Mining sequential alarm patterns in a telecommunication database

Pei Hsin Wu, Wen-Chih Peng, Ming Syan Chen

研究成果: Conference contribution同行評審

21 引文 斯高帕斯(Scopus)


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. Then, 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 procedure to determine the occurrence count of the sequential alarm patterns in accordance with the nature of alarms. 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. The quality of knowledge discovered is evaluated. The experimental results show that the proposed operations of data cleaning are able to improve the execution of our mining algorithm significantly and the knowledge obtained from the alarm data is very useful from the perspective of network operators for alarm prediction and alarm control.

主出版物標題Databases in Telecommunications II - VLDB 2001 International Workshop, Proceedings
編輯Willem Jonker
發行者Springer Verlag
ISBN(列印)354042623X, 9783540426233
出版狀態Published - 1 一月 2001
事件International Workshop on very Large Data Bases, VLDB 2001 - Rome, Italy
持續時間: 10 九月 200110 九月 2001


名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)


ConferenceInternational Workshop on very Large Data Bases, VLDB 2001

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