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Many applications generate and/or consume multi-variate temporal data, and experts often lack the means to adequately and systematically search for and interpret multi-variate observations. In this article, we first observe that multi-variate time series often carry localized multi-variate temporal features that are robust against noise. We then argue that these multi-variate temporal features can be extracted by simultaneously considering, at multiple scales, temporal characteristics of the time seriesalong with external knowledge, including variate relationships that are known a priori. Relying on these observations, we develop data models and algorithms to detectrobust multi-variate temporal(RMT) features that can be indexed for efficient and accurate retrieval and can be used for supporting data exploration and analysis tasks. Experiments confirm that the proposed RMT algorithm is highly effective and efficient in identifyingrobustmulti-scale temporal features of multi-variate time series.
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This content will become publicly available on January 1, 2026
Spiers Memorial Lecture: A retrospective view on the non-classical features revealed by advanced imaging of biominerals
This retrospective review focuses on two case studies that exemplify the non-classical features of biominerals, mollusk nacre for invertebrates, and bone for vertebrates, and how correlations within vitromodel systems helped unravel these features.
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- Award ID(s):
- 2314456
- PAR ID:
- 10618596
- Publisher / Repository:
- Royal Society of Chemistry
- Date Published:
- Journal Name:
- Faraday Discussions
- Edition / Version:
- on line
- Volume:
- online preprint
- ISSN:
- 1359-6640
- Format(s):
- Medium: X Size: 25.9MB
- Size(s):
- 25.9MB
- Sponsoring Org:
- National Science Foundation
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