“Join us in Vancouver, BC, Canada, October 2–3, 2010, for the Workshop on Managing Systems via Log Analysis and Machine Learning Techniques. Modern large-scale systems are challenging to manage. Fortunately, as these systems generate massive amounts of performance and diagnostic data, there is an opportunity to make system administration and development simpler via automated techniques to extract actionable information from the data. SLAML '10 workshop addresses this problem in two thrusts: (i) the analysis of raw system data logs and (ii) the application of machine learning to systems problems. The large overlap in these topics should promote a rich interchange of ideas between the areas.
The part related to logs is:
“Log Analysis: It is well known that raw system logs are an abundant source of information for the analysis and diagnosis of system problems and prediction of future system events. However, a lack of organization and semantic consistency between system data from various software and hardware vendors means that most of this information content is wasted. Current approaches to extracting information from the raw system data capture only a fraction of the information available and do not scale to the large systems common in business and supercomputing environments. It is thus a significant research challenge to determine how to better process and combine information from these data sources.”
The topics sought are:
“Topics include but are not limited to:
- Reports on publicly available sources of sample system logs
- Prediction of malfunction or misuse based on system data
- Statistical analysis of system logs
- Applications of Natural-Language Processing (NLP) to system data
- Techniques for system log analysis, comparison, standardization, compression, anonymization, and visualization
- Applications of log analysis to system administration problems
- Use of machine learning techniques to address reliability, performance, power management, security, fault diagnosis, scheduling, or manageability issues
- Challenges of scale in applying machine learning to large systems
- Integration of machine learning into real-world systems and processes
- Evaluating the quality of learned models, including assessing the confidence/reliability of models and comparisons between different methods”
P.S. This is posted by a scheduler; response to comments may be delayed since I might be away from computers.
Possibly related posts:
- Workshop on the Analysis of System Logs (WASL) 2010 CFP Out!