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Search queries on databases, such as Cloud Bigtable(Cloud Bigtable) is a openly accessible form of Bigtable used by Google system) frequently give back countless, just a little subset of which is significant to the client. Ranking and categorization, which can likewise be consolidated, have been proposed to reduce this data over-burden issue. Results order and most applicable data recovery for a given hunt question is the focus of this work. A natural way to organize citations is according to their concept hierarchies. In this framework, we present a dynamic navigation of queries used with Improved Distance Rank Algorithm. The dynamic navigation provides less query results compared with static navigation used by Google. Distance Rank algorithm depends on fortification learning with the distance between pages as punishment and it minimizes the distance values of pages and provides better results compared to the familiar Page Rank algorithm. This algorithm models a real user surfing the web. When users randomly browses the web, they selects the next pages based their background from the last pages and the current status of the web page. By combining these two algorithms we will provide more efficient results to the given search query.

<span>A Secure and Effective Multi-keyword Ranked Search Scheme on Encrypted Cloud Data. Cloud computing is providing people a very good knowledge on all the popular and relevant domains which they need in their daily life. For this, all the people who act as Data Owners must possess some knowledge on Cloud should be provided with more information so that it will help them to make the cloud maintenance and administration easy. And most important concern these days is privacy. Some sensitive data exposed in the cloud these days have security issues. So, sensitive information ought to be encrypted earlier before making the data externalized for confidentiality, which makes some keyword-based information retrieval methods outdated. But this has some other problems like the usage of this information becomes difficult and also all the ancient algorithms developed for performing search on these data are not so efficient now because of the encryption done to help data from breaches. In this project, we try to investigate the multi- keyword top-k search problem for encryption against privacy breaks and to establish an economical and secure resolution to the present drawback. we have a tendency to construct a special tree-based index structure and style a random traversal formula, which makes even identical question to supply totally different visiting ways on the index, and may additionally maintain the accuracy of queries unchanged below stronger privacy. For this purpose, we take the help of vector area models and TFIDF. The KNN set of rules are used to develop this approach.</span>

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