Research on Soft Error Detection Method for Self-Similar Data Streams Based on Structure Mapping

Self-similar data streams are characterized by their similarity across multiple time scales, exhibiting distinct nonlinear and discrete features. These characteristics complicate the accurate identification of data points associated with soft error features, thereby making it difficult to effectively discern the intricate relationship between the data flow and soft error data. This, in turn, severely impacts the accuracy of soft error detection in self-similar data streams. In this study, we propose a novel approach to address this challenge. Leveraging the suddenness and long-range correlation inherent in self-similar data streams, we construct a time series model to capture the data stream based on linear correlation and straight-line fitting features. By incorporating relationship parameters to fit neighboring flow points, we utilize a structural mapping model to establish the local angular relationship between the data streams and soft error data. Additionally, we construct a structural mapping network using flow features to achieve soft error detection in self-similar data streams. Experimental results demonstrate that our proposed method achieves high accuracy and low time overhead for soft error detection in self-similar data streams.