Libraries collect books, magazines and newspapers. Yes, that's what they always did. But today, the amount of digital information resources is growing at dizzying speed. Facing the demand of digital information resources available 24/7, there has been a significant shift regarding a library's core responsibilities. Today's libraries are curating large digital collections, indexing millions of full-text documents, preserving Terabytes of data for future generations, and at the same time exploring innovative ways of providing access to their collections. This is exactly where Hadoop comes into play. Libraries have to process a rapidly increasing amount of data as part of their day-to-day business and computing tasks like file format migration, text recognition, linguistic processing, etc., require significant computing resources. Many data processing scenarios emerge where Hadoop might become an essential part of the digital library's ecosystem. Hadoop is sometimes referred to as a hammer where you have to throw away everything that is not a nail. To remain in that metaphor: we will present some actual use cases for Hadoop in libraries, how we determine what are the nails in a library and what not, and some initial results.
Our libraries• The Hague, Netherlands • Vienna, Austria• Founded in 1798 • Founded in 14th century• 120.000 visitors per year • 300.000 visitors per year• 6 million documents • 8 million documents• 260 FTE • 300 FTE www.kb.nl www.onb.ac.at
DigitizationLibraries are rapidly transforming from physical…to digital…
TransformationCuration Lifecycle Model from Digital Curation Centre www.dcc.ac.uk
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?
2. Numbers“A good decision is based on knowledge and not on numbers” Plato, 400 BC
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
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
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
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
3. Challenges“What do you do with a million books?” Gregory Crane, 2006
Making it scaleScalability in terms of … • size • number • complexity • heterogeneity
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
Use cases (I)• Document recognition: From image to XML• Business case: • Better presentation options • Creation of eBooks • Full-text indexing
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
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
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/
Execution environment Cluster Taverna ServerFile server (REST API) Hadoop Apache Tomcat Jobtracker Web Application
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 20020110830130709 9684 46 16 3 image/jpeg http://URL at IP 22123 20020110830130710 9684 46 16 3 image/gif http://URL at IP 9794 20020110830130707 9684 46 16 3 image/jpeg http://URL at IP 40056 20020110830130704 9684 46 16 3 text/html http://URL at IP 13149 20020110830130712 9684 46 16 3 image/gif http://URL at IP 2285 20020110830130712 9684 46 16 3 text/html http://URL at IP 415 30120110830130710 9684 46 16 3 text/html http://URL at IP 7873 20020110830130712 9684 46 16 3 text/html http://URL at IP 632 30220110830130712 9684 46 16 3 image/png http://URL at IP 679 200
Scenarios (II) Web archiving: File format identification → Run file type identification on archived web content(W)ARC Container JPG (W)ARC RecordReader MapReduce Apache Tika GIF JPG image/jpg detect MIME based on HTM HERITRIX Map Web crawler Reduce read/write (W)ARC image/jpg 1 HTM image/gif 1 text/html 2 audio/midi 1 MID
Scenarios (II)Web archiving: File format identification→ Using MapReduce to calculate statistics DROID 6.01 TIKA 1.0
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 ...
Perform a simple Hive query to test if thedatabase has been created successfully
Outlook“Progress generally appears much greater than it really is” Johan Nestroy, 1847
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
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!
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)