Uncertain Query Processing using Vague Set or Fuzzy Set: Which One Is Better?

Abstract

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.

Inconsistent data processing using vague set and neutrosophic set for justifying better outcome

“…We are using the similarity measure formula [13] for two vague data to find the similarity value between two vague data.…”

“…In the present work, we have attempted to use two different similarity measure formulas which are worked upon vague and neutrosophic data to perform uncertain queries. Several authors have used fuzzy or vague set [9,10,11,12,13] to execute imprecise queries but no such work has been reported literature using neutrosophic set. In this paper, our objectives are to execute a query using vague and neutrosophic data and both data are uncertain in nature.…”

An Ideal Technique for Decision-Making Problems for Uncertain Data and Its Application In Medical Science

“…As uncertainty and indeterminacy is present in the real world situations there is a need to explore efficient decision making method for multiple criteria options. Such situations can be easily understood by Neutrosophic logic In various real problems the fuzzy sets, vague sets and rough sets( [4], [5], [6]) are applied on incomplete and uncertain information. The neutrosophic logic is basically the based on concept of different sets i.e classic, intuitionistic, fuzzy [7] and interval valued sets ( [8], [9]).…”

To Handle Uncertain Data for Medical Diagnosis Purpose Using Neutrosophic Set

“…Some cases it is very difficult to solve using fuzzy logic [1,2,3]. The vague set [5,6,7] is handled the uncertain data using truth and false values. Fuzzy and vague both data cannot be represented as three membership value, so they are unable to process uncertain data which is consists of true, indeterminacy and false, these three values.…”