STBins: Visual Tracking and Comparison of Multiple Data Sequences Using Temporal Binning

Ji Qi; Vincent Bloemen; Shihan Wang; Jarke Van Wijk; Huub Van De Wetering

doi:10.1109/TVCG.2019.2934289

STBins: Visual Tracking and Comparison of Multiple Data Sequences Using Temporal Binning

Ji Qi, Vincent Bloemen, Shihan Wang, Jarke Van Wijk, Huub Van De Wetering

Research output: Contribution to journal › Article › Academic › peer-review

9 Citations (Scopus)

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Abstract

While analyzing multiple data sequences, the following questions typically arise: how does a single sequence change over time, how do multiple sequences compare within a period, and how does such comparison change over time. This paper presents a visual technique named STBins to answer these questions. STBins is designed for visual tracking of individual data sequences and also for comparison of sequences. The latter is done by showing the similarity of sequences within temporal windows. A perception study is conducted to examine the readability of alternative visual designs based on sequence tracking and comparison tasks. Also, two case studies based on real-world datasets are presented in detail to demonstrate usage of our technique.

Original language	English
Article number	8805450
Pages (from-to)	1054-1063
Number of pages	10
Journal	IEEE transactions on visualization and computer graphics
Volume	26
Issue number	1
DOIs	https://doi.org/10.1109/TVCG.2019.2934289
Publication status	Published - Jan 2020

Keywords

data sequence
time series data
Visualization
n/a OA procedure

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10.1109/TVCG.2019.2934289

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title = "STBins: Visual Tracking and Comparison of Multiple Data Sequences Using Temporal Binning",

abstract = "While analyzing multiple data sequences, the following questions typically arise: how does a single sequence change over time, how do multiple sequences compare within a period, and how does such comparison change over time. This paper presents a visual technique named STBins to answer these questions. STBins is designed for visual tracking of individual data sequences and also for comparison of sequences. The latter is done by showing the similarity of sequences within temporal windows. A perception study is conducted to examine the readability of alternative visual designs based on sequence tracking and comparison tasks. Also, two case studies based on real-world datasets are presented in detail to demonstrate usage of our technique.",

keywords = "data sequence, time series data, Visualization, n/a OA procedure",

author = "Ji Qi and Vincent Bloemen and Shihan Wang and {Van Wijk}, Jarke and {Van De Wetering}, Huub",

note = "Funding Information: This work was supported by the Big Software on the Run (BSR) project in Netherlands. Publisher Copyright: {\textcopyright} 1995-2012 IEEE.",

year = "2020",

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STBins: Visual Tracking and Comparison of Multiple Data Sequences Using Temporal Binning

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