The notion of cause and effect is pervasive in human thinking and plays a significant role in our perception of time. The human mind is especially well-suited to detect instances of this concept of causality. However, as the number of actions and reactions in a system grows, it quickly becomes difficult to follow and gain an understanding of its general flow. We have developed a number of novel visualization techniques for causal relations based on animation, colors and patterns to provide an alternate graphical representation of causality in a system that facilitates quick overview. The Growing Squares and Growing Polygons techniques map the temporal parameter to geometric size and dependencies and information to color and texture, forming interactive influence maps of the system under execution.
We have empirically evaluated the performance of users solving tasks related to causal relations using both our techniques as well as standard Hasse (time-space) diagrams. Our results indicate a significant improvement using our techniques, both in terms of completion time as well as effectiveness. Moreover, our subjects showed strong preference for the new methods over standard tools.
2021 |
Arjun Choudhry, Mandar Sharma, Pramod Chundury, Thomas Kapler, Derek Gray, Naren Ramakrishnan, Niklas Elmqvist (2021): Once Upon A Time In Visualization: Understanding the Use of Textual Narratives for Causality. In: IEEE Transactions on Visualization & Computer Graphics, 28 (1), 2021. (Type: Article | Abstract | Links | BibTeX)@article{Choudhry2021,
title = {Once Upon A Time In Visualization: Understanding the Use of Textual Narratives for Causality},
author = {Arjun Choudhry and Mandar Sharma and Pramod Chundury and Thomas Kapler and Derek Gray and Naren Ramakrishnan and Niklas Elmqvist},
url = {http://users.umiacs.umd.edu/~elm/projects/causality/onceuponatime.pdf, PDF},
year = {2021},
date = {2021-01-01},
journal = {IEEE Transactions on Visualization & Computer Graphics},
volume = {28},
number = {1},
abstract = {Causality visualization can help people understand temporal chains of events, such as messages sent in a distributed system, cause and effect in a historical conflict, or the interplay between political actors over time. However, as the scale and complexity of these event sequences grows, even these visualizations can become overwhelming to use. In this paper, we propose the use of textual narratives as a data-driven storytelling method to augment causality visualization. We first propose a design space for how textual narratives can be used to describe causal data. We then present results from a crowdsourced user study where participants were asked to recover causality information from two causality visualizations--causal graphs and Hasse diagrams--with and without an associated textual narrative. Finally, we describe CAUSEWORKS, a causality visualization system for understanding how specific interventions influence a causal model. The system incorporates an automatic textual narrative mechanism based on our design space. We validate CAUSEWORKS through interviews with experts who used the system for understanding complex events.},
keywords = {}
}
Causality visualization can help people understand temporal chains of events, such as messages sent in a distributed system, cause and effect in a historical conflict, or the interplay between political actors over time. However, as the scale and complexity of these event sequences grows, even these visualizations can become overwhelming to use. In this paper, we propose the use of textual narratives as a data-driven storytelling method to augment causality visualization. We first propose a design space for how textual narratives can be used to describe causal data. We then present results from a crowdsourced user study where participants were asked to recover causality information from two causality visualizations--causal graphs and Hasse diagrams--with and without an associated textual narrative. Finally, we describe CAUSEWORKS, a causality visualization system for understanding how specific interventions influence a causal model. The system incorporates an automatic textual narrative mechanism based on our design space. We validate CAUSEWORKS through interviews with experts who used the system for understanding complex events. |
2007 |
Niklas Elmqvist, Philippas Tsigas (2007): CiteWiz: A Tool for the Visualization of Scientific Citation Networks. In: Information Visualization, 6 (3), pp. 215–232, 2007. (Type: Article | Abstract | Links | BibTeX)@article{Elmqvist2007c,
title = {CiteWiz: A Tool for the Visualization of Scientific Citation Networks},
author = {Niklas Elmqvist and Philippas Tsigas},
url = {http://www.umiacs.umd.edu/~elm/projects/citewiz/citewiz.pdf, Paper},
year = {2007},
date = {2007-01-01},
journal = {Information Visualization},
volume = {6},
number = {3},
pages = {215--232},
abstract = {We present CiteWiz, an extensible framework for visualization of scientific citation networks. The system is based on a taxonomy of citation database usage for researchers, and provides a timeline visualization for overviews and an influence visualization for detailed views. The timeline displays the general chronology and importance of authors and articles in a citation database, whereas the influence visualization is implemented using the Growing Polygons technique, suitably modified to the context of browsing citation data. Using the latter technique, hierarchies of articles with potentially very long citation chains can be graphically represented. The visualization is augmented with mechanisms for parent-child visualization and suitable interaction techniques for interacting with the view hierarchy and the individual articles in the dataset. We also provide an interactive concept map for keywords and co-authorship using a basic force-directed graph layout scheme. A formal user study indicates that CiteWiz is significantly more efficient than traditional database interfaces for high-level analysis tasks relating to influence and overviews, and equally efficient for low-level tasks such as finding a paper and correlating bibliographical data.},
keywords = {}
}
We present CiteWiz, an extensible framework for visualization of scientific citation networks. The system is based on a taxonomy of citation database usage for researchers, and provides a timeline visualization for overviews and an influence visualization for detailed views. The timeline displays the general chronology and importance of authors and articles in a citation database, whereas the influence visualization is implemented using the Growing Polygons technique, suitably modified to the context of browsing citation data. Using the latter technique, hierarchies of articles with potentially very long citation chains can be graphically represented. The visualization is augmented with mechanisms for parent-child visualization and suitable interaction techniques for interacting with the view hierarchy and the individual articles in the dataset. We also provide an interactive concept map for keywords and co-authorship using a basic force-directed graph layout scheme. A formal user study indicates that CiteWiz is significantly more efficient than traditional database interfaces for high-level analysis tasks relating to influence and overviews, and equally efficient for low-level tasks such as finding a paper and correlating bibliographical data. |
2004 |
Niklas Elmqvist, Philippas Tsigas (2004): Animated Visualization of Causal Relations Through Growing 2D Geometry. In: Information Visualization, 3 (3), pp. 154–172, 2004. (Type: Article | Abstract | Links | BibTeX)@article{Elmqvist2004a,
title = {Animated Visualization of Causal Relations Through Growing 2D Geometry},
author = {Niklas Elmqvist and Philippas Tsigas},
url = {http://www.umiacs.umd.edu/~elm/projects/causality/causality.pdf, Paper},
year = {2004},
date = {2004-01-01},
journal = {Information Visualization},
volume = {3},
number = {3},
pages = {154--172},
abstract = {Causality visualization is an important tool for many scientific domains that involve complex interactions between multiple entities (examples include parallel and distributed systems in computer science). However, traditional visualization techniques such as Hasse diagrams are not well-suited to large system executions, and users often have difficulties answering even basic questions using them, or have to spend inordinate amounts of time to do so. In this paper we present the Growing Squares and Growing Polygons methods, two sibling visualization techniques that were designed to solve this problem by providing efficient 2D causality visualization through the use of color, texture, and animation. Both techniques have abandoned the traditional linear timeline and instead map the time parameter to the size of geometrical primitives representing the processes; in the Growing Squares case, each process is a color-coded square that receives color influences from other process squares as messages reach it; in the Growing Polygons case, each process is instead an n-sided polygon consisting of triangular sectors showing color-coded influences from the other processes. We have performed user studies of both techniques, comparing them with Hasse diagrams, and they have been shown to be significantly more efficient than old techniques, both in terms of objective performance as well as the subjective opinion of the test subjects (the Growing Squares technique is, however, only significantly more efficient for small
systems).},
keywords = {}
}
Causality visualization is an important tool for many scientific domains that involve complex interactions between multiple entities (examples include parallel and distributed systems in computer science). However, traditional visualization techniques such as Hasse diagrams are not well-suited to large system executions, and users often have difficulties answering even basic questions using them, or have to spend inordinate amounts of time to do so. In this paper we present the Growing Squares and Growing Polygons methods, two sibling visualization techniques that were designed to solve this problem by providing efficient 2D causality visualization through the use of color, texture, and animation. Both techniques have abandoned the traditional linear timeline and instead map the time parameter to the size of geometrical primitives representing the processes; in the Growing Squares case, each process is a color-coded square that receives color influences from other process squares as messages reach it; in the Growing Polygons case, each process is instead an n-sided polygon consisting of triangular sectors showing color-coded influences from the other processes. We have performed user studies of both techniques, comparing them with Hasse diagrams, and they have been shown to be significantly more efficient than old techniques, both in terms of objective performance as well as the subjective opinion of the test subjects (the Growing Squares technique is, however, only significantly more efficient for small
systems). |
2003 |
Niklas Elmqvist, Philippas Tsigas (2003): Growing Squares: Animated Visualization of Causal Relations. In: Proceedings of the ACM Symposium on Software Visualization, pp. 17–26, 2003. (Type: Inproceeding | Abstract | Links | BibTeX)@inproceedings{Elmqvist2003a,
title = {Growing Squares: Animated Visualization of Causal Relations},
author = {Niklas Elmqvist and Philippas Tsigas},
url = {http://www.umiacs.umd.edu/~elm/projects/causality/causalviz.pdf, Paper},
year = {2003},
date = {2003-01-01},
booktitle = {Proceedings of the ACM Symposium on Software Visualization},
pages = {17--26},
abstract = {We present a novel information visualization technique for the graphical representation of causal relations, that is based on the metaphor of color pools spreading over time on a piece of paper. Messages between processes in the system affect the colors of their respective pool, making it possible to quickly see the influences each process has received. This technique, called Growing Squares, has been evaluated in a comparative user study and shown to be significantly faster and more efficient for sparse data sets than the traditional Hasse diagram visualization. Growing Squares were also more efficient for large data sets, but not significantly so. Test subjects clearly favored Growing Squares over old methods, naming the new technique easier, more efficient, and much more enjoyable to use.},
keywords = {}
}
We present a novel information visualization technique for the graphical representation of causal relations, that is based on the metaphor of color pools spreading over time on a piece of paper. Messages between processes in the system affect the colors of their respective pool, making it possible to quickly see the influences each process has received. This technique, called Growing Squares, has been evaluated in a comparative user study and shown to be significantly faster and more efficient for sparse data sets than the traditional Hasse diagram visualization. Growing Squares were also more efficient for large data sets, but not significantly so. Test subjects clearly favored Growing Squares over old methods, naming the new technique easier, more efficient, and much more enjoyable to use. |
Niklas Elmqvist, Philippas Tsigas (2003): Causality Visualization Using Animated Growing Polygons. In: Proceedings of the IEEE Symposium on Information Visualization, pp. 189–196, 2003. (Type: Inproceeding | Abstract | Links | BibTeX)@inproceedings{Elmqvist2003b,
title = {Causality Visualization Using Animated Growing Polygons},
author = {Niklas Elmqvist and Philippas Tsigas},
url = {http://www.umiacs.umd.edu/~elm/projects/causality/growing-polys.pdf, Paper},
year = {2003},
date = {2003-01-01},
booktitle = {Proceedings of the IEEE Symposium on Information Visualization},
pages = {189--196},
abstract = {We present Growing Polygons, a novel visualization technique for the graphical representation of causal relations and information flow in a system of interacting processes. Using this method, individual processes are displayed as partitioned polygons with color-coded segments showing dependencies to other processes. The entire visualization is also animated to communicate the dynamic execution of the system to the user. The results from a comparative user study of the method show that the Growing Polygons technique is significantly more efficient than the traditional Hasse diagram visualization for analysis tasks related to deducing information flow in a system for both small and large executions. Furthermore, our findings indicate that the correctness when solving causality tasks is significantly improved using our method. In addition, the subjective ratings of the users rank the method as superior in all regards, including usability, efficiency, and enjoyability.},
keywords = {}
}
We present Growing Polygons, a novel visualization technique for the graphical representation of causal relations and information flow in a system of interacting processes. Using this method, individual processes are displayed as partitioned polygons with color-coded segments showing dependencies to other processes. The entire visualization is also animated to communicate the dynamic execution of the system to the user. The results from a comparative user study of the method show that the Growing Polygons technique is significantly more efficient than the traditional Hasse diagram visualization for analysis tasks related to deducing information flow in a system for both small and large executions. Furthermore, our findings indicate that the correctness when solving causality tasks is significantly improved using our method. In addition, the subjective ratings of the users rank the method as superior in all regards, including usability, efficiency, and enjoyability. |