Evaluating Visual Analytics at the 2007 VAST Symposium Contest

Plaisant, Catherine; Grinstein, Georges; Scholtz, Jean; Whiting, Mark; O'Connell, Theresa; Laskowski, Sharon J.; Chien, L.; Tat, A.; Wright, William; Görg, Carsten; Liu, Zhicheng; Parekh, Nita; Singhal, K.; Stasko, John

doi:10.1109/mcg.2008.27

Cited by 36 publications

(32 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The analyst is not restricted to a single entry point to this loop, and instead can enter at the top or bottom before looping through the various steps [21]. The sensemaking process has been studied and observed on large, high-resolution displays as well as multiple monitor set-ups for individual users [5,7,22].…”

Section: Related Workmentioning

confidence: 99%

“…As a form of motivation, the solutions generated by the pairs of participants were scored and the participants received an additional financial award for the four highest scores. The rubric for evaluating the participants' verbal and written solutions was based on the strategy for scoring Visual Analytics Science and Technology (VAST) challenges [22]. The participants earned positive points for the people, events, and locations related to the solution and negative points for those that were irrelevant or incorrect.…”

Section: Participantsmentioning

confidence: 99%

See 1 more Smart Citation

Co-located Collaborative Sensemaking on a Large High-Resolution Display with Multiple Input Devices

Vogt

Bradel

Andrews

et al. 2011

Human-Computer Interaction – INTERACT 2011

View full text Add to dashboard Cite

Abstract. This study adapts existing tools (Jigsaw and a text editor) to support multiple input devices, which were then used in a co-located collaborative intelligence analysis study conducted on a large, high-resolution display. Exploring the sensemaking process and user roles in pairs of analysts, the two-hour study used a fictional data set composed of 50 short textual documents that contained a terrorist plot and subject pairs who had experience working together. The large display facilitated the paired sensemaking process, allowing teams to spatially arrange information and conduct individual work as needed. We discuss how the space and the tools affected the approach to the analysis, how the teams collaborated, and the user roles that developed. Using these findings, we suggest design guidelines for future colocated collaborative tools.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Participantsmentioning

confidence: 99%

Co-located Collaborative Sensemaking on a Large High-Resolution Display with Multiple Input Devices

Vogt

Bradel

Andrews

et al. 2011

Human-Computer Interaction – INTERACT 2011

View full text Add to dashboard Cite

show abstract

“…This paper examines extended field use of Jigsaw [39,40], a visual analytics system for helping analysts who work with large collections of documents. It complements an earlier comparative lab study of Jigsaw [20,21] as well as descriptions of Jigsaw's use in solving problems within the VAST Challenge [5,13,28]. In this paper, we profile six investigators who have been using Jigsaw in their own work, including three intelligence analysts, two academic researchers, and one business analyst.…”

Section: Introductionmentioning

confidence: 93%

Examining the Use of a Visual Analytics System for Sensemaking Tasks: Case Studies with Domain Experts

Kang

Stasko

2012

IEEE Trans. Visual. Comput. Graphics

Self Cite

View full text Add to dashboard Cite

Abstract-While the formal evaluation of systems in visual analytics is still relatively uncommon, particularly rare are case studies of prolonged system use by domain analysts working with their own data. Conducting case studies can be challenging, but it can be a particularly effective way to examine whether visual analytics systems are truly helping expert users to accomplish their goals. We studied the use of a visual analytics system for sensemaking tasks on documents by six analysts from a variety of domains. We describe their application of the system along with the benefits, issues, and problems that we uncovered. Findings from the studies identify features that visual analytics systems should emphasize as well as missing capabilities that should be addressed. These findings inform design implications for future systems.

show abstract

“…Our plan is to introduce ForceSPIRE to professional intelligence analysts and have them solve scenarios that model their daily task, such as one of the VAST datasets [58]. The observations and feedback from these users will provide ecological validity for semantic interaction.…”

Section: Future Directionsmentioning

confidence: 99%

“…To provide a realistic intelligence analysis task with a known ground truth to evaluate the performance of the users against, we plan to use one of the VAST Challenge Datasets [58]. give an idea of how much the user has guided the model through weighting of entities and documents, and how much the user has constrained the model through pinning documents in specific locations.…”

Section: Effects Of Semantic Interaction On Analytic Processmentioning

confidence: 99%

Semantic Interaction for Visual Analytics: Inferring Analytical Reasoning for Model Steering

Endert

2016

Synthesis Lectures on Visualization

View full text Add to dashboard Cite

User interaction in visual analytic systems is critical to enabling visual data exploration.Through interacting with visualizations, users engage in sensemaking, a process of developing and understanding relationships within datasets through foraging and synthesis. For example, two-dimensional layouts of high-dimensional data can be generated by dimension reduction models, and provide users with an overview of the relationships between information. However, exploring such spatializations can require expertise with the internal mechanisms and parameters of these models.The core contribution of this work is semantic interaction, capable of steering such models without requiring expertise in dimension reduction models, but instead leveraging the domain expertise of the user. Semantic interaction infers the analytical reasoning of the user with model updates, steering the dimension reduction model for visual data exploration. As such, it is an approach to user interaction that leverages interactions

show abstract

Evaluating Visual Analytics at the 2007 VAST Symposium Contest

Cited by 36 publications

References 4 publications

Co-located Collaborative Sensemaking on a Large High-Resolution Display with Multiple Input Devices

Co-located Collaborative Sensemaking on a Large High-Resolution Display with Multiple Input Devices

Examining the Use of a Visual Analytics System for Sensemaking Tasks: Case Studies with Domain Experts

Semantic Interaction for Visual Analytics: Inferring Analytical Reasoning for Model Steering

Contact Info

Product

Resources

About