Knowledge discovery from telecommunication network alarm databases

A telecommunication network produces daily large amounts of alarm data. The data contains hidden valuable knowledge about the behavior of the network. This knowledge can be used in filtering redundant alarms, locating problems in, the network, and possibly in predicting severe faults. We describe the TASA (Telecommunication Network Alarm Sequence Analyzer) system for discovering and browsing knowledge from large alarm databases. The system is built on the basis of viewing knowledge discovery as an interactive and iterative process, containing data collection, pattern discovery, rule postprocessing, etc. The system uses a novel framework for locating frequently occurring episodes from sequential data. The TASA system offers a variety of selection and ordering criteria for episodes, and supports iterative retrieval from the discovered knowledge. This means that a large part of the iterative nature of the KDD process can be replaced by iteration in the rule postprocessing stage. The user interface is based on dynamically generated HTML. The system is in experimental use, and the results are encouraging: some of the discovered knowledge is being integrated into the alarm handling software of telecommunication operators.

[1]  Gregory Piatetsky-Shapiro,et al.  Selecting and reporting What Is Interesting , 1996, Advances in Knowledge Discovery and Data Mining.

[2]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[3]  Gregory Piatetsky-Shapiro,et al.  The interestingness of deviations , 1994 .

[4]  M. A. Tanner,et al.  Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions, 3rd Edition , 1998 .

[5]  G. Jakobson,et al.  Alarm correlation , 1993, IEEE Network.

[6]  M. Tanner.  Tools for statistical inference: methods for the exploration of posterior distributions and likeliho , 1994 .

[7]  Heikki Mannila,et al.  Discovering Frequent Episodes in Sequences , 1995, KDD.

[8]  U. Fayyad.  Knowledge Discovery and Data Mining: An Overview , 1995 .

[9]  Heikki Mannila,et al.  Finding interesting rules from large sets of discovered association rules , 1994, CIKM '94.

[10]  Deborah L. McGuinness,et al.  Integrated Support for Data Archeology , 1993, Int. J. Cooperative Inf. Syst..

[11]  Heikki Mannila,et al.  Efficient Algorithms for Discovering Association Rules , 1994, KDD Workshop.

[12]  J. Kalbfleisch,et al.  The Statistical Analysis of Failure Time Data , 1980 .

[13]  Heikki Mannila,et al.  Pruning and grouping of discovered association rules , 1995 .

[14]  Heikki Mannila,et al.  Fast Discovery of Association Rules , 1996, Advances in Knowledge Discovery and Data Mining.