7th COMPUTATIONAL ARCHIVAL SCIENCE (CAS) WORKSHOP
PART OF: IEEE Big Data 2022 — http://bigdataieee.org/BigData2022/index.html
IMPORTANT DEADLINES:
- Friday Oct 28, 2022 (final): Due date for full workshop papers submission
- Friday Nov 4, 2022: Notification of paper acceptance to authors
- Tuesday Nov 15, 2022 (hard deadline): Camera-ready of accepted papers
- Dec 17-20, 2022: CAS#7 Workshop (exact date To Be Confirmed)
PAPER SUBMISSION:
- Please submit a full-length paper (up to 10 page IEEE 2-column format, reference pages don’t count in the 10 pages) through the online submission system at: https://wi-lab.com/cyberchair/2022/bigdata22/scripts/ws_submit.php
- Formatting Instructions: Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines: https://www.ieee.org/conferences/publishing/templates.html
COMPUTATIONAL ARCHIVAL SCIENCE: digital records in the age of big data
INTRODUCTION TO WORKSHOP [also see our CAS Portal]:
The large-scale digitization of analogue archives, the emerging diverse forms of born-digital archive, and the new ways in which researchers across disciplines (as well as the public)wish to engage with archival material, are resulting in disruptions to transitional archival theories and practices. Increasing quantities of ‘big archival data’ present challenges for the practitioners and researchers who work with archival material, but also offer enhanced possibilities for scholarship, through the application both of computational methods and tools to the archival problem space and of archival methods and tools to computational problems such as trusted computing, as well as, more fundamentally, through the integration of computational thinking with archival thinking.
Our working definition of Archival Computational Science (CAS) is:
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- A transdisciplinary field that integrates computational and archival theories, methods and resources, both to support the creation and preservation of reliable and authentic records/archives and to address large-scale records/archives processing, analysis, storage, and access, with aim of improving efficiency, productivity and precision, in support of recordkeeping, appraisal, arrangement and description, preservation and access decisions, and engaging and undertaking research with archival material.
OBJECTIVES
This workshop will explore the conjunction (and its consequences) of emerging methods and technologies around big data with archival practice (including record keeping) and new forms of analysis and historical, social, scientific, and cultural research engagement with archives.We aim to identify and evaluate current trends, requirements, and potential in these areas, to examine the new questions that they can provoke, and to help determine possible research agendas for the evolution of computational archival science in the coming years. At the same time, we will address the questions and concerns scholarship is raising about the interpretation of ‘big data’ and the uses to which it is put, in particular appraising the challenges of producing quality–meaning, knowledge and value–from quantity, tracing data and analytic provenance across complex ‘big data’ platforms and knowledge production ecosystems, and addressing data privacy issues.
This will be the 7th workshop at IEEE Big Data addressing Computational Archival Science (CAS), following on from workshops in 2016, 2017, 2018, 2019, 2020 and 2021. It 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. In addition to standard papers, the workshop (and the call for papers) will incorporate a student poster session for PhD and Master’s level students.
RESEARCH TOPICS COVERED:
Topics covered by the workshop include, but are not restricted to, the following:
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- 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
PROGRAM CHAIRS:
Dr. Mark Hedges
Department of Digital Humanities (DDH)
King’s College London, UK
Prof. Victoria Lemieux
School of Information
University of British Columbia, CANADA
Prof. Richard Marciano
Advanced Information Collaboratory (AIC)
College of Information Studies
University of Maryland, USA
PROGRAM COMMITTEE MEMBERS:
Dr. Bill Underwood
Advanced Information Collaboratory (AIC)
College of Information Studies
University of Maryland, USA
Dr. Tobias Blanke
Distinguished Professor in AI and Humanities
University of Amsterdam, THE NETHERLANDS