Mining Temporal Dependency among Proactive Data Services and Its Delivery to System-level Anomaly Prediction

Abstract

Motivated by the requirement of system-level anomaly prediction in the running of industrial processes, this paper proposes a new algorithm to mine temporal dependencies among services, by discovering frequent occurrence patterns among service outputted events. With temporal dependencies, the paper also explores a new type of graph-based service linking approach. These approaches are delivered to prediction of system-level anomalies in some real scenarios.