Empirical Study of Focus-Plus-Context and Aggregation Techniques for the Visualization of Streaming Data

Eric D. Ragan; Andrew S. Stamps; John R. Goodall

doi:10.1145/3399715.3399837

Empirical Study of Focus-Plus-Context and Aggregation Techniques for the Visualization of Streaming Data

Eric D. Ragan
, Andrew S. Stamps
, John R. Goodall

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

Abstract

Analysis of streaming data often involves both real-time monitoring of incoming data as well as contextual awareness of data history. A focus-plus-context approach can support both goals, with variable levels of visual aggregation making it possible to provide a high level of detail for incoming and recent data while providing contextual information about recent history. Visual aggregation reduces data resolution in order to show the context of data over large periods of time within a limited display space. With a controlled experiment, we evaluated the effectiveness of different types of aggregation for four types of stream-analysis tasks. Overall, the results show that a focus-plus-context design has little negative impact on the ability to successfully monitor and analyze streaming data, making it possible to show longer periods of time than other approaches. However, visual aggregation can be problematic for trend recognition tasks. This research demonstrates how the effectiveness of the visualization depends on the specifics of the analysis task.

Original language	English
Title of host publication	Proceedings of the Working Conference on Advanced Visual Interfaces, AVI 2020
Editors	Genny Tortora, Giuliana Vitiello, Marco Winckler
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450375351
DOIs	https://doi.org/10.1145/3399715.3399837
State	Published - Sep 28 2020
Event	2020 International Conference on Advanced Visual Interfaces, AVI 2020 - Salerno, Italy Duration: Sep 28 2020 → Oct 2 2020

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	2020 International Conference on Advanced Visual Interfaces, AVI 2020
Country/Territory	Italy
City	Salerno
Period	09/28/20 → 10/2/20

Funding

This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan http://energy.gov/downloads/doe-public-access-plan. This research is supported in part by the DARPA XAI program under Grant N66001-17-2-4031.

Keywords

Visualization
human-computer interaction
information visualization
streaming data

Access to Document

10.1145/3399715.3399837

Cite this

Ragan, E. D., Stamps, A. S., & Goodall, J. R. (2020). Empirical Study of Focus-Plus-Context and Aggregation Techniques for the Visualization of Streaming Data. In G. Tortora, G. Vitiello, & M. Winckler (Eds.), Proceedings of the Working Conference on Advanced Visual Interfaces, AVI 2020 (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3399715.3399837

Ragan, Eric D. ; Stamps, Andrew S. ; Goodall, John R. / Empirical Study of Focus-Plus-Context and Aggregation Techniques for the Visualization of Streaming Data. Proceedings of the Working Conference on Advanced Visual Interfaces, AVI 2020. editor / Genny Tortora ; Giuliana Vitiello ; Marco Winckler. Association for Computing Machinery, 2020. (ACM International Conference Proceeding Series).

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title = "Empirical Study of Focus-Plus-Context and Aggregation Techniques for the Visualization of Streaming Data",

abstract = "Analysis of streaming data often involves both real-time monitoring of incoming data as well as contextual awareness of data history. A focus-plus-context approach can support both goals, with variable levels of visual aggregation making it possible to provide a high level of detail for incoming and recent data while providing contextual information about recent history. Visual aggregation reduces data resolution in order to show the context of data over large periods of time within a limited display space. With a controlled experiment, we evaluated the effectiveness of different types of aggregation for four types of stream-analysis tasks. Overall, the results show that a focus-plus-context design has little negative impact on the ability to successfully monitor and analyze streaming data, making it possible to show longer periods of time than other approaches. However, visual aggregation can be problematic for trend recognition tasks. This research demonstrates how the effectiveness of the visualization depends on the specifics of the analysis task.",

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doi = "10.1145/3399715.3399837",

language = "English",

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Ragan, ED, Stamps, AS & Goodall, JR 2020, Empirical Study of Focus-Plus-Context and Aggregation Techniques for the Visualization of Streaming Data. in G Tortora, G Vitiello & M Winckler (eds), Proceedings of the Working Conference on Advanced Visual Interfaces, AVI 2020. ACM International Conference Proceeding Series, Association for Computing Machinery, 2020 International Conference on Advanced Visual Interfaces, AVI 2020, Salerno, Italy, 09/28/20. https://doi.org/10.1145/3399715.3399837

Empirical Study of Focus-Plus-Context and Aggregation Techniques for the Visualization of Streaming Data. / Ragan, Eric D.; Stamps, Andrew S.; Goodall, John R.
Proceedings of the Working Conference on Advanced Visual Interfaces, AVI 2020. ed. / Genny Tortora; Giuliana Vitiello; Marco Winckler. Association for Computing Machinery, 2020. (ACM International Conference Proceeding Series).

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

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AU - Stamps, Andrew S.

AU - Goodall, John R.

PY - 2020/9/28

Y1 - 2020/9/28

N2 - Analysis of streaming data often involves both real-time monitoring of incoming data as well as contextual awareness of data history. A focus-plus-context approach can support both goals, with variable levels of visual aggregation making it possible to provide a high level of detail for incoming and recent data while providing contextual information about recent history. Visual aggregation reduces data resolution in order to show the context of data over large periods of time within a limited display space. With a controlled experiment, we evaluated the effectiveness of different types of aggregation for four types of stream-analysis tasks. Overall, the results show that a focus-plus-context design has little negative impact on the ability to successfully monitor and analyze streaming data, making it possible to show longer periods of time than other approaches. However, visual aggregation can be problematic for trend recognition tasks. This research demonstrates how the effectiveness of the visualization depends on the specifics of the analysis task.

AB - Analysis of streaming data often involves both real-time monitoring of incoming data as well as contextual awareness of data history. A focus-plus-context approach can support both goals, with variable levels of visual aggregation making it possible to provide a high level of detail for incoming and recent data while providing contextual information about recent history. Visual aggregation reduces data resolution in order to show the context of data over large periods of time within a limited display space. With a controlled experiment, we evaluated the effectiveness of different types of aggregation for four types of stream-analysis tasks. Overall, the results show that a focus-plus-context design has little negative impact on the ability to successfully monitor and analyze streaming data, making it possible to show longer periods of time than other approaches. However, visual aggregation can be problematic for trend recognition tasks. This research demonstrates how the effectiveness of the visualization depends on the specifics of the analysis task.

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KW - human-computer interaction

KW - information visualization

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Ragan ED, Stamps AS, Goodall JR. Empirical Study of Focus-Plus-Context and Aggregation Techniques for the Visualization of Streaming Data. In Tortora G, Vitiello G, Winckler M, editors, Proceedings of the Working Conference on Advanced Visual Interfaces, AVI 2020. Association for Computing Machinery. 2020. (ACM International Conference Proceeding Series). doi: 10.1145/3399715.3399837

Empirical Study of Focus-Plus-Context and Aggregation Techniques for the Visualization of Streaming Data

Abstract

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Keywords

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