Paper Info
Title
Relational Representation Of Uncertain And Imprecise Time Assessments: An Application To Artworks Dating
Abstract
Imprecision and uncertainty appear together in many situations of real life and therefore soft computing techniques must be studied to tackle this problem. Imprecise and uncertain values are usually expressed by means of linguistic terms, especially when they have been provided by a human being. This is also the case of temporal information where, in addition to handling time constraints, we may also have both uncertainty and imprecision in the description, like in the sentence It is very possible that Giotto's Crucifix was painted by 1289. To manage both uncertainty (very possible) and imprecision (by 1289) in a separate way would lead to a quite complicated computation and a lack of comprehension by the users of the system. Because of these reasons, it is very desirable that both sources of imperfection of time values are combined into a single value that appropriately describes the intended information. In this work, we extend our previous research on this topic and we study how to adapt it to relational systems in order to be useful. The final goal is obtaining normalized fuzzy values that provide an equivalent information about the described temporal fact than the original ones, for making it possible to store and manage them in a fuzzy relational database. On the other hand, there will be some situations where more than one expert opinion about a time period must be taken into account and we need to find a representative value of them all in order to be stored and managed. For the sake of simplicity, comprehensibility, and the efficiency in computation (when using trapezoidal representation), the fuzzy average is used to find such a representative value. (C) 2017 Wiley Periodicals, Inc.
Year
DOI
Venue
2018
10.1002/int.21914
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
Field
DocType
Volume
Data mining,Normalization (statistics),Relational database,Computer science,Fuzzy logic,Artificial intelligence,Soft computing,Sentence,Comprehension,Machine learning,Computation
Journal
33
Issue
ISSN
Citations 
5
0884-8173
0
PageRank 
References 
Authors
0.34
7
3
Name
Order
Citations
PageRank
Juan Miguel Medina127224.24
Olga Pons227421.67
María Amparo Vila Miranda3118293.57