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Reducing and linking spatio-temporal datasets with kD-STR
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Steadman, Liam, Griffiths, Nathan, Jarvis, Stephen A., Bell, M., Helman, S. and Wallbank, C. (2020) Reducing and linking spatio-temporal datasets with kD-STR. In: 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities (ARIC 2020), Seattle, Washington, USA, 3-6 Nov 2020. Published in: ARIC '20: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities pp. 10-19. ISBN 9781450381659. doi:10.1145/3423455.3430317
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WRAP-reducing-linking-spatio-temporal-datasets-kD-STR-Jarvis-2020.pdf - Accepted Version - Requires a PDF viewer. Download (2632Kb) | Preview |
Official URL: https://doi.org/10.1145/3423455.3430317
Abstract
When linking spatio-temporal datasets, the kD-STR algorithm can be used to reduce the datasets and speed up the linking process. However, kD-STR can sacrifice accuracy in the linked dataset whilst retaining unnecessary information. To overcome this, we propose a preprocessing step that removes unnecessary information and an alternative heuristic for kD-STR that prioritises accuracy in the linked output. These are evaluated in a case study linking a road condition dataset with air temperature, rainfall and road traffic data. In this case study, we found the alternative heuristic achieved a 19% improvement in mean error for the linked air temperature features and an 18% reduction in storage used for the rainfall dataset compared to the original kD-STR heuristic. The results in this paper support our hypothesis that, at worse, our alternative heuristic will yield a similar error and storage overhead for linking scenarios as the original kD-STR heuristic. However, in some cases it can give a reduction that is more accurate when linking the datasets whilst using less storage than the original kD-STR algorithm.
| Item Type: | Conference Item (Paper) | ||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QH Natural history |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
| Library of Congress Subject Headings (LCSH): | Temporal databases , Data sets , Data reduction , Data reduction -- Computer programs , Spatial data mining | ||||||
| Journal or Publication Title: | ARIC '20: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities | ||||||
| Publisher: | ACM | ||||||
| ISBN: | 9781450381659 | ||||||
| Official Date: | November 2020 | ||||||
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| Page Range: | pp. 10-19 | ||||||
| DOI: | 10.1145/3423455.3430317 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Reuse Statement (publisher, data, author rights): | © ACM, 2020 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 ARIC '20: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities. Nov 2020 http://doi.acm.org/10.1145/3423455.3430317 | ||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||
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| Conference Paper Type: | Paper | ||||||
| Title of Event: | 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities (ARIC 2020) | ||||||
| Type of Event: | Workshop | ||||||
| Location of Event: | Seattle, Washington, USA | ||||||
| Date(s) of Event: | 3-6 Nov 2020 | ||||||
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