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 | |
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