CAS Workshop #2 (Boston, Dec. 13, 2017)

The 2nd Computational Archival Science (CAS) workshop was held at IEEE Big Data 2017 in Boston on Dec. 13.

Richard Marciano (UMD), Vicki Lemieux (UBC), and Mark Hedges (King’s College London)


See: for a list of computational methods and archival concepts associated with each of the presentations.

The workshop featured:

  • 14 presentations from France, Netherlands, UK, Canada, US, Taiwan
  • 2 demos from GE and US
  • 25 UMD iSchool students on talks/papers and panels on new CAS curricula development!

Moderator: Michael Kurtz

Students: LEFT TO RIGHT — Jennifer Proctor, Claire McDonald , Will Thomas discussed educational takeaways, and methods for incorporating CAS into the Master’s of Library and Information Science (MLIS) in order to better address the needs of today’s MLIS graduates looking to employ both ‘traditional’ archival principles in conjunction with computational methods.

They represented the seven graduate students at the U. Maryland iSchool who participated in the fall 2017 seminar exploring the eight case studies proposed in the 2017 Foundational Paper: “Archival records and training in the Age of Big Data”, Marciano, Lemieux, Hedges, Esteva, Underwood, Kurtz, Conrad, LINK, to be published in May 2018 in “Advances in Librarianship – Re-Envisioning the MLIS: Perspectives on the Future of Library and Information Science Education”, Editors: Lindsay C. Sarin, Johnna Percell, Paul T. Jaeger, & John Carlo Bertot.

Other UMD iSchool-related faculty / student talks included:

#3: Computational Curation of a Digitized Record Series of WWII Japanese-American Internment 
[William Underwood, Richard Marciano, Sandra Laib, Carl Apgar, Luis Beteta, Waleed Falak, Marisa Gilman, Riss Hardcastle, Keona Holden, Yun Huang, David Baasch, Brittni Ballard, Tricia Glaser, Adam Gray, Leigh Plummer, Zeynep Diker, Mayanka Jha, Aakanksha Singh, and Namrata Walanj — University of Maryland, USA]

Slides — Paper

  • Computational Methods: NLP, NER, GIS, Graph database,
    linked data
  • Archival Concepts: Digital curation, automated metadata extraction

#8: Heuristics for Assessing Computational Archival Science (CAS) Research: The Case of the Human Face of Big Data Project 
[Myeong Lee, Yuheng Zhang, Shiyun Chen, Edel Spencer, Jhon Dela Cruz, Hyeonggi Hong, and Richard Marciano — University of Maryland, USA]

Slides — Paper

  • Computational Methods: Heuristics for CAS research,
  • Archival Concepts: Iterative design, value-sensitive design

#14: A Typology of Blockchain Recordkeeping Solutions and Some Reflections on their Implications for the Future of Archival Preservation 
[Victoria Lemieux — University of British Columbia, CAN]

Slides — Paper

  • Computational Methods: Blockchain, computational validation, distributed ledger, computational trust
  • Archival Concepts: Recordkeeping, digital preservation,
    archival trust

Greg Jansen, University of Maryland, USA

DRAS-TIC for Linked Data and Memento
greg_jansen Greg showcased the next phase of DRAS-TIC software development and scalability testing. Digital Repository At Scale — That Invites Computation (DRAS-TIC) Funded through the NSF Brown Dog project (see: The next phase of DRAS-TIC development was funded by a two-year grant from the IMLS as the “DRAS-TIC Fedora” project. This will see our horizontal scaling NoSQL digital repository grow to support the Linked Data Platform and Memento APIs for versioned linked data. We aim to meet these stringent LDP requirements and continue to support distributed compute on the Cassandra back-end.

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