Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-3785
Title: Applied data science : using machine learning for alarm verification : a novel alarm verification service applying various machine learning algorithms can identify false alarms
Authors : Stampfli, Jan
Stockinger, Kurt
Published in : ERCIM News
Volume(Issue) : 107
Pages : 10
Publisher / Ed. Institution : European Research Consortium for Informatics and Mathematics
Issue Date: Oct-2016
License (according to publishing contract) : Not specified
Language : English
Subjects : Machine learning; Alarm verfication
Subject (DDC) : 004: Computer science
Abstract: False alarms triggered by sensors of alarm systems are a frequent and costly inconvenience for the emergency services and owners of alarm systems. Around 90% of false alarms are caused by either technical failures such as network down times or human error. To remedy this problem, we develop a novel alarm verification service by leveraging the power of an alarm data warehouse. In addition, we apply various machine learning algorithms to identify false alarms. The goal of our system is to help human responders in their decision about whether or not to trigger costly intervention forces.
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Publication type: Contribution to Magazine or Newspaper
DOI : 10.21256/zhaw-3785
ISSN: 0926-4981
1564-0094
URI: https://digitalcollection.zhaw.ch/handle/11475/7432
Published as part of the ZHAW project : SAVE - Smart Alarms & Verified Events
Appears in Collections:Publikationen School of Engineering

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