ÐIn a fuzzy relational database where a relation is a fuzzy set of tuples and ill-known data are represented by possibility distributions, nested fuzzy queries can be expressed in the Fuzzy SQL language, as defined in [25], [23]. Although it provides a very convenient way for users to express complex queries, a nested fuzzy query may be very inefficient to process with the naive evaluation meth...

Numerous biological databases such as Microbial Genome Database for Comparative Analysis (MBGD), Microbial Identification and Typing Database (HPA) and National Microbiological Database (NMD) have been developed in recent times which triggered impacts in the different research fields of biological sciences. It has been observed that due to adaptability, evolution, and emergence, the biological ...

Information imprecision and uncertainty exist in many real-world applications and for this reason fuzzy data modeling has been extensively investigated in various data models. Currently, huge amounts of electronic data are available on the Internet, and XML has been the de facto standard of information representation and exchange over the Web. This paper focuses on fuzzy XML data modeling, whic...

This paper proposes a general discussion of the handling of imprecise and uncertain information in temporal reasoning in the framework of fuzzy sets and possibility theory. The introduction of fuzzy features in temporal reasoning can be related to different issues. First, it can be motivated by the need of a gradual, linguistic-like description of temporal relations even in the face of complete...

This paper introduces new algorithms (Fuzzy relative of the CLARANS algorithm FCLARANS and Fuzzy c Medoids based on randomized search FCMRANS) for fuzzy clustering of relational data. Unlike existing fuzzy c-medoids algorithm (FCMdd) in which the within cluster dissimilarity of each cluster is minimized in each iteration by recomputing new medoids given current memberships, FCLARANS minimizes t...

The fuzzy logic theory proposed by Zadeh (1965) is based on intuitive reasoning and takes into account human subjectivity and imprecision. Unlike statistical data mining techniques such as cluster or regression analysis, fuzzy logic enables the use of nonnumerical values and introduces the notion of linguistic variables (Zadeh, 1975a, 1975b, 1975c). Using linguistic terms and variables hides Ab...

In this paper we deal with the problem of data summarization through the concept of fuzzy dependency. We introduce a projection operator which leads us to partition a database into two projections with a less amount of information. Thus, we can replace the original relation by those projections. In this process we must guarantee that we can recover the original relation, through the projections...