Movement as an aid to understanding graphs

Bovey, J. D.; Rodgers, Peter; Benoy, Florence

doi:10.1109/iv.2003.1218027

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…The only challenge is to distinguish the distance distortion and cluster overlapping introduced by the mapping. Uniquely different from other models, by tuning α i values, we can scrutinize the multidimensional dataset visually from different perspectives, which gives dynamic visual clues for distinguishing the visual overlapping 2 . In addition, since this model is a record-based mapping function, it is naturally parallel and can be implemented with the popular parallel processing models such as MapReduce [7] for large scale cloud-based data processing.…”

Section: The Vista Visualization Modelmentioning

confidence: 99%

“…The family of star coordinates systems [16,3] address the visual distortion problem with a more efficient way, which is also the basis of our approach. The advantage of stochastic animation in finding patterns, as we do with RandGen, is also explored in graph visualization [2] The three-phase framework "sampling or summarization -clustering/cluster analysis -disk labeling" is often used to incorporate the algorithms of high time complexity in exploring large datasets. As the size of data grows to very large, the rate between the size of the sampled or summarized dataset to the original size becomes very small, affecting the fidelity of the preserved clustering structure.…”

Section: Related Workmentioning

confidence: 99%

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CloudVista: Visual Cluster Exploration for Extreme Scale Data in the Cloud

Chen

Tian

et al. 2011

Lecture Notes in Computer Science

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Abstract. The problem of efficient and high-quality clustering of extreme scale datasets with complex clustering structures continues to be one of the most challenging data analysis problems. An innovate use of data cloud would provide unique opportunity to address this challenge. In this paper, we propose the CloudVista framework to address (1) the problems caused by using sampling in the existing approaches and (2) the problems with the latency caused by cloud-side processing on interactive cluster visualization. The CloudVista framework aims to explore the entire large data stored in the cloud with the help of the data structure visual frame and the previously developed VISTA visualization model. The latency of processing large data is addressed by the RandGen algorithm that generates a series of related visual frames in the cloud without user's intervention, and a hierarchical exploration model supported by cloud-side subset processing. Experimental study shows this framework is effective and efficient for visually exploring clustering structures for extreme scale datasets stored in the cloud.

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Section: The Vista Visualization Modelmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

CloudVista: Visual Cluster Exploration for Extreme Scale Data in the Cloud

Chen

Tian

et al. 2011

Lecture Notes in Computer Science

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“…To motivate the subjects we decided to make the investigation part of a game that could only be played successfully by subjects who understood the structure of the displayed graph Whilst there is previous work in animation [2,3,4] and user controlled movement of 3D graphs [7], we can find no evidence of other research groups developing systems to aid comprehension by the automated regular movement of graph or similar data structures. Our paper at IV03 [1] introduced movement for the understanding of graphs. The initial study described in that paper showed some promising results, but the outcome was ambiguous.…”

Section: Introductionmentioning

confidence: 99%

“…Various kinds of graph movement are possible (see [1] for a further discussion), however in this paper we used circular motion in which the nodes are out of phase with each other. That is, each node moves in small, smooth, circles of the same size and speed, but with different nodes at different points on the circle so that the nodes move relatively to each other.…”

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confidence: 99%

Using games to investigate movement for graph comprehension

Bovey

Benoy

Rodgers

2004

Proceedings of the Working Conference on Advanced Visual Interfaces

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We describe the results of empirical investigations that explore the effectiveness of moving graph diagrams to improve the comprehension of their structure. The investigations involved subjects playing a game that required understanding the structure of a number of graphs. The use of a game as the task was intended to motivate the exploration of the graph by the subjects. The results show that movement can be beneficial when there is nodenode or node-edge occlusion in the graph diagram but can have a detrimental effect when there is no occlusion, particularly if the diagram is small. We believe the positive result should generalise to other graph exploration tasks, and that graph movement is likely be useful as an additional graph exploration tool.

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Mobile liquid 2D scatter space (ML2DSS)

Waldeck¹,

Balfanz²

Proceedings. Eighth International Conference on Information Visualisation, 2004. IV 2004.

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This (video-) paper describes a user interface concept, which facilitates browsing of comparably large amounts of information on small screens. The viewing concept is based on a visually optimized star field display ([1] Ahlberg & Shneiderman 1994) and is introducing new concepts like liquid browsing (an expansion lens with pressure controlled magnetic force simulation), selection based filtering and representation manipulation, multimotion behavior tagging and continuous state animation. All this creates a very versatile information space with a liquid-like look and feel, which can be used in a very intuitive way, providing all the advantages of a mature interactive scatter plot and can easily be scaled to different screen sizes without any adjustment efforts. This paper focuses on visual design and interaction details and emphasizes the importance of the visual interactive quality for mobile information visualization.

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Movement as an aid to understanding graphs

Abstract: This paper describes a graph visualization method

Cited by 4 publications

References 6 publications

CloudVista: Visual Cluster Exploration for Extreme Scale Data in the Cloud

CloudVista: Visual Cluster Exploration for Extreme Scale Data in the Cloud

Using games to investigate movement for graph comprehension

Mobile liquid 2D scatter space (ML2DSS)

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