Predictive Visual Analytics : Approaches for Movie Ratings and Discussion of Open Research Challenges

Loading...
Thumbnail Image
Date
2014
Editors
Contact
Journal ISSN
Electronic ISSN
ISBN
Bibliographical data
Publisher
Series
URI (citable link)
DOI (citable link)
ArXiv-ID
International patent number
Link to the license
EU project number
Project
Open Access publication
Restricted until
Title in another language
Research Projects
Organizational Units
Journal Issue
Publication type
Contribution to a conference collection
Publication status
Published
Published in
An IEEE VIS 2014 Workshop : Visualization for Predictive Analytics ; Proceedings
Abstract
We present two original approaches for visual-interactive prediction of user movie ratings and box office gross after the opening weekend, as designed and awarded during VAST Challenge 2013. Our approaches are driven by machine learning models and interactive data exploration, respectively. They consider an array of different training data types, including categorical/discrete data, time series data, and sentiment data from social media. The two approaches are only first steps towards visual-interactive prediction, but have shown to deliver improved prediction results as compared to baseline non-interactive prediction, and may serve as starting points for other predictive applications. Furthermore, an abstract workflow for predictive visual analytics is derived. We also discuss promising challenges for future research in visual-interactive predictive analysis, including design space, evaluation, and model visualization.
Summary in another language
Subject (DDC)
004 Computer Science
Keywords
Visual Analytics, Interactive Prediction, System Design, Evaluation
Conference
An IEEE VIS 2014 Workshop : Visualization for Predictive Analytics, Nov 9, 2014 - Nov 9, 2014, Paris
Review
undefined / . - undefined, undefined. - (undefined; undefined)
Cite This
ISO 690EL-ASSADY, Mennatallah, Wolfgang JENTNER, Manuel STEIN, Fabian FISCHER, Tobias SCHRECK, Daniel A. KEIM, 2014. Predictive Visual Analytics : Approaches for Movie Ratings and Discussion of Open Research Challenges. An IEEE VIS 2014 Workshop : Visualization for Predictive Analytics. Paris, Nov 9, 2014 - Nov 9, 2014. In: An IEEE VIS 2014 Workshop : Visualization for Predictive Analytics ; Proceedings
BibTex
@inproceedings{ElAssady2014Predi-32741,
  year={2014},
  title={Predictive Visual Analytics : Approaches for Movie Ratings and Discussion of Open Research Challenges},
  url={http://predictive-workshop.github.io/papers/vpa2014_8.pdf},
  booktitle={An IEEE VIS 2014 Workshop : Visualization for Predictive Analytics ; Proceedings},
  author={El-Assady, Mennatallah and Jentner, Wolfgang and Stein, Manuel and Fischer, Fabian and Schreck, Tobias and Keim, Daniel A.}
}
RDF
<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/server/rdf/resource/123456789/32741">
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:contributor>Fischer, Fabian</dc:contributor>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dc:rights>terms-of-use</dc:rights>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/32741/1/El-Assady_0-305819.pdf"/>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:contributor>Keim, Daniel A.</dc:contributor>
    <dc:contributor>Schreck, Tobias</dc:contributor>
    <dcterms:issued>2014</dcterms:issued>
    <dc:creator>Stein, Manuel</dc:creator>
    <dcterms:abstract xml:lang="eng">We present two original approaches for visual-interactive prediction of user movie ratings and box office gross after the opening weekend, as designed and awarded during VAST Challenge 2013. Our approaches are driven by machine learning models and interactive data exploration, respectively. They consider an array of different training data types, including categorical/discrete data, time series data, and sentiment data from social media. The two approaches are only first steps towards visual-interactive prediction, but have shown to deliver improved prediction results as compared to baseline non-interactive prediction, and may serve as starting points for other predictive applications. Furthermore, an abstract workflow for predictive visual analytics is derived. We also discuss promising challenges for future research in visual-interactive predictive analysis, including design space, evaluation, and model visualization.</dcterms:abstract>
    <dcterms:title>Predictive Visual Analytics : Approaches for Movie Ratings and Discussion of Open Research Challenges</dcterms:title>
    <dc:creator>Fischer, Fabian</dc:creator>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/32741/1/El-Assady_0-305819.pdf"/>
    <dc:creator>Keim, Daniel A.</dc:creator>
    <dc:language>eng</dc:language>
    <dc:contributor>El-Assady, Mennatallah</dc:contributor>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/32741"/>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2016-01-27T14:38:24Z</dcterms:available>
    <dc:contributor>Stein, Manuel</dc:contributor>
    <dc:creator>Schreck, Tobias</dc:creator>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:contributor>Jentner, Wolfgang</dc:contributor>
    <dc:creator>El-Assady, Mennatallah</dc:creator>
    <dc:creator>Jentner, Wolfgang</dc:creator>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2016-01-27T14:38:24Z</dc:date>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
  </rdf:Description>
</rdf:RDF>
Internal note
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Contact
Test date of URL
2016-01-27
Examination date of dissertation
Method of financing
Comment on publication
Alliance license
Corresponding Authors der Uni Konstanz vorhanden
International Co-Authors
Bibliography of Konstanz
Yes
Refereed