Fast Dynamic Voronoi Treemaps

Sud, Avneesh; Fisher, Danyel; Lee, Huai-Ping

doi:10.1109/isvd.2010.16

Cited by 29 publications

(26 citation statements)

References 19 publications

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“…It has a time complexity of O(n 2 ) for each iteration which is the same as Balzer's algorithm but worse than Lloyd's CVT algorithm (O(nlog(n))). Some acceleration techniques such as Sud et al [33] and Gotz et al [14] are available but it still remains a challenging task to layout Voronoi treemaps for large datasets in real time. To have the benefits of both real time interaction and high-quality layouts, DICON supports both rectangular treemap icons as well as the optimized Voronoi icons.…”

Section: Discussionmentioning

confidence: 99%

DICON: Interactive Visual Analysis of Multidimensional Clusters

Cao

Gotz

Sun

et al. 2011

IEEE Trans. Visual. Comput. Graphics

101

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Fig. 1. DICON is a dynamic icon-based visualization technique that helps users understand, evaluate, and adjust complex multidimensional clusters. It provides visual cues describing the quality of a cluster as well as its multiple attributes, and can be embedded within many kinds of visualizations such as maps, scatter plots, and graphs.Abstract-Clustering as a fundamental data analysis technique has been widely used in many analytic applications. However, it is often difficult for users to understand and evaluate multidimensional clustering results, especially the quality of clusters and their semantics. For large and complex data, high-level statistical information about the clusters is often needed for users to evaluate cluster quality while a detailed display of multidimensional attributes of the data is necessary to understand the meaning of clusters. In this paper, we introduce DICON, an icon-based cluster visualization that embeds statistical information into a multi-attribute display to facilitate cluster interpretation, evaluation, and comparison. We design a treemap-like icon to represent a multidimensional cluster, and the quality of the cluster can be conveniently evaluated with the embedded statistical information. We further develop a novel layout algorithm which can generate similar icons for similar clusters, making comparisons of clusters easier. User interaction and clutter reduction are integrated into the system to help users more effectively analyze and refine clustering results for large datasets. We demonstrate the power of DICON through a user study and a case study in the healthcare domain. Our evaluation shows the benefits of the technique, especially in support of complex multidimensional cluster analysis.

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Section: Discussionmentioning

confidence: 99%

DICON: Interactive Visual Analysis of Multidimensional Clusters

Cao

Gotz

Sun

et al. 2011

IEEE Trans. Visual. Comput. Graphics

101

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“…Sud et al [SFL10] present an improved implementation of the sampling approach using graphics hardware and General Purpose GPU techniques. While actual running times are reduced significantly, the advantage of parallelization on the graphics card could not be used for all parts of the algorithm and the asymptotic running time for a single iteration remained quadratic in the number of sites.…”

Section: Methods For Voronoi Treemapsmentioning

confidence: 99%

Computing Voronoi Treemaps: Faster, Simpler, and Resolution‐independent

Nocaj

Brandes

2012

Computer Graphics Forum

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Voronoi treemaps represent hierarchies as nested polygons. We here show that, contrary to the apparent popular belief, utilization of an algorithm for weighted Voronoi diagrams is not only feasible, but also more efficient than previous low-resolution approximations, even when the latter are implemented on graphics hardware. More precisely, we propose an instantiation of Lloyd's method for centroidal Voronoi diagrams with Aurenhammer's algorithm for power diagrams that yields an algorithm running in O(n log n) rather than Ω(n 2 ) time per iteration, with n the number of sites. We describe its implementation and present evidence that it is faster also in practice.

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“…Mental‐map preservation is also a problem in this case. This problem has been investigated and addressed using dynamic voronoi treemaps [SFL10], which are designed to be stable against changes to the primary attribute, as well as to zooming. Dealing with changing hierarchies in a streaming context seems even more problematic, but because trees are special cases of graphs, solutions might be drawn from techniques for dynamic graph drawing.…”

Section: Analysing Design Challenges and Trade‐offsmentioning

confidence: 99%

Human Factors in Streaming Data Analysis: Challenges and Opportunities for Information Visualization

Dasgupta

Arendt

Franklin

et al. 2017

Computer Graphics Forum

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Real‐world systems change continuously. In domains such as traffic monitoring or cyber security, such changes occur within short time scales. This results in a streaming data problem and leads to unique challenges for the human in the loop, as analysts have to ingest and make sense of dynamic patterns in real time. While visualizations are being increasingly used by analysts to derive insights from streaming data, we lack a thorough characterization of the human‐centred design problems and a critical analysis of the state‐of‐the‐art solutions that exist for addressing these problems. In this paper, our goal is to fill this gap by studying how the state of the art in streaming data visualization handles the challenges and reflect on the gaps and opportunities. To this end, we have three contributions in this paper: (i) problem characterization for identifying domain‐specific goals and challenges for handling streaming data, (ii) a survey and analysis of the state of the art in streaming data visualization research with a focus on how visualization design meets challenges specific to change perception and (iii) reflections on the design trade‐offs, and an outline of potential research directions for addressing the gaps in the state of the art.

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Fast Dynamic Voronoi Treemaps

Cited by 29 publications

References 19 publications

DICON: Interactive Visual Analysis of Multidimensional Clusters

DICON: Interactive Visual Analysis of Multidimensional Clusters

Computing Voronoi Treemaps: Faster, Simpler, and Resolution‐independent

Human Factors in Streaming Data Analysis: Challenges and Opportunities for Information Visualization

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