To ensure reliable network performance, anomaly detection is an important part of the telecommunication operators’ work. This includes that operators need to timely intervene with the network, should they encounter indications of network performance degradation. In this paper, we describe the results of an initial experiment for anomaly detection with regard to network performance, using topic modeling on base station run-time variable data collected from live Radio Access Networks (RANs). The results show that topic modeling clusters semantically related data in the same way as human experts would and that the anomalies in our test cases could be identified in latent Dirichlet allocation (LDA) topic models. Our experiment further reveals which information provided by the topic model is particularly usable to support human anomaly detection in this application domain.
CC BY 4.0
Received: 31 January 2019 / Accepted: 18 June 2019 / Published online: 2 August 2019
H. Joe Steinhauer joe.steinhauer@his.se
Open access funding provided by University of Skövde. This work was supported by the Swedish Knowledge Foundation under grant BISON—Big Data Fusion—in cooperation with Huawei Technologies Sweden AB. We would like to thank Anders Åhlén for sharing his knowledge throughout our work. The topic modeling was performed using the package topicmodels (Grün and Hornik 2011) in R (R Core Team 2017), and the LDAvis visualization was enabled by Sievert and Shirley (2014).