The paper "Detecting Large-Scale System Problems by Mining Console Logs" by Wei Xu, Ling Huang, Armando Fox, David Patterson, and Michael I. Jordan proposes a methodology to automatically detect system runtime problems in large-scale datacenter services by mining console logs. The authors argue that console logs, which are often voluminous and intermixed with messages from multiple software components, are a rich source of information that can be used to identify operational issues. They combine source code analysis with information retrieval to create composite features from console logs, which are then analyzed using machine learning to detect operational problems. The method is validated on two real-world systems: the Darkstar online game server and the Hadoop File System. The authors demonstrate high accuracy in detecting real problems with few false positives, and show that their approach can analyze 24 million lines of console logs in 3 minutes. The methodology is scalable and requires no changes to the service software, human input, or knowledge of the software's internals. The paper also introduces a visualization technique that distills the results into a one-page decision tree, making the analysis more operator-friendly.The paper "Detecting Large-Scale System Problems by Mining Console Logs" by Wei Xu, Ling Huang, Armando Fox, David Patterson, and Michael I. Jordan proposes a methodology to automatically detect system runtime problems in large-scale datacenter services by mining console logs. The authors argue that console logs, which are often voluminous and intermixed with messages from multiple software components, are a rich source of information that can be used to identify operational issues. They combine source code analysis with information retrieval to create composite features from console logs, which are then analyzed using machine learning to detect operational problems. The method is validated on two real-world systems: the Darkstar online game server and the Hadoop File System. The authors demonstrate high accuracy in detecting real problems with few false positives, and show that their approach can analyze 24 million lines of console logs in 3 minutes. The methodology is scalable and requires no changes to the service software, human input, or knowledge of the software's internals. The paper also introduces a visualization technique that distills the results into a one-page decision tree, making the analysis more operator-friendly.