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Visualization of streaming data : observing change and context in information visualization techniques

Visualization of streaming data : observing change and context in information visualization techniques

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KRSTAJIC, Milos, Daniel A. KEIM, 2013. Visualization of streaming data : observing change and context in information visualization techniques. 2013 IEEE International Conference on Big Data. Silicon Valley, CA, USA, 6. Okt 2013 - 9. Okt 2013. In: 2013 IEEE International Conference on Big Data. 2013 IEEE International Conference on Big Data. Silicon Valley, CA, USA, 6. Okt 2013 - 9. Okt 2013. IEEE, pp. 41-47. ISBN 978-1-4799-1293-3

@inproceedings{Krstajic2013-10Visua-26222, title={Visualization of streaming data : observing change and context in information visualization techniques}, year={2013}, doi={10.1109/BigData.2013.6691713}, isbn={978-1-4799-1293-3}, publisher={IEEE}, booktitle={2013 IEEE International Conference on Big Data}, pages={41--47}, author={Krstajic, Milos 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/123456789/26222"> <dc:creator>Krstajic, Milos</dc:creator> <dcterms:title>Visualization of streaming data : observing change and context in information visualization techniques</dcterms:title> <dc:creator>Keim, Daniel A.</dc:creator> <dcterms:abstract xml:lang="eng">Visualizing data streams poses numerous challenges in the data, image and user space. In the era of big data, we need incremental visualization methods that will allow the analysts to explore data faster and help them make important decisions on time. In this paper, we have reviewed several well-known information visualization methods that are commonly used to visualize static datasets and analyzed their degrees of freedom. By observing which independent visual variables can change in each method, we described how these changes are related to the attribute and structure changes that can occur in the data stream. Most of the changes in the data stream lead to potential loss of temporal and relational context between the new data and the past data. We present potential directions for measuring the amount of change and loss of context by reviewing related work and identify open issues for future work in this domain.</dcterms:abstract> <dc:rights>deposit-license</dc:rights> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/26222"/> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2014-02-05T19:44:32Z</dcterms:available> <dcterms:rights rdf:resource="http://nbn-resolving.org/urn:nbn:de:bsz:352-20140905103605204-4002607-1"/> <dcterms:issued>2013-10</dcterms:issued> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2014-02-05T19:44:32Z</dc:date> <dc:contributor>Krstajic, Milos</dc:contributor> <dc:contributor>Keim, Daniel A.</dc:contributor> <dcterms:bibliographicCitation>2013 IEEE International Conference on Big Data : Proceedings ; October 6-9, 2013, Santa Clara, CA, USA / ed. by Xiaohua Hu [...]. - Piscataway ; IEEE, 2013. - S.41-47. - ISBN 978-1-4799-1292-6</dcterms:bibliographicCitation> <dc:language>eng</dc:language> </rdf:Description> </rdf:RDF>

Dateiabrufe seit 01.10.2014 (Informationen über die Zugriffsstatistik)

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