Today's large and complex telecommunication networks produce large amounts of alarms daily. The sequence of alarms contains valuable knowledge about the behavior of the network, but much of the knowledge is fragmented and hidden in the vast amount of data. Regularities in the alarms can be used in fault management applications, e.g., for filtering redundant alarms, locating problems in the network, and possibly in predicting severe faults. In this paper we describe TASA (Telecommunication Alarm Sequence Analyzer), a novel system for discovering interesting regularities in the alarms. In the core of the system are algorithms for locating frequent alarm episodes from the alarm stream and presenting them as rules. Discovered rules can then be explored with flexible information retrieval tools that support iteration. The user interface is hypertext, based on HTML, and can be used with a standard WWW browser. TASA is in experimental use and has already discovered rules that have been integrated into the alarm handling software of an operator.
[1]
U. Fayyad.
Knowledge Discovery and Data Mining: An Overview
,
1995
.
[2]
Heikki Mannila,et al.
Finding interesting rules from large sets of discovered association rules
,
1994,
CIKM '94.
[3]
Heikki Mannila,et al.
Knowledge discovery from telecommunication network alarm databases
,
1996,
Proceedings of the Twelfth International Conference on Data Engineering.
[4]
Heikki Mannila,et al.
Efficient Algorithms for Discovering Association Rules
,
1994,
KDD Workshop.
[5]
G. Jakobson,et al.
Alarm correlation
,
1993,
IEEE Network.
[6]
Yves Raynaud,et al.
Integrated Network Management IV
,
1995,
IFIP — The International Federation for Information Processing.
[7]
William Frawley,et al.
Knowledge Discovery in Databases
,
1991
.
[8]
Heikki Mannila,et al.
Discovering Frequent Episodes in Sequences
,
1995,
KDD.