Steering the Craft: UI Elements and Visualizations for Supporting Progressive Visual Analytics
Progressive visual analytics (PVA) has emerged in recent years to manage the latency of data analysis systems. When analysis is performed progressively, rough estimates of the results are generated quickly and are then improved over time. Analysts can therefore monitor the progression of the results...
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| Published in | Computer graphics forum Vol. 36; no. 3; pp. 491 - 502 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Blackwell Publishing Ltd
01.06.2017
Wiley |
| Series | Eurographics Conference on Visualization (EuroVis 2017) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Progressive visual analytics (PVA) has emerged in recent years to manage the latency of data analysis systems. When analysis is performed progressively, rough estimates of the results are generated quickly and are then improved over time. Analysts can therefore monitor the progression of the results, steer the analysis algorithms, and make early decisions if the estimates provide a convincing picture. In this article, we describe interface design guidelines for helping users understand progressively updating results and make early decisions based on progressive estimates. To illustrate our ideas, we present a prototype PVA tool called InsightsFeed for exploring Twitter data at scale. As validation, we investigate the tradeoffs of our tool when exploring a Twitter dataset in a user study. We report the usage patterns in making early decisions using the user interface, guiding computational methods, and exploring different subsets of the dataset, compared to sequential analysis without progression. |
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| AbstractList | Progressive visual analytics
(PVA) has emerged in recent years to manage the latency of data analysis systems. When analysis is performed progressively, rough estimates of the results are generated quickly and are then improved over time. Analysts can therefore monitor the progression of the results, steer the analysis algorithms, and make early decisions if the estimates provide a convincing picture. In this article, we describe interface design guidelines for helping users understand progressively updating results and make early decisions based on progressive estimates. To illustrate our ideas, we present a prototype PVA tool called
I
nsights
F
eed
for exploring Twitter data at scale. As validation, we investigate the tradeoffs of our tool when exploring a Twitter dataset in a user study. We report the usage patterns in making early decisions using the user interface, guiding computational methods, and exploring different subsets of the dataset, compared to sequential analysis without progression. Progressive visual analytics (PVA) has emerged in recent years to manage the latency of data analysis systems. When analysis is performed progressively, rough estimates of the results are generated quickly and are then improved over time. Analysts can therefore monitor the progression of the results, steer the analysis algorithms, and make early decisions if the estimates provide a convincing picture. In this article, we describe interface design guidelines for helping users understand progressively updating results and make early decisions based on progressive estimates. To illustrate our ideas, we present a prototype PVA tool called InsightsFeed for exploring Twitter data at scale. As validation, we investigate the tradeoffs of our tool when exploring a Twitter dataset in a user study. We report the usage patterns in making early decisions using the user interface, guiding computational methods, and exploring different subsets of the dataset, compared to sequential analysis without progression. Progressive visual analytics (PVA) has emerged in recent years to manage the latency of data analysis systems. When analysisis performed progressively, rough estimates of the results are generated quickly and are then improved over time. Analysts cantherefore monitor the progression of the results, steer the analysis algorithms, and make early decisions if the estimates providea convincing picture. In this article, we describe interface design guidelines for helping users understand progressively updatingresults and make early decisions based on progressive estimates. To illustrate our ideas, we present a prototype PVA tool calledI NSIGHTS F EED for exploring Twitter data at scale. As validation, we investigate the tradeoffs of our tool when exploringa Twitter dataset in a user study. We report the usage patterns in making early decisions using the user interface, guidingcomputational methods, and exploring different subsets of the dataset, compared to sequential analysis without progression. |
| Author | Elmqvist, Niklas Fekete, Jean‐Daniel Badam, Sriram Karthik |
| Author_xml | – sequence: 1 givenname: Sriram Karthik surname: Badam fullname: Badam, Sriram Karthik organization: University of Maryland – sequence: 2 givenname: Niklas surname: Elmqvist fullname: Elmqvist, Niklas organization: University of Maryland – sequence: 3 givenname: Jean‐Daniel surname: Fekete fullname: Fekete, Jean‐Daniel organization: INRIA |
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| Cites_doi | 10.1109/TVCG.2007.70539 10.1109/TVCG.2014.2346452 10.3115/1614025.1614037 10.1109/TPAMI.2002.1017616 10.1109/TVCG.2014.2346578 10.1145/2207676.2207741 10.1609/icwsm.v8i1.14550 10.1109/TVCG.2011.185 10.1109/TVCG.2014.2346574 10.1108/eb026526 10.1145/2514.2517 10.1609/aaai.v31i1.10628 10.1145/2556288.2557195 10.1016/S0167-739X(98)00047-8 10.1109/TVCG.2015.2462356 10.1609/icwsm.v4i1.14009 10.1109/INFVIS.2004.60 10.1007/978-3-540-70956-5_7 |
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| Copyright | 2017 The Author(s) Computer Graphics Forum © 2017 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd. 2017 The Eurographics Association and John Wiley & Sons Ltd. Attribution |
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| Snippet | Progressive visual analytics (PVA) has emerged in recent years to manage the latency of data analysis systems. When analysis is performed progressively, rough... Progressive visual analytics (PVA) has emerged in recent years to manage the latency of data analysis systems. When analysis is performed progressively, rough... Progressive visual analytics (PVA) has emerged in recent years to manage the latency of data analysis systems. When analysisis performed progressively, rough... |
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| SubjectTerms | Analytics Categories and Subject Descriptors (according to ACM CCS) Computer Science Crafts Data analysis Decision analysis Decisions Estimates H.5.2 [Information Interfaces]: User Interfaces—GUI Human-Computer Interaction Sequential analysis Social networks Steering Systems analysis |
| Title | Steering the Craft: UI Elements and Visualizations for Supporting Progressive Visual Analytics |
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