Frequent Pattern Mining for XML Query-Answering Support
Alfiya Iqbal Ahmed Shaikh1, Sanchika Bajpai2
1Alfiya Iqbal Ahmed Shaikh, Master Student, Department of Computer Engineering, Bhivrabai Sawant Institute of Technology & Research, Pune University, Wagholi, Pune (Maharashtra), India.
2Sanchika Bajpai, Assistant Professor, Department of Computer Engineering, Bhivrabai Sawant Institute of Technology & Research, Pune University, Wagholi, Pune (Maharashtra), India.
Manuscript received on 10 July 2014 | Revised Manuscript received on 20 July 2014 | Manuscript Published on 30 July 2014 | PP: 89-92 | Volume-4 Issue-2, July 2014 | Retrieval Number: B1735074214/14©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Extracting information from semi structured documents is difficult task. It is more crucial as there is a huge amount of digital information on the Internet is growing rapidly. Sometimes, documents are often so large that the data set returned as answer to a query may be large to even convey interpretable knowledge. This paper describes an approach which takes RSS feeds as input for which Tree-Based Association Rules (TARs): mined rules are used. It provides more approximate and intentional information on both the structure and the contents of Extensible Markup Language (XML) documents which can then be stored in XML format as well. This generated mined knowledge is later used to provide: 1) The gist of the structure and the content of the XML document and 2) Quick and more approximate answers to queries. This paper focuses on the second feature. In this paper we show a novel approach for finding frequent patterns in XML documents.
Keywords: XML, Document, RSS, TARs, Approximate.
Scope of the Article: Patterns and Frameworks