Dynamic Navigation of Query Results Based on Hash-Based Indexing Using Improved Distance PageRank Algorithm

World Wide Web as we know unlimited source of data, which contains list of Internet pages and infinite links. During last ten years, the size of Web has grown, as millions of web pages are adding to Web every day. So it is the most important source of information and more popular manner of communication. The main purpose of the Web data mining is to provide the hyperlink structure for the Internet pages. User-entered queries on most of websites, such as Cloud Bigtable (Cloud Bigtable is a publicly available version of Bigtable used by Google system), most of the time result in huge number of documents and hyperlinks, but only few results are related to user query, but they may not be present at top place. Web page ranking and classification of Web queries, used in combination, overcome the problem of non-relevant results for a given query. Web page results classification and most relevant information retrieval on educational data sets is our proposed methodology. In our methodology, we propose a solution to this problem by categorization of Web queries dynamically using hash-based indexing data structure and resulting web pages are ranked by using improved distance PageRank algorithm. Using our proposed method, we reduced most of non-relevant results and most important results based on content and number of hyperlinks as top results for given query.