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Evaluating aggregate functions on possibilistic data
The need for extending information management systems to handle the imprecision of information found in the real world has been recognized. Fuzzy set theory together with possibility theory represent a uniform framework for extending the relational database model with these features. However, none of the existing proposals for handling imprecision in the literature has dealt with queries involving a functional evaluation of a set of items, traditionally referred to as aggregation. Two kinds of aggregate operators, namely, scalar aggregates and aggregate functions, exist. Both are important for most real-world applications, and are thus being supported by traditional languages like SQL or QUEL. This paper presents a framework for handling these two types of aggregates in the context of imprecise information. We consider three cases, specifically, aggregates within vague queries on precise data, aggregates within precisely specified queries on possibilistic data, and aggregates within vague queries on imprecise data. These extensions are based on fuzzy set-theoretical concepts such as the extension principle, the sigma-count operation, and the possibilistic expected value. The consistency and completeness of the proposed operations is shown
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
definition level. FSM authorizes an entity to be partially member
of its class according to a given degree of membership that reflects
the level to which the entity verifies the extent properties of this
class. This paper deals with the conceptual design of FSM and
adresses some implementation issues.ou
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
first 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
Spatial database implementation of fuzzy region connection calculus for analysing the relationship of diseases
Analyzing huge amounts of spatial data plays an important role in many
emerging analysis and decision-making domains such as healthcare, urban
planning, agriculture and so on. For extracting meaningful knowledge from
geographical data, the relationships between spatial data objects need to be
analyzed. An important class of such relationships are topological relations
like the connectedness or overlap between regions. While real-world
geographical regions such as lakes or forests do not have exact boundaries and
are fuzzy, most of the existing analysis methods neglect this inherent feature
of topological relations. In this paper, we propose a method for handling the
topological relations in spatial databases based on fuzzy region connection
calculus (RCC). The proposed method is implemented in PostGIS spatial database
and evaluated in analyzing the relationship of diseases as an important
application domain. We also used our fuzzy RCC implementation for fuzzification
of the skyline operator in spatial databases. The results of the evaluation
show that our method provides a more realistic view of spatial relationships
and gives more flexibility to the data analyst to extract meaningful and
accurate results in comparison with the existing methods.Comment: ICEE201
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
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