The document discusses concept drift in process mining and proposes a new online learning model called Diversity for Dealing with Drifts (DDD) to handle concept drift more accurately than existing methods. DDD uses an ensemble of classifiers with varying diversity levels to make predictions both before and after a concept drift is detected. It is compared to an existing early drift detection method (EDDM) using real-world datasets, and is shown to achieve higher accuracy, including when false positive drift detections occur. The document provides details on the DDD algorithm and compares its performance to the existing approach.