...
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In this paper, a new methodology for simulating bootstrap samples of fuzzy numbers is proposed. Unlike the classical bootstrap, it allows enriching a resampling scheme with values from outside the initial sample. Although a secondary sample may contain results beyond members of the primary set, they are generated smartly so that the crucial characteristics of the original observations remain invariant. Two methods for generating bootstrap samples preserving the representation (i.e., the value and the ambiguity or the expected value and the width) of fuzzy numbers belonging to the primary sample are suggested and numerically examined with respect to other approaches and various statistical properties.
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Various fuzzy data models such as fuzzy relational databases, fuzzy object-oriented databases, fuzzy object-relational databases and fuzzy XML have been proposed in the literature in order to represent and process fuzzy informatio...
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Various fuzzy data models such as fuzzy relational databases, fuzzy object-oriented databases, fuzzy object-relational databases and fuzzy XML have been proposed in the literature in order to represent and process fuzzy information in databases and XML. But little work has been done in modeling fuzzy data types. Actually in the fuzzy data models, each fuzzy value is associated with a fuzzy data type. Explicit representations of fuzzy data types are the foundation of fuzzy data processing. To fill this gap, in this paper, we propose several fuzzy data types, including fuzzy simple data types, fuzzy collection data types and fuzzy defined data types. We further investigate how to declare the fuzzy data types in the fuzzy object-oriented database model and fuzzy XML Schema. The proposed fuzzy data types can meet the requirement of modeling fuzzy data in the fuzzy databases and fuzzy XML.
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Temporal databases offer a common framework to those database applications that need to store or handle different types of temporal data from a variety of sources. They allow the concept of time to be handled from the point of vie...
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Temporal databases offer a common framework to those database applications that need to store or handle different types of temporal data from a variety of sources. They allow the concept of time to be handled from the point of view of meaning, representation, and manipulation. Although at first sight the incorporation of time into a database might appear to be a direct and simple task, it is, however, quite complex: not only must new structures and specific operators be included, but the semantics of conventional DML sentences (insert, update, or delete) and queries must be appropriately changed. In addition, temporal information is not always as precise as desired since it might be affected by imprecision due to the use of natural language or to the nature of the information source. In this paper, we deal with the problem of the update (and, implicitly, insert and delete) and query operations when time is expressed by means of a fuzzy interval of dates.
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Uncertain information extensively exists in data and knowledge intensive applications, where fuzzy data play an import role in nature. Fuzzy set theory has been extensively applied to extend various database models and resulted in...
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Uncertain information extensively exists in data and knowledge intensive applications, where fuzzy data play an import role in nature. Fuzzy set theory has been extensively applied to extend various database models and resulted in numerous contributions. This paper concentrates on a crucial issue in fuzzy data management: fuzzy data modeling in XML. An up-to-date overview of the current state of the art in fuzzy XML data modeling is provided in the paper. The paper serves as identifying possible research opportunities in the area of fuzzy XML data management in addition to providing a generic overview of the approaches proposed to modeling fuzzy XML data. (C) 2015 Published by Elsevier B.V.
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Fuzzy sets generalize the concept of sets by considering that elements belong to a class (or fulfil a property) with a degree of membership (or certainty) ranging between 0 and 1. Fuzzy sets have been used in diverse areas to mode...
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Fuzzy sets generalize the concept of sets by considering that elements belong to a class (or fulfil a property) with a degree of membership (or certainty) ranging between 0 and 1. Fuzzy sets have been used in diverse areas to model gradual transitions as opposite to abrupt changes. In econometrics and statistics, this has been especially relevant in clustering, regression discontinuity designs, and imprecise data modelling, to name but a few. Although the membership functions vary between 0 and 1 as the probabilities, the nature of the imprecision captured by the fuzzy sets is usually different from stochastic uncertainty. The aim is to illustrate the advantages of combining fuzziness, imprecision, or partial knowledge with randomness through various key methodological problems. Emphasis will be placed on the management of non-precise data modelled through (fuzzy) sets. Software to apply the reviewed methodology will be suggested. Some open problems that could be of future interest will be discussed.
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Data envelopment analysis (DEA) requires input and output data to be precisely known. This is not always the case in real applications. This paper proposes two new fuzzy DEA models constructed from the perspective of fuzzy arithme...
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Data envelopment analysis (DEA) requires input and output data to be precisely known. This is not always the case in real applications. This paper proposes two new fuzzy DEA models constructed from the perspective of fuzzy arithmetic to deal with fuzziness in input and output data in DEA. The new fuzzy DEA models are formulated as linear programming models and can be solved to determine fuzzy efficiencies of a group of decision-making units (DMUs). An analytical fuzzy ranking approach is developed to compare and rank the fuzzy efficiencies of the DMUs. The proposed fuzzy DEA models and ranking approach are applied to evaluate the performances of eight manufacturing enterprises in China.
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This note is a rejoinder on our paper in this issue. It attempts to provide some clarifications and thoughts in connection with the discussions/comments made about it by Didier Dubois and Sebastien Destercke. We hope our comments ...
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This note is a rejoinder on our paper in this issue. It attempts to provide some clarifications and thoughts in connection with the discussions/comments made about it by Didier Dubois and Sebastien Destercke. We hope our comments are at the level of the discussants'.
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The first aim is to emphasize the use of fuzziness in data analysis to capture information that has been traditionally disregarded with a cost in the precision of the conclusions. Fuzziness can be considered in the data analysis p...
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The first aim is to emphasize the use of fuzziness in data analysis to capture information that has been traditionally disregarded with a cost in the precision of the conclusions. Fuzziness can be considered in the data analysis process at various stages, but the main target in this paper will be fuzziness in the data. Depending on the nature of the fuzzy data or the aim to which they are handled, different approaches should be applied. We attempt to contribute to the clarification of such a difference while focusing on the so-called ontic approach in contrast to the epistemic approach. The second aim is to underline the need of considering robust methods to reduce the misleading impact of outliers in fuzzy data analysis. We propose trimming as a general and intuitive method to discard outliers. We exemplify this approach with the case of the ontic fuzzy trimmed mean/variance and highlight the differences with the epistemic case. All the discussions and developments are illustrated by means of a case-study concerning the perception of lengths of men and women. (C) 2015 Elsevier B.V. All rights reserved.
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Since it can result in significant benefits for a company or an individual, the inclusion of information extracted from subjective social media content into a decision making process is becoming a more frequent activity. However, ...
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Since it can result in significant benefits for a company or an individual, the inclusion of information extracted from subjective social media content into a decision making process is becoming a more frequent activity. However, such benefits are usually linked to the usability of the extracted information, which, among other aspects, depends on the reliability of its source. In this regard, people whose understandings of a topic are alike to the understanding possessed by an information seeker can be considered fairly reliable information sources. Hence, we propose a novel technique for detecting social media users with whom an information seeker shares a similar understanding of a given topic. Through this technique, posts on social media are digested to build a kind of database consisting of augmented Atanassov fuzzy sets, or AAIFSs for short, each resembling a collection of experience-based evaluations given by a particular source with respect to a given topic. Since such AAIFSs can be used in comparisons in which not only the extents but also the contexts of those evaluations are taken into account for computation, extracting more reliable (and usable) information is possible. An illustrative example shows how the proposed technique works and how it can help to detect sources having a common understanding of a topic. (c) 2019 Elsevier B.V. All rights reserved.
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