Volume : IV, Issue : XII, December - 2015

Query Recommendations System with filtration and relaxation method.

Laxmi H Patil, Manjusha Tatiya

Abstract :

Collaborative data mining is a very important task in information mining. Where user who are unknown about SQL concepts to fire a query to search a particular result face a great difficulty in data mining task. To help those users our query recommendation system is developed. This system continuously monitor the user behaviour and find the matching pattern or data from recorded SQL query log, to find out the older member with similar information need. And then this query system is able to get a recommendation based on the o obtained data by assuming that current user may find needed data. In this work we are record the active user session as well as all previous user session. From the current user session we collect fragments of query, by the help of this fragment we find out similar result from query log. At basic level SQL suggestions are selected by profiling the users past behavior and compå them with other users. First–time users, however, may not have the necessary knowledge to know where to start their exploration. Other times, users may simply overlook queries that retrieve important information. The queries are fragmented and ranked according to relevance and the relevant queries are retrieved using ‘User’s querying Behaviour’. This time we try find out the effect of query relaxation method on previously defined fragment based recommendations and also explore the sequence based approach.

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Article: Download PDF   DOI : 10.36106/ijsr  

Cite This Article:

Laxmi H Patil, Manjusha Tatiya Query Recommendations System with Filtration and Relaxation Method. International Journal of Scientific Research, Vol : 4, Issue : 12 December 2015

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