Experimental results on a constraint based sequential pattern mining for telecommunication alarm data

A telecommunication system produces daily a large amount of alarm data which contains hidden valuable information about the system behavior The knowledge discovered from alarm data can be used in finding problems in networks and possibly in predicting severe faults. In this paper we devise a solution procedure for mining sequential alarm patterns from the alarm data of a GSM system. First, by observing the features of the alarm data, we develop operations for data cleaning without compromising the quality of sequential alarm patterns obtained. After the data cleaning procedure, we transform the alarm data into a set of alarm sequences. Note that the consecutive alarm events exist in the alarm sequences, and it is complicated to count the occurrence counts of events and extract patterns. Hence, we devise a new counting method to determine the occurrence count of the sequential alarm patterns in accordance with the nature of alarms. More importantly, by utilizing time constraints to restrict the time difference between two alarm events, we devise a mining algorithm to discover useful sequential alarm patterns. The proposed mining algorithm is implemented and applied to test against a set of real alarm data provided by a cellular phone company