A 3D Visualization Technique for Large Scale Time-Varying Data

Imoto, Maiko; Itoh, Takayuki

doi:10.1109/iv.2010.12

Cited by 14 publications

(3 citation statements)

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“…Heatmap has advantages over other representations from the standpoint of cluttering reduction and display space utilization for overviews. Imoto et al presented a technique that extracts interesting portions of time-varying data on a heatmap [5]. Ziegler et al [10] also presented a heatmap-based technique applying Pixel Bar Charts.…”

Section: B Time-varying Data Visualizationmentioning

confidence: 99%

A Heatmap-Based Time-Varying Multi-variate Data Visualization Unifying Numeric and Categorical Variables

Suematsu

Yagi

Itoh

et al. 2014

2014 18th International Conference on Information Visualisation

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Abstract-Most time-varying data in our daily life is multivariate. Moreover, most of such time-varying data contains both numeric and categorical values. It is often meaningful to visualize both of them as they are often correlated. We aim to visualize every value in such time-varying data in a single display space so that we can discover interesting relationships among the values of the time-varying data. This paper presents a heatmap-based time-varying data visualization technique which displays both numeric and categorical values in a single display space. The technique assigns time to the horizontal axis of the display space, and vertically arranges the series of colored belts corresponding to the time-sequence values. It generates one belt for a numeric value, and multiple belts for a categorical value. It clusters the belts according to the similarity of color sequences, and re-arranges the belts based on the clustering result. This paper shows an example of the visualization result applying a time-varying multi-variate marketing dataset.

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Section: B Time-varying Data Visualizationmentioning

confidence: 99%

A Heatmap-Based Time-Varying Multi-variate Data Visualization Unifying Numeric and Categorical Variables

Suematsu

Yagi

Itoh

et al. 2014

2014 18th International Conference on Information Visualisation

Self Cite

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“…Therefore, to strike a good balance, we suggest to use group-wise voxels in conjunction with time intervals for generating input TACs. SAX has been applied to detect frequent or outlier patterns for time-varying data [5]. We modify the original algorithms [12,18] to accommodate the characteristics of time-varying volumetric data in order to better differentiate SAX words, improve computation efficiency, and reduce the number of nodes shown in the iTree.…”

Section: Sax and Isaxmentioning

confidence: 99%

iTree: Exploring time-varying data using indexable tree

Wang

2013

2013 IEEE Pacific Visualization Symposium (PacificVis)

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Significant advances have been made in time-varying data analysis and visualization, mainly in improving our ability to identify temporal trends and classify the underlying data. However, the ability to perform cost-effective data querying and indexing is often not incorporated, which posts a serious limitation as the size of timevarying data continue to grow. In this paper, we present a new approach that unifies data compacting, indexing and classification into a single framework. We achieve this by transforming the timeactivity curve representation of a time-varying data set into a hierarchical symbolic representation. We further build an indexable version of the data hierarchy, from which we create the iTree for visual representation of the time-varying data. A hyperbolic layout algorithm is employed to draw the iTree with a large number of nodes and provide focus+context visualization for interaction. We achieve effective querying, searching and tracking of time-varying data sets by enabling multiple coordinated views consisting of the iTree, the symbolic view and the spatial view.

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“…Wang et al presented a technique for important polyline selection [7]. Recently we presented two time-varying data visualization techniques, featuring sketch query on the clustered view [6], and pattern display on the heatmap [4].…”

Section: Introductionmentioning

confidence: 99%