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In this paper, an ontology system is proposed to represent the knowledge structure enabling fuzzy information to be stored in fuzzy databases. This proposal allows users or applications to simplify the metadata definition process that is necessary for representing and managing imprecise and classic information in these databases. This ontology then acts as an interface that formalizes the representation of such structures and allows access to them. The instances obtained from this ontology represent the schemas that describe domain information in a database. The description of fuzzy and classic database schemas allows access to online public databases for which no other semantic description is associated. This paper also presents another ontology to represent these schemas as instances. Not only does this ontology allow fuzzy data values to be stored (because of the definition of fuzzy data types as classes of the ontology) but it also enables schema tables and attributes to be defined. C 2008 Wiley Periodicals, Inc.

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OLAP Vs. OLTP in the middle tier.

In this paper we deal with the problem of mining for approximate dependencies (AD) in relational databases. We introduce a definition of AD based on the concept of association rule, by means of suitable definitions of the concepts of item and transaction. This definition allow us to measure both the accuracy and support of an AD. We provide an interpretation of the new measures based on the complexity of the theory (set of rules) that describes the dependence, and we employ this interpretation to compare the new measures with existing ones. A methodology to adapt existing association rule mining algorithms to the task of discovering ADs is introduced. The adapted algorithms obtain the set of ADs that hold in a relation with accuracy and support greater than user-defined thresholds. The experiments we have performed show that our approach performs reasonably well over large databases with real-world data.

This work presents an overview of the text mining area, considering the most common techniques, and including proposals based on the application of fuzzy sets. Besides, some of the most frequent text mining applications are mentioned. We discuss the existing approaches, which we call text data mining, in relation to the recently proposed paradigm of text knowledge mining, and we conclude that both are different and complementary, in the sense that they are able to extract different knowledge pieces from text by using different reasoning mechanisms. Future challenges related to text knowledge mining are also briefly outlined.