Imminence Monitoring of Critical Events: A Representation Learning Approach

Abstract

Complex event monitoring is an important problem in data streams that has drawn much attention. Most previous work assumes that the user knows and provides a complex event pattern for the system to continuously monitor. However, we observe that in many real applications, such as healthcare, security, and businesses, there are heterogeneous substreams and a diverse set of attributes. Often there is no simple uniform pattern prior to a critical event; nor is there clean simple language to describe the pattern leading to the critical event. People often only know it after the fact - e.g., when something undesirable happens. We propose a novel approach based on relational machine learning and representation learning. We propose and learn probabilistic state machine patterns, which are used to monitor and predict the imminence of critical events. Our experiments demonstrate the efficiency and effectiveness of our approach, as well as its clear superiority over the closest previous approaches such as IL-Miner and LSTM based early prediction.