Abstract
Keyword search on XML document has received wide attention. Many search semantics and algorithms have been proposed for XML keyword queries. But the existing approaches fall short in their abilities to support keyword queries over fuzzy XML documents. To overcome this limitation, in this paper, we discuss how to obtain and evaluate top-k smallest lowest common ancestor (SLCA) results of keyword queries on fuzzy XML documents. We define the fuzzy SLCA semantics on the fuzzy XML document, and then propose a novel encoding scheme to denote different types of nodes in fuzzy XML documents. After these, we propose two efficient algorithms to find k SLCA results with highest possibilities for a given keyword query on the fuzzy XML document. First one is an algorithm which can obtain the top-k SLCA results and their possibilities based on the stack technique. The second algorithm can obtain top-k SLCA results of keyword queries based on a set of SLCA’s properties. Finally, we compare and evaluate the performances of the two algorithms.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
