A Machine Learning Approach to Rapid Development of XML Mapping Queries

Authors: 
Morishima, A.; Kitagawa, H.; Matsumoto, A.
Author: 
Morishima, A
Kitagawa, H
Matsumoto, A
Year: 
2004
Venue: 
ICDE 2004
URL: 
http://csdl.computer.org/dl/proceedings/icde/2004/2065/00/20650276.pdf
Citations: 
13
Citations range: 
10 - 49
AttachmentSize
Morishima2004AMachineLearningApproachto.pdf276.34 KB

This paper presents XLearner, a novel tool that helps
the rapid development of XML mapping queries written
in XQuery. XLearner is novel in that it learns XQuery
queries consistent with given examples (fragments) of intended
query results. XLearner combines known learning
techniques, incorporates mechanisms to cope with issues
specific to the XQuery learning context, and provides a systematic
way for the semi-automatic development of queries.
This paper describes the XLearner system. It presents algorithms
for learning various classes of XQuery, shows that
a minor extension gives the system a practical expressive
power, and reports experimental results to demonstrate how
XLearner outputs reasonably complicated queries with only
a small number of interactions with the user.