What process helps in analyzing logs to determine sequences of events from a recent outage?

Event correlation is the process that plays a crucial role in analyzing logs to determine sequences of events surrounding a recent outage. It involves aggregating logs from various sources and identifying patterns or relationships between different entries. By correlating events based on timestamps, sources, and types of activities, this method helps pinpoint the chain of events that led to the outage and can highlight anomalies that require further investigation.

This approach improves the efficiency of troubleshooting by allowing administrators to focus on significant events that are likely related to the outage rather than sifting through large volumes of data indiscriminately. It also provides insights into cascading failures or unexpected interactions between different systems, which are essential for understanding the full spectrum of an outage's causes.

In contrast, data mining typically involves extracting useful patterns or insights from large datasets, but it is not specifically focused on the linear analysis of logs related to a specific incident. Log capture refers to the process of collecting logs from various devices and systems, which is a necessary precursor to log analysis but does not provide the analytical framework needed to sequence events. Error analysis focuses on identifying and diagnosing particular errors but does not inherently involve the broader context of event relationships critical to understanding an outage.