Generative Data Models for Validation and Evaluation of Visualization Techniques


Dateien zu dieser Ressource

Prüfsumme: MD5:6f6da06c4eb9a216c6c127db28947d81

SCHULZ, Christoph, Arlind NOCAJ, Mennatallah EL-ASSADY, Michael HUND, Christin SCHÄTZLE, Miriam BUTT, Daniel A. KEIM, Ulrik BRANDES, Daniel WEISKOPF, 2016. Generative Data Models for Validation and Evaluation of Visualization Techniques. BELIV Workshop 2016. Baltimore, MD, USA, 24. Okt 2016 - 24. Okt 2016. In: BELIV '16 : Proceedings of the Sixth Workshop on Beyond Time and Errors on Novel Evaluation Methods for Visualization. New York:ACM Press, pp. 112-124. ISBN 978-1-4503-4818-8. Available under: doi: 10.1145/2993901.2993907

@inproceedings{Schulz2016Gener-37469, title={Generative Data Models for Validation and Evaluation of Visualization Techniques}, year={2016}, doi={10.1145/2993901.2993907}, isbn={978-1-4503-4818-8}, address={New York}, publisher={ACM Press}, booktitle={BELIV '16 : Proceedings of the Sixth Workshop on Beyond Time and Errors on Novel Evaluation Methods for Visualization}, pages={112--124}, author={Schulz, Christoph and Nocaj, Arlind and El-Assady, Mennatallah and Hund, Michael and Schätzle, Christin and Butt, Miriam and Keim, Daniel A. and Brandes, Ulrik and Weiskopf, Daniel} }

<rdf:RDF xmlns:dcterms="" xmlns:dc="" xmlns:rdf="" xmlns:bibo="" xmlns:dspace="" xmlns:foaf="" xmlns:void="" xmlns:xsd="" > <rdf:Description rdf:about=""> <dc:creator>Nocaj, Arlind</dc:creator> <dc:creator>Brandes, Ulrik</dc:creator> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dcterms:issued>2016</dcterms:issued> <bibo:uri rdf:resource=""/> <dc:creator>Schätzle, Christin</dc:creator> <dc:contributor>Schätzle, Christin</dc:contributor> <dc:contributor>Butt, Miriam</dc:contributor> <dspace:hasBitstream rdf:resource=""/> <dc:creator>Weiskopf, Daniel</dc:creator> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dspace:isPartOfCollection rdf:resource=""/> <dcterms:title>Generative Data Models for Validation and Evaluation of Visualization Techniques</dcterms:title> <dc:creator>Butt, Miriam</dc:creator> <dc:contributor>Nocaj, Arlind</dc:contributor> <dcterms:hasPart rdf:resource=""/> <dc:rights>terms-of-use</dc:rights> <dspace:isPartOfCollection rdf:resource=""/> <dc:contributor>Keim, Daniel A.</dc:contributor> <dc:date rdf:datatype="">2017-02-16T07:54:41Z</dc:date> <dc:language>eng</dc:language> <dcterms:rights rdf:resource=""/> <dc:contributor>Schulz, Christoph</dc:contributor> <dc:creator>Schulz, Christoph</dc:creator> <dcterms:isPartOf rdf:resource=""/> <dc:creator>Keim, Daniel A.</dc:creator> <dc:contributor>Brandes, Ulrik</dc:contributor> <dc:creator>El-Assady, Mennatallah</dc:creator> <dcterms:available rdf:datatype="">2017-02-16T07:54:41Z</dcterms:available> <dcterms:abstract xml:lang="eng">We argue that there is a need for substantially more research on the use of generative data models in the validation and evaluation of visualization techniques. For example, user studies will require the display of representative and uncon-founded visual stimuli, while algorithms will need functional coverage and assessable benchmarks. However, data is often collected in a semi-automatic fashion or entirely hand-picked, which obscures the view of generality, impairs availability, and potentially violates privacy. There are some sub-domains of visualization that use synthetic data in the sense of generative data models, whereas others work with real-world-based data sets and simulations. Depending on the visualization domain, many generative data models are "side projects" as part of an ad-hoc validation of a techniques paper and thus neither reusable nor general-purpose. We review existing work on popular data collections and generative data models in visualization to discuss the opportunities and consequences for technique validation, evaluation, and experiment design. We distill handling and future directions, and discuss how we can engineer generative data models and how visualization research could benefit from more and better use of generative data models.</dcterms:abstract> <dc:contributor>El-Assady, Mennatallah</dc:contributor> <dc:contributor>Hund, Michael</dc:contributor> <dcterms:isPartOf rdf:resource=""/> <dc:creator>Hund, Michael</dc:creator> <dc:contributor>Weiskopf, Daniel</dc:contributor> </rdf:Description> </rdf:RDF>

Dateiabrufe seit 16.02.2017 (Informationen über die Zugriffsstatistik)

Schulz_0-370172.pdf 383

Das Dokument erscheint in:

KOPS Suche


Mein Benutzerkonto