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Needle in the Haystack: The Technology of Internet Search

Needle in the Haystack: The Technology of Internet Search. Randy H. Katz The United Microelectronics Corporation Distinguished Professor Computer Science Division, EECS Department University of California, Berkeley Berkeley, CA 94720-1776 USA randy@cs.Berkeley.edu. Outline.

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Needle in the Haystack: The Technology of Internet Search

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  1. Needle in the Haystack:The Technology of Internet Search Randy H. Katz The United Microelectronics Corporation Distinguished Professor Computer Science Division, EECS Department University of California, Berkeley Berkeley, CA 94720-1776 USA randy@cs.Berkeley.edu

  2. Outline • Historical Background • Information Tsunami • Anatomy of a Web Page • Anatomy of Web Access • The Challenge of Search • Google’s Page Rank Algorithm • Fun and Games with Internet Search • New Directions

  3. Search is BIG!

  4. And the World is Going Digital

  5. Outline • Historical Background • Information Tsunami • Anatomy of a Web Page • Anatomy of Web Access • The Challenge of Search • Google’s Page Rank Algorithm • Fun and Games with Internet Search • New Directions

  6. ARPANet 1969 NSFNet 1985 Commercial Internet 1995 Marc Andreessen NCSA Mosaic1993 Jim Clark Netscape1995 Historical Background:The Perfect Storm World Wide Web Tim Berners-Lee URL/HTTP/HTML 1989 Bill Atkinson Hypercard 1987 SGML 1986 Ted Nelson Xanadu Hypertext 1965-1990 Autodesk Est. $15.5 Billion spent on-line Thanksgivings to Xmas 2004, up 28% since 2003 Vannevar Bush “As We May Think” MEMEX 1947

  7. Outline • Historical Background • Information Tsunami • Anatomy of a Web Page • Anatomy of Web Access • The Challenge of Search • Google’s Page Rank Algorithm • Fun and Games with Internet Search • New Directions

  8. Information Tsunami • Bit: Binary digit – either a 0 or 1 • Byte: 8 bits • 1 byte: single character • 10 bytes: a single word • 100 bytes: Telegram or punched card • Kilobyte: 1,000 or 103 bytes • 1 kilobyte: Very short story • 2 kilobytes: Typewritten page • 10 kilobytes: Encyclopedia page • 50 kilobytes: Compressed document image page • 100 kilobytes: Low-res photo • 200 kilobytes: Box of punched cards http://www.sims.berkeley.edu/research/projects/how-much-info/index.html

  9. Information Tsunami • Megabyte: 1,000,000 or 106 bytes • 1 megabyte: Small novel or 3.5in floppy disk • 2 megabytes: Hi-res photo • 5 megabytes: Complete works of Shakespeare • 10 megabytes: Minute of hi-fi sound • 100 megabytes: 1m shelved books • 500 megabytes: CD-ROM • Gigabyte: 1,000,000,000 or 109 bytes • 1 gigabyte: Pickup truck filled with paper • 2 gigabytes: Movie on a DVD • 50 gigabytes: Floor of books • 100 gigabytes: Floor of academic journals • 500 gigabytes: Biggest FTP site http://www.sims.berkeley.edu/research/projects/how-much-info/index.html

  10. Information Tsunami • Terabyte: 1,000,000,000,000 or 1012 bytes • 1 terabyte: 50,000 trees made into paper and printed or 1 day of EOS data • 2 terabytes: Academic research library • 10 terabytes: Printed collection of the U.S. Library of Congress • 50 terabytes: Contents of a large mass storage system • 400 terabytes: National Climate Data Center (NOAA) database • Petabyte: 1,000,000,000,000,000 or 1015 bytes • 1 petabytes: 3 years of Earth Observing System (EOS) data • 2 petabytes: All U.S. academic research libraries • 8 petabytes: All information available on the Web • 200 petabytes: All printed material (2001) http://www.sims.berkeley.edu/research/projects/how-much-info/index.html

  11. Information Tsunami • Exabyte: 1,000,000,000,000,000,000 or 1018 bytes • 2 exabytes: Total volume of information generated worldwide annually • 5 exabytes: All words ever spoken by humans • Zettabyte: 1,000,000,000,000,000,000,000 or 1021 bytes • Yottabyte: 1,000,000,000,000,000,000,000,000 or 1024 bytes http://www.sims.berkeley.edu/research/projects/how-much-info/index.html

  12. Outline • Historical Background • Information Tsunami • Anatomy of a Web Page • Anatomy of Web Access • The Challenge of Search • Google’s Page Rank Algorithm • Fun and Games with Internet Search • New Directions

  13. Anatomy of aWeb Page:Randy’s Home Page • URL: Uniform Resource Locator • Images • Text

  14. Anatomy of a Web Page:Randy’s Home Page <html> <head> <title>Professor Randy Howard Katz University of California Berkeley Computer Science Division Home Page</title> <meta name="description“ content="Home Page of Berkeley Computer Science Professor Randy Howard Katz"> <meta name="keywords“ content="Katz Randy Howard Berkeley Professor University California Electrical Engineering Computer Science Department RAID Redundant Arrays Inexpensive Disks SPUR Snoop Wireless Communications Networks Programmable Network Elements"> </head> <body> <p><img height="269" src="Randy_2004.jpg" width="182" align="bottom" naturalsizeflag="0">&nbsp;&nbsp; <img height="269" src="RHK85a.jpg" width="177" align="bottom" naturalsizeflag="0">&nbsp;&nbsp; </p> <p><font size="-1">2005 vs. 1985 ... The hair is grayer, but the smirk remains the same!<br> <br> "... Katz, a thin, almost gaunt man with horn-rimmed glasses magnifying sunken eyes. ..."<br> --George Johnson, WIRED Magazine, (January 2000), page 150.</font></p><p><img src="VISIONAR.JPG" align="bottom"> </p> …

  15. Text Images Links!

  16. Anatomy of a Web Page:Randy’s Web Page <hr align="left"> <h1>Professor Randy H. Katz</h1> <h3>Electrical Engineering and Computer Science Department</h3> <p><a href="http://www.umc.com.tw/"><img hspace="6" src="UMCLogo.gif" align="left"> </a> <b><font size="+1">The <a href="http://www.umc.com.tw/">United Microelectronics Corporation</a> Distinguished Professor</font></b></p> <p><font size="-1"><br clear="left"> Ph.D., University of California, Berkeley, 1980.<br> M.S., University of California, Berkeley, 1978.<br> A.B., Cornell University, 1976.<br> </font></p>

  17. Outline • Historical Background • Information Tsunami • Anatomy of a Web Page • Anatomy of Web Access • The Challenge of Search • Google’s Page Rank Algorithm • Fun and Games with Internet Search • New Directions

  18. (1) (2) (3) (4) Anatomy of Web Access Naming System (DNS): Name-to-Address Mapping IP address Web Page In HTML Taiwan Link URL http://www.umc.com.tw/ Web Browser Web Server

  19. (5) (6) (7) (8) Anatomy of Web AccessContent Caching Naming System (DNS) Origin IP Web Page In HTML Content Network DNS Edge Cache IP Taiwan Link URL Content Distribution …/English/about/index.asp Edge Cache San Jose Web Browser Origin Web Server

  20. Outline • Historical Background • Information Tsunami • Anatomy of a Web Page • Anatomy of Web Access • The Challenge of Search • Google’s Page Rank Algorithm • Fun and Games with Internet Search • New Directions

  21. Challenges of Search • How to find all the pages on the Web? • How to order the pages by relevance? • How to make searchable the content on those pages? • How to keep it all up-to-date? • Web Crawlers/SpiderBots • Network software executing in parallel that follow links in the Web to find content • Web pages “scraped” for more links follow • Web revisited on the order of once every two-three days • Indexers • Web pages “scraped” for search terms to build indexes • (Google) Page rank algorithm: order a page within the index based (roughly) on how many pages refer to it

  22. Pre-Web 1993 1995 1997 1999 2001 2003 2005 Quick (and Incomplete) History of Search Engines a9.comAlltheWebAsk JeevesClustyGigablastEz2FindTeomaWiseNutGoHookWalhelloKartoo CMU Lycos 1st Commercial Search Engine Stanford Yahoo! Directories Yahoo! acquires Inktomi Yahoo! acquires Overture (AlltheWeb, AltaVista) Battle for Popularity: Webcrawler (UWash) HotBot (Wired)Excite (Stanford) Infoseek (ABC) Inktomi (Berkeley) AltaVista (DEC)Google (Stanford) UMinn Veronica & Archieservicesfor gopher & ftp MIT Wandex/ WWW Wanderer Aliweb Yahoo! deploysjointtechnology

  23. Search Challenges and Issues • Web growing faster than search engines can index • Web pages updated frequently, forcing frequent revisits • Key word only searches results in many false positives • Difficult to index dynamically generated sites: the so-called “invisible web” • Some search engines order results by financial “placement” considerations rather than relevance • Some sites trick search engine to display them first for some keywords—results in polluted search results, with more relevant links pushed down among the results

  24. Outline • Historical Background • Information Tsunami • Anatomy of a Web Page • Anatomy of Web Access • The Challenge of Search • Google’s Page Rank Algorithm • Fun and Games with Internet Search • New Directions

  25. Page Ranking Algorithms • Web page relevancy • Many hits, how to insure the best/most relevant web pages are presented first in answer to a search • Location and Frequency of Keywords • Index terms in page title raise its relevance for that term • Keywords near “top” of page more relevant than bottom • High keyword frequency boosts relevance • If search engine strategy is known, page developers will “game” the strategy to get their pages ranked higher

  26. Google’s Page Rank Algorithm • Which is the most important page?

  27. Google’s Page Rank Algorithm • Googlese from their web page: • PageRank relies on the uniquely democratic nature of the web by using its vast link structure as an indicator of an individual page's value. Google interprets a link from page A to page B as a vote, by page A, for page B. But, Google looks at more than the sheer volume of votes, or links a page receives; it also analyzes the page that casts the vote. Votes cast by pages that are themselves "important" weigh more heavily and help to make other pages "important.”

  28. Google Page Rank Algorithm • Basic idea: • Page’s rank determined by the number of links to the page (also known as citations) • If citing page is more important (has a high page rank/authority page) then the pages it cites are more important • If citing page has many links, then cited page is less important (normalize for number of links on citing page) PR(P) is page rank of page P, T1, …, TN are pages that cite P, C(P) is the # links from Page P, D is a “decay factor”, e.g., 0.85 then: PR(P) = (1 – d) + d (PR(T1)/C(T1) + … + PR(Tn)/C(Tn)) • See http://www-db.stanford.edu/~backrub/google.html

  29. GoogleConceptualArchitecture

  30. Google Server Architecture Spell Checker Google Web Server • Index servers: search term partitioned and mapped to doc list • Intersect to find document list, sort by page rank • Document IDs used to extract text from Doc Servers • Over 100,000 processors (and growing) in Googleplex Ad Server Doc Server Doc Server Doc Server Doc Server Doc Server Doc Server Doc Server Doc Server Index Server Doc Server

  31. Outline • Historical Background • Information Tsunami • Anatomy of a Web Page • Anatomy of Web Access • The Challenge of Search • Google’s Page Rank Algorithm • Fun and Games with Internet Search • New Directions

  32. Fun and Games • Google Scholar • Googling Someone • Google News • Comparison Shopping • Google Whacks

  33. Google Scholar

  34. Google Randy

  35. Google Randy Katz “Google Index” Advertising Placement

  36. Google News

  37. Comparison Shopping

  38. elgooG

  39. Google Whacks

  40. Business ModelAd Placement and Click-Thru Old data (2002): Google is now market leader in ad revenue 2004 revenue through 9/30/04: $2.1B

  41. Outline • Historical Background • Information Tsunami • Anatomy of a Web Page • Anatomy of Web Access • The Challenge of Search • Google’s Page Rank Algorithm • Fun and Games with Internet Search • New Directions

  42. Top 10 Search Engines 10. DMOZ.org 9. Alltheweb.com 8. KartOO.com 7. MSN.com 6. Dogpile.com 5. AskJeeves.com 4. About.com 2. Yahoo.com 2. Vivismio.com 1. Google.com

  43. Clustering

  44. Google Video Search

  45. Google Video Search

  46. Amazon’s A9

  47. Amazon’s A9

  48. A9’s Yellow Pages

  49. A9’s Yellow Pages

  50. Innovations Now andYet to Come • Index ever larger portions of the Web, even beyond traditional web pages, e.g., video • Better quality/higher relevance searches • Better presentation of results, e.g., clustering, site information • Better exploitation of semantic relationships for improved page ranking, more personalization, e.g., user’s zip code • More services (Web, news groups, blogs, comparison shopping, video/audio, yellow pages, etc.) • Integrate with desktop machine

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