By Topic

Predictive modeling in XML compression

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
$33 $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

2 Author(s)
Luoma, Olli ; Department of Information Technology and Turku Centre for Computer Science 20014 University of Turku, FINLAND ; Teuhola, J.

Since its advent, the Extensible Markup Language (XML) has gained tremendous popularity in many different application areas. However, XML data is generally very verbose and redundant, and thus it requires a lot of disk space to store and bandwidth to transfer. To overcome this problem, many methods for compressing XML documents have been proposed. In general, data compression requires a model which is used to predict the next symbol in the data. In this paper, we compare different models suitable for XML compression. We also present a novel modeling method and measure the information content in a set of XML documents using different modeling methods.

Published in:

Digital Information Management, 2007. ICDIM '07. 2nd International Conference on  (Volume:2 )

Date of Conference:

28-31 Oct. 2007