Statistical analysis and prioritisation of alarms in mobile networks

Authors:
Stefan Wallin;Viktor Leijon;Leif Landen
Affiliations:
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
Venue:
International Journal of Business Intelligence and Data Mining
Year:
2009

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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.