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Adaptive Query Recommendation Techniques for Log Files Mining to Analysis User’s Session Pattern

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Durga Choudhary, Subhash Chandra Jat, Pankaj Kumar Sharma
10.5120/ijca2016908085

Durga Choudhary, Subhash Chandra Jat and Pankaj Kumar Sharma. Article: Adaptive Query Recommendation Techniques for Log Files Mining to Analysis User’s Session Pattern. International Journal of Computer Applications 133(17):22-27, January 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Durga Choudhary and Subhash Chandra Jat and Pankaj Kumar Sharma},
	title = {Article: Adaptive Query Recommendation Techniques for Log Files Mining to Analysis User’s Session Pattern},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {133},
	number = {17},
	pages = {22-27},
	month = {January},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

System log files are very important part of any web application. System log files serves as the purpose of directory in various aspect of knowledge mining. There is a wide variety of logs to stock knowledge about the search patterns of the users. There might be lots of formats of availability of logs, each of web application can develop format of its own logs. Generally, IP, date and time of the request, result for the request (with code), transaction size, protocol, request description, browser and operating system used by the user are some of the important attributes of every request that get into the record of the log file. This paper presents the user’s behavioral search pattern by the query log files.

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Keywords

Log Files, Web Mining, Query Recommendation Techniques.