Log-driven predictive analysis of remaining time for emergency response processes

Abstract

The cross-organizational, messaging and resource attributes of emergency response processes effectively improve the accuracy of remaining time prediction. For this purpose, a log-driven analysis method for remaining time prediction of emergency response processes is proposed. Firstly, the attributes such as cross-organization, messaging and resources of the emergency response process are encoded, and then the vector representation of the emergency response process is obtained. Secondly, the vector representations of the emergency response processes are fed into a deep neural network prediction model for learning. Finally, we experimented with an emergency response process log to demonstrate the proposed method.