10,939 research outputs found
Implementing imperfect information in fuzzy databases
Information in real-world applications is often
vague, imprecise and uncertain. Ignoring the inherent imperfect
nature of real-world will undoubtedly introduce some deformation of human perception of real-world and may eliminate several
substantial information, which may be very useful in several
data-intensive applications. In database context, several fuzzy
database models have been proposed. In these works, fuzziness
is introduced at different levels. Common to all these proposals is
the support of fuzziness at the attribute level. This paper proposes
ïŹrst a rich set of data types devoted to model the different kinds
of imperfect information. The paper then proposes a formal
approach to implement these data types. The proposed approach
was implemented within a relational object database model but it
is generic enough to be incorporated into other database models.ou
Some notes on an extended query language for FSM
FSM is a database model that has been recently proposed by the authors. FSM uses basic concepts of
classification, generalization, aggregation and association that are commonly used in semantic modelling and
supports the fuzziness of real-world at attribute, entity, class and relations intra and inter-classes levels. Hence, it
provides tools to formalize and conceptualize real-world within a manner adapted to human perception of and
reasoning about this real-word. In this paper we briefly review basic concepts of FSM and provide some notes on an
extended query language adapted to it.ou
Conceptual design and implementation of the fuzzy semantic model
FSM is one of few database models that support
fuzziness, uncertainty and impreciseness of real-world at the class
deïŹnition level. FSM authorizes an entity to be partially member
of its class according to a given degree of membership that reïŹects
the level to which the entity veriïŹes the extent properties of this
class. This paper deals with the conceptual design of FSM and
adresses some implementation issues.ou
Extending fuzzy semantic model by advanced decision rules
This paper extends FSM, a recently proposed semantic data model that supports fuzziness, imprecision and uncertainty of real-world. More precisely, the paper proposes four new concepts, decisional grouping, inhibition, multiplicity and selection, which allows enhancing the modeling of real-world applications. It integrates these concepts in FSM by the definition of new decision rules
Data modeling dealing with uncertainty in fuzzy logic
This paper shows models of data description that incorporate uncertainty like models of data extension EER, IFO among others. These database modeling tools are compared with the pattern FuzzyEER proposed by us, which is an extension of the EER model in order to manage uncertainty with fuzzy logic in fuzzy databases. Finally, a table shows the components of EER tool with the representation of all the revised models.The past and the future of information systems: 1976-2006 and beyondRed de Universidades con Carreras en InformĂĄtica (RedUNCI
Image databases: Problems and perspectives
With the increasing number of computer graphics, image processing, and pattern recognition applications, economical storage, efficient representation and manipulation, and powerful and flexible query languages for retrieval of image data are of paramount importance. These and related issues pertinent to image data bases are examined
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