This document discusses integrating Hadoop into libraries to help scale up their digitization efforts of cultural heritage materials. It provides background on two libraries' digitization projects and data volumes. It then outlines challenges of scaling up like use cases exploring document recognition, file format migration, and web archiving using Hadoop. Scenarios demonstrate running analytics on book metadata and page images and web archives stored in Hadoop.

1. SCAP
E
The Elephant in the Library
Integrating Hadoop
Clemens Neudecker Sven Schlarb
@cneudecker @SvenSchlarb

2. Contents
1. Background: Digitization of cultural heritage
2. Numbers: Scaling up!
3. Challenges: Use cases and scenarios
4. Outlook

3. 1. Background
“The digital revolution is far more
significant than the invention of
writing or even of printing”
Douglas Engelbart

4. Then

5. Our libraries
• The Hague, Netherlands
• Founded in 1798
• 120.000 visitors per year
• 6 million documents
• 260 FTE
www.kb.nl
• Vienna, Austria
• Founded in 14th century
• 300.000 visitors per year
• 8 million documents
• 300 FTE
www.onb.ac.at

6. Digitization
Libraries are rapidly transforming from physical…
to digital…

7. Transformation
Curation Lifecycle Model from Digital Curation Centre www.dcc.ac.uk

8. Now

9. Digital Preservation

10. Our data – cultural heritage
• Traditionally
• Bibliographic and other metadata
• Images (Portraits/Pictures, Maps, Posters, etc.)
• Text (Books, Articles, Newspapers, etc.)
• More recently
• Audio/Video
• Websites, Blogs, Twitter, Social Networks
• Research Data/Raw Data
• Software? Apps?

11. 2. Numbers
“A good decision is based on knowledge
and not on numbers”
Plato, 400 BC

12. Numbers (I)
National Library of the Netherlands
• Digital objects
• > 500 million files
• 18 million digital publications (+ 2M/year)
• 8 million newspaper pages (+ 4M/year)
• 152.000 books (+ 100k/year)
• 730.000 websites (+ 170k/year)
• Storage
• 1.3 PB (currently 458 TB used)
• Growing approx. 150 TB a year

13. Numbers (II)
Austrian National Library
• Digital objects
• 600.000 volumes being digitised during the next
years (currently 120.000 volumes, 40 million pages)
• 10 million newspapers and legal texts
• 1.16 billion files in web archive from
> 1 million domains
• Several 100.000 images and portraits
• Storage
• 84 TB
• Growing approx. 15 TB a year

14. Numbers (III)
• Google Books Project
• 2012: 20 million books scanned
(approx. 7,000,000,000 pages)
• www.books.google.com
• Europeana
• 2012: 25 million digital objects
• All metadata licensed CC-0
• www.europeana.eu/portal

15. Numbers (IV)
• Hathi Trust
• 3,721,702,950 scanned pages
• 477 TBytes
• www.hathitrust.org
• Internet Archive
• 245 billion web pages archived
• 10 PBytes
• www.archive.org

16. Numbers (V)
• What can we expect?
• Enumerate 2012: only about 4% digitised so far
• Strong growth of born digital information
Source: www.idc.com Source: security.networksasia.net

17. 3. Challenges
“What do you do with a million books?”
Gregory Crane, 2006

18. Making it scale
Scalability in terms of …
• size
• number
• complexity
• heterogeneity

19. SCAPE
• SCAPE = SCAlable Preservation Environments
• €8.6M EU funding, Feb 2011 – July 2014
• 20 partners from public sector, academia, industry
• Main objectives:
• Scalability
• Automation
• Planning
www.scape-project.eu

20. Use cases (I)
• Document recognition: From image to XML
• Business case:
• Better presentation options
• Creation of eBooks
• Full-text indexing

21. Use cases (II)
• File type migration: JP2k  TIFF
• Business case:
• Originally migration
to JP2k to reduce
storage costs
• Reverse process
used in case JP2k
becomes obsolete

22. Use cases (III)
• Web archiving: Characterization of web content
• Business case:
• What is in a Top Level Domain?
• What is the distribution of file formats?
• http://www.openplanetsfoundation.org/blogs/2013-01-09-
year-fits
xkcd.com/688

23. Use cases (IV)
• Digital Humanities: Making sense of the millions
• Business case:
• Text mining & NLP
• Statistical analysis
• Semantic enrichment
• Visualizations Source: www.open.ac.uk/

24. Enter the Elephants…
Source: Biopics

25. Experimental Cluster

26. Apache Tomcat
Web Application
Execution environment
Taverna Server
(REST API)
Hadoop
Jobtracker
File server
Cluster

27. Scenarios (I)
Log file analysis
• Metadata log files generated by the web crawler
during the harvesting process
(no mime type identification – just the mime types
returned by the web server)
20110830130705 9684 46 16 3 image/jpeg http://URL at IP 17311 200
20110830130709 9684 46 16 3 image/jpeg http://URL at IP 22123 200
20110830130710 9684 46 16 3 image/gif http://URL at IP 9794 200
20110830130707 9684 46 16 3 image/jpeg http://URL at IP 40056 200
20110830130704 9684 46 16 3 text/html http://URL at IP 13149 200
20110830130712 9684 46 16 3 image/gif http://URL at IP 2285 200
20110830130712 9684 46 16 3 text/html http://URL at IP 415 301
20110830130710 9684 46 16 3 text/html http://URL at IP 7873 200
20110830130712 9684 46 16 3 text/html http://URL at IP 632 302
20110830130712 9684 46 16 3 image/png http://URL at IP 679 200

28. Scenarios (II)
Web archiving: File format identification
→ Run file type identification on archived web content
(W)ARC Container
JPG
GIF
HTM
HTM
MID
(W)ARC RecordReader
based on
HERITRIX
Web crawler
read/write (W)ARC
MapReduce
JPG
Apache Tika
detect MIME
Map
Reduce
image/jpg
image/jpg 1
image/gif 1
text/html 2
audio/midi 1

29. Scenarios (II)
Web archiving: File format identification
→ Using MapReduce to calculate statistics
DROID 6.01 TIKA 1.0

30. Scenarios (III)
File format migration
• Risk of format obsolescence
• Quality assurance
• File format validation
• Original/target image
comparison
• Imagine runtime of 1 minute
per image for 200 million
pages ...

31. Parallel execution of
file format validation
using Mapper
● Jpylyzer (Python)
● Jhove2 (Java)

32. ● Feature extraction
requires sharing
resources between
processing steps
● Challenge to model
more complex image
comparison scenarios,
e.g. book page
duplicates detection
or digital book
comparison

33. Scenarios (IV)
Book page analysis

34. Create text file containing JPEG2000 input file paths and read
image metadata using Exiftool via the Hadoop Streaming API

35. Jp2PathCreator HadoopStreamingExiftoolRead
find
/NAS/Z119585409/00000001.jp2
/NAS/Z119585409/00000002.jp2
/NAS/Z119585409/00000003.jp2
…
/NAS/Z117655409/00000001.jp2
/NAS/Z117655409/00000002.jp2
/NAS/Z117655409/00000003.jp2
…
/NAS/Z119585987/00000001.jp2
/NAS/Z119585987/00000002.jp2
/NAS/Z119585987/00000003.jp2
…
/NAS/Z119584539/00000001.jp2
/NAS/Z119584539/00000002.jp2
/NAS/Z119584539/00000003.jp2
…
/NAS/Z119599879/00000001.jp2l
/NAS/Z119589879/00000002.jp2
/NAS/Z119589879/00000003.jp2
...
...
NAS
reading files from NAS
1,4 GB 1,2 GB
: ~ 5 h + ~ 38 h = ~ 43 h
60.000 books
24 Million pages
Z119585409/00000001 2345
Z119585409/00000002 2340
Z119585409/00000003 2543
…
Z117655409/00000001 2300
Z117655409/00000002 2300
Z117655409/00000003 2345
…
Z119585987/00000001 2300
Z119585987/00000002 2340
Z119585987/00000003 2432
…
Z119584539/00000001 5205
Z119584539/00000002 2310
Z119584539/00000003 2134
…
Z119599879/00000001 2312
Z119589879/00000002 2300
Z119589879/00000003 2300
...
Reading image metadata

36. Create text file containing HTML input file paths and create
one sequence file with the complete file content in HDFS

37. HtmlPathCreator SequenceFileCreator
find
/NAS/Z119585409/00000707.html
/NAS/Z119585409/00000708.html
/NAS/Z119585409/00000709.html
…
/NAS/Z138682341/00000707.html
/NAS/Z138682341/00000708.html
/NAS/Z138682341/00000709.html
…
/NAS/Z178791257/00000707.html
/NAS/Z178791257/00000708.html
/NAS/Z178791257/00000709.html
…
/NAS/Z967985409/00000707.html
/NAS/Z967985409/00000708.html
/NAS/Z967985409/00000709.html
…
/NAS/Z196545409/00000707.html
/NAS/Z196545409/00000708.html
/NAS/Z196545409/00000709.html
...
Z119585409/00000707
Z119585409/00000708
Z119585409/00000709
Z119585409/00000710
Z119585409/00000711
Z119585409/00000712
NAS
reading files from NAS
1,4 GB 997 GB (uncompressed)
: ~ 5 h + ~ 24 h = ~ 29 h
60.000 books
24 Million pages
SequenceFile creation

38. Execute Hadoop MapReduce job using the sequence file created
before in order to calculate the average paragraph block width

39. Z119585409/00000001
Z119585409/00000002
Z119585409/00000003
Z119585409/00000004
Z119585409/00000005
HTML Parsing
...
Map Reduce
: ~ 6 h
60.000 books
24 Million pages
Z119585409/00000001 2100
Z119585409/00000001 2200
Z119585409/00000001 2300
Z119585409/00000001 2400
Z119585409/00000002 2100
Z119585409/00000002 2200
Z119585409/00000002 2300
Z119585409/00000002 2400
Z119585409/00000003 2100
Z119585409/00000003 2200
Z119585409/00000003 2300
Z119585409/00000003 2400
Z119585409/00000004 2100
Z119585409/00000004 2200
Z119585409/00000004 2300
Z119585409/00000004 2400
Z119585409/00000005 2100
Z119585409/00000005 2200
Z119585409/00000005 2300
Z119585409/00000005 2400
Z119585409/00000001 2250
Z119585409/00000002 2250
Z119585409/00000003 2250
Z119585409/00000004 2250
Z119585409/00000005 2250
HadoopAvBlockWidthMapReduce
SequenceFile Textfile

40. Create Hive table and load generated data into the Hive database

41. Analytic Queries
CREATE TABLE htmlwidth
(hid STRING, hwidth INT)
: ~ 6 h
60.000 books
24 Million pages
HiveLoadExifData & HiveLoadHocrData
htmlwidth
hid hwidth
Z119585409/00000001 1870
Z119585409/00000002 2100
Z119585409/00000003 2015
Z119585409/00000004 1350
Z119585409/00000005 1700
jp2width
jid jwidth
Z119585409/00000001 2250
Z119585409/00000002 2150
Z119585409/00000003 2125
Z119585409/00000004 2125
Z119585409/00000005 2250
Z119585409/00000001 1870
Z119585409/00000002 2100
Z119585409/00000003 2015
Z119585409/00000004 1350
Z119585409/00000005 1700
Z119585409/00000001 2250
Z119585409/00000002 2150
Z119585409/00000003 2125
Z119585409/00000004 2125
Z119585409/00000005 2250
CREATE TABLE jp2width
(hid STRING, jwidth INT)

42. Analytic Queries
jp2width htmlwidth
select jid, jwidth, hwidth from jp2width inner join htmlwidth on jid = hid
: ~ 6 h
60.000 books
24 Million pages
HiveSelect
jid jwidth
Z119585409/00000001 2250
Z119585409/00000002 2150
Z119585409/00000003 2125
Z119585409/00000004 2125
Z119585409/00000005 2250
hid hwidth
Z119585409/00000001 1870
Z119585409/00000002 2100
Z119585409/00000003 2015
Z119585409/00000004 1350
Z119585409/00000005 1700
jid jwidth hwidth
Z119585409/00000001 2250 1870
Z119585409/00000002 2150 2100
Z119585409/00000003 2125 2015
Z119585409/00000004 2125 1350
Z119585409/00000005 2250 1700

43. Perform a simple Hive query to test if the
database has been created successfully

44. Outlook
“Progress generally appears much
greater than it really is”
Johan Nestroy, 1847

45. What have WE learned?
• We need to carefully assess the efforts for data
preparation vs. the actual processing load
• HDFS prefers large files over many small ones,
is basically “append-only”
• There is still much more the Hadoop ecosystem
has to offer, e.g. YARN, Pig, Mahout

46. What can YOU do?
• Come join our “Hadoop in cultural heritage”
hackathon on 2-4 December 2013, Vienna
(See http://www.scape-project.eu/events )
• Check out some tools from our github at
https://github.com/openplanets/ and help
us make them better and more scalable
• Follow us at @SCAPEProject and spread the word!

47. What’s in it for US?
• Digital (free) access to centuries of cultural
heritage data, 24x7 and from anywhere
• Ensuring our cultural history is not lost
• New innovative applications using cultural
heritage data (education, creative industries)

48. Thank you! Questions?
(btw, we’re hiring)
www.kb.nl
www.onb.ac.at
www.scape-project.eu
www.openplanetsfoundation.org