1. Lazy Preservation: Reconstructing Websites from the Web Infrastructure Frank McCown Advisor: Michael L. Nelson Old Dominion University Computer Science Department Norfolk, Virginia, USA Dissertation Defense October 19, 2007

2. 2 Outline Motivation Lazy preservation and the Web Infrastructure Web repositories Responses to 10 research questions Contributions and Future Work

3. 3 Black hat: http://img.webpronews.com/securitypronews/110705blackhat.jpg Virus image: http://polarboing.com/images/topics/misc/story.computer.virus_1137794805.jpg Hard drive: http://www.datarecoveryspecialist.com/images/head-crash-2.jpg

4. 4 Preservation: Fortress Model 1.Get a lot of $ 2.Buy a lot of disks, machines, tapes, etc. 3.Hire an army of staff 4.Load a small amount of data 5.“Look upon my archive ye Mighty, and despair!” Image from: http://www.itunisie.com/tourisme/excursion/tabarka/images/fort.jpg 5 easy steps for preservation: Slide from: http://www.cs.odu.edu/~mln/pubs/differently.ppt

6. 6 …I was doing a little “maintenance” on one of my sites and accidentally deleted my entire database of about 30 articles. After I finished berating myself for being so stupid, I realized that my hosting company would have a backup, so I sent an email asking them to restore the database. Their reply stated that backups were “coming soon”…OUCH!

7. Web Infrastructure

8. Lazy Preservation How much preservation can be had for free? (Little to no effort for web producer/publisher before website is lost) High-coverage preservation of works of unknown importance Built atop unreliable, distributed members which cannot be controlled Usually limited to crawlable web 8

9. Dissertation Objective 9 To demonstrate the feasibility of using the WI as a preservation service – lazy preservation – and to evaluate how effectively this previously unexplored service can be utilized for reconstructing lost websites.

10. Research Questions (Dissertation p. 3) 1.What types of resources are typically stored in the WI search engine caches, and how up-to-date are the caches? 2.How successful is the WI at preserving short-lived web content? 3.How much overlap is there with what is found in search engine caches and the Internet Archive? 4.What interfaces are necessary for a member of the WI (a web repository) to be used in website reconstruction? 5.How does a web-repository crawler work, and how can it reconstruct a lost website from the WI? 10

11. Research Questions cont. 6.What types of websites do people lose, and how successful have they been recovering them from the WI? 7.How completely can websites be reconstructed from the WI? 8.What website attributes contribute to the success of website reconstruction? 9.Which members of the WI are the most helpful for website reconstruction? 10.What methods can be used to recover the server-side components of websites from the WI? 11

12. WI Preliminaries: Web Repositories 12

13. 13 How much of the Web is indexed? Estimates from “The Indexable Web is More than 11.5 billion pages” by Gulli and Signorini (WWW’05) Internet Archive?

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16. Cached Image 16

17. Cached PDF http://www.fda.gov/cder/about/whatwedo/testtube.pdf MSN version Yahoo versionGoogle version canonical

18. Types of Web Repositories Depth of holdings –Flat – only maintain last version of resource crawled –Deep – maintain multiple versions, each with a timestamp Access to holdings –Dark – no outside access to resources –Light – minimal access restrictions 18

19. Accessing the WI Screen-scraping the web user interface (WUI) Application programming interface (API) WUIs and APIs do not always produce the same responses; the APIs may be pulling from smaller indexes 1 19 1 McCown & Nelson, Agreeing to Disagree: Search Engines and their Public Interfaces, JCDL 2007

20. Research Questions 1-3: Characterizing the WI Experiment 1: Observe the WI finding and caching new web content that is decaying. Experiment 2: Examine the contents of the WI by randomly sampling URLs 20

21. 21 Timeline of Web Resource

22. 22 Web Caching Experiment May – Sept 2005 Create 4 websites composed of HTML, PDFs, and images –http://www.owenbrau.com/http://www.owenbrau.com/ –http://www.cs.odu.edu/~fmccown/lazy/http://www.cs.odu.edu/~fmccown/lazy/ –http://www.cs.odu.edu/~jsmit/http://www.cs.odu.edu/~jsmit/ –http://www.cs.odu.edu/~mln/lazp/http://www.cs.odu.edu/~mln/lazp/ Remove pages each day Query GMY every day using identifiers McCown et al., Lazy Preservation: Reconstructing Websites by Crawling the Crawlers, ACM WIDM 2006.

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27. Observations Internet Archive found nothing Google was the most useful web repository from a preservation perspective –Quick to find new content –Consistent access to cached content –Lost content reappeared in cache long after it was removed Images are slow to be cached, and duplicate images are not cached 27

28. 28 Experiment: Sample Search Engine Caches Feb 2007 Submitted 5200 one-term queries to Ask, Google, MSN, and Yahoo Randomly selected 1 result from first 100 Download resource and cached page Check for overlap with Internet Archive McCown and Nelson, Characterization of Search Engine Caches, Archiving 2007.

29. 29 Distribution of Top Level Domains

30. 30 Cached Resource Size Distributions 976 KB977 KB 1 MB 215 KB

31. 31 Cache Freshness and Staleness crawled and cached changed on web server crawled and cached Stale time Fresh Staleness = max(0, Last-modified HTTP header – cached date)

32. 32 Cache Staleness 46% of resource had Last-Modified header 71% also had cached date 16% were at least 1 day stale

33. 33 Overlap with Internet Archive

34. 34 Overlap with Internet Archive Ave of 46% URLs from search engines were archived

35. Research Question 4 of 10: Repository Interfaces Minimum interface requirement: What resource r do you have stored for the URI u?“ r  getResource(u) 35

36. Deep Repositories What resource r do you have stored for the URI u at datestamp d?“ r  getResource(u, d) 36

37. Lister Queries What resources R do you have stored from the site s? R  getAllUris(s) 37

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39. Other Interface Commands Get list of dates D stored for URI u D  getResourceList(u) Get crawl date d for URI u d  getCrawlDate(u) 39

40. Research Question 5 of 10: Web-Repository Crawling 40

41. 41 Web-repository Crawler

42. 42 Written in Perl First version completed in Sept 2005 Made available to the public in Jan 2006 Run as a command line program warrick.pl --recursive --debug --output-file log.txt http://foo.edu/~joe/ Or on-line using the Brass queuing system http://warrick.cs.odu.edu/

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44. Research Question 6 of 10: Warrick usage 44

45. 45 Ave 38.2%

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47. Research Questions 7 and 8: Reconstruction Effectiveness Problem with usage data: Difficult to determine how successful reconstructions actually are –Brass tells Warrick to recover all resources, even if not part of “current” website –When were websites actually lost? –Were URLs spelled correctly? Spam? –Need actual website to compare against reconstruction, especially if wanting to determine which factors determine website’s recoverability 47

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49. 49 Measuring the Difference (r c, r m, r a ) changed missing added Apply Recovery Vector for each resource Compute Difference Vector for website

50. 50 Reconstruction Diagram added 20% identical 50% changed 33% missing 17%

51. 51 McCown and Nelson, Evaluation of Crawling Policies for a Web- Repository Crawler, HYPERTEXT 2006

52. 52 Reconstruction Experiment 300 websites chosen randomly from Open Directory Project (dmoz.org) Crawled and reconstructed each website every week for 14 weeks Examined change rates, age, decay, growth, recoverability McCown and Nelson, Factors Affecting Website Reconstruction from the Web Infrastructure, JCDL 2007

53. 53 Success of website recovery each week *On average, 61% of a website was recovered on any given week.

54. 54 Recovery by TLD

55. 55 Which Factors Are Significant? External backlinks Internal backlinks Google’s PageRank Hops from root page Path depth MIME type Query string params Age Resource birth rate TLD Website size Size of resources

56. 56 Regression Analysis No surprises: all variables are significant, but overall model only explains about half of the observations Three most significant variables: PageRank, hops and age (R-squared = 0.1496)

57. 57 Observations Most of the sampled websites were relatively stable –One third of the websites never lost a single resource –Half of the websites never added any new resources The typical website can expect to get back 61% of its resources if it were lost today (77% textual, 42% images and 32% other) How to improve recovery from WI? Improve PageRank, decrease number of hops to resources, create stable URLs

58. Research Question 9 of 10: Web Repository Contributions 58 Real usage data Experimental results

59. Research Question 10 of 10: Recovering the web server’s components 59 Database Perl script config Static files (html files, PDFs, images, style sheets, Javascript, etc.) Web Infrastructure Web Server Dynamic page Recoverable Not Recoverable

60. 60 Injecting Server Components into Crawlable Pages Erasure codes HTML pagesRecover at least m blocks

61. Server Encoding Experiment Create a digital library using Eprints software and populate with 100 research papers Monarch DL: http://blanche-03.cs.odu.edu/http://blanche-03.cs.odu.edu/ Encode Eprints server components (Perl scripts, MySQL database, config files) and inject into all HTML pages Reconstruct each week 61

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63. 63 Web resources recovered each week

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65. Contributions 1.Novel solution to pervasive problem of website loss: lazy preservation, after-the- fact recovery for little to no work required for the content creator 2.WI is characterized: behavior to consume and retain new web content, types of resources it contains, overlap between flat and deep repositories 65

66. Contributions cont. 3.Model for resource availability is developed from initial creation to its potential unavailability 4.Developed new type of crawler: web- repository crawler. Architecture, interfaces for crawling web repositories, rules for canonicalizing URLs, three crawling policies are evaluated 66

67. Contributions cont. 5.Developed statistical model to measure reconstructed website, reconstruction diagram to summarize reconstruction success. 6.Discovered the three most significant variables that determine how successfully a web resource will be recovered from the WI: Google's PageRank, hops from the root page, resource age. 67

68. Contributions cont. 7.Proposed and experimentally validated a novel solution to recover a website's server components from WI 8.Created website reconstruction service which is currently being used by the public to reconstruct more than 100 lost websites a month 68

69. Future Work Improvements to Warrick: increase used repositories, discovery of URLs, soft 404s Determining or predicting loss- save websites if detecting they are about to or already have disappeared Investigate other sources of lazy preservation: browser caches More extensive overlap studies of WI 69

70. Related Publications Deep web –IEEE Internet Computing 2006 Link rot –IWAW 2005 Lazy Preservation / WI –D-Lib Magazine 2006 –WIDM 2006 –Archiving 2007 –Dynamics of Search Engines: An Introduction (chapter) –Content Engineering (chapter) –International Journal on Digital Libraries 2007 Search engine contents and interfaces –ECDL 2005 –WWW 2007 –JCDL 2007 Obsolete web file formats –IWAW 2005 Warrick –HYPERTEXT 2006 –Archiving 2007 –JCDL 2007 –IWAW 2007 –Communications of the ACM 2007 (to appear) 70

71. 71 Thank You Can’t wait until I’m old enough to run Warrick!

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73. 73 Web Repository Characteristics TypeMIME typeFile extGoogleYahooLiveIA HTML text text/html html CCCC Plain text text/plain txt, ans MMMC Graphic Interchange Format image/gif gif MMMC Joint Photographic Experts Group image/jpeg jpg MM M C Portable Network Graphic image/png png MM M C Adobe Portable Document Format application/pdf pdf MMMC JavaScript application/javascript js MMC Microsoft Excel application/vnd.ms-excel xls M~SMC Microsoft PowerPoint application/vnd.ms- powerpoint ppt MMMC Microsoft Word application/msword doc MMMC PostScript application/postscript ps M~SC CCanonical version is stored MModified version is stored (modified images are thumbnails, all others are html conversions) ~SIndexed but not stored

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75. 75 Some Difference Vectors D = (changed, missing, added) (0,0,0) – Perfect recovery (1,0,0) – All resources are recovered but changed (0,1,0) – All resources are lost (0,0,1) – All recovered resources are at new URIs

76. 76 How Much Change is a Bad Thing? LostRecovered

77. 77 How Much Change is a Bad Thing? LostRecovered

78. 78 Assigning Penalties Apply to each resource (P c, P m, P a ) Penalty Adjustment Or Difference vector

79. 79 Defining Success success = 1 – d m Equivalent to percent of recovered resources 01 Less successful More successful

80. 80 Recovery of Textual Resources

81. 81 Birth and Decay

82. 82 Recovery of HTML Resources

83. 83 Recovery by Age

84. 84 Mild Correlations Hops and –website size (0.428) –path depth (0.388) Age and # of query params (-0.318) External links and –PageRank (0.339) –Website size (0.301) –Hops (0.320)

85. 85 Regression Parameter Estimates

86. 86 Similarity vs. Staleness