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Frequent XML Query Caching in ebXML based E-commerce Applications using Association Rule Mining

Anju Vijayan, V. Uma, G. Aghila Published in Information Sciences

International Journal of Applied Information Systems
Year of Publication: 2013
© 2012 by IJAIS Journal
10.5120/ijais13-450924
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  1. Anju Vijayan, V Uma and G Aghila. Article: Frequent XML Query Caching in ebXML based E-commerce Applications using Association Rule Mining. International Journal of Applied Information Systems 5(6):8-11, April 2013. BibTeX

    @article{key:article,
    	author = "Anju Vijayan and V. Uma and G. Aghila",
    	title = "Article: Frequent XML Query Caching in ebXML based E-commerce Applications using Association Rule Mining",
    	journal = "International Journal of Applied Information Systems",
    	year = 2013,
    	volume = 5,
    	number = 6,
    	pages = "8-11",
    	month = "April",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

Abstract

Mining frequent XML query patterns and caching the results can improve the performance of XML based systems. Temporal features of queries can be used to guide cache replacement. Many approaches have been proposed to mine frequent XML query patterns and caching the results. Those approaches mine frequent subquery patterns from historical queries. But for ebXML (electronic business using XML) based applications in e-commerce, most of the queries will be of same structure. In such cases instead of mining subqueries, frequent queries can be mined directly. In this work an efficient system for frequent XML query caching in ebXML based e-commerce applications is proposed which incorporates temporal features of frequent queries for cache replacement. Association rules are mined between frequent queries to discover temporal patterns among them. Semantically closer infrequent queries are clustered and their associations with frequent queries are also mined. These rules are used to guide cache replacement so that subsequent query results will not get replaced from cache.

Reference

  1. T. Chang, S. Chen, Frequent Xml Query pattern mining for ebXML applications in Ecommerce, Expert Systems with Applications, An International journal archive, Volume 39 Issue 2, February 2012, Pages 2183-2193.
  2. L. Chen, S. S. Bhowmick, L. T. Chia, Mining Positive and Negative Association Rules from XML Query Patterns for Caching, Proceedings of the 10th international conference on Database Systems, 2005, Pages 736-747.
  3. G. Li, J. Feng, J. Wang, Y. Zang, L. Zhou, Incremental pattern mining from xml queries for caching, Data Mining, Sixth International Conference on Computing & Processing Hardware /Software, 2006, Pages 350-361.
  4. L. H. Yang, M. L. Li, W. Hsu, Efficient mining of XML query patterns for caching, Proceedings of the 29th international conference on Very large data bases, 2003, Pages 69-80.
  5. C. Hua, Frequent Query Patterns Guided XML Caching and Materialization, Wireless Communications Networking and Mobile Computing, International Conference on Communication Networking Broadcasting, 2007, Pages 3673-3676.
  6. Y. Bei, G. Chen, L. Shou, X. Li, J. Dong , Bottom up discovery of frequent rooted unordered subtrees, Information sciences, Volume 179, Issues 1-2, 2 January 2009, Pages 70-88.
  7. X. Wu, C. Zhang, S. Zhang, Mining both positive and negative association rules. In Proc. of ICML, 2002, Pages 381-405.
  8. Jin, Z. Yong, Ye, S. Ping, ebXML compatible agent communication language, Proceedings of international conference on computational and information sciences, 2011, Pages 361-365.
  9. ebXML. Available from: http://www. ebxml. org/

Keywords

requent XML query mining, Frequent XML query caching, ebXML