1. ZHAW digitalcollection
  2. School of Engineering
  3. Publikationen
  4. Publikationen School of Engineering
Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-3487
Full metadata record
DC FieldValueLanguage
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.publisherAssociation for Computing Machineryde_CH
dc.rightshttps://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.ddc006: Spezielle Computerverfahrende_CH
dc.titleA hybrid approach for alarm verification using stream processing, machine learning and text analyticsde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.21256/zhaw-3487-
zhaw.conference.detailsEDBT 2018, Vienna, Austria, 26-29 March 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 Technologyde_CH
zhaw.webfeedDatalabde_CH
zhaw.funding.zhawSAVE - Smart Alarms & Verified Eventsde_CH
Appears in collections:Publikationen School of Engineering

Files in This Item:
File Description SizeFormat 
AlarmVerfication_EDBT2018.pdf1.71 MBAdobe PDFThumbnail
View/Open
Show simple item record
Sima, A.-C., Stockinger, K., Affolter, K., Braschler, M., Monte, P., & Kaiser, L. (2018). A hybrid approach for alarm verification using stream processing, machine learning and text analytics. Proceedings of the 21st International Conference on Extending Database Technology. https://doi.org/10.21256/zhaw-3487
Sima, A.-C. et al. (2018) ‘A hybrid approach for alarm verification using stream processing, machine learning and text analytics’, in Proceedings of the 21st International Conference on Extending Database Technology. Association for Computing Machinery. Available at: https://doi.org/10.21256/zhaw-3487.
A.-C. Sima, K. Stockinger, K. Affolter, M. Braschler, P. Monte, and L. Kaiser, “A hybrid approach for alarm verification using stream processing, machine learning and text analytics,” in Proceedings of the 21st International Conference on Extending Database Technology, 2018. doi: 10.21256/zhaw-3487.
SIMA, Ana-Claudia, Kurt STOCKINGER, Katrin AFFOLTER, Martin BRASCHLER, Peter MONTE und Lukas KAISER, 2018. A hybrid approach for alarm verification using stream processing, machine learning and text analytics. In: Proceedings of the 21st International Conference on Extending Database Technology. Conference paper. Association for Computing Machinery. 2018. ISBN 978-3-89318-078-3
Sima, Ana-Claudia, Kurt Stockinger, Katrin Affolter, Martin Braschler, Peter Monte, and Lukas Kaiser. 2018. “A Hybrid Approach for Alarm Verification Using Stream Processing, Machine Learning and Text Analytics.” Conference paper. In Proceedings of the 21st International Conference on Extending Database Technology. Association for Computing Machinery. https://doi.org/10.21256/zhaw-3487.
Sima, Ana-Claudia, et al. “A Hybrid Approach for Alarm Verification Using Stream Processing, Machine Learning and Text Analytics.” Proceedings of the 21st International Conference on Extending Database Technology, Association for Computing Machinery, 2018, https://doi.org/10.21256/zhaw-3487.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.