Geo-Search Engine based on Map/Reduce

Geo-location is becoming increasingly important in web search. Search engines can often return better results to users by analyzing features such as user location or geographic terms and specific places in web pages and user queries. Diversified needs of Internet users and the enormous development in the global network, using search engines is an undeniable necessity. In addition, due to the large volume of data on the Web, data mining operations, indexing and the query, the search engines takes a lot of overhead. One of the methods to increase the efficiency of search engines, limit queries to files and pages related to a specific geographic area. Despite significant improvements in the efficiency of the search engines place high -volume data processing in search engines requires a distributed model is scalable. Moreover using distributed systems can improve the performance of Search Engine. One of the distributed storage, processing methods and programming model is a map/Reduce than could be implemented on Apache and Hadoop platform. In this paper we provided a method, by combining geo-search engine and map/Reduce offer a new architectural model for search engine that improve the performance of search engine in a great way.

Keywords:

Map , Reduce , Hadoop , World Wide Web , geo-search phrases , Geo-location , Geo-Search Engine based on Map , Reduce