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CFP 7th Computational Archival Science (CAS) Workshop

  • 1.  CFP 7th Computational Archival Science (CAS) Workshop

    Posted 24 days ago
    Announcing the 7th Computational Archival Science (CAS) workshop to be held in Osaka, Japan mid-December 2022 this year (co-hosted by Mark Hedges, Vicki Lemieux, and Richard Marciano).  
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    This will be the 7th workshop at IEEE Big Data addressing Computational Archival Science (CAS), following on from workshops in 20162017201820192020 and 2021It also builds on three earlier workshops on 'Big Humanities Data' organized by the same chairs at the 2013-2015 conferences, and more directly on a 2016 symposium held in April 2016 at the University of Maryland.
    All papers accepted for the workshop will be included in the Conference Proceedings published by the IEEE Computer Society Press.
    Topics include (but are not limited to): 

    • Application of analytics to archival material, including AI, ML, text-mining, data-mining, sentiment analysis, network analysis.
    • Analytics in support of archival processing, including e-discovery, identification of personal information, appraisal, arrangement and description.
    • Scalable services for archives, including identification, preservation, metadata generation, integrity checking, normalization, reconciliation, linked data, entity extraction, anonymization and reduction.
    • New forms of archives, including Web, social media, audiovisual archives, and blockchain.
    • Cyber-infrastructures for archive-based research and for development and hosting of collections
    • Big data and archival theory and practice
    • Digital curation and preservation
    • Crowd-sourcing and archives
    • Big data and the construction of memory and identity
    • Specific big data technologies (e.g. NoSQL databases) and their applications
    • Corpora and reference collections of big archival data
    • Linked data and archives
    • Big data and provenance
    • Constructing big data research objects from archives
    • Legal and ethical issues in big data archives

    Richard Marciano
    University of Maryland, College Park
    Annapolis MD