A Window-based Approach for Mining Long Duration Event-sequences
| dc.contributor.author | Vrotsou, Katerina | en_US |
| dc.contributor.author | Nordman, Aida | en_US |
| dc.contributor.editor | Turkay, Cagatay and Vrotsou, Katerina | en_US |
| dc.date.accessioned | 2020-05-24T13:31:31Z | |
| dc.date.available | 2020-05-24T13:31:31Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | This paper presents an interactive sequence mining approach for exploring long duration event-sequences and identifying interesting patterns within them. The approach extends previous work on exploratory sequence mining by using a sliding window to split the sequence prior to mining. Patterns are interactively grown and visualized through a tree representation, while a set of accompanying views allows for identified patterns to be explored in the context in which they occur. The approach is motivated and exemplified in the domain of air traffic control and, in particular, air traffic controller training. | en_US |
| dc.description.sectionheaders | Visual Analysis of High Dimensional and Temporal Data | |
| dc.description.seriesinformation | EuroVis Workshop on Visual Analytics (EuroVA) | |
| dc.identifier.doi | 10.2312/eurova.20201087 | |
| dc.identifier.isbn | 978-3-03868-116-8 | |
| dc.identifier.issn | 2664-4487 | |
| dc.identifier.pages | 55-59 | |
| dc.identifier.uri | https://doi.org/10.2312/eurova.20201087 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/eurova20201087 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.rights | Attribution 4.0 International License | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | ] |
| dc.subject | Human centered computing | |
| dc.subject | Visual analytics | |
| dc.subject | Information systems | |
| dc.subject | Data mining | |
| dc.title | A Window-based Approach for Mining Long Duration Event-sequences | en_US |
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