%0 Journal Article %1 4359494 %A Aigner, W. %A Miksch, S. %A Muller, W. %A Schumann, H. %A Tominski, C. %D 2008 %J Visualization and Computer Graphics, IEEE Transactions on %K data_analysis visualization %N 1 %P 47-60 %R 10.1109/TVCG.2007.70415 %T Visual Methods for Analyzing Time-Oriented Data %U http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4359494 %V 14 %X 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.

@article{4359494, 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.}, added-at = {2013-04-04T18:09:29.000+0200}, author = {Aigner, W. and Miksch, S. and Muller, W. and Schumann, H. and Tominski, C.}, biburl = {https://www.bibsonomy.org/bibtex/2b6604dfd00ec36446a8dda93d063cc87/alex_ruff}, description = {IEEE Xplore - Visual Methods for Analyzing Time-Oriented Data}, doi = {10.1109/TVCG.2007.70415}, interhash = {c1272f34ceedba9b5179e4d19cd56cc6}, intrahash = {b6604dfd00ec36446a8dda93d063cc87}, issn = {1077-2626}, journal = {Visualization and Computer Graphics, IEEE Transactions on}, keywords = {data_analysis visualization}, number = 1, pages = {47-60}, timestamp = {2013-04-04T18:09:29.000+0200}, title = {Visual Methods for Analyzing Time-Oriented Data}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4359494}, volume = 14, year = 2008 }