dc.contributor.author | Whilden, Allison | en_US |
dc.contributor.author | Karis, Dirk | en_US |
dc.contributor.author | Setlur, Vidya | en_US |
dc.contributor.author | Degtyar, Rodion | en_US |
dc.contributor.author | Que, Jonathan | en_US |
dc.contributor.author | Lymperopoulos, Filippos | en_US |
dc.contributor.editor | Agus, Marco | en_US |
dc.contributor.editor | Aigner, Wolfgang | en_US |
dc.contributor.editor | Hoellt, Thomas | en_US |
dc.date.accessioned | 2022-06-02T15:50:44Z | |
dc.date.available | 2022-06-02T15:50:44Z | |
dc.date.issued | 2022 | |
dc.identifier.isbn | 978-3-03868-184-7 | |
dc.identifier.uri | https://doi.org/10.2312/evs.20221094 | |
dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/evs20221094 | |
dc.description.abstract | We present Blocks, a formalism that enables the building of visualizations by specifying layout, data relationships, and level of detail (LOD) for specific portions of the visualization. Users can create and manipulate Blocks on a canvas interface through drag-and-drop interaction, controlling the LOD of the data attributes for tabular style visualizations. We conducted a user study to compare how 24 participants employ Blocks and Tableau to complete a target visualization task. Findings from the study suggest that Blocks is a useful mechanism for creating visualizations with embedded microcharts, conditional formatting, and custom layouts. We describe future directions for extending Blocks in visual analysis interfaces. | en_US |
dc.publisher | The Eurographics Association | en_US |
dc.rights | Attribution 4.0 International License | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.subject | CCS Concepts Human-centered computing --> Information visualization | |
dc.subject | CCS Concepts Human | |
dc.subject | centered computing | |
dc.subject | Information visualization | |
dc.title | Blocks: Creating Rich Tables with Drag-and-Drop Interaction | en_US |
dc.description.seriesinformation | EuroVis 2022 - Short Papers | |
dc.description.sectionheaders | Visual Analysis and Machine Learning | |
dc.identifier.doi | 10.2312/evs.20221094 | |
dc.identifier.pages | 61-65 | |
dc.identifier.pages | 5 pages | |