Similarity-Driven Visual-Interactive Prediction of Movie Ratings and Box Office Results
| Type of Publication: | Contribution to a conference |
| URI (citable link): | http://nbn-resolving.de/urn:nbn:de:bsz:352-265328 |
| Author: | Al-Masoudi, Feeras; Seebacher, Daniel; Schreiner, Mario; Stein, Manuel; Rohrdantz, Christian; Fischer, Fabian; Simon, Svenja; Schreck, Tobias; Keim, Daniel |
| Year of publication: | 2013 |
| Conference: | IEEE VIS, Oct 13, 2013 - Oct 18, 2013, Atlanta, Georgia, USA |
| Published in: | VIS 2013 : IEEE International Conference on Visual Analytics Science and Technology ; 13 - 18 October 2013, Atlanta, Georgia, USA |
| Summary: |
We present an approach developed in course of the VAST 2013 Mini Challenge: Visualize the Box Office. We follow a similaritydriven methodology to predict ratings and box office results based on historic data. An array of interactive visualizations allow analysts to explore structured and unstructured data, activate their domain background knowledge, and come up with predictions as a weighted sum of historically observed figures. We describe the workflow, our developed system, present results obtained during the Challenge execution, and discuss our method in light of extension possibilities.
|
| Subject (DDC): | 004 Computer Science |
| Link to License: | Terms of use |
| Bibliography of Konstanz: | Yes |
Cite This
Files in this item
![[PDF]](/themes/Kops/images/mimes/pdf.png "PDF file"){kind=small}
AL-MASOUDI, Feeras, Daniel SEEBACHER, Mario SCHREINER, Manuel STEIN, Christian ROHRDANTZ, Fabian FISCHER, Svenja SIMON, Tobias SCHRECK, Daniel KEIM, 2013. Similarity-Driven Visual-Interactive Prediction of Movie Ratings and Box Office Results. IEEE VIS. Atlanta, Georgia, USA, Oct 13, 2013 - Oct 18, 2013. In: VIS 2013 : IEEE International Conference on Visual Analytics Science and Technology ; 13 - 18 October 2013, Atlanta, Georgia, USA
@inproceedings{AlMasoudi2013Simil-26532, title={Similarity-Driven Visual-Interactive Prediction of Movie Ratings and Box Office Results}, year={2013}, booktitle={VIS 2013 : IEEE International Conference on Visual Analytics Science and Technology ; 13 - 18 October 2013, Atlanta, Georgia, USA}, author={Al-Masoudi, Feeras and Seebacher, Daniel and Schreiner, Mario and Stein, Manuel and Rohrdantz, Christian and Fischer, Fabian and Simon, Svenja and Schreck, Tobias and Keim, Daniel} }
<rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:void="http://rdfs.org/ns/void#" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/26532"> <dcterms:abstract xml:lang="eng">We present an approach developed in course of the VAST 2013 Mini Challenge: Visualize the Box Office. We follow a similaritydriven methodology to predict ratings and box office results based on historic data. An array of interactive visualizations allow analysts to explore structured and unstructured data, activate their domain background knowledge, and come up with predictions as a weighted sum of historically observed figures. We describe the workflow, our developed system, present results obtained during the Challenge execution, and discuss our method in light of extension possibilities.</dcterms:abstract> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2014-02-26T10:26:10Z</dcterms:available> <dc:contributor>Stein, Manuel</dc:contributor> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2014-02-26T10:26:10Z</dc:date> <dc:creator>Seebacher, Daniel</dc:creator> <dc:language>eng</dc:language> <dc:contributor>Fischer, Fabian</dc:contributor> <dcterms:rights rdf:resource="https://kops.uni-konstanz.de/page/termsofuse"/> <dcterms:issued>2013</dcterms:issued> <dc:creator>Al-Masoudi, Feeras</dc:creator> <dc:creator>Fischer, Fabian</dc:creator> <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/26532/2/Seebacher_265328.pdf"/> <dc:rights>terms-of-use</dc:rights> <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/26532/2/Seebacher_265328.pdf"/> <dc:contributor>Rohrdantz, Christian</dc:contributor> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dc:creator>Stein, Manuel</dc:creator> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/36"/> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/36"/> <dc:contributor>Seebacher, Daniel</dc:contributor> <dc:creator>Keim, Daniel</dc:creator> <dc:creator>Simon, Svenja</dc:creator> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/26532"/> <dcterms:title>Similarity-Driven Visual-Interactive Prediction of Movie Ratings and Box Office Results</dcterms:title> <dc:contributor>Simon, Svenja</dc:contributor> <dc:creator>Schreck, Tobias</dc:creator> <dc:creator>Schreiner, Mario</dc:creator> <dc:creator>Rohrdantz, Christian</dc:creator> <dc:contributor>Schreiner, Mario</dc:contributor> <dc:contributor>Al-Masoudi, Feeras</dc:contributor> <dc:contributor>Schreck, Tobias</dc:contributor> <dcterms:bibliographicCitation>Vortrag gehalten bei: VIS 2013 : IEEE International Conference on Visual Analytics Science and Technology ; 13-18 October 2013, Atlanta, Georgia, USA</dcterms:bibliographicCitation> <dc:contributor>Keim, Daniel</dc:contributor> </rdf:Description> </rdf:RDF>
Downloads since Oct 1, 2014 (Information about access statistics)
| Seebacher_265328.pdf | 305 |
This item appears in the following Collection(s)
Search KOPS
Browse
My Account
