In practice, we often come across situations where it is
necessary to make decisions based on incomplete or uncertain data.
In control systems it may be due to the unknown exact mathematical
model, or its excessive complexity (e.g. nonlinearity) when it is
necessary to simplify it, respectively, to solve it using a rule base. In
the case of databases, searching data we compare a similarity
measure with of the requirements of the selection with stored data,
where both the select query and the data itself may contain vague
terms, for example in the form of linguistic qualifiers. In this paper,
we focus on the processing of uncertain data in databases and
demonstrate it on the example multi-criteria decision making in the
selection of variants, specified by higher number of technical
parameters.
[1] P. Moravek, "Processing of Uncertain Information in Databases" (in
Czech), Diploma project, BUT in Brno, FME, 2009, 78 pp.
[2] V. Novak, Fundamentals of Fuzzy Modelling (in Czech). Praha: BEN -
technická literatura, 2003.
[3] J. Galindo, Handbook of Research on Fuzzy Information Processing in
Databases. Information Science Reference, 2008.
[4] J. Galindo, Fuzzy Databases: Modeling, Design and Implementation.
Idea Group Publishing, 2005.
[1] P. Moravek, "Processing of Uncertain Information in Databases" (in
Czech), Diploma project, BUT in Brno, FME, 2009, 78 pp.
[2] V. Novak, Fundamentals of Fuzzy Modelling (in Czech). Praha: BEN -
technická literatura, 2003.
[3] J. Galindo, Handbook of Research on Fuzzy Information Processing in
Databases. Information Science Reference, 2008.
[4] J. Galindo, Fuzzy Databases: Modeling, Design and Implementation.
Idea Group Publishing, 2005.
@article{"International Journal of Information, Control and Computer Sciences:64178", author = "Petr Morávek and Miloš Šeda", title = "Fuzzy Processing of Uncertain Data", abstract = "In practice, we often come across situations where it is
necessary to make decisions based on incomplete or uncertain data.
In control systems it may be due to the unknown exact mathematical
model, or its excessive complexity (e.g. nonlinearity) when it is
necessary to simplify it, respectively, to solve it using a rule base. In
the case of databases, searching data we compare a similarity
measure with of the requirements of the selection with stored data,
where both the select query and the data itself may contain vague
terms, for example in the form of linguistic qualifiers. In this paper,
we focus on the processing of uncertain data in databases and
demonstrate it on the example multi-criteria decision making in the
selection of variants, specified by higher number of technical
parameters.", keywords = "fuzzy logic, linguistic variable, multicriteria decision", volume = "5", number = "12", pages = "1736-5", }