In this paper we attempt to make a theoretical comparison between fuzzy sets and vague sets in processing uncertain queries. We have designed an architecture to process uncertain i.e. fuzzy or vague queries. In the architecture we have presented an algorithm to find the membership value that generates the fuzzy or vague representation of the attributes with respect to the given uncertain query. Next, a similarity measure is used to get each tuples similarity value with the uncertain query for both fuzzy and vague sets. Finally, a decision maker will supply a threshold or α-cut value based on which a corresponding SQL statement is generated for the given uncertain query. This SQL retrieves different result sets from the database for fuzzy or vague data. It has been shown with examples that vague sets give more accurate result in comparison with fuzzy sets for any uncertain query.

In order to model the real world with imprecise and uncertain information, various extensions of the classical relational data model have been studied in literature using fuzzy set theory. However, vague set, as a generalized fuzzy set, has more powerful ability to process fuzzy information than fuzzy set. In this paper, we have proposed a vague relational database model and have defined a new kind of vague functional dependency (called  -vfd) based on the notion of  -equality of tuples and the idea of similarity measure of vague sets. Next, we present a set of sound vague inference rules which are similar to Armstrong"s axioms for the classical case. Finally, partial  -vfd and vague key have been studied with the new notion of  -vfd and also tested with examples. General TermsVague Database Design KeywordsVague set, similarity measure of vague sets,  -vfd, partial  -vfd, vague key.

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Precancer atlases have the potential to revolutionize how we think about the topographic and morphologic structures of precancerous lesions in relation to cellular, molecular, genetic, and pathophysiologic states. This mini review uses the Human Tumor Atlas Network (HTAN), established by the National Cancer Institute (NCI), to illustrate the construction of cellular and molecular three-dimensional atlases of human cancers as they evolve from precancerous lesions to advanced disease. We describe the collaborative nature of the network and the research to determine how and when premalignant lesions progress to invasive cancer, regress or obtain a state of equilibrium. We have attempted to highlight progress made by HTAN in building precancer atlases and discuss possible future directions. It is hoped that the lessons from our experience with HTAN will help other investigators engaged in the construction of precancer atlases to crystallize their thoughts on logistics, rationale, and implementation.