Call for Paper - March 2023 Edition
IJCA solicits original research papers for the March 2023 Edition. Last date of manuscript submission is February 20, 2023. Read More

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

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

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

	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}


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.


  1. S. K. Pani Et El(2011), Web Usage Mining: A Survey On Pattern Extraction From Web Logs, Web Usage Mining: A Survey On Pattern Extraction From Web Logs, International Journal Of Instrumentation, Control & Automation (Ijica), Volume 1, Issue 1,
  2. Mehak(2013), Web Usage Mining: An Analysis, Journal Of Emerging Technologies In Web Intelligence, Vol. 5, No. 3,
  3. Govind Murari(2013), Web Content Mining: Its Techniques and Uses, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 11
  4. Priyanka Verma(2014), Web Usage Mining Framework For Data Cleaning And Ip Address Identification, International Journal Of Advanced Studies In Computer Science And Engineering Ijascse, Volume 3, Issue 8
  5. Shiva Asadianfam And Masoud Mohammadi(2014), Identify Navigational Patterns Of Web Users, International Journal Of Computer-Aided Technologies (Ijcax) Vol.1,No.1,
  6. Dr. Girish S. Katkar(2014), Use Of Log Data For Predictive Analytics Through Data Mining, Current Trends In Technology And Science Volume: 3, Issue: 3
  7. Pooja Kherwa (2015). Data Preprocessing: A Milestone Of Web Usage Mining , International Journal Of Engineering Science And Innovative Technology (Ijesit) Volume 4, Issue 2.
  8. Shivaprasad G.(2015), Knowledge Discovery From Web Usage Data: An Efficient Implementation Of Web Log Preprocessing Techniques, International Journal Of Computer Applications (0975 – 8887) Volume 111 – No 13
  9. V. Bharanipriya & V. Kamakshi Prasad WEB CONTENT MINING TOOLS: A COMPARATIVE STUDY.
  10. M.Santhanakumar(2015), Web Usage Based Analysis Of Web Pages Using Rapidminer, Wseas Transactions On Computers, Volume 14.
  11. Chandana S. Khatavkar(2015), A Hybrid Approach For Clustering Weblog(2015). International Journal Of Advanced Research In Computer Science And Software Engineering, Volume 5,Issue3.
  12. Liu Y., Miao J., Zhang M., Ma S. and Ru L., “How do users describe their information need: Query recommendation based on snippet click model,” Expert Systems with Applications, vol. 38, no. 11, pp. 13847-13856,2011.
  13. Baeza-Yates R., Hurtado C. and Mendoza M., "Query recommendation using query logs in search engines,” In Current Trends in Database Technology-EDBT 2004 Workshops, pp. 588-596, 2005.
  14. Dupret G. and Mendoza M., “Recommending better queries based on click-through data,” In Proceedings of the 12th International Symposium on String Processing and Information Retrieval, pp. 41-44, 2005.
  15. Dupret G. and Mendoza M., “Automatic query recommendation using click-through data,” In Professional Practice in Artificial Intelligence, pp. 303-312,2006.
  16. Burke R., “Hybrid web recommender systems,” In The adaptive web, Springer Berlin Heidelberg, pp. 377-408, 2007
  17. Li L., Yang Z., Liu L. and Kitsuregawa M., “Query-URL Bipartite Based Approach to Personalized Query Recommendation,” In AAAI, vol. 8, pp. 1189-1194, 2008.
  18. Ma H., Yang H., King I. and Lyu M. R, “Learning latent semantic relations from clickthrough data for query suggestion,” In Proceedings of the 17th ACM conference on Information and knowledge management, pp.709-718,2008.


Log Files, Web Mining, Query Recommendation Techniques.