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Authors: João Moreira 1 ; 2 ; Filipa Castanheira 2 ; Daniel Mendes 3 ; 4 and Daniel Gonçalves 1 ; 2

Affiliations: 1 INESC-ID, Lisboa, Portugal ; 2 Instituto Superior Técnico, Lisboa, Portugal ; 3 Faculdade de Engenharia da Universidade do Porto, Porto, Portugal ; 4 INESC TEC, Porto, Portugal

Keyword(s): Information Visualization, Big Data, Streaming, Time-Series, User Study, Animated Transitions.

Abstract: Visualizations for Streaming Big Data need to handle high volumes of information in real-time, making it challenging to convey significant data changes without confusing users. A simple first approach would be switching from the current visual idiom to another, highlighting a significant change. Unfortunately, there are no guidelines to design effective transitions between two visual idioms in Streaming Big Data. Therefore, we created a tree of animation concepts to serve as a starting point for designing such animated transitions. The concepts represent several ways in which a visual idiom can be transformed into another. We chose three visual idioms to test our idea and arranged several concepts to apply at each possible pairing (six possibilities). For each pairing, we tested the accuracy of people’s perceptions. Finally, we conducted a user study with 100 participants, where each participant answered various questions about transitions between two visual idioms shown in several v ideos. We concluded that to conceive appropriate animated transitions for Streaming Big Data (which also applies just for Data Streaming) that allow users to understand the changes in incoming data, varying how the proposed concepts are applied is not enough, highlighting the need for future research to address this challenge. (More)

CC BY-NC-ND 4.0

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Paper citation in several formats:
Moreira, J.; Castanheira, F.; Mendes, D. and Gonçalves, D. (2022). Designing Animated Transitions for Dynamic Streaming Big Data. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - IVAPP, ISBN 978-989-758-555-5; ISSN 2184-4321, pages 139-145. DOI: 10.5220/0010787600003124

@conference{ivapp22,
author={João Moreira. and Filipa Castanheira. and Daniel Mendes. and Daniel Gon\c{C}alves.},
title={Designing Animated Transitions for Dynamic Streaming Big Data},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - IVAPP,},
year={2022},
pages={139-145},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010787600003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - IVAPP,
TI - Designing Animated Transitions for Dynamic Streaming Big Data
SN - 978-989-758-555-5
IS - 2184-4321
AU - Moreira, J.
AU - Castanheira, F.
AU - Mendes, D.
AU - Gonçalves, D.
PY - 2022
SP - 139
EP - 145
DO - 10.5220/0010787600003124