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.

We developed the HS-bitmap index for efficient information retrieval. The HS-bitmap index is a hierarchical document-term matrix: the original document-term matrix is called the leaf matrix and an upper matrix is the summary of its lower matrix. Our experiment results show the HS-bitmap index performs better than the inverted index with a minor space overhead.