Google Case What were the key factors behind Google’s early success? The main key factor for Google’s early success was the creation of PageRank algorithm. Stanford graduate students Sergey Brin and Larry Page transformed the spam problem of all search engines into an opportunity by creating an algorithm that favored pages that were referenced by other pages rather than simply counting key words. The page’s importance was determined by counting its inbound links‚ weighting the links more heavily
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East Lansing‚ Michigan. He received his Bachelor of science in computer engineering from Michigan University and a Master of science in computer science from Stanford University. Proficient in computer science and extremely intelligent‚ he invented Pagerank‚ a powerful search algorithm that returned highly successful results to users’ queries (Page et al‚ 1999). Together with his friend Sergey Brin‚ they founded Google in 1998. Google soon found itself at the top of search engines and from a small‚
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SWOT Analysis: SWOT analysis is used to strategically plan and identify a company’s internal strengths and weaknesses and the external environment that creates opportunities and threats. Company’s use their external opportunities to reinforce internal strengths and improve internal weaknesses in an attempt to achieve organization goals. The internal factors of strengths and weaknesses are measured by its impact on the goals and objectives of the organization. A company’s strengths are its resources
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obviously‚ that the most important factor behind Google’s success is their effort concentrated on developing search engine. They managed to turn the problem with the key word spam on the web into the attractive opportunity being solving it‚ when PageRank algorithm were created by Sergey Brin and Larry Page. The new system works like this: there were created reliable searches through the amount of websites‚ which than link to a certain page‚ or otherwise «votes» to weight the relevance of the search
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results – www.webminer.web44.net. The implementation of a Web Crawler was done using ‘Breadth First Search Algorithm’. This helped me learn the various aspects used by the modern search engines in retrieving data from the World Wide Web. An effective ‘PageRank algorithm’ was implemented for fast searching using ‘Lazy Learning
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comfortable to search for its users in different countries. • Google uses state of the art search technology to index pages regularly in order to give most updated results to its users. • Google also weights the votes and ranks web pages with its PageRank technology to give its user access to most important pages first. • Google is not biased towards
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quickly enough "to cause problems for Stanford’s computing infrastructure." The first iteration of Google production servers was built with inexpensive hardware and was designed to be very fault-tolerant (Picture) They called this new technology PageRank‚ where a website’s relevance was determined by the number of pages‚ and the importance of those pages‚ that linked back to the original site. (Picture) Page and Brin originally nicknamed their new search engine "BackRub"‚ because the system checked
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Case Study The rise of Google‚ now a $6.1 billion company‚ has been fast and fierce. Founders Sergey Brin and Larry Page met in 1995 as Stanford University graduate students. They created a search engine that combined the technologies of Page ’s PageRank system‚ which evaluates a page ’s importance based on the external links to it‚ and Erin ’s Web crawler‚ which visits Web sites and records a summary of their content. Because Google was so effective‚ it quickly became the search engine of choice
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Breadth-first BASED WEB Crawling Application May Phyu Htun Computer University (Mandalay) mphyutun@gmail.com. Abstract The large size and the dynamic nature of the Web highlight the need for continuous support and updating of Web-based information retrieval systems. Crawlers facilitate the process by following the hyperlinks in Web pages to automatically download a partial snapshot of the Web. Traversing the web graph in breadth-first search order is a good crawling. This system is
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