Abstract
Context: A large amount of information about system behavior is stored in logs that record system changes. Such information can be exploited to discover anomalies of a system and the operations that cause them. Given their large size, manual inspection of logs is hard and infeasible in a desired timeframe (e.g., real-time), especially for critical systems.
Objective: This study proposes a semi-automated method for reconstructing sequences of tasks of a system, revealing system anomalies, and associating tasks and anomalies to code components.
Method: The proposed approach uses unsupervised machine learning (Latent Dirichlet Allocation) to discover latent topics in messages of log events and introduces a novel technique based on pattern recognition to derive the semantic of such topics (topic labelling). The approach has been applied to the big data generated by the ALMA telescope system consisting of more than 2000 log events collected in about five hours of telescope operation.
Results: With the application of our approach to such data, we were able to model the behavior of the telescope over 16 different observations. We found five different behavior models and three different types of errors. We use the models to interpret each error and discuss its cause.
Conclusions: With this work, we have also been able to discuss some of the known challenges in log mining. The experience we gather has been then summarized in lessons learned.