PanoramicData : Data Analysis through Pen & Touch

Interactively exploring multidimensional datasets requires frequent switching among a range of distinct but inter-related tasks (e.g., producing different visuals based on different column sets, calculating new variables, and observing the interactions between sets of data). Existing approaches eith...

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Veröffentlicht in:IEEE transactions on visualization and computer graphics. - 1996. - 20(2014), 12 vom: 26. Dez., Seite 2112-21
1. Verfasser: Zgraggen, Emanuel (VerfasserIn)
Weitere Verfasser: Zeleznik, Robert, Drucker, Steven M
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2014
Zugriff auf das übergeordnete Werk:IEEE transactions on visualization and computer graphics
Schlagworte:Journal Article Research Support, Non-U.S. Gov't
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520 |a Interactively exploring multidimensional datasets requires frequent switching among a range of distinct but inter-related tasks (e.g., producing different visuals based on different column sets, calculating new variables, and observing the interactions between sets of data). Existing approaches either target specific different problem domains (e.g., data-transformation or data-presentation) or expose only limited aspects of the general exploratory process; in either case, users are forced to adopt coping strategies (e.g., arranging windows or using undo as a mechanism for comparison instead of using side-by-side displays) to compensate for the lack of an integrated suite of exploratory tools. PanoramicData (PD) addresses these problems by unifying a comprehensive set of tools for visual data exploration into a hybrid pen and touch system designed to exploit the visualization advantages of large interactive displays. PD goes beyond just familiar visualizations by including direct UI support for data transformation and aggregation, filtering and brushing. Leveraging an unbounded whiteboard metaphor, users can combine these tools like building blocks to create detailed interactive visual display networks in which each visualization can act as a filter for others. Further, by operating directly on relational-databases, PD provides an approachable visual language that exposes a broad set of the expressive power of SQL including functionally complete logic filtering, computation of aggregates and natural table joins. To understand the implications of this novel approach, we conducted a formative user study with both data and visualization experts. The results indicated that the system provided a fluid and natural user experience for probing multi-dimensional data and was able to cover the full range of queries that the users wanted to pose 
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