Map-Reduced Model for Topic Sensitive Page Ranking

Author(s):

Dupinder Kaur , Chaudhary Devi Lal University, Sirsa(HR)

Keywords:

Crawler, Fetching, Map-Reduce, Pagerank, Trustrank

Abstract:

Search engines are effectively used today to find the content on web as well as it can rank the fetched page(s) in a useful manner. When a user search for a content in the search engine, the search engine fetches the web pages from the database. Most search engines work by crawling the web pages and then they build an inverted index by listing all the words or other strings found in which page. When a search query is fired, the terms are searched in the inverted index and all the web pages which contains the search term. A TrustRank is calculated based on several factors .After fetching the web pages and calculating the TrustRank, it is matched with the Pageranking value the overall rank of a web page and the fetched pages are sorted and displayed according to their ranks. Hence Page-ranking plays an important role in case of a search engine. If we analyze the web a little; we can observe that it forms a graph where each node represents a web page and each edge represents one hyper link. More specially we can consider this graph to be directed. Considering these factors a map-reduce model is developed and which can be easily implemented in any Hadoop like environment. Map-Reduce Model has the advantages of parallel processing in a cluster and sparseness of the web matrix. In this paper, Topic sensitive PageRank is studied with map-reduce model and a comparison is made with iterative model.

Other Details:

Manuscript Id	:	IJSTEV3I3071
Published in	:	Volume : 3, Issue : 3
Publication Date	:	01/10/2016
Page(s)	:	138-141

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