Publikation: RescueMark : Visual Analytics of Social Media Data for Guiding Emergency Response in Disaster Situations Award for Skillful Integration of Language Model
Lade...
Dateien
Datum
2019
Herausgeber:innen
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
URI (zitierfähiger Link)
DOI (zitierfähiger Link)
Internationale Patentnummer
Link zur Lizenz
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Open Access Green
Core Facility der Universität Konstanz
Titel in einer weiteren Sprache
Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published
Erschienen in
CHANG, Remco, ed., Daniel A. KEIM, ed., Ross MACIEJEWSKI, ed.. 2019 IEEE Conference on Visual Analytics Science and Technology (VAST) : Proceedings. Piscataway, NJ: IEEE, 2019, pp. 120-121. ISBN 978-1-72812-284-7. Available under: doi: 10.1109/VAST47406.2019.8986898
Zusammenfassung
This paper presents RescueMark, a web-based visual analytics tool for analyzing disaster situations and guiding emergency response. In disaster situations operators must take quick and effective decisions to solve critical problems. RescueMark provides spatial, topic and temporal event exploration supporting decision making for resource allocation and determine damaged areas of the city. We describe the data analysis and visualization process of the social media data applied to extract the relevant information.
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
Human-centered computing, Visualization, Visualization application domains, Visual analytics
Konferenz
2019 IEEE Conference on Visual Analytics Science and Technology (VAST), 20. Okt. 2019 - 25. Okt. 2019, Vancouver, BC, Canada
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690
JEITLER, Astrik Veronika, Alpin TÜRKOGLU, Denis MAKAROV, Timo JOCKERS, Juri F. BUCHMÜLLER, Udo SCHLEGEL, Daniel A. KEIM, 2019. RescueMark : Visual Analytics of Social Media Data for Guiding Emergency Response in Disaster Situations Award for Skillful Integration of Language Model. 2019 IEEE Conference on Visual Analytics Science and Technology (VAST). Vancouver, BC, Canada, 20. Okt. 2019 - 25. Okt. 2019. In: CHANG, Remco, ed., Daniel A. KEIM, ed., Ross MACIEJEWSKI, ed.. 2019 IEEE Conference on Visual Analytics Science and Technology (VAST) : Proceedings. Piscataway, NJ: IEEE, 2019, pp. 120-121. ISBN 978-1-72812-284-7. Available under: doi: 10.1109/VAST47406.2019.8986898BibTex
@inproceedings{Jeitler2019Rescu-50585, year={2019}, doi={10.1109/VAST47406.2019.8986898}, title={RescueMark : Visual Analytics of Social Media Data for Guiding Emergency Response in Disaster Situations Award for Skillful Integration of Language Model}, isbn={978-1-72812-284-7}, publisher={IEEE}, address={Piscataway, NJ}, booktitle={2019 IEEE Conference on Visual Analytics Science and Technology (VAST) : Proceedings}, pages={120--121}, editor={Chang, Remco and Keim, Daniel A. and Maciejewski, Ross}, author={Jeitler, Astrik Veronika and Türkoglu, Alpin and Makarov, Denis and Jockers, Timo and Buchmüller, Juri F. and Schlegel, Udo 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/123456789/50585"> <dc:creator>Türkoglu, Alpin</dc:creator> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2020-08-27T07:43:10Z</dc:date> <dc:creator>Makarov, Denis</dc:creator> <dc:language>eng</dc:language> <dc:creator>Keim, Daniel A.</dc:creator> <dc:contributor>Jeitler, Astrik Veronika</dc:contributor> <dc:contributor>Keim, Daniel A.</dc:contributor> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/> <dc:rights>terms-of-use</dc:rights> <dcterms:abstract xml:lang="eng">This paper presents RescueMark, a web-based visual analytics tool for analyzing disaster situations and guiding emergency response. In disaster situations operators must take quick and effective decisions to solve critical problems. RescueMark provides spatial, topic and temporal event exploration supporting decision making for resource allocation and determine damaged areas of the city. We describe the data analysis and visualization process of the social media data applied to extract the relevant information.</dcterms:abstract> <dcterms:issued>2019</dcterms:issued> <dc:contributor>Türkoglu, Alpin</dc:contributor> <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/50585/1/Jeitler_2-y97wz7grp4sv4.pdf"/> <dcterms:title>RescueMark : Visual Analytics of Social Media Data for Guiding Emergency Response in Disaster Situations Award for Skillful Integration of Language Model</dcterms:title> <dc:creator>Buchmüller, Juri F.</dc:creator> <dc:contributor>Buchmüller, Juri F.</dc:contributor> <dc:creator>Jeitler, Astrik Veronika</dc:creator> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2020-08-27T07:43:10Z</dcterms:available> <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/50585/1/Jeitler_2-y97wz7grp4sv4.pdf"/> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/> <foaf:homepage rdf:resource="http://localhost:8080/"/> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/50585"/> <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/> <dc:contributor>Makarov, Denis</dc:contributor> <dc:contributor>Schlegel, Udo</dc:contributor> <dc:creator>Schlegel, Udo</dc:creator> <dc:creator>Jockers, Timo</dc:creator> <dc:contributor>Jockers, Timo</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