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An evaluation of the run-time and task-based performance of event detection techniques for Twitter

An evaluation of the run-time and task-based performance of event detection techniques for Twitter

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WEILER, Andreas, Michael GROSSNIKLAUS, Marc H. SCHOLL, 2016. An evaluation of the run-time and task-based performance of event detection techniques for Twitter. In: Information Systems. 62, pp. 207-219. ISSN 0306-4379. eISSN 1873-6076

@article{Weiler2016-12evalu-33581, title={An evaluation of the run-time and task-based performance of event detection techniques for Twitter}, year={2016}, doi={10.1016/j.is.2016.01.003}, volume={62}, issn={0306-4379}, journal={Information Systems}, pages={207--219}, author={Weiler, Andreas and Grossniklaus, Michael and Scholl, Marc H.} }

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/33581"> <dc:contributor>Weiler, Andreas</dc:contributor> <dcterms:title>An evaluation of the run-time and task-based performance of event detection techniques for Twitter</dcterms:title> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/33581"/> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2016-04-13T14:39:08Z</dc:date> <dc:contributor>Grossniklaus, Michael</dc:contributor> <dcterms:issued>2016-12</dcterms:issued> <dcterms:rights rdf:resource="http://nbn-resolving.de/urn:nbn:de:bsz:352-20150914100631302-4485392-8"/> <dc:contributor>Scholl, Marc H.</dc:contributor> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2016-04-13T14:39:08Z</dcterms:available> <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, data throughput, or memory usage. In particular, neither a quantitative nor a comparative evaluation of these aspects has been performed to date. In this article, 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> <dc:language>eng</dc:language> <dc:creator>Grossniklaus, Michael</dc:creator> <dc:creator>Scholl, Marc H.</dc:creator> <dc:creator>Weiler, Andreas</dc:creator> </rdf:Description> </rdf:RDF>

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