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dc.contributor.authorLeman, Scotland C.en
dc.contributor.authorHouse, Leanna L.en
dc.contributor.authorMaiti, Dipayanen
dc.contributor.authorEndert, Alexen
dc.contributor.authorNorth, Chrisen
dc.date.accessioned2017-02-19T01:47:56Zen
dc.date.available2017-02-19T01:47:56Zen
dc.date.issued2013-03-20en
dc.identifier.issn1932-6203en
dc.identifier.urihttp://hdl.handle.net/10919/75065en
dc.description.abstractTypical data visualizations result from linear pipelines that start by characterizing data using a model or algorithm to reduce the dimension and summarize structure, and end by displaying the data in a reduced dimensional form. Sensemaking may take place at the end of the pipeline when users have an opportunity to observe, digest, and internalize any information displayed. However, some visualizations mask meaningful data structures when model or algorithm constraints (e.g., parameter specifications) contradict information in the data. Yet, due to the linearity of the pipeline, users do not have a natural means to adjust the displays. In this paper, we present a framework for creating dynamic data displays that rely on both mechanistic data summaries and expert judgement. The key is that we develop both the theory and methods of a new human-data interaction to which we refer as ‘‘ Visual to Parametric Interaction’’ (V2PI). With V2PI, the pipeline becomes bidirectional in that users are embedded in the pipeline; users learn from visualizations and the visualizations adjust to expert judgement. We demonstrate the utility of V2PI and a bi-directional pipeline with two examples.en
dc.format.extent12 pagesen
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.publisherPLOSen
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000317562600001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsCreative Commons CC0 1.0 Universal Public Domain Dedicationen
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/en
dc.titleVisual to Parametric Interaction (V2PI)en
dc.typeArticle - Refereeden
dc.description.versionPublished versionen
dc.contributor.departmentComputer Scienceen
dc.contributor.departmentStatisticsen
dc.title.serialPLOS ONEen
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0050474en
dc.identifier.volume8en
dc.identifier.issue3en
dc.type.dcmitypeTexten
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/Computer Scienceen
pubs.organisational-group/Virginia Tech/Scienceen
pubs.organisational-group/Virginia Tech/Science/COS T&R Facultyen
pubs.organisational-group/Virginia Tech/Science/Statisticsen


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Creative Commons CC0 1.0 Universal Public Domain Dedication
License: Creative Commons CC0 1.0 Universal Public Domain Dedication