Mining Linked data for Search Optimization

Abstract

Searching information in internet is a natural activity in current situation. People are searching in web for all sorts of information, what they need in their daily life. The information in the web is spread among lots of data. Some information‟s are and frequently searched by the user. The searched information of different users may be matched. Frequent data indicates that, those data might be related, which are searched in a particular time. Those related (raw linked data) might be used in future to give some interesting information or to optimize the search of a user. Searching data in broad (web) area is quite difficult process to retrieve information. Searching in the web is also matter of cost and need good throughput if the traditional search engines are followed. In short area of data searching, it is quite effective to fetch specific information. It is a challenging issue to reduce the search area and categorize the reduced data in such a way that the important data is not lost. In this project we have used Apriori algorithm to mine a huge set of data to generate some optimized linked data which decreases the search area of search engine and also gives an optimized result to the users who are searching the information. We have proposed a model for the implementation with a tool and also demonstrated how the distributed computing can be used to optimize huge data if required.