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Web Archive Profiling Through Fulltext Search

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Web Archive Profiling Through Fulltext Search, my presentation at TPDL 2016, Hanover, Germany.

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Web Archive Profiling Through Fulltext Search

  1. 1. Web Archive Profiling Through Fulltext Search Sawood Alam and Michael L. Nelson Computer Science Department, Old Dominion University Norfolk, Virginia - 23529 Herbert Van de Sompel Los Alamos National Laboratory, Los Alamos, NM David S. H. Rosenthal Stanford University Libraries, Stanford, CA Supported in part by the IIPC and NSF 1526700
  2. 2. Unorganized Collections 2
  3. 3. Organized Collections 3
  4. 4. Collection Understanding 4
  5. 5. Memento Aggregator 5
  6. 6. Memento Aggregator 6
  7. 7. Memento Aggregator 7
  8. 8. Memento Aggregator 8
  9. 9. Memento Aggregator 9
  10. 10. Memento Aggregator 10
  11. 11. From: Michael Nelson [mailto:mln@cs.odu.edu] Sent: Wednesday, December 02, 2015 12:33 PM To: Jones, Gina Cc: Rourke, Patrick; Grotke, Abigail Subject: Re: WebSciDL Hi Gina, I'll investigate. memgator is software that one my students wrote, but I suspect the traffic you're seeing is b/c it is deployed in http://oldweb.today/ can you share the IP addr from where you're seeing the traffic? I presume the requests are for Memento TimeMaps? It should not being actually scraping HTML pages. regards, Michael On Wed, 2 Dec 2015, Jones, Gina wrote: > Hi Michael, we have a slight configuration issue with the current OW > set up for our webarchives. I think, from looking at the logs, that > "MemGator:1.0-rc3 <@WebSciDL>" is really causing some issues on our wayback. > Do you know who is running this scraper? Itʼs not part of memento is it? > > Gina Jones > Web Archiving Team > Library of Congress From: Ilya Kreymer <ikreymer@gmail.com> Date: Wed, 2 Dec 2015 10:33:56 -0800 Subject: high traffic on oldweb! To: Herbert Van de Sompel <hvdsomp@gmail.com>, Sawood Alam <ibnesayeed@gmail.com> Hi Herbert, Sawood, Herbert: Perhaps you are lucky that I am not using the LANL aggregator, as the traffic has gotten really high, and also I was asked to remove an archive due to the traffic it was causing temporarily.. I am thinking that ability to remove source archives quickly is an important aspect of an aggregator. Sawood: Hopefully yours will support something like this so I don't need to restart the container to change the archivelist ;) Ilya Broadcasting is Bad 11
  12. 12. Availability and Overlap ● Archives are sparse ● Broadcasting is wasteful, both clients and archives suffer 12
  13. 13. Memento Routing 13
  14. 14. Routing Pros & Cons ● Pros ○ Minimizes traffic and resources consumption ○ Improves throughput ● Cons ○ Upfront profile maintenance cost ○ May miss Mementos (false negatives) 14
  15. 15. Why Small Archives Matter? 15
  16. 16. Why Small Archives Matter? ● 400B+ web pages at IA do not cover everything ● Top three archives after IA produce full TimeMap 52% of the time (AlSum, et al., TPDL 2013) ● Targeted crawls ● Special focus archives ● Restricted resources ● Private archives ● Censorship 16
  17. 17. While the Internet Archive was Down... $ memgator -f cdxj example.org | cut -c-4 | grep -v "^@" | uniq -c 2 2002 1 2005 1 2008 6 2009 67 2010 17 2011 64 2012 108 2013 108 2014 186 2015 51 2016 17
  18. 18. Archive Profile ● High-level summary of an archive ● Predicts presence of mementos of a URI-R in an archive ● Provides various statistics about the holdings ● Small in size ● Publicly available ● Easy to update and partially patch ● Useful for Memento query routing and other things 18
  19. 19. Profiling Strategies ● Sample URI Profiling (AlSum, et al., TPDL 2013) ● CDX Profiling (Alam, et al., TPDL 2015) ● Response Cache Profiling (Bornand, et al., JCDL 2016) ● Fulltext Search Profiling 19
  20. 20. Methodology Top Nouns time year people way man day thing child mr government 20 Random Dict analogies unbolt consonant coils stolidly cigar decrepit rhododendron cannibal honeydew Dynamic Words Discovery the ‫وﻛﺎﻟﺔ‬ war angry ‫أﻧﺑﺎء‬ the arab ‫اﻟﻌرﺑﻲ‬ middle news ‫اﻟﻐﺎﺿب‬ east service on arabic a politics poetry source war art
  21. 21. Random Searcher Model (RSM) 21 START STOP Seed Vocabulary NextWord() ExtractWords() Search() Select a random link from the search results Vocabulary seeding needed? Termination condition reached? GenerateProfile() Store search results No Yes YesNo Fetch the contents of the selected document
  22. 22. RSM Illustration Teaching Resources Adjunct Toolkit NC NET Academy PD Planning Tools Regional Centers Campus Liaisons Nontraditional Careers College Tech Prep NC ACCESS Co op Education Green Technology You are here NC NET Teaching Resources Discipline Specific English English Self Paced Modules Writing Across the Curriculum NC NET Western Center Incorporating Visuals in Workplace Documents Sections 1 2 Wake Tech Community College Incorporating Visuals in Workplace Documents Section 3 Wake Tech Community College All self paced modules can be accessed through the NC NET Blackboard server Log in with the user name faculty and the password nc net Once connected you can view the courses by topic or alphabetically by title English Webliography North Carolina Community College System 2012
  23. 23. RSM Modes ● Static: Externally supplied static word list ● PopularityBiased: Refresh Vocabulary after every search attempt and consider term frequency for selecting next search keyword ● EqualOpportunity: Refresh Vocabulary after every search attempt and ignore term frequency for selecting next search keyword ● Conservative: Discover new words only when the Vocabulary is exhausted 23
  24. 24. Profiling Policies & Archive-It Dataset Policy # Keys Example URIR 30,800,406 uk,co,bbc,news,)/Images/Logo.png?height=80&width=200 HxP1 1,724,284 uk,co,bbc,news,)/Images DDom 91,629 uk,co,bbc,)/ H1P0 212 uk,)/ Sample URI: https://www.news.BBC.co.uk/Images/Logo.png?width=80&height=40 24 For a detailed list of profiling policies please refer to: Alam, et al.: Web Archive Profiling Through CDX Summarization. IJDL (2016) 17: 223-238
  25. 25. Searches vs Coverage 25 100% in 11K searches 100% in 27K searches 100% in 337K searches 100% in 1.9M searches
  26. 26. RSM Operation Mode Costs Mode Query Cost HTTP Cost Remarks Static C C Suitable for specialized collection with known top keywords PopularityBiased C 2 * C Human like model, but costly EqualOpportunity C 2 * C Human like model, but costly Conservative C C + (where << C) Suitable for any collection and works without any supplementary materials with very little overhead 26
  27. 27. Routing Confusion Matrix Predicted Actual Present in the Archive Not in the Archive Routed to the Archive True Positive (TP) False Positive (FP) Not Routed to the Archive False Negative (FN) True Negative (TN) Routing Confusion Matrix Recall Accuracy 27
  28. 28. Accuracy, Recall, & Coverage (10-100%) 28 DMOZ IA Wayback UK WaybackMemento Proxy Low Accuracy (high FP) => Archives & Aggregator suffer Low Recall (high FN) => Users suffer
  29. 29. Profile Policy Recommendations ● IF complete CDX is available THEN ○ Generate HxP1 profile ● ELSE IF fulltext search is available THEN ○ Generate DDom profile ● ELSE ○ Generate H1P0 or other smaller profiles using Sample URIs Note: It is possible to perform less detailed queries on more specific (higher order) profiles, but not the other way 29
  30. 30. RSM Mode Recommendations ● IF the collection is about a specific topic in a specific language AND a suitable top keywords list is available THEN ○ Use Static mode ● ELSE ○ Use Conservative mode 30
  31. 31. Who Knows Term Frequency for Estonian Nouns? 31 https://en.wiktionary.org/wiki/Category:Estonian_nouns
  32. 32. Future Work ● Evaluation of combination profiles such as URI-Key along with Datetime ● Utilize archive profile to generate rank ordered list of archive ● Profiles for usage other than Memento routing, such as, site classification based profiles (e.g., news, wiki, social media, blog etc.) 32
  33. 33. Conclusions ● Evaluated the search cost as a function of archive holdings’ coverage and profiling policy ● Developed the Random Searcher Model ● Correctly route 80% requests while maintaining 0.9 Recall by only discovering 10% of the archive holdings and generating a profile that costs less than 1% of the complete knowledge profile 33

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