STBins
| Autor: | Vincent Bloemen, Ji Qi, Huub van de Wetering, Jarke J. van Wijk, Shihan Wang |
|---|---|
| Přispěvatelé: | Algorithms, Geometry and Applications, Visualization, EAISI Health, Amsterdam Machine Learning lab (IVI, FNWI) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Sequence
data sequence Similarity (geometry) business.industry Computer science 020207 software engineering Pattern recognition 02 engineering and technology Computer Graphics and Computer-Aided Design Readability n/a OA procedure Visualization Data visualization Signal Processing 0202 electrical engineering electronic engineering information engineering Task analysis Eye tracking time series data Computer Vision and Pattern Recognition Artificial intelligence business Software |
| Zdroj: | IEEE transactions on visualization and computer graphics, 26(1):8805450, 1054-1063. IEEE IEEE Transactions on Visualization and Computer Graphics, 26(1):8805450, 1054-1063. IEEE Computer Society IEEE Transactions on visualization and computer graphics, 26(1), 1054-1063. IEEE Computer Society |
| ISSN: | 1077-2626 |
| DOI: | 10.1109/tvcg.2019.2934289 |
| Popis: | While analyzing multiple data sequences, the following questions typically arise: how does a single sequence change over time, how do multiple sequences compare within a period, and how does such comparison change over time. This paper presents a visual technique named STBins to answer these questions. STBins is designed for visual tracking of individual data sequences and also for comparison of sequences. The latter is done by showing the similarity of sequences within temporal windows. A perception study is conducted to examine the readability of alternative visual designs based on sequence tracking and comparison tasks. Also, two case studies based on real-world datasets are presented in detail to demonstrate usage of our technique. |
| Databáze: | OpenAIRE |
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