LFPeers: Temporal Similarity Search in Covid-19 Data
| dc.contributor.author | Burmeister, Jan | en_US |
| dc.contributor.author | Bernard, Jürgen | en_US |
| dc.contributor.author | Kohlhammer, Jörn | en_US |
| dc.contributor.editor | Vrotsou, Katerina and Bernard, Jürgen | en_US |
| dc.date.accessioned | 2021-06-12T11:22:15Z | |
| dc.date.available | 2021-06-12T11:22:15Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | While there is a wide variety of visualizations and dashboards to help understand the data of the Covid-19 pandemic, hardly any of these support important analytical tasks, especially of temporal attributes. In this paper, we introduce a general concept for the analysis of temporal and multimodal data and the system LFPeers that applies this concept to the analysis of countries in a Covid-19 dataset. Our concept divides the analysis in two phases: a search phase to find the most similar objects to a target object before a time point t0, and an exploration phase to analyze this subset of objects after t0. LFPeers targets epidemiologists and the public who want to learn from the Covid-19 pandemic and distinguish successful and ineffective measures. | en_US |
| dc.description.sectionheaders | Temporal Data and Clustering | |
| dc.description.seriesinformation | EuroVis Workshop on Visual Analytics (EuroVA) | |
| dc.identifier.doi | 10.2312/eurova.20211098 | |
| dc.identifier.isbn | 978-3-03868-150-2 | |
| dc.identifier.pages | 49-53 | |
| dc.identifier.uri | https://doi.org/10.2312/eurova.20211098 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/eurova20211098 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.title | LFPeers: Temporal Similarity Search in Covid-19 Data | en_US |
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