Abstract
Monitoring events on communication and computing systems becomes more and more challenging due to the increasing complexity and diversity of these systems. Several supporting tools have been created to assist system administrators in monitoring an enormous number of events daily. The main function of these tools is to filter as many as possible events and present non-trivial events to the administrators for fault analysis and detection. However, non-trivial events never decrease on large systems, such as cloud computing systems, while investigating events is time consuming. This paper proposes an approach for evaluating the severity level of an event using a classification and regression decision tree. The approach aims to build a decision tree based on the features of old events, then use this tree to decide the severity level of new events. The administrators take advantages of this decision to determine proper actions for the non-trivial events. We have implemented and experimented the approach for software bug datasets obtained from bug tracking systems. The experimental results reveal that the accuracy scores for different decision trees are above 70 % and some detailed analyses are provided.
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This research activity is funded by Vietnam National University in Ho Chi Minh City (VNU-HCM) under the grant number C2015-28-02
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Tran, H.M., Van Nguyen, S., Le, S.T., Vu, Q.T. (2015). Fault Data Analytics Using Decision Tree for Fault Detection. In: Dang, T., Wagner, R., Küng, J., Thoai, N., Takizawa, M., Neuhold, E. (eds) Future Data and Security Engineering. FDSE 2015. Lecture Notes in Computer Science(), vol 9446. Springer, Cham. https://doi.org/10.1007/978-3-319-26135-5_5
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