Run-Time and Task-Based Performance of Event Detection Techniques for Twitter

Weiler, Andreas; Grossniklaus, Michael; Scholl, Marc H.

Run-Time and Task-Based Performance of Event Detection Techniques for Twitter

Dateien

Weiler_0-300914.pdfGröße: 350.98 KBDownloads: 801

Datum

2015

Publikationstyp

Beitrag zu einem Konferenzband

unikn.publication.listelement.citation.prefix.version.undefined

ZDRAVKOVIC, Jelena, ed. and others. Advanced Information Systems Engineering : 27th International Conference, CAiSE 2015, Stockholm, Sweden, June 8-12, 2015, Proceedings. Cham: Springer, 2015, pp. 35-49. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-319-19068-6. Available under: doi: 10.1007/978-3-319-19069-3_3

Zusammenfassung

Twitter’s increasing popularity as a source of up to date news and information about current events has spawned a body of research on event detection techniques for social media data streams. Although all proposed approaches provide some evidence as to the quality of the detected events, none relate this task-based performance to their run-time performance in terms of processing speed or data throughput. In particular, neither a quantitative nor a comparative evaluation of these aspects has been performed to date. In this paper, we study the run-time and task-based performance of several state-of-the-art event detection techniques for Twitter. In order to reproducibly compare run-time performance, our approach is based on a general-purpose data stream management system, whereas task-based performance is automatically assessed based on a series of novel measures.

Fachgebiet (DDC)

004 Informatik

Schlagwörter

Event detection, Performance evaluation, Twitter streams

Konferenz

27th International Conference, CAiSE 2015, 8. Juni 2015 - 12. Juni 2015, Stockholm

Zitieren

ISO 690

WEILER, Andreas, Michael GROSSNIKLAUS, Marc H. SCHOLL, 2015. Run-Time and Task-Based Performance of Event Detection Techniques for Twitter. 27th International Conference, CAiSE 2015. Stockholm, 8. Juni 2015 - 12. Juni 2015. In: ZDRAVKOVIC, Jelena, ed. and others. Advanced Information Systems Engineering : 27th International Conference, CAiSE 2015, Stockholm, Sweden, June 8-12, 2015, Proceedings. Cham: Springer, 2015, pp. 35-49. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-319-19068-6. Available under: doi: 10.1007/978-3-319-19069-3_3

BibTex

@inproceedings{Weiler2015RunTi-31937,
  year={2015},
  doi={10.1007/978-3-319-19069-3_3},
  title={Run-Time and Task-Based Performance of Event Detection Techniques for Twitter},
  number={9097},
  isbn={978-3-319-19068-6},
  issn={0302-9743},
  publisher={Springer},
  address={Cham},
  series={Lecture Notes in Computer Science},
  booktitle={Advanced Information Systems Engineering : 27th International Conference, CAiSE 2015, Stockholm, Sweden, June 8-12, 2015, Proceedings},
  pages={35--49},
  editor={Zdravkovic, Jelena},
  author={Weiler, Andreas and Grossniklaus, Michael and Scholl, Marc H.}
}

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/123456789/31937">
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2015-10-08T11:46:10Z</dcterms:available>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dc:creator>Grossniklaus, Michael</dc:creator>
    <dc:language>eng</dc:language>
    <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/31937"/>
    <dcterms:title>Run-Time and Task-Based Performance of Event Detection Techniques for Twitter</dcterms:title>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2015-10-08T11:46:10Z</dc:date>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/31937/1/Weiler_0-300914.pdf"/>
    <dc:contributor>Weiler, Andreas</dc:contributor>
    <dc:contributor>Grossniklaus, Michael</dc:contributor>
    <dc:creator>Weiler, Andreas</dc:creator>
    <dc:rights>terms-of-use</dc:rights>
    <dcterms:issued>2015</dcterms:issued>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/31937/1/Weiler_0-300914.pdf"/>
    <dcterms:abstract xml:lang="eng">Twitter’s increasing popularity as a source of up to date news and information about current events has spawned a body of research on event detection techniques for social media data streams. Although all proposed approaches provide some evidence as to the quality of the detected events, none relate this task-based performance to their run-time performance in terms of processing speed or data throughput. In particular, neither a quantitative nor a comparative evaluation of these aspects has been performed to date. In this paper, we study the run-time and task-based performance of several state-of-the-art event detection techniques for Twitter. In order to reproducibly compare run-time performance, our approach is based on a general-purpose data stream management system, whereas task-based performance is automatically assessed based on a series of novel measures.</dcterms:abstract>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:creator>Scholl, Marc H.</dc:creator>
    <dc:contributor>Scholl, Marc H.</dc:contributor>
  </rdf:Description>
</rdf:RDF>

Universitätsbibliographie

Ja