Mining Sequential Alarm Patterns in a Mobile Communication System

• Main Authors: Pei-Hsin Wu, 吳佩欣
• Other Authors: Ming-Syan Chen
• Format: Others
• Language: en_US
• Published: 2001
• Online Access: http://ndltd.ncl.edu.tw/handle/65826277271236642796

碩士 === 國立臺灣大學 === 電機工程學研究所 === 89 === A mobile communication 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 thesis, 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 to extract patterns. Hence, we devise an innovative procedure 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 successfully applied to 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.