158 Efficient Query Processing For Imprecise Data Jaydev Mishra Computer Science and Engineering Department, College of Engineering and Management Kolaghat, India, jsm03@cemk.ac.in Received Date : June 14, 2022 Accepted Date : July 19, 2022 Published Date : August 06, 2022 ABSTRACT In real world applications we often need to test the queries based on fuzzy data. For example, some one can specify as “find students’ whose age is around 17 years old.”; “find tall person”. “find employee with high salary”; “find country with low population” etc. This fuzziness in measurement is captured in this paper. To test such fuzzy queries, we have developed an algorithm that is applicable universally to any type of database. In this paper first we have designed architecture to test fuzzy query. In the architecture we have defined an algorithm to find the membership value for each tuple of the relation based on the fuzzy attributes on which fuzzy query is made. Next Decision Maker (DM) will supply a threshold value or  -cut based on which corresponding SQL of the given fuzzy query will be generated. This SQL will retrieve the resultant tuples from the database. Finally we have tested our algorithm with an example. Key words: fuzzy data, fuzzy-equality,  -tolerance value 1. INTRODUCTION Data is often partially known i.e., fuzzy in many real world applications. Fuzziness is introduced in the classical model to deal with such imprecise information and several extensions of the model are available in literature [1]-[4]. When data is of fuzzy nature, the concept of classical database model has been extended by using the concept of fuzzy logic and is defined as fuzzy database model. Query processing for imprecise data plays a crucial role in fuzzy database model. Since for a human being the main communication is natural language uses many fuzzy, ambiguity and vague information etc. For example, some one can specify as “find all rounder for cricket team”; “find patient with high sugar level”; etc. To handle such vague information several authors have been developed the theoretical foundation for the fuzzy query language of fuzzy database [5]-[8]. While developing the theory on fuzzy query processing several authors have defined different membership functions based on different fuzzy attributes for testing fuzzy equality of numbers [4], [7], [8]. In this work while processing uncertain query from a relation we have proposed a unique algorithm to find closeness value of each tuple of the relation with respect to any fuzzy attribute data given in the uncertain query. After obtaining closeness membership value for each tuple, decision maker provide  -cut value to obtain desire output of the given uncertain query. The paper is organized as follows: In section 2, we have revisited some basic definitions of fuzzy set theory and defined  -equality of two fuzzy numbers. In section 3, we have described the architecture for testing fuzzy query also in this section we have designed an algorithm to find closeness membership value of two fuzzy numbers. In section 4 we have tested the proposed architecture with two real life examples. Finally, the concluding remarks have been given in section 5. 2. BASIC DEFINITIONS In this section, we first review some basic definitions from fuzzy set theory that will be useful throughout the paper and then define fuzzy equality. 2.1 Basic Preliminaries on Fuzzy Set Theory Let U = {u1,u2,...,un} be a universe of discourse. 2.1.1 Fuzzy Set A fuzzy set A in the universe of discourse U is characterized by the membership function A  given by A  : U → [0, 1] and A is defined as the set of ordered pairs A = {(u, A  (u)): u U}, where A  (u) is the grade of membership of element u in the set A. 2.1.2 Fuzzy Union If A and B are two fuzzy sets of the universe U, then the fuzzy union of A and B is denoted by A  fuzzy B and is defined as A  fuzzy B ={(x, max{ A  (x), B  (x)}): x U}. 2.1.3 Fuzzy Intersection If A and B are two fuzzy sets of the universe U, then the fuzzy intersection of A and B is denoted by A  fuzzy B and is defined as A  fuzzy B ={(x, min{ A  (x), ISSN 2278-3091 Volume 11, No.4, July – August 2022 International Journal of Advanced Trends in Computer Science and Engineering Available Online at http://www.warse.org/IJATCSE/static/pdf/file/ijatcse031142022.pdf https://doi.org/10.30534/ijatcse/2022/031142022