By Topic

Searching XML data by SLCA on a MapReduce cluster

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Mengjie Zhou ; Shanghai Key Lab. of Trustworthy Comput., East China Normal Univ., Shanghai, China ; Haoji Hu ; Minqi Zhou

XML keyword search is a popular topic in research field, and the Smallest Lowest Common Ancestor (SLCA) concept is fundamental for XML keyword search algorithms. With the rapid growth of XML data in internet, we are confronted with big data issues, it's becoming a new research direction for managing massive XML data now. Conventional centralized data management technologies are limited in the aspects of efficiency, throughout and maintenance cost. MapReduce framework is a recent trend to process large-scale data. It is implemented on clusters built by numbers of business machines, to conquer limitations mentioned above by parallel computation. In this paper, we provide a SLCA-based keyword search implementation for large-scale XML data sets on a MapReduce cluster. Main steps of our implementation include XML data partition, parse and sort, index setup and SLCA computation. We conduct some experiments to evaluate the effectiveness of the proposed method.

Published in:

Universal Communication Symposium (IUCS), 2010 4th International

Date of Conference:

18-19 Oct. 2010