This document summarizes Sawood Alam's doctoral research on developing efficient techniques for profiling web archives to improve memento aggregation. Some key points: - It proposes using concise archive profiles to predict what content archives have in order to minimize traffic when routing memento requests. - It evaluates different profiling policies and strategies like using CDX files, full-text search, and URI sampling to balance accuracy and resource costs. - The results show profiling policies can achieve up to 80% routing accuracy with less than 1% of the cost of a complete archive index, while maintaining over 90% recall of available mementos. - Future work includes expanding the profiles to include language, date/time

1. Web Archive Profiling
For
Efficient Memento Aggregation
Sawood Alam
Old Dominion University, Norfolk, Virginia - 23529
Advisor: Michael L. Nelson
Doctoral Consortium TPDL’16
September 5, 2016
Supported in part by the International Internet Preservation Consortium (IIPC)

2. Motivation
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3. Motivation
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4. Motivation
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5. Memento Aggregator
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6. Memento Aggregator
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7. Memento Aggregator
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8. Memento Aggregator
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9. Memento Aggregator
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10. Memento Aggregator
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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
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12. Availability and Overlap
● Archives are sparse
● Broadcasting is wasteful, both clients and archives suffer
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13. Memento Routing
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14. Routing Pros & Cons
● Pros
○ Minimizes traffic and resources consumption
○ Improves throughput
● Cons
○ Upfront profile maintenance cost
○ May miss Mementos (false negatives)
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15. Why Small Archives Matter?
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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
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17. While the IA 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. Research Questions
● What do individual web archives hold?
● How much do we need to know about an
archive’s holdings?
● What is the optimal level of summarization for
better accuracy and increased freshness?
● What are various ways to learn about archives’
holdings?
● How to store and update archives’ profiles to
efficiently scale?
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19. 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
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20. Profiling Policies
● Complete URI-R Profiling (1 URI-R = 1 Profile Key)
○ bbc.co.uk/images/logo.png?w=90
○ cnn.com/2014/03/15/?id=128734
● TLD-only Profiling (1 TLD = 1 Profile Key)
○ com)/
○ uk)/
● Middle Ground
○ uk,co)/
○ uk,co,bbc)/images
○ uk,co,bbc)/0/2/1
○ com,cnn)/ 201309 ar
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21. Available Profiling Resources
Client request
Archive Response
CDX Records
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22. Profiling Strategies
● Sample URI Profiling
● CDX Profiling
● Response Cache Profiling
● Fulltext Search Profiling
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23. Sample Profile
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24. Probability Rank
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25. Archives
Archive URI-Rs URI-Ms Index Size
Archive-It 1.9B 5.3B 1.8TB
UKWA 0.7B 1.7B 0.5TB
Stanford 12M 25M 8.3GB
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26. Sample Query Sets
Sample
(1M URIs Each)
In
Archive-It
In
UKWA
In
Stanford
Union
{AIT, UK,
SU}
DMOZ 4.097% 3.594% 0.034% 7.575%
MementoProxy 4.182% 0.408% 0.046% 4.527%
IAWayback 3.716% 0.519% 0.039% 4.165%
UKWayback 0.108% 0.034% 0.002% 0.134%
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27. Evaluation
● Generate profiles with 23 policies
● Relate CDX Size, URI-M, URI-R, and URI-Key
● Analyze profile growth
● Estimate Relative Cost
● Evaluate Routing Efficiency
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28. Resource Requirement
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29. CDX Size vs URI-M (UKWA 10 Years)
Alpha: 175 bytes per CDX line
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30. URI-M vs URI-R (UKWA 10 Years)
Gamma: 2.46
K : 2.686
Beta: 0.911
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31. Space Cost (UKWA 7 Years)
Phi: 8.5e-07 -- 0.70583
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32. Time Cost (UKWA 7 Years)
Tau: 5.7e-05 -- 6.2e-05
CDX: 45GB
URI-Ms: 181M
URI-Rs: 96M
Time: 3 hours
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33. Archive-It
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34. Fulltext Search Cost
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35. Partial Knowledge
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36. Cost vs Accuracy
Group Policies Cost Accuracy
G1 H1P0/TLD Bound by # of TLDs ≈ 0.01
G2
H3P0, DDom, DSub,
DPth, DQry
< 0.01 ≈ 0.78
G3 DIni ≈ 2 * G2 ≈ 0.88
G4 HxP1 ≈ 5 * G3 ≈ 0.94
G5 Higher HmPn 0.4 -- 0.7 Not Explored
G6 URIR 1.0 1.0
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37. Work Plan
✓ Baseline Profiling Through CDX Files
✓ Profile Serialization
✓ Fulltext Search Profiling
✓ Sample URI Dataset
➢ Instrumenting Memento Aggregator
➢ Multidimensional Profiling
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38. Publications
TPDL15 Web Archive Profiling Through CDX Summarization
TCDL15 Profiling Web Archives - For Efficient Memento Query Routing
IJDL16 Web Archive Profiling Through CDX Summarization
JCDL16 Poster: MemGator - A Portable Concurrent Memento Aggregator
TPDL16 Web Archive Profiling Through Fulltext Search
RFC Object Resource Stream (ORS) and CDX-JSON (CDXJ) Formats
C4LJ MemGator - A Portable Concurrent Memento Aggregator Architecture
JCDL17 Scalable, Maintainable, and Extensible Web Archive Profile Serialization for Efficient Lookup
JCDL17 URI, Time, and Language Profiling from Live Archives via URI Sampling and Fulltex Search
SIGIR17 Memento Aggregator Routing Based on Probability Distribution of Memento Availability with
Archive Profiles
IJDL17 Archive X-Ray - Web Archive Profiling for Efficient Memento Aggregation
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39. Future Work
● Language profiles
● 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.)
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40. Conclusions
● Generated profiles with different policies for three archives
● Examined cost-precision tradeoffs of various policies
● Related CDX Size, URI-M, URI-R, and URI-Key
● Gained up to 80% routing accuracy with <1% relative cost
while maintaining 0.9 recall
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