| Publisher | Hewlett Packard | ||
|---|---|---|---|
| Format | 58.3KB PDF | Date added | 02 May 2005 |
| Topics | Knowledge and Data Management | ||
| Downloads | 4 | ||
Recent research activity has shown encouraging results for performance debugging and failure diagnosis and detection in systems by using approaches based on automatically inducing models and deriving correlations from observed data. This paper explores research questions and preliminary results regarding the next steps in advancing this line of work. It specifically formulates three challenges which are included in the paper. These challenges arises directly from the application of statistical and machine learning techniques to real-world systems, and it contends that through domain knowledge of this same application it may identify well-engineered solutions that allow the full potential of statistical and machine learning approaches to be realized.
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