Enterprise information system automatic fault diagnosis and analysis method based on decision tree - Xin JIN; Longchuan YAN; Jun LIU; Shulin ZHANG (Dianxin kexue)
With the rapid growth of the scale and complexity of enterprise information systems, traditional fault diagnosis and analysis methods relying on human experiences and manual operations cost more and more labor and time. To solve this problem, an automatic algorithm was proposed. The algorithm exploits information from system operation monitoring indicators and alarm data, based on decision tree, to automatically diagnose and analyze faults of enterprise information systems. The algorithm was verified, and the decision tree model and training method was simulated and analyzed comparatively under R language environment, using alarm data extracted from real operation data of a typical large-scale enterprise system. The experiment results show that this algorithm is able to achieve fast automatic fault diagnosis accurately, and is much helpful on improving efficiencies of information system fault processing.