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The authors present DIADS, an integrated DIAgnosis tool for Databases and Storage Area Networks (SANs). Existing diagnosis tools in this domain have a database-only or SAN-only focus. DIADS is a first-of-a-kind framework based on a careful integration of information from the database and SAN subsystems; and is not a simple concatenation of database-only and SAN only modules. This approach not only increases the accuracy of diagnosis, but also leads to significant improvements in efficiency. DIADS uses a novel combination of non-intrusive machine learning techniques (e.g., Kernel Density Estimation) and domain knowledge encoded in a new symptoms database design.
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