ROOT CAUSE ANALYSIS IN A COMMUNICATION NETWORK VIA PROBABILISTIC NETWORK STRUCTURE

摘要

The disclosure relates to technology for determining a root cause of anomalous behaviors in networks. First indicators (KQIs) are categorized into first groups (states) and second indicators (KPIs) are categorized into second groups. A conditional probability is estimated by calculating a probability that the second indicators will result in degradation of the first indicators based on historical data using association rule learning. The second indicators having the conditional probability associated with degradation of the first indicators are mapped to a corresponding one of the first groups in a probabilistic network structure based on a detected degradation of the first indicators in the historical data. Then it is determined whether the second indicators mapped to the corresponding first groups satisfy a threshold when degradation of the first indicators is detected, and each of the second indicators resulting in degradation of the first indicator are ranked according to a corresponding conditional probability.