Progressive Presentation of Large Hierarchies Using Treemaps

Visualization and Data Analysis 2011

Giménez

Schumann

et al. 2011

Self Cite

Visual data presentations require adaptation for appropriate display on a viewing device that is limited in resources such as computing power, screen estate, and/or bandwidth. Due to the complexity of suitable adaptation, the few proposed solutions available are either too resource-intensive or inflexible to be applied broadly. Effective use and acceptance of data visualization on constrained viewing devices require adaptation approaches that are tailored to the requirements of the user and the capabilities of the viewing device.We propose a predictive device adaptation approach that takes advantage of progressive data refinement. The approach relies on hierarchical data structures that are created once and used multiple times. By incrementally reconstructing the visual presentation on the client with increasing levels of detail and resource utilization, we can determine when to truncate the refinement of detail so as to use the resources of the device to their full capacities. To determine when to finish the refinement for a particular device, we introduce a profile-based strategy which also considers user preferences. We discuss the whole adaptation process from the storage of the data into a scalable structure to the presentation on the respective viewing device. This particular implementation is shown for two common data visualization methods, and empirical results we obtained from our experiments are presented and discussed.

Section: Device Adaptation and Progressive Refinement In Visualizationmentioning

confidence: 99%

Section: Device Adaptation and Progressive Refinement In Visualizationmentioning

confidence: 99%

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A flexible low-complexity device adaptation approach for data presentation

Visualization and Data Analysis 2011

Giménez

Schumann

et al. 2011

Self Cite

“…Future implementations of our method could significantly influence areas which use progressive refinement, such as Rosenbaum's [10] technique for device adaptation. As progressive refinement methods require generation of LODhierarchies for many types and sizes of multidimensional data, our method provides much of the necessary functionality.…”

Section: Conclusion and Possible Future Researchmentioning

confidence: 99%

Using R-Trees for Interactive Visualization of Large Multidimensional Datasets

Giménez

Advances in Visual Computing

Hlawitschka

et al. 2010

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Abstract. Large, multidimensional datasets are difficult to visualize and analyze. Visualization interfaces are constrained in resolution and dimension, so cluttering and problems of projecting many dimensions into the available low dimensions are inherent. Methods of real-time interaction facilitate analysis, but often these are not available due to the computational complexity required to use them. By organizing the dataset into a level-of-detail (LOD) hierarchy, our proposed method solves problems of both inefficient interaction and visual cluttering. We do this by introducing an implementation of R-trees for large multidimensional datasets. We introduce several useful methods for interaction, by queries and refinement, to explain the relevance of interaction and show that it can be done efficiently with R-trees. We examine the applicability of hierarchical parallel coordinates to datasets organized within an R-tree, and build upon previous work in hierarchical star coordinates to introduce a novel method for visualizing bounding hyperboxes of internal R-tree nodes. Finally, we examine two datasets using our proposed method and present and discuss results.

“…1,14 Depending on the application domain, this can lead to faster data browsing, node search, or higher accuracy of task completion. These claims stem from their following inherent characteristics:…”

mentioning

confidence: 99%

Evaluation of progressive treemaps to convey tree and node properties

Visualization and Data Analysis 2012

Hamann

2012

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In this paper we evaluate progressive treemaps. Progressive refinement has a long tradition in image communication, but is a relatively new approach for information presentation. Besides technical benefits it also promises to provide advantages important for the conveyance of data properties. In this first user study in this domain, we focus on the additional value of progressive refinement for traditional treemaps to convey the topology of a given hierarchical data set and properties of its nodes. To achieve this, we compare the results gained for common squarified treemap displays with and without progression for various related tasks and set-ups. The results we obtained indicate that progressive treemaps allow for a better conveyance of topological features and node properties in most set-ups. We also assessed the opinions of our study participants and found that progressive treemaps also lead to a better confidence about the given answers and provide more assistance and user friendliness.