Database Reference
In-Depth Information
quantifi ers (Galindo, 1999; Galindo et al., 2001a; Yager, 1983; Zadeh, 1983) and the fuzzy
(min,max) notation.
These fuzzy extensions have a novel meaning and offer great expressiveness to the
conceptual model. However, we think that FuzzyEER can be extended even more. Besides,
we must study possible problems and improvements in the implementation of the resulting
model.
An interesting study to facilitate the task of using fuzzy quantifi ers on the part of
designers would be to classify the quantifi ers which can be used in natural language, and
study the relationship between them.
The next step will be the automatic implementation of the model, including the neces-
sary triggers to activate the fuzzy constraints described, and the study of different tools to
facilitate the query of stored data, especially with regard to the fuzzy belonging of a super-
class to different subclasses. For this last objective we can use and extend the fuzzy query
language FSQL (FuzzySQL), an extension of the popular SQL which allows dealing with
imprecise data (Galindo et al., 1998; Galindo, 1999). We are now studying how subclasses
can inherit properties of their superclasses with such fuzzy extensions.
Another research line is to achieve notational constructs to allow a greater selection
of other fuzzy integrity constraints; for example, relaxing the constraints proposed in Davis
et al. (1989).
Anther target is the modeling of a real application for a real estate agency, using all
these ideas and some new ones. We started with the defi nition presented in Galindo et al.
(1999) and one fi rst approach is in Urrutia et al. (2002) and Urrutia (2003). Another research
line was published in Aranda et al. (2002).
REFERENCES
Aranda, M.C., Galindo, J., & Urrutia, A. (2002). Museos digitales en Internet: Modelo EER
difuso y recuperación de imágenes basada en contenido. IV
Turismo y tecnologías de
í
la información y las comunicaciones (TuriTec'2002)
(pp. 411-425). Málaga (Spain),
ISBN: 84-600-9813-3.
Chaudhry, N., Moyne, J., & Rundensteiner, E.A. (1994). A design methodology for data-
bases with uncertain data.
7th International Working Conference on Scientifi c and
Statistical Database Management
(pp. 32-41). Charlottesville, VA. Available: www.
Statistical Database Management
(pp. 32-41). Charlottesville, VA. Available: www.
mitexsolutions.com
Chaudhry, N., Moyne, J., & Rundensteiner, E.A. (1999). An extended database design
methodology for uncertain data management.
Information Sciences, 121
, 83-112.
Chen, G.Q., & Kerre, E.E. (1998). Extending ER/EER concepts towards fuzzy conceptual
data modeling.
IEEE International Conference on Fuzzy Systems, 2,
1320-1325.
Chen, P. (1976).
The Entity-Relationship Model-Toward a unifi ed view of data. ACM Trans-
actions on Database Systems (TODS), 1
(1), 9-36.
Connolly, T., Begg, C., & Strachon, A. (2001).
Data bases system, a practical approach to
design, implementation and management
(2nd Edition). Addison Wesley.
design, implementation and management
Davis, J.P., & Bonnell, R.D. (1989). Modeling semantic constraints with logic in the EARL data
model.
Proc. Fifth International Conference on Data Engineering
(pp. 226-233).
design, implementation and management
(2nd Edition). Addison Wesley.
Proc. Fifth International Conference on Data Engineering
De Luca A., & Termini S. (1974). Entropy of L-Fuzzy Sets.
Information and Control, 24
,
55-73.
Proc. Fifth International Conference on Data Engineering
(pp. 226-233).
