About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
ICAC 2007
Conference paper
Adaptive multi-levels dictionaries and singular value decomposition techniques for autonomic problem determination
Abstract
An autonomic problem determination system can adapt to changing environments, react to existing or new error condition and predict possible problems. In this report, we propose such a system using dynamic and adaptive multi-levels dictionaries and "Singular Value Decomposition techniques" (SVD). Compared to standard SVD, our system uses an iterative method that enables dynamic interaction between events and the current dictionaries with its entries being updated continuously to reflect relative importance of each event, thereby accelerating its convergence. The system captures knowledge in a hierarchical form for complex knowledge representation. It does not require a formal knowledge model or intensive training by examples. It is efficient with sufficient accuracy for autonomic problem determination. © 2007 IEEE.