Interactive exploration of interesting findings in the Telecommunication Network Alarm Sequence Analyzer (TASA)

Abstract

In this paper we describe the final version of a knowledge discovery system, Telecommunication Network Alarm Sequence Analyzer (TASA), for telecommunication networks alarm data analysis. The system is based on the discovery of recurrent, temporal patterns of alarms in databases; these patterns, episode rules, can be used in the construction of real-time alarm correlation systems. Also association rules are used for identifying relationships between alarm properties. TASA uses a methodology for knowledge discovery in databases (KDD) where one first discovers large collections of patterns at once, and then performs interactive retrievals from the collection of patterns. The proposed methodology suits very well such KDD formalisms as association and episode rules, where large collections of potentially interesting rules can be found efficiently. When searching for the most interesting rules, simple threshold-like restrictions, such as rule frequency and confidence may satisfy a large number of rules. In TASA, this problem can be alleviated by templates and pattern expressions that describe the form of rules that are to be selected or rejected. Using templates the user can flexibly specify the focus of interest, and also iteratively refine it. Different versions of TASA have been in prototype use in four telecommunication companies since the beginning of 1995. TASA has been found useful in, e.g. finding long-term, rather frequently occurring dependencies, creating an overview of a short-term alarm sequence, and evaluating the alarm data base consistency and correctness.