Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-3487
Publication type: Conference paper
Type of review: Peer review (publication)
Title: A hybrid approach for alarm verification using stream processing, machine learning and text analytics
Authors: Sima, Ana-Claudia
Stockinger, Kurt
Affolter, Katrin
Braschler, Martin
Monte, Peter
Kaiser, Lukas
DOI: 10.21256/zhaw-3487
Proceedings: Proceedings of the 21st International Conference on Extending Database Technology
Conference details: EDBT 2018, Vienna, Austria, 26-29 March 2018
Issue Date: 2018
Publisher / Ed. Institution: ACM
ISBN: 978-3-89318-078-3
Language: English
Subjects: Database technology; Stream processing; Machine learning; Text analytics
Subject (DDC): 006: Special computer methods
Abstract: 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%.
URI: https://digitalcollection.zhaw.ch/handle/11475/2180
Fulltext version: Published version
License (according to publishing contract): CC BY-NC-ND 4.0: Attribution - Non commercial - No derivatives 4.0 International
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Published as part of the ZHAW project: SAVE - Smart Alarms & Verified Events
Appears in collections:Publikationen School of Engineering

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