Efficient Query Processing For Imprecise Data

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.