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2/18

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  1. 2/18 • Date: Fri, 15 Feb 2002 12:53:45 -0700Subject: IOC awards presidency also to Gore(RNN)-- In a surprising, but widely anticipated move, the International Olympic Committee president just came on TV and announced that IOC decided to award a presidency to Albert Gore Jr. too. Gore Jr. won the popular vote initially, but to the surprise of TV viewers world wide, Bush was awarded thepresidency by the electoral college judges.Mr. Bush, who "beat" gore,  still gets to keep his presidency.  "We decided to put the two men on an equal footing and we are not going to start doing the calculations of all the different votes that (were) given. Besides, who knows what those seniors in Palm Beach were thinking?" said the IOC president.  The specific details of shared presidency are still being worked out--but it is expected that Gore will be the president during the day, when Mr. Bush typically is busy in the Gym working out.In a separate communique the IOC  suspended Florida for an indefinite period from the union.Speaking from his home (far) outside Nashville, a visibly elated Gore profusely thanked Canadian people for starting this trend. He also remarked that this will be the first presidents' day when the sitting president can be on both coasts simultaneously. When last seen, he was busy using the "Gettysburg" template in the latest MS Powerpoint to prepare an eloquent  speech for his inauguration-cum-first-state-of-the-union.--RNNRelated Sites:   Gettysburg Powerpoint template: http://www.norvig.com/Gettysburg/

  2. Agenda:Page Rank issues (computation; Collusion etc)Crawling Announcements: Next class: INTERACTIVE (read Google paper and come prepared with smart questions/comments/answers) Homework 2 socket closed.. Question: Are you reading the papers????????

  3. Adding PageRank to a SearchEngine • Weighted sum of importance+similarity with query • Score(q, d) = wsim(q, p) + (1-w)  R(p), if sim(q, p) > 0 = 0, otherwise • Where • 0 < w < 1 • sim(q, p), R(p) must be normalized to [0, 1].

  4. Stability of Rank Calculations (From Ng et. al. ) The left most column Shows the original rank Calculation -the columns on the right are result of rank calculations when 30% of pages are randomly removed

  5. Assuming a=0.8 and K=[1/3] Rank(A)=0.37 Rank(B)=0.6672 Rank(C)=0.6461 Rank(A)=Rank(B)=Rank(C)= 0.5774 Effect of collusion on PageRank C C A A B B Moral: By referring to each other, a cluster of pages can artificially boost their rank (although the cluster has to be big enough to make an appreciable difference. Solution: Put a threshold on the number of intra-domain links that will count Counter: Buy two domains, and generate a cluster among those..

  6. What about non-principal eigen vectors? • Principal eigen vector gives the authorities (and hubs) • What do the other ones do? • They may be able to show the clustering in the documents (see page 23 in Kleinberg paper) • The clusters are found by looking at the positive and negative ends of the secondary eigen vectors (ppl vector has only +ve end…)

  7. Practicality • Challenges • M no longer sparse (don’t represent explicitly!) • Data too big for memory (be sneaky about disk usage) • Stanford version of Google : • 24 million documents in crawl • 147GB documents • 259 million links • Computing pagerank “few hours” on single 1997 workstation • But How? • Next discussion from Haveliwala paper…

  8. Efficient Computation: Preprocess • Remove ‘dangling’ nodes • Pages w/ no children • Then repeat process • Since now more danglers • Stanford WebBase • 25 M pages • 81 M URLs in the link graph • After two prune iterations: 19 M nodes

  9. Source node (32 bit int) Outdegree (16 bit int) Destination nodes (32 bit int) 0 4 12, 26, 58, 94 1 3 5, 56, 69 2 5 1, 9, 10, 36, 78 Representing ‘Links’ Table • Stored on disk in binary format • Size for Stanford WebBase: 1.01 GB • Assumed to exceed main memory

  10. source node =  dest node Dest Links (sparse) Source Algorithm 1 s Source[s] = 1/N while residual > { d Dest[d] = 0 while not Links.eof() { Links.read(source, n, dest1, … destn) for j = 1… n Dest[destj] = Dest[destj]+Source[source]/n } d Dest[d] = c * Dest[d] + (1-c)/N /* dampening */ residual = Source – Dest /* recompute every few iterations */ Source = Dest }

  11. Analysis of Algorithm 1 • If memory is big enough to hold Source & Dest • IO cost per iteration is | Links| • Fine for a crawl of 24 M pages • But web ~ 800 M pages in 2/99 [NEC study] • Increase from 320 M pages in 1997 [same authors] • If memory is big enough to hold just Dest • Sort Links on source field • Read Source sequentially during rank propagation step • Write Dest to disk to serve as Source for next iteration • IO cost per iteration is | Source| + | Dest| + | Links| • If memory can’t hold Dest • Random access pattern will make working set = | Dest| • Thrash!!!

  12. Block-Based Algorithm • Partition Dest into B blocks of D pages each • If memory = P physical pages • D < P-2 since need input buffers for Source & Links • Partition Links into B files • Linksi only has some of the dest nodes for each source • Linksi only has dest nodes such that • DD*i <= dest < DD*(i+1) • Where DD = number of 32 bit integers that fit in D pages source node  = dest node Dest Links (sparse) Source

  13. Partitioned Link File Source node (32 bit int) Outdegr (16 bit) Num out (16 bit) Destination nodes (32 bit int) 0 4 2 12, 26 Buckets 0-31 1 3 1 5 2 5 3 1, 9, 10 0 4 1 58 Buckets 32-63 1 3 1 56 2 5 1 36 0 4 1 94 Buckets 64-95 1 3 1 69 2 5 1 78

  14. Block-based Page Rank algorithm

  15. Analysis of Block Algorithm • IO Cost per iteration = • B*| Source| + | Dest| + | Links|*(1+e) • e is factor by which Links increased in size • Typically 0.1-0.3 • Depends on number of blocks • Algorithm ~ nested-loops join

  16. Comparing the Algorithms

  17. PageRank Convergence…

  18. PageRank Convergence…

  19. Summary of Key Points • PageRank Iterative Algorithm • Rank Sinks • Efficiency of computation – Memory! • Single precision Numbers. • Don’t represent M* explicitly. • Break arrays into Blocks. • Minimize IO Cost. • Number of iterations of PageRank. • Weighting of PageRank vs. doc similarity.

  20. 2/24 Shopping at job fairs Push my resume [But] jobs aren't what I seek I will be your walking student advertisement Can't live on my research stipend Everybody wants a Google shirt HP, Amazon Pixar, Cray, and Ford I just can't decide Help me score the most free pens and free umbrellas or a coffee mug from Bell Labs Everybody wants a Google.. [Un]til I find a steady funder I'll make do with cheap-a## plunder Everybody wants a Google.. Wait! You will never never never need it It's free; I couldn't leave it Everybody wants a Google shirt Shameless corp'rate carrion crows Turn your backs and show your logos Everybody wants a Google shirt ("Everybody Wants a Google Shirt" is based on "Everybody Wants to Rule the World" by Tears for Fears. Alternate lyrics by Andy Collins, Kate Deibel, Neil Spring, Steve Wolfman, and Ken Yasuhara.)

  21. Discussion • What parts of Google did you find to be in line with what you learned until now? • What parts of Google were different?

  22. Fancy hits? Why two types of barrels? How is indexing parallelized? How does Google show that it doesn’t quite care about recall? How does Google avoid crawling the same URL multiple times? What are some of the memory saving things they do? Do they use TF/IDF? Do they normalize? (why not?) Can they support proximity queries? How are “page synopses” made? Some points…

  23. Beyond Google (and Pagerank) • Are backlinks reliable metric of importance? • It is a “one-size-fits-all” measure of importance… • Not user specific • Not topic specific • There may be discrepancy between back links and actual popularity (as measured in hits) • The “sense” of the link is ignored (this is okay if you think that all publicity is good publicity) • Mark Twain on Classics • “A classic is something everyone wishes they had already read and no one actually had..” (paraphrase) • Google may be its own undoing…(why would I need back links when I know I can get to it through Google?) • Customization, customization, customization… • Yahoo sez about their magic bullet.. (NYT 2/22/04) • "If you type in flowers, do you want to buy flowers, plant flowers or see pictures of flowers?"

  24. The rest of the slides on Google as well as crawling were notspecifically discussed one at a time, but have been discussed in essence(read “you are still responsible for them”)

  25. SPIDER CASE STUDY

  26. Robot (4) • How to extract URLs from a web page? Need to identify all possible tags and attributes that hold URLs. • Anchor tag: <a href=“URL” … > … </a> • Option tag: <option value=“URL”…> … </option> • Map: <area href=“URL” …> • Frame: <frame src=“URL” …> • Link to an image: <img src=“URL” …> • Relative path vs. absolute path: <base href= …>

  27. Focused Crawling • Classifier: Is crawled page P relevant to the topic? • Algorithm that maps page to relevant/irrelevant • Semi-automatic • Based on page vicinity.. • Distiller:is crawled page P likely to lead to relevant pages? • Algorithm that maps page to likely/unlikely • Could be just A/H computation, and taking HUBS • Distiller determines the priority of following links off of P

  28. Anatomy of Google(circa 1999) Slides from http://www.cs.huji.ac.il/~sdbi/2000/google/index.htm

  29. The “google” paper Discusses google’s Architecture circa 99 Search Engine Size over Time Number of indexed pages, self-reported Google: 50% of the web?

  30. Google Search Engine Architecture URL Server- Provides URLs to be fetched Crawler is distributed Store Server - compresses and stores pages for indexing Repository - holds pages for indexing (full HTML of every page) Indexer - parses documents, records words, positions, font size, and capitalization Lexicon - list of unique words found HitList – efficient record of word locs+attribs Barrels hold (docID, (wordID, hitList*)*)* sorted: each barrel has range of words Anchors - keep information about links found in web pages URL Resolver - converts relative URLs to absolute Sorter - generates Doc Index Doc Index - inverted index of all words in all documents (except stop words) Links - stores info about links to each page (used for Pagerank) Pagerank - computes a rank for each page retrieved Searcher - answers queries SOURCE: BRIN & PAGE

  31. Major Data Structures • Big Files • virtual files spanning multiple file systems • addressable by 64 bit integers • handles allocation & deallocation of File Descriptions since the OS’s is not enough • supports rudimentary compression

  32. Major Data Structures (2) • Repository • tradeoff between speed & compression ratio • choose zlib (3 to 1) over bzip (4 to 1) • requires no other data structure to access it

  33. Major Data Structures (3) • Document Index • keeps information about each document • fixed width ISAM (index sequential access mode) index • includes various statistics • pointer to repository, if crawled, pointer to info lists • compact data structure • we can fetch a record in 1 disk seek during search

  34. Major Data Structures (4) • Lexicon • can fit in memory for reasonable price • currently 256 MB • contains 14 million words • 2 parts • a list of words • a hash table

  35. Major Data Structures (4) • Hit Lists • includes position font & capitalization • account for most of the space used in the indexes • 3 alternatives: simple, Huffman , hand-optimized • hand encoding uses 2 bytes for every hit

  36. Major Data Structures (4) • Hit Lists (2)

  37. Major Data Structures (5) • Forward Index • partially ordered • used 64 Barrels • each Barrel holds a range of wordIDs • requires slightly more storage • each wordID is stored as a relative difference from the minimum wordID of the Barrel • saves considerable time in the sorting

  38. Major Data Structures (6) • Inverted Index • 64 Barrels (same as the Forward Index) • for each wordID the Lexicon contains a pointer to the Barrel that wordID falls into • the pointer points to a doclist with their hit list • the order of the docIDs is important • by docID or doc word-ranking • Two inverted barrels—the short barrel/full barrel

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