Open AccessOperation Anomaly Monitoring of Customer Service Data Analysis Platform Based on Improved FP-Growth Algorithm
Author(s) -
Jing Yang,
Li Gong,
Kunpeng Liu,
Letian Xiu
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2209/1/012030
Subject(s) - computer science , anomaly detection , data mining , service (business) , real time computing , data set , algorithm , artificial intelligence , economy , economics
Aiming at the problems of long time-consuming monitoring and poor monitoring accuracy in traditional customer service data analysis platform operation abnormality monitoring methods, a customer service data analysis platform operation abnormality monitoring method based on the improved FP-Growth algorithm is designed. Obtain customer service data sets, classify data types, filter customer behavior, identify the operating status of the data analysis platform, improve the FP-Growth algorithm to build a rule configuration model, set the platform safety factor threshold, and keep the reconstruction error of customer service data to a minimum Within the scope, optimize the abnormal monitoring mode. The experimental results show that the average recovery time of the proposed customer service data analysis platform operation anomaly monitoring method is 5.239s, and the average platform operation anomaly accuracy rate is 97.3%, indicating that the customer service data analysis platform integrated with the improved FP-Growth algorithm operates abnormally The monitoring method performs better.