ITIS 1210 Introduction to Web-Based Information Systems

1. ITIS 1210Introduction to Web-Based Information Systems Internet Research Two How Search Engines Rank Pages & Constructing Complex Searches

2. How do Search Engines Crawl? • Repeat 8 billion times. • Gathering data from the Web is like browsing: • Visit a page. • Record all the words on the page • Choose a link you haven’t seen/recorded • Click on the link.

3. Crawling the Web • 8 billion seconds = 133 million minutes • = 2 million hours • = 93 thousand days • = 256 years One person with a Web browser, following one link per second. How long does it take to browse the surface Web (8 billion pages)?

4. Crawling the Web • One week = six hundred thousand seconds • Six hundred thousand / eight billion = thirteen thousand How many people would it take to crawl the surface Web in a week? If each person follows one link per second (with no sleep):

5. Challenges: Remembering where you’ve been Remembering where you haven’t been Storing all the data

6. A (small) Server Farm

7. The Deep Web • Not all pages get crawled: • Private pages on Intranets (company networks) • Pages that people don’t want crawled • Dynamic content pages (from databases) • Dynamic content pages make the size of the Internet infinite!

8. Dynamic Content Example zillow.com Won’t be indexed

9. Identifying High Quality Web Pages Google has ranked billions of Web pages by "quality". You enter your search terms: UNC Charlotte HCI Google finds the highest quality page associated with these search terms.

10. Google Pagerank 10% of time 90% of time • Pagerank measures how likely you are to reach a particular page through random surfing (either 1 or 2). • The main idea is that links to your page from important web pages indicate that your page is important. Pretend you're surfing the Web randomly. To move from page to page you could: 1) type in an address (www.sis.uncc.edu) includes using a bookmark OR 2) follow a link.

11. Computing Pagerank(what’s the probability of getting to this page?) Q A, B, C, ... L(A), L(B), L(C),... = Web page = Pages pointing to Q = number of links on each page Pagerank of Q: R(Q) = (1-d) + d·(R(A)/L(A) + R(B)/L(B) + ...) d represents the relative chance of following a link to page Q and 1-drepresents the relative chance of going directly to page Q (via typing inthe address or using a bookmark): Usually these are: d=0.9 (1-d) = 0.1

12. Computing Pagerank Pretend the Web has only four pages: W X Y Z Links: W X Y  W Y  Z Z  W L(W)=1 L(X)=0 L(Y)=2 L(Z)=1 Which page has the highest “quality”?

13. Computing Pagerank Links: W  X Y  W Y  Z Z W L(W)=1 L(X)=0 L(Y)=2 L(Z)=1 R(W) = (1-d) + d * (R(Y)/L(Y) + R(Z)/L(Z)) = 0.1 + 0.9 * (R(Y)/2 + R(Z)/1)) R(X) = 0.1 + 0.9 * R(W) R(Y) = 0.1 R(Z) = 0.1 + 0.9 *(R(Y)/2) Now, solve for: R(W), R(X), R(Y), R(Z)

14. Computing Values for R(W), R(X), R(Y) and R(Z) We could use algebra to find the values, in the same way we could solve for x and y in: x = 1 + 2x + y y = 2 + x + 3y

15. Algebraic Solution w = R(W) x = R(X) y = R(Y) z = R(Z) w = 0.1 + 0.45y + 0.9z x = 0.1 + 0.9w y = 0.1 z = 0.1 + 0.45y w = 0.2775 x = 0.34795 y = 0.1 z = 0.145 But solving for eight billion variables is hard. Instead, we'll use fixed point iteration.

16. Solution by Fixed-Point Iteration Start with initial estimates of PageRank for each page: R(W) = 1.0 R(X) = 1.0R(Y) = 1.0R(Z) = 1.0 Apply equations to compute new estimates: new R(W) = 0.1 + 0.9 * (R(Y)/2 + R(Z)) = 0.1 + 0.9 * (1.0/2 + 1.0) = 1.45 new R(X) = 0.1 + 0.9 *R(W) =0.1 + 0.9 *1.0 = 1.0 newR(Y) = 0.1 new R(Z) = 0.1 + 0.9 * (R(Y)/2) = 0.1 + 0.9 * (1.0/2) = 0.55

17. Solution by Fixed-Point Iteration Start with updated estimates: R(W) = 1.45 R(X) = 1.0R(Y) = 0.1R(Z) = 0.55 Apply equations to compute new estimates: new R(W) = 0.1 + 0.9 * (R(Y)/2 + R(Z)) = 0.1 + 0.9 * (0.1/2 + 0.55) = 0.64 new R(X) = 0.1 + 0.9 *R(W) =0.1 + 0.9 *1.45 = 1.405 newR(Y) = 0.1 new R(Z) = 0.1 + 0.9 * (R(Y)/2) = 0.1 + 0.9 * (0.1/2) = 0.145

18. Solution by Iteration iteration R(W) R(X) R(Y) R(Z) 0 1.00000 1.00000 1.00000 1.00000 1 1.45000 1.00000 0.10000 0.55000 2 0.64000 1.40500 0.10000 0.14500 Compute new estimates from the old untilthe estimates stop changing. Note that this is the same answer as the traditional algebraic approach, but this way scales better. 3 0.27550 0.67600 0.10000 0.14500 4 0.27550 0.34795 0.10000 0.14500 5 0.27550 0.34795 0.10000 0.14500 ... ... ... ...

19. Final Pageranks highest page X R(X) = 0.34795 . page W R(W) = 0.2755 . . page Z R(Z) = 0.14500 lowest page Y R(Y) = 0.10000

20. Final Pageranks 0.14500 0.10000 Z Y 2 1 W X 1 0 0.34795 0.27550

21. How does Google Use Pagerank? You enter search terms, such as “UNC Charlotte HCI” Google finds all the pages that have all those words on them Of all those pages, Google will list the ones with the highest page rank first, but… …other ‘magic ingredients’ are used by Google: trade secrets of their algorithms.

22. Introduction • Basic queries are somewhat limited • One or two keywords • Simple relationships • Limited syntax • Complex queries provide more power • Keywords & phrase can be connected to form more complex relationships • Search filters can be employed to limit results

23. Understanding Boolean Operators • Syntax • Rules for combining simple words to form complex sentences • Search engine syntax implemented by applying Boolean logic • George Boole • 1815-1864

24. Understanding Boolean Operators • Boolean logic • Keywords act as nouns • Boolean operators act as conjunctions • They define the connections between keywords • Illustrated with Venn diagrams • John Venn • 1834-1923

25. Understanding Boolean Operators All web pages containing the word cats W W W

26. Understanding Boolean Operators All web pages containing the word dogs W W W

27. Understanding Boolean Operators All web pages containing the words cats and dogs Searches containing both words Intersection of the two sets W W W

28. Understanding Boolean Operators All web pages containing the words cats or dogs Searches containing either word Union of the two sets W W W

29. Understanding Boolean Operators All web pages containing the words cats and not dogs Searches containing one word but not the other Exclusion of the dogs set W W W

30. Understanding Boolean Operators All web pages containing the words dogs and not cats Searches containing one word but not the other Exclusion of the cats set W W W

31. Understanding Boolean Operators • Boolean operators • AND • OR • NOT • Instruct the engine on how to combine keywords to produce results • Always use capital letters to avoid confusion with and, or, not as keywords

32. Understanding Boolean Operators • AND • All these keywords must be on the Web page • OR • These keywords may or may not be on the Web page • At least one of them must be • NOT • None of these keywords can be on the Web page

33. Understanding Boolean Operators • Default operator • Some engines have a default Boolean operator • Usually AND • Might be OR • Some engines may search for multiple words as phrases

34. Understanding Boolean Operators • Boolean operators may be • Allowed on main page • Confined to Advanced search pages • Some engines use symbols instead • + for AND • - for NOT • No space between sign and word: • +solar +energy -windmill

35. Narrowing Searches with AND • AND • Limits results • Forces inclusion of a stop word • Indicates that all keywords must be found on Web page • Adding more ANDed keywords limits search more • Results should be more relevant because the keyword list has expanded

36. Narrowing Searches with AND • Example: • “solar energy association” AND Portland Portland Solar energy association W W W

37. Narrowing Searches with AND • Example: • Henry +I same as “Henry I” I Henry W W W

38. Expanding Searches with OR • OR expands results • Useful if you didn’t get enough returns from your first search • The more keywords you add, the more results you should get • Every page returned must have at least one of the keywords on it • Good to use when you have synonyms

39. Expanding Searches with OR • Example: • oregon OR northwest oregon northwest W W W

40. Restricting Queries with AND NOT • AND NOT excludes the keyword that follows NOT • Limits your search • Produces fewer results • Useful if first search returns irrelevant results • Use AND NOT to get rid of those results

41. Restricting Queries with AND NOT • Equivalent forms: • cats AND NOT dogs • cats AND-NOT dogs • cats NOT dogs • cats –dogs

42. Restricting Queries with AND NOT • Example: • “solar energy association” AND portland AND NOT maine portland Solar energy association maine

43. Multiple Boolean Operators • Boolean operators allow you to focus a search • Any logical combination of operators is allowed • If it makes sense when spoken like a sentence it’s probably OK to use • Order of operations is usually left to right • Use parentheses to organize terms

44. Multiple Boolean Operators • Bad example: • constitution +american OR “united states” american constitution “united states”

45. Multiple Boolean Operators • Good example: • constitution +(american OR “united states”) american constitution “united states”