Context Based XML Data and Diversification for Keyword Search Queries
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Abstract
In searching process user enter particular candidate searching keyword and with the help of searching algorithm respective searching query is executed on targeted dataset and result is return as an output of that algorithm. In this case it is expected that meaningful keyword has to be entered by user to get appropriate result set. In case of confusing bunch of keywords or ambiguity in it or short and indistinctness in it causes an irrelevant searching result. Also searching algorithms works on exact result fetching which can be irrelevant in case problem in input query and keyword. This problem statement is focused in this system. By considering the keyword and its relevant context in XML data , searching should be done using automatically diversification process of XML keyword search. In this way system may satisfy user, as user gets the analytical result set based on context of searching keywords. For more efficiency and to deal with big data, HADOOP platform is used. baseline efficient algorithms are proposed to incrementally compute top-k qualified query candidates as the diversified search intentions. Compare selection criteria are targeted: the k selected query candidates are most relevant to the given query while they have to cover maximal number of distinct results on real and synthetic data sets demonstrates the effectiveness diversification model and the efficiency of algorithms
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