When this occurs, the system linearizes the cluster, resulting in the formation of separate data chunks labeled [B] and [C]. The key to efficiently tracking these occurrences lies in observing the time at which the chunk fee rate undergoes changes rather than monitoring the sponsor's entry time. This approach is validated by the fact that both chunks [B] and [C] share the same update time, which is marked as 1500. This figure not only signifies the creation time of the chunks but also doubles as the point in time when the chunk fee rate was last updated. This logic extends to the combined entity [A, D], where once again, the timestamp 1500 indicates the most recent update to the chunk fee rate.

This information suggests a streamlined process for tracking changes within the system, providing an efficient method to monitor the evolution of data chunks over time. By focusing on the last update time for chunk fee rates, it simplifies the complexities associated with the linearization of clusters and the subsequent data management tasks.