Simulation of Potential Anomaly Identification for Big Data Terminals Based on Spark Computing
This article mainly focuses on identifying and simulating potential abnormal situations in real-time processing of big data terminals.A new method is proposed based on the Spark computing framework to monitor and identify possible abnormal behavior in big data terminals.By using real-time data flow analysis and machine learning techniques,potential abnormal situations can be detected and processed in a timely manner,improving data processing efficiency and quality,and ultimately enhancing system reliability and stability.
big data terminalanomaly recognitionSpark computingreal-time data flow analysismachine learning
NETL
NSTL
万方数据
