Handling indefinite and maybe information in logical fuzzy relational databases

Abstract

In this article, fuzzy set theory uses an extension of the classical logical relational database model. A logical fuzzy relational database model was developed with the aim of manipulating imprecise information and adding deduction capabilities to the database system. The essence of this work is the detailed discussion on fuzzy definite, fuzzy indefinite, and fuzzy maybe information and the development of an information theoretical approach of query evaluation on the logical fuzzy relational database. We define redundancies among fuzzy tuples and the operator of their removal. A complete set of fuzzy relational operations in relational algebra and the calculus of linguistically quantified propositions are included also.

Answers to queries concerning uncertain and imprecise information in fuzzy relational databases

“…Therefore, a method in determining the matching strengths of answers to a query is required. In Hsieh et al's work, 16,22 all subtuples in Sat~t! are considered as indefinite maybe answers to the query, and they have defined matching information and extra information to evaluate the matching strength of each answer to a query.…”

“…The advantage of the logical construction of fuzzy relational databases 17,18 is to present clearly the semantics of fuzzy information. We 16,22 started by considering the logical database models by Reiter, 5 Liu and Sunderraman, 7 and Vila et al, 17 extending them using fuzzy set theory to handle fuzzy disjunctive information, and developing a new method for measuring the quality of each tuple as an answer to the fuzzy SPJ operations.…”

“…Therefore the independent extra degree can be interpreted as the minimum amount of extra information required by the database system to ensure that the tuple satisfies a query. 16,22 The independent extra degree is the distance between the I min and I w measurements. Extra degree is the amount of truth information required for the subtuplẽ t i , m r~ti !!…”

“…Therefore, the measure of matching degree between membership values of a tuple and the query is not always enough to determine which answers are the better answers. 16,22 Moreover, only considering the matching degree is also not sufficient to determine the quality of answers to fuzzy disjunctive tuples, because two tuples with similar disjunctive forms may return the same answer and the matching interpretation to a query. Measure of uncertainty 4,23 is another method for handling query evaluation with imperfect information.…”