Classification and Visualization of Alarm Data Based on Heterogeneous Distance

Boxu Zhao and Guiming Luo
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Boxu Zhao: School of Software, Tsinghua University, Beijing, China
Guiming Luo: School of Software, Tsinghua University, Beijing, China

International Journal of Data Warehousing and Mining (IJDWM), 2018, vol. 14, issue 2, 60-80

Abstract: Alarm classification and visualization of historical data is significant and sophisticated in the area of smart management in telecom network due to alarm flood and propagation. In this article, we propose a heterogeneous distance to compute the similarity distance matrix of alarms, which is applied to alarm classification. By using Multidimensional Scaling, alarm data in high dimension is translated into a 2-dimensional graph in alarm windows. Then alarm attention and relation are clearly shown by comparing current and past alarms. Experiments show MDS based on the heterogeneous distance has a better classifying effect than other distance measures. The case study demonstrates the method can show alarm correlation easily and help to locate faults when applied to the analysis of telecom alarm data.

Date: 2018
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