Larry Page and Sergey Brin created Google in 1998 after developing a search engine called BackRub at Stanford. In 2000, Google introduced AdWords and their toolbar. They became AOL's search partner that year. Google's services beyond search include Gmail, Maps, Drive, and more. Their PageRank algorithm and use of anchor text helped make Google a popular search engine.
Implementing page rank algorithm using hadoop map reduceFarzan Hajian
The document describes how to implement PageRank, an algorithm for ranking the importance of web pages, using Hadoop MapReduce. PageRank is calculated iteratively by treating each web page as a "random surfer" that follows links with certain probabilities based on the page's own importance ranking. The MapReduce implementation involves multiple stages where mappers distribute PageRank values to outbound links and reducers calculate new PageRank values based on the formula. The process iterates until PageRank values converge within a set threshold.
This document provides an introduction to MapReduce and Hadoop, including an overview of computing PageRank using MapReduce. It discusses how MapReduce addresses challenges of parallel programming by hiding details of distributed systems. It also demonstrates computing PageRank on Hadoop through parallel matrix multiplication and implementing custom file formats.
The Google Pagerank algorithm - How does it work?Kundan Bhaduri
This document discusses how Google uses Markov chains and the PageRank algorithm to rank web pages. It begins by explaining Markov chains and how they can model random user behavior on the web. It then describes how Google implemented PageRank as a non-absorbing Markov chain to calculate the probability of a random user reaching any given page. The document outlines issues with applying this to the large-scale web, and proposes techniques like the power method to efficiently approximate PageRank values for the trillion-page internet graph. Finally, it provides an example of how links between related high-authority sites can increase the PageRank of a given page.
This is a academic work for developing a crawler that can classify the Web Content using SVM and Naive Bayes for Machine Learning, implemented with Elasticsearch, Crawler4J and Apache Spark.
Web crawlers, also known as robots or bots, are programs that systematically browse the internet and index websites for search engines. Crawlers follow links from seed URLs and download pages to extract new URLs to crawl. They use techniques like breadth-first crawling to efficiently discover as much of the web as possible. Crawlers must have policies to select pages, revisit sites, be polite to not overload websites, and coordinate distributed crawling. Their high-performance architecture is crucial for search engines to comprehensively index the large and constantly changing web.
Large Scale Graph Processing with Apache Giraphsscdotopen
This document summarizes a talk on large scale graph processing using Apache Giraph. It begins with an introduction of the speaker and their research interests. It then provides an overview of graphs and challenges with graph processing using Hadoop/MapReduce. It describes Google's Pregel framework for graph processing and how Apache Giraph is an open source implementation of Pregel. Example graph algorithms like PageRank and connected components are demonstrated in Giraph. Experimental results show Giraph providing a 10x performance improvement over Hadoop for PageRank. The talk concludes that many problems can be modeled as networks and solved using graph processing frameworks like Giraph.
The document discusses web crawlers, which are programs that download web pages to help search engines index websites. It explains that crawlers use strategies like breadth-first search and depth-first search to systematically crawl the web. The architecture of crawlers includes components like the URL frontier, DNS lookup, and parsing pages to extract links. Crawling policies determine which pages to download and when to revisit pages. Distributed crawling improves efficiency by using multiple coordinated crawlers.
Larry Page and Sergey Brin created Google in 1998 after developing a search engine called BackRub at Stanford. In 2000, Google introduced AdWords and their toolbar. They became AOL's search partner that year. Google's services beyond search include Gmail, Maps, Drive, and more. Their PageRank algorithm and use of anchor text helped make Google a popular search engine.
Implementing page rank algorithm using hadoop map reduceFarzan Hajian
The document describes how to implement PageRank, an algorithm for ranking the importance of web pages, using Hadoop MapReduce. PageRank is calculated iteratively by treating each web page as a "random surfer" that follows links with certain probabilities based on the page's own importance ranking. The MapReduce implementation involves multiple stages where mappers distribute PageRank values to outbound links and reducers calculate new PageRank values based on the formula. The process iterates until PageRank values converge within a set threshold.
This document provides an introduction to MapReduce and Hadoop, including an overview of computing PageRank using MapReduce. It discusses how MapReduce addresses challenges of parallel programming by hiding details of distributed systems. It also demonstrates computing PageRank on Hadoop through parallel matrix multiplication and implementing custom file formats.
The Google Pagerank algorithm - How does it work?Kundan Bhaduri
This document discusses how Google uses Markov chains and the PageRank algorithm to rank web pages. It begins by explaining Markov chains and how they can model random user behavior on the web. It then describes how Google implemented PageRank as a non-absorbing Markov chain to calculate the probability of a random user reaching any given page. The document outlines issues with applying this to the large-scale web, and proposes techniques like the power method to efficiently approximate PageRank values for the trillion-page internet graph. Finally, it provides an example of how links between related high-authority sites can increase the PageRank of a given page.
This is a academic work for developing a crawler that can classify the Web Content using SVM and Naive Bayes for Machine Learning, implemented with Elasticsearch, Crawler4J and Apache Spark.
Web crawlers, also known as robots or bots, are programs that systematically browse the internet and index websites for search engines. Crawlers follow links from seed URLs and download pages to extract new URLs to crawl. They use techniques like breadth-first crawling to efficiently discover as much of the web as possible. Crawlers must have policies to select pages, revisit sites, be polite to not overload websites, and coordinate distributed crawling. Their high-performance architecture is crucial for search engines to comprehensively index the large and constantly changing web.
Large Scale Graph Processing with Apache Giraphsscdotopen
This document summarizes a talk on large scale graph processing using Apache Giraph. It begins with an introduction of the speaker and their research interests. It then provides an overview of graphs and challenges with graph processing using Hadoop/MapReduce. It describes Google's Pregel framework for graph processing and how Apache Giraph is an open source implementation of Pregel. Example graph algorithms like PageRank and connected components are demonstrated in Giraph. Experimental results show Giraph providing a 10x performance improvement over Hadoop for PageRank. The talk concludes that many problems can be modeled as networks and solved using graph processing frameworks like Giraph.
The document discusses web crawlers, which are programs that download web pages to help search engines index websites. It explains that crawlers use strategies like breadth-first search and depth-first search to systematically crawl the web. The architecture of crawlers includes components like the URL frontier, DNS lookup, and parsing pages to extract links. Crawling policies determine which pages to download and when to revisit pages. Distributed crawling improves efficiency by using multiple coordinated crawlers.
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