In this presentation, I'm discussing general techniques to summarize web archives timemap to generate thumbnails. The techniques depend on similarity features on the HTML text such as Simhash and DOM tree.

1. Thumbnail Summarization
Techniques For Web Archives
Ahmed AlSum*
Stanford University Libraries
Stanford CA, USA
aalsum@stanford.edu
Michael L. Nelson
Old Dominion University
Norfolk VA, USA
mln@cs.odu.edu
The 36th European Conference on Information Retrieval.
ECIR 2014, Amsterdam, Netherlands, 2014
* The research has been conducted while Ahmed AlSum was at Old Dominion University
ECIR 2014 Amsterdam, Netherlands

2. What is a Web Archive?
http://www.cs.odu.edu
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3. Memento Terminology
URI-R, R
URI-M, M
URI-T, TM
http://www.amazon.com
http://web.archive.org/web/20110411070244/http://amazon.com
Original Resource
Memento
TimeMap
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4. Thumbnails in Web Archive
Internet Archive UK Web Archive
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5. Thumbnail Creation Challenges
• Scalability in Time
• IA may need 361 years to create thumbnail for each memento
using one hundred machines.
• Scalability in Space
• IA will need 355 TB to store 1 thumbnail per each memento.
• Page quality
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6. Thumbnail Usage Challenges
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• This is partial view of the first 700 thumbnails out of
10,500 available mementos for www.apple.com
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7. From 10,500 Mementos to 69 Thumbnails.
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8. How many thumbnails do we need?
www.unfi.com on the live Web
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9. How many thumbnails do we need?
www.unfi.com on the live Web
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10. 40 Thumbnails are good.
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11. METHODOLOGY
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12. Visual Similarity and Text Similarity
SimilarDifferent
HTML Text
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13. Correlation between
Visual Similarity and Text Similarity
• Text Similarity
• SimHash
• DOM Tree
• Embedded resources
• Memento Datetime (Capture time)
• Visual Similarity
• Number of different pixels
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14. Text Similarity
SimHash
• Compute 64-bit SimHash fingerprints with k = 4 for two
pages, then Calculate the distance using Hamming
Distance
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12 Sep 2012 - 00:12:27 12 Sep 2012 - 19:54:05
Distance
12 bits
Simhash: 147EDAA9977E9400 Simhash: 157EFAAC97189100

15. Text Similarity
DOM Tree
• Transfer each webpage to DOM tree
• Calculate the difference using Levenshtein Distance
• Levenshtein distance: is the number of operations to insert, update, and delete.
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Pawlik, M., & Augsten, N. (2011). RTED: a robust algorithm for the tree edit distance. Proceedings of the VLDB Endowment, 5(4), 334–345.

16. Text Similarity
Embedded resources
• Extract the embedded resources from each page
• Calculate the total number of new resources that have
been added and the resources that have been removed.
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Addition
Removal
Total 4 11
Images 1 9
JS 1 0
CSS 2 2
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17. Text Similarity
Memento datetime
• Calculate the difference between the record capture time
for both pages in seconds.
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12 Sep 2012 - 00:12:27 12 Sep 2012 - 19:54:05
Difference
70942 sec

18. Visual Similarity
• The number of different pixels between two thumbnails,
we resize them into different dimensions (e.g., 64x64 and
128x128). We calculate the Manhattan distance between
each pair
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12 Sep 2012 - 00:12:27 12 Sep 2012 - 19:54:05
Distance
0.65

19. EXPERIMENT
Calculate the correlation between Visual Similarity and
Text Similarity
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20. Fortune 500
• 499,540 mementos from 488
TimeMaps.
• For each Memento, we download the
HTML and capture the thumbnail using
PhantomJS.
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Dataset

21. Correlation between
Visual Similarity and Text Similarity
SimHash DOM tree
Embedded resources Memento Datetime
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SimHash [Charikar 2002], DOM tree [Pawlik 2011], Memento Datetime [Van de Sompel 2013]
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22. SELECTION ALGORITHMS
Using text similarity features to predict the visual
similarity.
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23. #1: Threshold Grouping
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24. #1: Threshold Grouping
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25. #2: Clustering technique
• Input:
• TimeMap with n mementos
• A set of features.
• For example, F = {SimHash, Memento-Datetime}
• Task:
• Cluster n mementos in K clusters.
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26. #2: Clustering technique
SimHash Feature SimHash and Datetime Features
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Park, H.-S., & Jun, C.-H. (2009). A simple and fast algorithm for K-medoids clustering. Expert Systems with Applications, 36(2, Part 2), 3336–3341.
ECIR 2014 Amsterdam, Netherlands

27. #3: Time Normalization
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28. Selection Algorithms Comparison
Threshold Grouping K clustering Time Normalization
TimeMap Reduction 27% 9% to 12% 23%
Image Loss 28 78 - 101 109
# Features 1 feature 1 or more 1 feature
Preprocessing required Yes Yes No
Efficient processing Medium Extensive Light
Incremental Yes No Yes
Online/offline Both Both Both
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29. Generalization outside the Web Archive
• Summarize a website of n pages with only k thumbnails
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30. Conclusions
• We explored the similarity between the text and visual
appearance of the web page.
• We found that SimHash difference between HTML text and
Levenshtein distance between HTML DOM tree have the highest
correlation
• We presented three algorithms to select k thumbnails
from n mementos per TimeMap.
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aalsum@stanford.edu
@aalsum
ECIR 2014 Amsterdam, Netherlands