Authors: Stefan Wallin, Viktor Leijon, Leif Landen
Addresses: Data Ductus Nord AB, Torget 6 SE-931 31 Skelleftea, Sweden; Lulea University of Technology, Department of Computer Science and Electrical Engineering, SE-931 87 Skelleftea, Sweden. ' Lulea University of Technology, Department of Computer Science and Electrical Engineering, SE-971 87 Lulea, Sweden. ' Data Ductus Nord AB, Torget 6 SE-931 31 Skelleftea, Sweden
Abstract: Telecom service providers are faced with an overwhelming flow of alarms, which makes good alarm classification and prioritisation very important. This paper first provides statistical analysis of data collected from a real-world alarm flow and then presents a quantitative characterisation of the alarm situation. Using data from the trouble ticketing system as a reference, we examine the relationship between mechanical classification of alarms and the human perception of them. Using this knowledge of alarm flow properties and trouble ticketing information, we suggest a neural network-based approach for alarm classification. Tests using live data show that our prototype assigns the same severity as a human expert in 50% of all cases, compared to 17% for a naïve approach.
Keywords: communication systems operations; communication systems management; neural networks; alarm systems; mobile networks; alarms; statistical analysis; alarm flow; trouble ticketing; mechanical classification; human perception; alarm classification.
International Journal of Business Intelligence and Data Mining, 2009 Vol.4 No.1, pp.4 - 21
Available online: 21 May 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article