THe HathiTrust Research Center: Digital Humanities at Scale


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This is my keynote for the Innovation Conference sponsored by the FSU Libraries and PLAN held in Panama City, Florida on August 17, 2012.

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  • == Corpus Usage Patterns (cont'd) === 1)  In this use case, the user may wish to select a chapter from each book in the collection he/she assembled and perform analysis over these chapters2) In this use case, analysis only on some special pages of each book (e.g. foreword, or first page of each chapter) in his/her collection.3) in this case, analysis on special contents of each book (e.g. table of contents, indices, glossaries
  • THe HathiTrust Research Center: Digital Humanities at Scale

    1. 1. The HathiTrust Research Center: Digital Humanities at Scale Robert H. McDonald | @mcdonald Executive Committee-HathiTrust Research Center (HTRC) Deputy Director-Data to Insight Center Associate Dean-University Libraries Indiana University
    2. 2. Presentation Overview• What is the HathiTrust Research Center (HTRC)?• How does the HTRC Work?• Overview of Current HTRC Research – Applied Technology for Access to the HT Corpus – New Models for Non-Consumptive Research• New questions for scaleable Digital Humanities Computing?• How to get engaged with the HTRC?
    3. 3. HathiTrust Research Center (HTRC): The HTRC is dedicated to enable computational access for nonprofit and educational users to published works inthe public domain and, in the future, onlimited terms to works in-copyright from the HathiTrust.
    4. 4. Big Data and Access Problem HTRC HT Volume HT Store andVolume Volume Index Store Store (IUB) (UM) XSEDE (IUPUI) Compute FutureGrid Allocation Computation Cloud UIUC Compute Allocation IU Compute Allocation
    5. 5. New questions require computational access to large corpusInvestigate way in which concepts of philosophy are used in physics through – Extracting argumentative structure from large dataset using mixture of automated and social computing techniques – Capture evidence for conjecture that availability of such analyses will enable innovative interdisciplinary research – Digging into Data 2012 award, Colin Allen, IU
    6. 6. New questions, cont.Document through software text analysis techniques, the appearance, frequency and context of terms, concepts and usages related to human rights in a selection of English- language novels. – Ronnie Lipschutz of UCSC is currently doing this analysis on one of Jane Austen’s books. He’d like to extend the work to encompass a far larger corpus.
    7. 7. New questions, cont.Identify all 18th century published books in HathiTrust corpus, and apply topic modeling to create a consistent overall subject metadata
    8. 8. Towards an HT Research Center• Started in response to proposed Google Settlement - June 2009  Specific Funding set aside by Google to build a public research center  Worked to identify key stakeholders from HT institutions to collaborate and write RFP• Developed specific RFP for HathiTrust to solicit proposals – Summer/Fall 2009 – HTRC RFP Working Group• RFP Released – Winter 2010
    9. 9. HTRC RFP Working Group or• Kat Hagedorn-HathiTrust • Robert H. McDonald-Indiana• Jeremy York-HathiTrust • Qiaozhu Mei-Michigan• Melissa Levine-HathiTrust • Shawn Newsam-California-• John Wilkin-HathiTrust Merced• Steve Abney-Michigan • John Ober- California Digital Library• Jack Bernard-Michigan • Beth Plale-Indiana• Geoffrey Fox-Indiana • Scott Poole-Illinois• David Goldberg-California-Irvine • Sarah Shreeves-Illinois • John Unsworth-Illinois
    10. 10. HTRC Governance• Reports to the HathiTrust Board of Governors – Laine Farley-CDL is liaison to the HT BOG• HTRC Executive Committee – Co-Directors: Beth Plale (IU) – J. Stephen Downie (UIUC) – IU-Robert H. McDonald (IU) – UIUC-Beth Sandore (UIUC) – Marshall Scott Poole (UIUC) – John Unsworth (Brandeis)• HTRC Advisory Board (See members next slide)• Google Public Domain agreement – in place for IU and UIUC
    11. 11. HTRC Advisory Board• Cathy Blake, University of Illinois, Urbana-Champaign• Beth Cate, Indiana University• Greg Crane, Tufts University• Laine Farley, California Digital Library• Brian Geiger, University of California at Riverside• David Greenbaum, University of California at Berkeley• Fotis Jannidis, University of Wurzberg, Germany• Matthew Jockers, Stanford University• Jim Neal, Columbia University• Bill Newman, Indiana University• Bethany Nowviskie, University of Virginia• Andrey Rzhetsky, University of Chicago• Pat Steele, University of Maryland• Craig Stewart, Indiana University• David Theo Goldberg, University of California at Irvine• John Towns, National Center for Supercomputing Applications• Madelyn Wessel, University of Virginia
    13. 13. The study• Dr. John Unsworth, a representative of HTRC, distributed invitations to participate in this study via email to 22 researchers given Google Digital Humanities Research Awards.• Interviews were conducted via telephone, Skype®, or face-to-face, and all were audio recorded. All participants agreed to IRB permission statement via email.• A semi-structured interview protocol was developed with input from HTRC to elicit responses from participants on primary goals of project.
    14. 14. Select Findings• Optical Character Recognition – Steps should be taken to improve OCR quality if and when possible – Scalability of scanned image viewing is necessary for OCR reference and correction – Metadata should expose the quality of OCR
    15. 15. Select Findings• “Would like better metadata about text languages, particularly in multi-text documents and on language by sections within text. Automatic language identification functions would be helpful, but human-created metadata is preferred, particularly for documents with low OCR quality.”• “primary issue was retrieving the bibliographic records in usable form, unparsed by Google. […] process took 10 months to design the queries and get the data.”
    16. 16.  HathiTrust is large corpusproviding opportunity for newforms of computationinvestigation. The bigger the data, the lessable we are to move it to aresearcher’s desktop machine Future research on largecollections will requirecomputation moves to thedata, not vice versa
    17. 17. HTRC Goals• HTRC will provide a persistent and sustainable structure to enable original and cutting edge research.• Stimulate the development in community of new functionality and tools to enable new discoveries that would not be possible without the HTRC.
    18. 18. HTRC Goals (cont.)• Leverage data storage and computational infrastructure at Indiana U and UIUC,• Provision secure computational and data environment for scholars to perform research using HathiTrust Digital Library.• Center will break new ground, allowing scholars to fully utilize content of HathiTrust Library while preventing intellectual property misuse within confines of current U.S. copyright law.
    19. 19. HathiTrust Research Center• Analysis on 10,000,000+ volumes of HathiTrust Type of Data (Public Estimated initial digital repository domain and copyrighted size: 300-500 TB• Founded 2011 works) Solr Indexes 6.8 TB (3 indexes)• Working with OCR File system rsync 2.3 TB• Large-scale data storage (pairtree) and access Fast volume access store 3.7 TB (Cassandra)• HPC and Cloud Versions of collection (5) 11.5 TB (2.3TB x 5) Volume store indexes 6.8 TB8/17/2012 19
    20. 20. Corpus Usage Patterns Chapter 1 Chapter 1 Access by Chapter 1 chapter Page IV Access by Page IV page Page IV Table of Contents Access by Table of 1…………. #Contents 1…………. 2………… # Table of ## Contents special contents 2…………# 1…………. # # 2…………# # (table of contents, index, glossary)8/17/2012 20
    21. 21. HTRC Timeline• Phase I: an 18-mo development cycle – Began 01 July 2011 – Demo of capability September 2012 (14 mo mark)• Phase II: broad availability of resource, begins 01 January 2013
    22. 22. HTRC UnCamp• HTRC UnCamp to be held at Indiana University – September 10-11, 2012• Goals of HTRC UnCamp – Demo of initial HTRC API and SOA Infrastructure per 4 use cases as based on original RFP submission of 2010 – Researcher and Librarian input on next steps for HTRC services
    23. 23. • Web services architecture and protocols• Registry of services and algorithms• Solr full text indexes• noSQL store as volume store• Large scale computing• openID authentication• Portal front-end, programmatic access• SEASR mining algorithms8/17/2012 23
    24. 24. HTRC Architecture Overview Portal access High level apps Nam Token Sentence Direct e filter tokenizer programmatic entity access (by Meandre Latent semantic Word programs running analysis position on HTRC machines) Data API access interface Algorithm Security CassandraRegistry (WS02) (OAuth) cluster volume store Application Solr index submission AuditCompute resources Storage resources
    25. 25. Web portal Desktop SEASR client CI logon Agent SEASR analytics WSO2 registry services, collections, data (NCSA) framework service capsule images Blacklight HTRC Data API v0.1 Agent Agent instance Agent instance Agent Access instance instance control (e.g. Solr index Programmatic Grouper) access e.g., Task Meandre deployment Orchestration Non-consumptive Volume store Data capsules Volume store (Cassandra) Volume store (Cassandra) NCSA local resources (Cassandra) Future Grid HathiTrust NCSA HPC resources rsync corpus Page/volume Penguin on tree (file system) Demand University of Michigan8/17/2012 25
    26. 26. One access point: through SEASR8/17/2012 26
    27. 27. SEASR: workflowused to generatetagcloudQuery = title:Abraham LincolnAND publishDate:1920 8/17/2012 27
    28. 28. Query = author:Withers, Hartley 8/17/2012 28
    29. 29. Workflow invokes HTRC Solr index and HTRC data API. 8/17/2012 29
    30. 30. HTRC Solr index• The Solr Data API 0.1 test version available. – Preserves all query syntax of original Solr, – Prevents user from modification, – Hides the host machine and port number HTRC Solr is actually running on, – Creates audit log of requests, and – Provides filtered term vector for words starting with user-specified letter• Test version service soon available
    31. 31. HTRC Solr index: most used API patterns• ===== query•• =====faceted search•*:*&fac et=on&facet.field=genre• ===get frequency and offset of words starting with letter• 2000011575976/w• ===== banned modification request:• http://localhost:8983/solr/update?stream.body=<delete><qu ery>id:298253</query></delete>&commit=truethanks,
    32. 32. Requirement: run big jobs onlarge scale (free or nearlyfree (leveraged)) computeresources
    33. 33. Data Capsules VM Cluster HTRC Volume Store and Index Remote Provide secure Desktop VM Or VNC Submit secureScholars capsule FutureGrid map/reduce Data Computation Capsule images Cloud to FutureGrid. Receive and review resultsSecure Data Capsule
    34. 34. HTRC FutureGrid Single source 4GB chunk + of write from user buffer Map map node. < HTRC block store Mapreduce Read only node (0) 1GB per map headnode. node Take experiment with Partitions 4GB chunk 5M vols (4TB Map 4TB evenly uncompressed data.) node (1) amongst ReduceBlock abstraction at vol 1000 nodes. size or larger. Trusted because run 4GB chunk Map as user HTRC. node (2) Data User buffer Capsule needs to be node User buffer for secondary copied to data user each map node. 4GB chunk Map submits for use in node computation (999) Data Capsule nodes Provenance capture (through Karma provenance tool)
    35. 35. Creating Functionality forNon-consumptive Research Key notions
    36. 36. Notion of a Proxy• “researcher” in Google Book Settlement definition means a human• An avatar is a virtual life form acting on behalf of person• Computer program acts on behalf of person• Computer program (i.e., proxy) must be able to read Google texts, otherwise computational analysis is impossible to carry out.• So non-consumption applied to books applies to human consumption.
    37. 37. Non-consumptive research – implementation definition• No action or set of actions on part of users, either acting alone or in cooperation with other users over duration of one or multiple sessions can result in sufficient information gathered from collection of copyrighted works to reassemble pages from collection.• Definition disallows collusion between users, or accumulation of material over time. Differentiates human researcher from proxy which is not a user. Users are human beings.
    38. 38. Notion of Algorithm• Computational analysis is accomplished through algorithms – An algorithm carries out one coherent analysis task: sort list of words, compute word frequency for text• Researcher’s computational analysis often requires running sequence of algorithms. Important distinction for implementing non- consumptive research is “who owns the algorithm”?
    39. 39. Infrastructure for computational analysis• When needing to support computation over 10+M volume corpus, algorithms must be co- located with data.• That is, algorithms must be located where repository is located, and not on user’s desktop.• When computational analysis is to be non- consumptive, likely one location for the data.
    40. 40. Who owns algorithm?• HTRC owns the algorithms, – use Software Environment for Advancement of Scholarly Research (SEASR) suite of algorithms – we are examining security requirements of users, algorithms, and data
    41. 41. User owns and submits their algorithms• HTRC recently received funding from Alfred P. Sloan foundation to prototype “data capsule framework” that provisions for non- consumptive research.• Founded on principle of “trust but verify”. Informatics-savvy humanities scholar is given freedom to experiment with new algorithms on protected information, but technological mechanisms in place to prevent undesirable behavior (leakage.)
    42. 42. Non-consumptive, user-owned algorithms infrastructure; requirements:• Implements non-consumptive• Openness – users not limited to using known set of algorithms• Efficiency – Not possible to analyze algorithms for conformance prior to running• Low cost and scale – Run at large-scale and low cost to scholarly community of users• Long term value –adoption for other purposes
    43. 43. Categories of algorithms. Can fair use be determined based on categorization ofalgorithm? Or is all computational use fair use?8/17/2012 43
    44. 44. Algorithm results fair use?• Center supplied – Easier because we know category of algorithm• User supplied – HTRC is not examining code, so open question
    45. 45. HTRC Philosophy and NCR• Finally, results of computational research that conforms to restrictions of non-consumptive research must belong to researcher
    46. 46. How to Engage• Building partnership with researchers and research communities is key goal of the HathiTrust Research Center• HTRC can give technical advice to researchers as they look for funding opportunities involving access to research data• Upcoming “Fix the OCR and Metadata Shortage Community Challenge” : help us address key weaknesses of HT corpus
    47. 47. HTRC Upcoming Events• JADH2012 Sept 15, 2012 - HathiTrust Research Center: Pushing the Frontiers of Large Scale Text Analytics – J. Stephen Downie-UIUC• HathiTrust Research Center UnCamp – Sept 10-11, 2012 – Indiana University• Demonstration Presentation for the HathiTrust Digital Library Board of Governors – Sept 2012
    48. 48. Thank You• This presentation was made possible with content provided by many HTRC colleagues Beth Plale, Marshall Scott Poole, John Unsworth, J. Stephen Downie, Stacy Kowalczyk, Yiming Sun, Guangchen Ruan, Loretta Auvil, Kirk Hess, and many others…• The HTRC Non-Consumptive Research Grant is graciously funded by the Alfred P. Sloan Foundation• IU D2I-PTI is graciously funded by The Lilly Endowment, Inc.• HTRC -• IU D2I Center -
    49. 49. Contact Information• Robert H. McDonald – Email – – Chat – rhmcdonald on googletalk | skype – Twitter - @mcdonald – Blog –