Mining sequential alarm patterns in a telecommunication database

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

21 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationDatabases in Telecommunications II - VLDB 2001 International Workshop, Proceedings
EditorsWillem Jonker
PublisherSpringer Verlag
Pages37-51
Number of pages15
ISBN (Print)354042623X, 9783540426233
DOIs
StatePublished - 1 Jan 2001
EventInternational Workshop on very Large Data Bases, VLDB 2001 - Rome, Italy
Duration: 10 Sep 200110 Sep 2001

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2209
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Workshop on very Large Data Bases, VLDB 2001
CountryItaly
CityRome
Period10/09/0110/09/01

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