Model-based alarm correlation in cellular phone networks

Fröhlich, Peter; Nejdl, Wolfgang; Jobmann, K.; Wietgrefe, Hermann

doi:10.1109/mascot.1997.567613

Cited by 28 publications

(27 citation statements)

References 5 publications

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“…Most research efforts related to alarm handling focus on correlation (Frölich et al, 1997;Jakobson and Weissman, 1993;Liu et al, 1999;Meira, 1997;Penido et al, 1999). This mainly falls into the prediction and detection categories above.…”

Section: Related Workmentioning

confidence: 99%

Statistical analysis and prioritisation of alarms in mobile networks

Wallin

Leijon

Landen³

2009

IJBIDM

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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 system operations and management; neural network applications; alarm systems.Reference to this paper should be made as follows: Wallin, S., Leijon, V. and Landén, L. (2009) 'Statistical analysis and prioritisation of alarms in mobile networks', Int.

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Section: Related Workmentioning

confidence: 99%

Statistical analysis and prioritisation of alarms in mobile networks

Wallin

Leijon

Landen³

2009

IJBIDM

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“…Research studies in automation of diagnosis in the RAN of cellular networks have been focused on alarm correlation (Frohlich, Nejdl, Jobmann, & Wietgrefe, 1997;Wietgrefe, 2002;Wietgrefe et al, 1997). In current cellular networks, most systems are semi-intelligent and generate alarm messages when errors occur.…”

Section: Diagnosis In Cellular Networkmentioning

confidence: 99%

Automatic diagnosis of mobile communication networks under imprecise parameters

Barco

Díez

Wille

et al. 2009

Expert Systems with Applications

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“…Most research efforts related to alarm handling focus on correlation [6], [8], [11], [12], [13]. The aim is "the determination of the cause" [16].…”

Section: Related Workmentioning

confidence: 99%

Telecom Alarm Prioritization Using Neural Networks

Wallin¹,

Landen²

2008

22nd International Conference on Advanced Information Networking and Applications - Workshops (Aina Workshops 2008)

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Abstract-Telecom Service Providers are faced with an overwhelming flow of alarms. Network administrators need to judge which alarms to resolve in order to maintain the service quality. The problem is that it is hard to pick the most important alarms. Which alarms have the highest priority? A solution that automatically assigns priorities to alarms would increase the efficiency of Network Management Centers. We have prototyped a solution that uses neural networks to assign alarm priority. The neural network learns from network administrators by using the manually assigned priorities in trouble-tickets. Our tests are based on live-data from a large mobile service provider and we show that neural networks can learn to assign relevant priorities to 75% of the alarms.Index Terms-Communication system operations and management, Neural network applications, Alarm Systems.

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Model-based alarm correlation in cellular phone networks

Cited by 28 publications

References 5 publications

Statistical analysis and prioritisation of alarms in mobile networks

Statistical analysis and prioritisation of alarms in mobile networks

Automatic diagnosis of mobile communication networks under imprecise parameters

Telecom Alarm Prioritization Using Neural Networks

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