The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Visual Methods for Analyzing Time-Oriented Data
Abstract
Providing appropriate methods to facilitate the analysis of time-oriented data is a key issue in many application domains. In this paper, we focus on the unique role of the parameter time in the context of visually driven data analysis. We will discuss three major aspects - visualization, analysis, and the user. It will be illustrated that it is necessary to consider the characteristics of time when generating visual representations. For that purpose we take a look at different types of time and present visual examples. Integrating visual and analytical methods has become an increasingly important issue. Therefore, we present our experiences in temporal data abstraction, principal component analysis, and clustering of larger volumes of time-oriented data. The third main aspect we discuss is supporting user-centered visual analysis. We describe event-based visualization as a promising means to adapt the visualization pipeline to needs and tasks of users.
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Cites
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The eyes have it: a task by data type taxonomy for information visualizations
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A symbolic representation of time series, with implications for streaming algorithms
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Illuminating the Path: The Research and Development Agenda for Visual Analytics
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Experiencing SAX: A novel symbolic representation of time series
Lin, J., Keogh, E., Wei, L., & Lonardi, S. (2007) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-007-0064-z · Cited by 1,618
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ThemeRiver: visualizing thematic changes in large document collections
Havre, S., Hetzler, E., Whitney, P., & Nowell, L. (2002) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/2945.981848 · Cited by 672
A symbolic representation of time series, with implications for streaming algorithms
Lin, J., Keogh, E., Lonardi, S., & Chiu, B. (2003) · doi:10.1145/882085.882086 · Cited by 642
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Analysis and visualisation of movement: an interdisciplinary review
Demšar, U., Buchin, K., Cagnacci, F., Safi, K., Speckmann, B., Van de Weghe, N., Weiskopf, D., & Weibel, R. (2015) · Movement Ecology · doi:10.1186/s40462-015-0032-y · Cited by 128
Screenomics: a new approach for observing and studying individuals’ digital lives.
Ram, N., Yang, X., Cho, M.-J., Brinberg, M., Muirhead, F., Reeves, B., & Robinson, T. N. (2020) · Journal of Adolescent Research · doi:10.1177/0743558419883362 · Cited by 83
“One may zoom in or out across time scales to examine specific behaviors of finite length, cyclic or acyclic patterns of behavior, or long-term trends in behavior that match the times scale relevant to theoretical questions (Aigner, Miksch, Muller, Schumann, & Tominski, 2008).”
Background
Metadata
- Authors
- Aigner, W., Miksch, S., Muller, W., Schumann, H., & Tominski, C.
- Venue
- IEEE Transactions on Visualization and Computer Graphics
- Published
- 2008-01
- Volume
- 14
- Issue
- 1
- Pages
- 47-60
- ISSN
- 1077-2626 (ISSN-L)
- OpenAlex
- W2163487757
- Cited by
- 284
- References
- 28
- FWCI
- 17.89
- Type
- journal article
- Language
- en
- Open access
- closed
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- Aigner, W., Miksch, S., Muller, W., Schumann, H., & Tominski, C. (2008). Visual Methods for Analyzing Time-Oriented Data. IEEE Transactions on Visualization and Computer Graphics. https://doi.org/10.1109/tvcg.2007.70415
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
