Research on application software operation fault diagnosis method based on big data mining

The current fault diagnosis methods can only diagnose whether the software has a fault, but can’t correlate the fault cause with the fault performance, the diagnosis accuracy is not high, and it is difficult to meet the demand of fault diagnosis with the surge of operation response. In order to optimize the operation management level of application software and improve the fault diagnosis accuracy of application software, the application software operation fault diagnosis method based on big data mining will be studied. The data is preprocessed according to the dynamic changes of data flow when the application software is running. The parallel optimized FP_Growth algorithm is used to mine the association relation of the running data. A high-dimensional clustering algorithm is used to diagnose software running faults with mined association rules as initial clustering conditions. The simulation results show that the accuracy of fault diagnosis is improved by at least 25.1% after the application of data mining, and the efficiency of fault diagnosis is improved to some extent. Moreover, the proposed method is suitable for different types of application software.