Uncertainty Propagation and Trust Building in Visual Analytics

Zitieren

Dateien zu dieser Ressource

Prüfsumme: MD5:cd19fd5edbb92eb2dafeb1388e162e7e

SACHA, Dominik, Hansi SENARATNE, Bum Chul KWON, Daniel A. KEIM, 2014. Uncertainty Propagation and Trust Building in Visual Analytics. IEEE VIS 2014. Paris, 9. Nov 2014 - 14. Nov 2014. In: Provenance for Sensemaking : IEEE VIS 2014 Workshop, 10 November 2014, Paris, France. IEEE VIS 2014. Paris, 9. Nov 2014 - 14. Nov 2014

@inproceedings{Sacha2014Uncer-30217, title={Uncertainty Propagation and Trust Building in Visual Analytics}, year={2014}, booktitle={Provenance for Sensemaking : IEEE VIS 2014 Workshop, 10 November 2014, Paris, France}, author={Sacha, Dominik and Senaratne, Hansi and Kwon, Bum Chul and Keim, Daniel A.} }

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/30217"> <dc:creator>Sacha, Dominik</dc:creator> <dc:creator>Kwon, Bum Chul</dc:creator> <dc:contributor>Senaratne, Hansi</dc:contributor> <dcterms:rights rdf:resource="http://nbn-resolving.de/urn:nbn:de:bsz:352-20150305140228786-3747162-5"/> <dc:contributor>Sacha, Dominik</dc:contributor> <dcterms:abstract xml:lang="eng">Visual analytics combines human and machine abilities to generate new knowledge from data. Within this process, uncertainty often plays an important role in hindering the sensemaking process and analysis tasks. On the machine side, uncertainty builds up from the data source level to the visual output. On the human side, these uncertainties often result in “lack of knowledge or trust” or “overtrust.” Such human’s biased interpretation can be resolved if we can measure uncertainties and users’ trust at each stage and provide proper mitigation in time. We believe that we can achieve this by tracing data provenance and analytic provenance accurately and reflecting them on the system output. Therefore, our first goal is to identify the roles of uncertainty and trust along the entire visual analytics knowledge generation process. In addition, we aim to capture how uncertainty and trust can be derived from data and analytic provenance. In this workshop, we introduce a framework that describes the roles of uncertainty and trust, and introduce open research questions with potential solutions.</dcterms:abstract> <dc:contributor>Keim, Daniel A.</dc:contributor> <dc:language>eng</dc:language> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2015-03-11T15:13:22Z</dc:date> <dcterms:title>Uncertainty Propagation and Trust Building in Visual Analytics</dcterms:title> <dc:creator>Senaratne, Hansi</dc:creator> <dcterms:issued>2014</dcterms:issued> <dc:contributor>Kwon, Bum Chul</dc:contributor> <dc:creator>Keim, Daniel A.</dc:creator> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2015-03-11T15:13:22Z</dcterms:available> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/30217"/> </rdf:Description> </rdf:RDF>

Dateiabrufe seit 11.03.2015 (Informationen über die Zugriffsstatistik)

Sacha_0-284009.pdf 115

Das Dokument erscheint in:

KOPS Suche


Stöbern

Mein Benutzerkonto