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Titel: A hybrid approach for alarm verification using stream processing, machine learning and text analytics
Autoren: Sima, Ana-Claudia
Stockinger, Kurt
Affolter, Katrin
Braschler, Martin
Monte, Peter
Kaiser, Lukas
Tagungsband: Proceedings of the 21st International Conference on Extending Database Technology (EDBT)
Angaben zur Konferenz: International Conference on Extending Database Technology (EDBT), March 26-29, 2018
Verlag / Hrsg. Institution: ACM
Erscheinungsdatum: 2018
Sprache: Englisch / English
Schlagwörter: Database technology; Stream processing; Machine learning; Text analytics
Fachgebiet (DDC): 004: Informatik
Zusammenfassung: False 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%.
Departement: School of Engineering
Organisationseinheit: Institut für Angewandte Informationstechnologie (InIT)
Publikationstyp: Konferenz: Paper / Conference Paper
DOI: 10.21256/zhaw-3487
ISBN: 978-3-89318-078-3
Enthalten in den Sammlungen:Publikationen Soziale Arbeit

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