Congressional samples for approximate answering of group-by queries
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
IEEE Transactions on Visualization and Computer Graphics
High Dimensional Brushing for Interactive Exploration of Multivariate Data
VIS '95 Proceedings of the 6th conference on Visualization '95
A Scalable Framework for Information Visualization
INFOVIS '00 Proceedings of the IEEE Symposium on Information Vizualization 2000
Dynamic sample selection for approximate query processing
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Cluster Validity Indices for Graph Partitioning
IV '04 Proceedings of the Information Visualisation, Eighth International Conference
By Chance is not Enough: Preserving Relative Density through non Uniform Sampling
IV '04 Proceedings of the Information Visualisation, Eighth International Conference
Clutter Reduction in Multi-Dimensional Data Visualization Using Dimension Reordering
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Feature congestion: a measure of display clutter
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
by chance enhancing interaction with large data sets through statistical sampling
Proceedings of the Working Conference on Advanced Visual Interfaces
XmdvtoolQ:: quality-aware interactive data exploration
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Nugget discovery in visual exploration environments by query consolidation
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
An optimization-based approach to dynamic data transformation for smart visualization
Proceedings of the 13th international conference on Intelligent user interfaces
Theoretical Foundations of Information Visualization
Information Visualization
Surveying the complementary role of automatic data analysis and visualization in knowledge discovery
Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration
Data, information, and knowledge in visualization
IEEE Computer Graphics and Applications - Special issue title on generating 3D building models a VR playground for teaching math
ACM SIGKDD Explorations Newsletter
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Journal of Visual Languages and Computing
The notion of overview in information visualization
International Journal of Human-Computer Studies
ACM Transactions on Intelligent Systems and Technology (TIST)
Conceptualizing Visual Uncertainty in Parallel Coordinates
Computer Graphics Forum
A screen space quality method for data abstraction
EuroVis'08 Proceedings of the 10th Joint Eurographics / IEEE - VGTC conference on Visualization
A salience-based quality metric for visualization
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
Visual reconstructability as a quality metric for flow visualization
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
An information-theoretic observation channel for volume visualization
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
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Data abstraction techniques are widely used in multiresolution visualization systems to reduce visual clutter and facilitate analysis from overview to detail. However, analysts are usually unaware of how well the abstracted data represent the original dataset, which can impact the reliability of results gleaned from the abstractions. In this paper, we define two data abstraction quality measures for computing the degree to which the abstraction conveys the original dataset: the Histogram Difference Measure and the Nearest Neighbor Measure. They have been integrated within XmdvTool, a public-domain multiresolution visualization system for multivariate data analysis that supports sampling as well as clustering to simplify data. Several interactive operations are provided, including adjusting the data abstraction level, changing selected regions, and setting the acceptable data abstraction quality level. Conducting these operations, analysts can select an optimal data abstraction level. Also, analysts can compare different abstraction methods using the measures to see how well relative data density and outliers are maintained, and then select an abstraction method that meets the requirement of their analytic tasks.