This study focuses on implementing a log analysis strategy that combines a neural network algorithm and wavelet transform. Wavelet transform allows us to extract the important hidden information and features of the original time series log data and offers a precise framework for the analysis of input information. While neural network algorithm constitutes a powerfulnonlinear function approximation which can provide detection and prediction functions. The combination of the two techniques is based on the idea of using wavelet transform to denoise the log data by decomposing it into a set of coefficients, then feed the denoised data into a neural network. The experimental outputs reveal that this strategy can have a better ability to identify the patterns among problems in a log dataset, and make predictions with a better accuracy. This strategy can help the platform maintainers to adopt corresponding actions to eliminate risks before the occurrence of serious damages.
| Identifer | oai:union.ndltd.org:csusb.edu/oai:scholarworks.lib.csusb.edu:etd-1690 |
| Date | 01 March 2018 |
| Creators | Liu, Chunyu |
| Publisher | CSUSB ScholarWorks |
| Source Sets | California State University San Bernardino |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses, Projects, and Dissertations |
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