Building Highly-Coordinated Visualizations in Improvise

Weaver,

doi:10.1109/infvis.2004.12

Cited by 138 publications

(106 citation statements)

References 27 publications

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“…Its implementation for single data sources where charts share measures and categories is fairly easy. Nevertheless, if measures and categories need to be matched across data sources, then matching approaches need to be provided [21,32]. When providing coordinated views, designers should remember that novice users confuse linked selections of visual queries to applying global data filters based on the selections.…”

Section: Discussion and Design Guidelinesmentioning

confidence: 99%

“…These do not target novice visualization users, and usually require additional programming to create the visualizations, to synchronize them in dashboard views, and to connect them to data-sources. Several environments, such as Improvise [32] or Snap-Together [21], allow the construction of highly customized, powerful and fully linked visualizations (for a comprehensive list see [25]). Nevertheless their setup cost and learning curve is usually high and they target visualization experts rather than novice users.…”

Section: Visualization Creationmentioning

confidence: 99%

“…All our visual representations are essentially linked coordinated views [25,32] of the underlying datasets (unless users give them local filters, discussed later). We thus extend the dynamic query functionality to any visual selection or query in the dashboard: if the user selects in one chart bars representing the 3 quarters of a year, these data instances are also highlighted on all other charts (Fig 4.a).…”

Section: Supporting Visual Analytics Processmentioning

confidence: 99%

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Exploration Views: Understanding Dashboard Creation and Customization for Visualization Novices

Elias

Bezerianos

2011

Lecture Notes in Computer Science

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Abstract. With the increase of visualization platforms targeting novices, researchers are now focusing on gathering insights regarding novice user practices. We describe the design and evaluation of Exploration Views (EV), a system that allows novice visualization users to easily build and customize Business Intelligence information dashboards. EV provides an intuitive environment for dynamically creating, rearranging, searching and exploring multiple visual data representations from diverse data-sources. These aspects aid users to better retrieve, experiment and familiarize themselves with their data. We evaluated EV with both novice and expert dashboard designers and report here (i) how novice users interact with the system, (ii) differences in how novice and expert users react to a dashboard systems that targets both, and (iii) provide new design guidelines for practitioners building dashboard applications, on the needs of novice visualization users.

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Section: Discussion and Design Guidelinesmentioning

confidence: 99%

Section: Visualization Creationmentioning

confidence: 99%

Section: Supporting Visual Analytics Processmentioning

confidence: 99%

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Exploration Views: Understanding Dashboard Creation and Customization for Visualization Novices

Elias

Bezerianos

2011

Lecture Notes in Computer Science

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“…To this end, we have developed interactive visualization software for exploration and analysis of individual differences in grouping data (that is indicative of different conceptualizations of events). Using Improvise, an integrated environment for developing highly interactive visual analysis tools (Weaver, 2004), we have so far built two tools that display similarity matrix data sets in matrix and graph form. Improvise supports the rapid, data-driven and analysis-driven development of visual analysis tools that provide multiple interactively coupled ("coordinated") views onto multivariate data.…”

Section: (Re-) Analysis Of the Datamentioning

confidence: 99%

Analyzing Cognitive Conceptualizations Using Interactive Visual Environments

Klippel

Weaver²,

Robinson

2011

Cartography and Geographic Information Science

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ABSTRACT. The conceptualization of spatio-temporal information is an interdisciplinary research area. The focus of this article is on human conceptualizations of spatio-temporal geographic phenomena (also referred to as events). Identifying and understanding human conceptualizations is a crucial component in defining the semantics of spatiotemporal information. However, most research focuses primarily on how humans imbue dynamic phenomena with meaning on a general level. In contrast, this article is concerned with contextual factors (specifically: individual differences) that are too often neglected in general theories and in the analysis of behavioral data. In other words, we are interested in individual or group strategies of participants that are not detected by classical analysis methods. Research on individual difference is gaining widespread attention in cognitive and spatial sciences and it is time to consider individual differences in the area of conceptualizing spatio-temporal information. To be able to shed light on individual differences in behavioral data on how people conceptualize events, we have developed software solutions and combined them with established similarity measures. These software solutions allow analysts to effectively explore individual differences. We demonstrate the feasibility of our approach, its usefulness in analyzing behavioral data, and results that can be obtained through this individualized analysis by reanalyzing four sets of experimental data we previously collected.

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“…As both the scientific visualization and information visualization communities test out an increasing number of visualization techniques for human sense-making and situation awareness activities, we design the RimSim architecture to incorporate solutions that show promise for the role-play scenarios we wish to train. Whole improvisional visualization frameworks like Prefuse [10] or Improvise [11] can be incorporated into a RimSim client or handcrafted solutions can be tried on the fly as well. A relational data services component mediates data organization between the simulation services and the dynamic visualization services.…”

Section: Rimsim Architecturementioning

confidence: 99%