The fuzzy relations as operators and crisp values as operands. For A Y , where A is an attribute, is a fuzzy relation, and Y is a crisp value, Y is a fuzzy number. In this chapter three types of fuzzy relations, which are “ close to ( around )”, “ at least ”, and “ at most ”.
Published in Chapter:
Probabilistic Ranking Method of XML Fuzzy Query Results
Wei Yan (Liaoning University, China)
Copyright: © 2016
|Pages: 24
DOI: 10.4018/978-1-4666-8767-7.ch007
Abstract
Fuzzy query processing for XML database systems is an important issue. Based on the fuzzy set theory, XML fuzzy query can be expressed exploiting fuzzy predicates. To deal with the ranking problem of XML fuzzy query results, this chapter proposes a novel ranking approach. Firstly, according to the workload of XML documents, this chapter speculates how much the users care about each attribute node and assign a corresponding weight to it. Then, a membership degree ranking method, which ranks the fuzzy query results according to corresponding membership degree, is presented. Furthermore, this chapter proposes the probabilistic ranking method, which improves the PIR method. The improved probabilistic ranking method considers the relevance between the nodes specified by fuzzy query and the nodes unspecified by fuzzy query. Finally, top-k ranking algorithm of XML fuzzy query results is presented. The efficiency and effectiveness of the approach are also demonstrated by experimental results.