Uncertain query processing using vague sets: A better approach

In the real world, vaguely specified data values appear in many applications such as sensor information, expert systems, decision analysis, medical sciences, management and engineering problems and so on. Fuzzy set theory has been proposed to handle such vagueness by generalizing the notion of membership in a set. A vague set, which is conceived as a further generalization of fuzzy set, uses the idea of interval-based membership instead of point-based membership as in the case of fuzzy sets. Such interval-based membership in vague sets is more expressive in capturing vagueness of data. 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 queries and 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. The proposed architecture has been verified with a real life example which shows that vague sets give more accurate result in comparison to fuzzy sets for any uncertain query. Thus a vague relational database model may be more fruitful in processing real life data and queries than the conventional fuzzy data models.