Publikation: Visual Integration of Meteorological and Sensor Data for Identifying Suspicious Company Behavior
Lade...
Dateien
Zu diesem Dokument gibt es keine Dateien.
Datum
2017
Herausgeber:innen
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
DOI (zitierfähiger Link)
Internationale Patentnummer
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Core Facility der Universität Konstanz
Titel in einer weiteren Sprache
Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published
Erschienen in
FISHER, Brian, ed., Shixia LIU, ed., Tobias SCHRECK, ed.. 2017 IEEE Conference on Visual Analytics Science and Technology (VAST), Proceedings. Piscataway, NJ: IEEE, 2017, pp. 225-226. ISBN 978-1-5386-3163-8. Available under: doi: 10.1109/VAST.2017.8585436
Zusammenfassung
We present an approach developed in course of the VAST 2017 Mini-Challenge 2. To help the ornithologist Mitch to investigate the noxious gases emitted by the four companies south of the nature preserve, we employ a combination of interactive visualizations that allow for an exploration of the data. In this paper, we present our visual-interactive approach for analyzing suspicious patterns in the data. By taking the wind data into consideration, as well, our approach allows the retrieval of patterns in the chemical releases and identify key polluters.
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
Konferenz
2017 IEEE Conference on Visual Analytics Science and Technology (VAST), 3. Okt. 2017 - 6. Okt. 2017, Phoenix, AZ
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690
SEEBACHER, Daniel, Bruno SCHNEIDER, Michael BEHRISCH, 2017. Visual Integration of Meteorological and Sensor Data for Identifying Suspicious Company Behavior. 2017 IEEE Conference on Visual Analytics Science and Technology (VAST). Phoenix, AZ, 3. Okt. 2017 - 6. Okt. 2017. In: FISHER, Brian, ed., Shixia LIU, ed., Tobias SCHRECK, ed.. 2017 IEEE Conference on Visual Analytics Science and Technology (VAST), Proceedings. Piscataway, NJ: IEEE, 2017, pp. 225-226. ISBN 978-1-5386-3163-8. Available under: doi: 10.1109/VAST.2017.8585436BibTex
@inproceedings{Seebacher2017Visua-44808, year={2017}, doi={10.1109/VAST.2017.8585436}, title={Visual Integration of Meteorological and Sensor Data for Identifying Suspicious Company Behavior}, isbn={978-1-5386-3163-8}, publisher={IEEE}, address={Piscataway, NJ}, booktitle={2017 IEEE Conference on Visual Analytics Science and Technology (VAST), Proceedings}, pages={225--226}, editor={Fisher, Brian and Liu, Shixia and Schreck, Tobias}, author={Seebacher, Daniel and Schneider, Bruno and Behrisch, Michael} }
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/123456789/44808"> <dcterms:abstract xml:lang="eng">We present an approach developed in course of the VAST 2017 Mini-Challenge 2. To help the ornithologist Mitch to investigate the noxious gases emitted by the four companies south of the nature preserve, we employ a combination of interactive visualizations that allow for an exploration of the data. In this paper, we present our visual-interactive approach for analyzing suspicious patterns in the data. By taking the wind data into consideration, as well, our approach allows the retrieval of patterns in the chemical releases and identify key polluters.</dcterms:abstract> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dc:language>eng</dc:language> <foaf:homepage rdf:resource="http://localhost:8080/"/> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/44808"/> <dcterms:issued>2017</dcterms:issued> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/> <dc:contributor>Behrisch, Michael</dc:contributor> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/> <dcterms:title>Visual Integration of Meteorological and Sensor Data for Identifying Suspicious Company Behavior</dcterms:title> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2019-02-01T13:38:01Z</dc:date> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2019-02-01T13:38:01Z</dcterms:available> <dc:creator>Behrisch, Michael</dc:creator> <dc:creator>Schneider, Bruno</dc:creator> <dc:creator>Seebacher, Daniel</dc:creator> <dc:contributor>Seebacher, Daniel</dc:contributor> <dc:contributor>Schneider, Bruno</dc:contributor> </rdf:Description> </rdf:RDF>
Interner Vermerk
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Prüfungsdatum der Dissertation
Finanzierungsart
Kommentar zur Publikation
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja