Discovery of Knowledge from Query Groups
Now a day’s web usage is increasing and most of the queries submitted to server are informational rather than navigational. Users access complicated informational and task-oriented goals like arranging for travelling, managing banking transactions, or planning their decisions on buying new products. However, the primary option for accessing data on-line continues to be through keyword search. A complex task such as managing bank transactions should be broken down into a number of subtasks (queries) over a period of time. As an example, a user may first search on target accounts branch names, etc. So there is need for maintaining user search history which incorporates a sequence of four queries displayed in reverse timely order together with their corresponding U.R.L clicks. This paper explains the how to maintain user search history and missing knowledge from query group. This paper uses one pass algorithm in order to generate knowledge from user search groups.
KeywordsQuery groups Sliding window model knowledge discovery
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