Parallelization of pagerank and hits algorithm

Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text and hyperlink database of at least 24 million pages is available at http:

Parallelization of pagerank and hits algorithm

The Anatomy of a Search Engine

Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text and hyperlink database of at least 24 million pages is available at http: Search engines index tens to hundreds of millions of web pages involving a comparable number of distinct terms.

They answer tens of millions of queries every day. Despite the importance of large-scale search engines on the web, very little academic research has been done on them.

Furthermore, due to rapid advance in technology and web proliferation, creating a web search engine today is very different from three years ago. This paper provides an in-depth description of our large-scale web search engine -- the first such detailed public description we know of to date.

Apart from the problems of scaling traditional search techniques to data of this magnitude, there are new technical challenges involved with using the additional information present in hypertext to produce better search results.

This paper addresses this question of how to build a practical large-scale system which can exploit the additional information present in hypertext.

Parallelization of pagerank and hits algorithm

Also we look at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want. There are two versions of this paper -- a longer full version and a shorter printed version.

The web creates new challenges for information retrieval. The amount of information on the web is growing rapidly, as well as the number of new users inexperienced in the art of web research.

People are likely to surf the web using its link graph, often starting with high quality human maintained indices such as Yahoo! Human maintained lists cover popular topics effectively but are subjective, expensive to build and maintain, slow to improve, and cannot cover all esoteric topics.

Automated search engines that rely on keyword matching usually return too many low quality matches. To make matters worse, some advertisers attempt to gain people's attention by taking measures meant to mislead automated search engines. We have built a large-scale search engine which addresses many of the problems of existing systems.

It makes especially heavy use of the additional structure present in hypertext to provide much higher quality search results.

We chose our system name, Google, because it is a common spelling of googol, or and fits well with our goal of building very large-scale search engines. As of November,the top search engines claim to index from 2 million WebCrawler to million web documents from Search Engine Watch.

It is foreseeable that by the yeara comprehensive index of the Web will contain over a billion documents.

Parallelization of pagerank and hits algorithm

At the same time, the number of queries search engines handle has grown incredibly too. In NovemberAltavista claimed it handled roughly 20 million queries per day. With the increasing number of users on the web, and automated systems which query search engines, it is likely that top search engines will handle hundreds of millions of queries per day by the year In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext.

Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text. The Anatomy of a Large-Scale Hypertextual Web Search Engine Sergey Brin and Lawrence Page {sergey, page}@rutadeltambor.com Computer Science Department, Stanford University, Stanford, CA