Mobile Network Failure Event Detection and Forecasting With Multiple User Activity Data Sets

Authors

  • Motoyuki Oki NTT Communications Corporation
  • Koh Takeuchi NTT Communication Science Laboratories
  • Yukio Uematsu NTT Communications Corporation

Keywords:

Event Detection, Event Forecasting, Network Failure Event, Multiple Data Sets, Ensemble Model

Abstract

As the demand for mobile network services increases, immediate detection and forecasting of network failure events have become important problems for service providers. Several event detection approaches have been proposed to tackle these problems by utilizing social data. However, these approaches have not tried to solve event detection and forecasting problems from multiple data sets, such as web access logs and search queries. In this paper, we propose a machine learning approach that incorporates multiple user activity data into detecting and forecasting failure events. Our approach is based on a two-level procedure. First, we introduce a novel feature construction method that treats both the imbalanced label problem and the data sparsity problem of user activity data. Second, we propose a model ensemble method that combines outputs of supervised and unsupervised learning models for each data set and gives accurate predictions of network service outage. We demonstrate the effectiveness of the proposed models by extensive experiments with real-world failure events occurred at a network service provider in Japan and three user activity data sets.

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Published

2018-04-27

How to Cite

Oki, M., Takeuchi, K., & Uematsu, Y. (2018). Mobile Network Failure Event Detection and Forecasting With Multiple User Activity Data Sets. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11422