Success Pattern Finding With Regards To Textual Content Exploration

Abstract

Various algorithms such as Object Rank and PageRank, the latter created by Larry Page and used in Google Search Engine, were highly expensive as they required a PageRank style iterative computation over the full graph. BinRank, a hybrid algorithm proposed uses an index of pre computed results for some/or all keywords being used by the user Dynamic authority based online keyword search algorithms, such as Object rank and personalized page rank leverage semantic link in formation to provide high quality, high  recall search in databases and the web Conceptually, these  algorithms require a query time  page rank style iterative computation over the full graph. This computation is too  expensive for large graphs and not feasible at query time . Alternatively, building an index of pre computed results for some  or all keywords involves very expensive processing. We introduce  BinRank,a system that approximates ObjectRank results by utilizing a hybrid approach inspired by materialize d views in traditional query processing. The issue addressed in this paper is would like to provide an approach which intends to provide an approximation to BinRank by integrating it with Hubrank and parallelize i.e execute the activities simultaneously to reduce query execution time and also increase the relevance of the results. Bin Rank system which  approximates Object Rank results by utilizing a hybrid approach inspired by materialized views in traditional query  processing.