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  5. Defoe: A Spark-Based Toolbox for Analysing Digital Historical Textual Data

Defoe: A Spark-Based Toolbox for Analysing Digital Historical Textual Data

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Resource type
Conference paper (unpublished)
Creator (person)
Filgueira, Rosa
Jackson, Michel
Terras, Melissa
Roubickova, Anna
Beavan, David
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Hobson, Timothy
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Coll Ardanuy, Mariona
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Colavizza, Giovanni
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Hetherington, James
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Krause, Amy
Hauswedell, Tessa
Nyhan, Julianne
Ahnert, Ruth
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Date published
2019
Abstract
This work presents defoe, a new scalable and portable digital eScience toolbox that enables historical research. It allows for running text mining queries across large datasets, such as historical newspapers and books in parallel via Apache Spark. It handles queries against collections that comprise several XML schemas and physical representations. The proposed tool has been successfully evaluated using five different large-scale historical text datasets and two HPC environments, as well as on desktops. Results shows that defoe allows researchers to query multiple datasets in parallel from a single command-line interface and in a consistent way, without any HPC environment-specific requirements.
Contributor (organisation)
British Library
Living with Machines
Project(s)
Living with Machines
Funder
Funder nameAwards
Arts and Humanities Research Council
AH/S01179X/1
Event title
2019 IEEE 15th International Conference on e-Science (e-Science) - San Diego, United States
Publisher
IEEE
Place of publication
San Diego, CA, USA
Official URL
https://doi.org/10.1109/eScience.2019.00033
Related URL
https://escience2019.sdsc.edu/
DOI
10.1109/eScience.2019.00033
Keywords
text mining
XML schemas
digitised primary historical sources
humanities research
digital tools
distributed queries
Apache Spark
High-Performance Computing
Additional information
The attached file is the authors' accepted manuscript of this paper published by IEEE.
Collection
Living with Machines
Managed by the British Library and supported by the AHRC

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