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kD-STR : a method for spatio-temporal data reduction and modelling
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Steadman, Liam, Griffiths, Nathan, Jarvis, Stephen A., Bell, Mark, Helman, Shaun and Wallbank, Caroline (2021) kD-STR : a method for spatio-temporal data reduction and modelling. ACM/IMS Transactions on Data Science, 2 (3). 17. doi:10.1145/3439334 ISSN 2691-1922.
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WRAP-kD-STR-method-spatio-temporal-data-reduction-Griffiths-2020.pdf - Accepted Version - Requires a PDF viewer. Download (3193Kb) | Preview |
Official URL: https://doi.org/10.1145/3439334
Abstract
Analysing and learning from spatio-temporal datasets is an important process in many domains, including transportation, healthcare and meteorology. In particular, data collected by sensors in the environment allows us to understand and model the processes acting within the environment. Recently, the volume of spatio-temporal data collected has increased significantly, presenting several challenges for data scientists. Methods are therefore needed to reduce the quantity of data that needs to be processed in order to analyse and learn from spatio-temporal datasets. In this article, we present the -Dimensional Spatio-Temporal Reduction method (D-STR) for reducing the quantity of data used to store a dataset whilst enabling multiple types of analysis on the reduced dataset. D-STR uses hierarchical partitioning to find spatio-temporal regions of similar instances, and models the instances within each region to summarise the dataset. We demonstrate the generality of D-STR with three datasets exhibiting different spatio-temporal characteristics and present results for a range of data modelling techniques. Finally, we compare D-STR with other techniques for reducing the volume of spatio-temporal data. Our results demonstrate that D-STR is effective in reducing spatio-temporal data and generalises to datasets that exhibit different properties.
Item Type: | Journal Article | ||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
Journal or Publication Title: | ACM/IMS Transactions on Data Science | ||||||
Publisher: | ACM | ||||||
ISSN: | 2691-1922 | ||||||
Official Date: | July 2021 | ||||||
Dates: |
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Volume: | 2 | ||||||
Number: | 3 | ||||||
Article Number: | 17 | ||||||
DOI: | 10.1145/3439334 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Re-use Statement: | "© ACM, 2021. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in 2(3) ACM/IMS Transactions on Data Science http://doi.acm.org/10.1145/3439334 | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 27 November 2020 | ||||||
Date of first compliant Open Access: | 2 August 2021 | ||||||
Related URLs: | |||||||
Open Access Version: |
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