Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-3487
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dc.contributor.authorSima, Ana-Claudia-
dc.contributor.authorStockinger, Kurt-
dc.contributor.authorAffolter, Katrin-
dc.contributor.authorBraschler, Martin-
dc.contributor.authorMonte, Peter-
dc.contributor.authorKaiser, Lukas-
dc.date.accessioned2018-01-24T15:31:29Z-
dc.date.available2018-01-24T15:31:29Z-
dc.date.issued2018-
dc.identifier.isbn978-3-89318-078-3de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/2180-
dc.description.abstractFalse alarms triggered by security sensors incur high costs for all parties involved. According to police reports, a large majority of alarms are false. Recent advances in machine learning can enable automatically classifying alarms. However, building a scalable alarm verification system is a challenge, since the system needs to: (1) process thousands of alarms in real-time, (2) classify false alarms with high accuracy and (3) perform historic data analysis to enable better insights into the results for human operators. This requires a mix of machine learning, stream and batch processing -- technologies which are typically optimized independently. We combine all three into a single, real-world application. This paper describes the implementation and evaluation of an alarm verification system we developed jointly with Sitasys, the market leader in alarm transmission in central Europe. Our system can process around 30K alarms per second with a verification accuracy of above 90%.de_CH
dc.language.isoende_CH
dc.publisherACMde_CH
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/de_CH
dc.subjectDatabase technologyde_CH
dc.subjectStream processingde_CH
dc.subjectMachine learningde_CH
dc.subjectText analyticsde_CH
dc.subject.ddc004: Informatikde_CH
dc.titleA hybrid approach for alarm verification using stream processing, machine learning and text analyticsde_CH
dc.typeKonferenz: Paperde_CH
dc.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Angewandte Informationstechnologie (InIT)de_CH
dc.identifier.doi10.21256/zhaw-3487de_CH
zhaw.conference.detailsInternational Conference on Extending Database Technology (EDBT), March 26-29, 2018de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsProceedings of the 21st International Conference on Extending Database Technology (EDBT)de_CH
zhaw.id.projectSAVE - Smart Alarm & Verfied Events (CTI funding)de_CH
zhaw.monitoring.oajournalNot specifiedde_CH
zhaw.webfeedDatalabde_CH
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