Framework for mining event correlations and time lags in large event sequences

Abstract

Event correlation is the task of detecting dependencies between events in event sequences, e.g., for predictive maintenance based on log-files. In this work, a new data-driven, generic framework for event correlation is presented. First, we use a fast preliminary test statistic to determine candidate event type pairs. Next, the precise distribution of the time lag between those pairs is calculated. For this purpose, a new efficient iterative method is developed that aligns two event sequences and finds the optimal event assignments. In our experiments, the proposed method is orders of magnitude faster than state-of-the-art methods but always yields similar (or even better) results.