ScagnosticsViewer: Tracking time series patterns via scagnostics meatures

Ngan V.T. Nguyen; Bao D.Q. Nguyen; Tommy Dang; Jon Hass

doi:10.1145/3430036.3430072

ScagnosticsViewer: Tracking time series patterns via scagnostics meatures

Ngan V.T. Nguyen, Bao D.Q. Nguyen, Tommy Dang, Jon Hass

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Visual analytics studies the integration of human perceptional ability and the data exploration process. Besides, high dimensional time series has become more and more popular data in daily applications and various domains. It is essential to study visual techniques for this type of data set. This paper is an applied work that extends successes of the scatterplot matrix (SPLOM) for cross-sectional data to the high dimensional temporal data sets. We have built a web-based application that contains various visual techniques to display the temporal data sets by SPLOM at each snapshot and animate the SPLOM to illustrate the evolution of the data sets. Two real data sets in different domains are utilized to demonstrate the advances of this approach.

Original language	English
Title of host publication	Proceedings of the 13th International Symposium on Visual Information Communication and Interaction, VINCI 2020
Editors	Quang Vinh Nguyen, Ying Zhao, Michael Burch, Michel Westenberg
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450387507
DOIs	https://doi.org/10.1145/3430036.3430072
State	Published - Dec 8 2020
Event	13th International Symposium on Visual Information Communication and Interaction, VINCI 2020 - Virtual, Online, Netherlands Duration: Dec 8 2020 → Dec 10 2020

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	13th International Symposium on Visual Information Communication and Interaction, VINCI 2020
Country	Netherlands
City	Virtual, Online
Period	12/8/20 → 12/10/20

Keywords

scatterplot
scatterplot matrix

Access to Document

10.1145/3430036.3430072

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Nguyen, N. V. T., Nguyen, B. D. Q., Dang, T., & Hass, J. (2020). ScagnosticsViewer: Tracking time series patterns via scagnostics meatures. In Q. V. Nguyen, Y. Zhao, M. Burch, & M. Westenberg (Eds.), Proceedings of the 13th International Symposium on Visual Information Communication and Interaction, VINCI 2020 [3430072] (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3430036.3430072

Nguyen, Ngan V.T. ; Nguyen, Bao D.Q. ; Dang, Tommy ; Hass, Jon. / ScagnosticsViewer : Tracking time series patterns via scagnostics meatures. Proceedings of the 13th International Symposium on Visual Information Communication and Interaction, VINCI 2020. editor / Quang Vinh Nguyen ; Ying Zhao ; Michael Burch ; Michel Westenberg. Association for Computing Machinery, 2020. (ACM International Conference Proceeding Series).

@inproceedings{227e94c6090540adb68119b0ffc736d1,

title = "ScagnosticsViewer: Tracking time series patterns via scagnostics meatures",

abstract = "Visual analytics studies the integration of human perceptional ability and the data exploration process. Besides, high dimensional time series has become more and more popular data in daily applications and various domains. It is essential to study visual techniques for this type of data set. This paper is an applied work that extends successes of the scatterplot matrix (SPLOM) for cross-sectional data to the high dimensional temporal data sets. We have built a web-based application that contains various visual techniques to display the temporal data sets by SPLOM at each snapshot and animate the SPLOM to illustrate the evolution of the data sets. Two real data sets in different domains are utilized to demonstrate the advances of this approach. ",

keywords = "scatterplot, scatterplot matrix",

author = "Nguyen, {Ngan V.T.} and Nguyen, {Bao D.Q.} and Tommy Dang and Jon Hass",

year = "2020",

month = dec,

day = "8",

doi = "10.1145/3430036.3430072",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

editor = "Nguyen, {Quang Vinh} and Ying Zhao and Michael Burch and Michel Westenberg",

booktitle = "Proceedings of the 13th International Symposium on Visual Information Communication and Interaction, VINCI 2020",

}

Nguyen, NVT, Nguyen, BDQ, Dang, T & Hass, J 2020, ScagnosticsViewer: Tracking time series patterns via scagnostics meatures. in QV Nguyen, Y Zhao, M Burch & M Westenberg (eds), Proceedings of the 13th International Symposium on Visual Information Communication and Interaction, VINCI 2020., 3430072, ACM International Conference Proceeding Series, Association for Computing Machinery, 13th International Symposium on Visual Information Communication and Interaction, VINCI 2020, Virtual, Online, Netherlands, 12/8/20. https://doi.org/10.1145/3430036.3430072

ScagnosticsViewer : Tracking time series patterns via scagnostics meatures. / Nguyen, Ngan V.T.; Nguyen, Bao D.Q.; Dang, Tommy; Hass, Jon.

Proceedings of the 13th International Symposium on Visual Information Communication and Interaction, VINCI 2020. ed. / Quang Vinh Nguyen; Ying Zhao; Michael Burch; Michel Westenberg. Association for Computing Machinery, 2020. 3430072 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Visual analytics studies the integration of human perceptional ability and the data exploration process. Besides, high dimensional time series has become more and more popular data in daily applications and various domains. It is essential to study visual techniques for this type of data set. This paper is an applied work that extends successes of the scatterplot matrix (SPLOM) for cross-sectional data to the high dimensional temporal data sets. We have built a web-based application that contains various visual techniques to display the temporal data sets by SPLOM at each snapshot and animate the SPLOM to illustrate the evolution of the data sets. Two real data sets in different domains are utilized to demonstrate the advances of this approach.

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KW - scatterplot matrix

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BT - Proceedings of the 13th International Symposium on Visual Information Communication and Interaction, VINCI 2020

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Nguyen NVT, Nguyen BDQ, Dang T, Hass J. ScagnosticsViewer: Tracking time series patterns via scagnostics meatures. In Nguyen QV, Zhao Y, Burch M, Westenberg M, editors, Proceedings of the 13th International Symposium on Visual Information Communication and Interaction, VINCI 2020. Association for Computing Machinery. 2020. 3430072. (ACM International Conference Proceeding Series). https://doi.org/10.1145/3430036.3430072