RankBooster: Visual Analysis of Ranking Predictions

dc.contributor.authorPuri, Abisheken_US
dc.contributor.authorKu, Bon Kyungen_US
dc.contributor.authorWang, Yongen_US
dc.contributor.authorQu, Huaminen_US
dc.contributor.editorKerren, Andreas and Garth, Christoph and Marai, G. Elisabetaen_US
dc.date.accessioned2020-05-24T13:52:18Z
dc.date.available2020-05-24T13:52:18Z
dc.date.issued2020
dc.description.abstractRanking is a natural and ubiquitous way to facilitate decision-making in various applications. However, different rankings are often used for the same set of entities, with each ranking method placing emphasis on different factors. These factors can also be multi-dimensional in nature, compounding the problem. This complexity can make it challenging for an entity which is being ranked to understand what they can do to improve their rankings, and to analyze the effect of changes in various factors to their overall rank. In this paper, we present RankBooster, a novel visual analytics system to help users conveniently investigate ranking predictions.We take university rankings as an example and focus on helping universities to better explore their rankings, where they can compare themselves to their rivals in key areas as well as overall. Novel visualizations are proposed to enable efficient analysis of rankings, including a Scenario Analysis View to show a high-level summary of different ranking scenarios, a Relationship View to visualize the influence of each attribute on different indicators and a Rival View to compare the ranking of a university and those of its rivals. A case study demonstrates the usefulness and effectiveness of RankBooster in facilitating the visual analysis of ranking predictions and helping users better understand their current situation.en_US
dc.description.sectionheadersRendering, Images, and Applications
dc.description.seriesinformationEuroVis 2020 - Short Papers
dc.identifier.doi10.2312/evs.20201068
dc.identifier.isbn978-3-03868-106-9
dc.identifier.pages175-179
dc.identifier.urihttps://doi.org/10.2312/evs.20201068
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/evs20201068
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/]
dc.subjectHuman centered computing
dc.subjectVisual analytics
dc.subjectInformation visualization
dc.titleRankBooster: Visual Analysis of Ranking Predictionsen_US
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