Efficient Schema Extraction from a Collection of XML Documents

<p>The eXtensible Markup Language (XML) has become the standard format for data exchange on the Internet, providing interoperability between different business applications. Such wide use results in large volumes of heterogeneous XML data, i.e., XML documents conforming to different schemas. Although schemas are important in many business applications, they are often missing in XML documents. In this thesis, we present a suite of algorithms that are effective in extracting schema information from a large collection of XML documents. We propose using the cost of NFA simulation to compute the Minimum Length Description to rank the inferred schema. We also studied using frequencies of the sample inputs to improve the precision of the schema extraction. Furthermore, we propose an evaluation framework to quantify the quality of the extracted schema. Experimental studies are conducted on various data sets to demonstrate the efficiency and efficacy of our approach.</p>

Name thesis:degree_name

Master of Science

Discipline thesis:degree_discipline

Department of Mathematics and Computer Science

Year

2011

Author dc:creator

• Parthepan, Vijayeandra

Contributors dc:contributor

• Dr. Guangming Xing (Direcotor), Dr. Qi Li, Dr. Zhonghang Xia

• document mark up language
• data mining
• eXtensible Markup Language
• Databases and Information Systems
• Programming Languages and Compilers

Repository record dc:identifier

https://digitalcommons.wku.edu/theses/1061

OAI identifier oai:identifier

oai:digitalcommons.wku.edu:theses-2064