Event abstraction is a technique that can be utilized to improve the readability and manageability of complex process models derived from event logs. It involves the identification and aggregation of low-level events into higher-level, more abstract events or activities, based on their semantics and the context in which they occur.

Here are some ways in which event abstraction can be effectively utilized to improve the readability and manageability of complex process models:

1. Reduction of model complexity: By aggregating low-level events into higher-level activities, event abstraction reduces the overall complexity of the process model, making it easier to comprehend and analyze. This is particularly useful when dealing with event logs that contain a large number of low-level events, which can result in overly complex and cluttered process models.

2. Improved process understanding: Abstract activities often better align with the conceptual understanding of the process by domain experts. By representing the process in terms of higher-level activities, event abstraction can help bridge the gap between the low-level event data and the domain knowledge, facilitating better communication and understanding among stakeholders.

3. Hierarchical modeling: Event abstraction enables the creation of hierarchical process models, where lower-level events are grouped into higher-level activities, which can be further aggregated into even more abstract activities or subprocesses. This hierarchical representation can provide different levels of abstraction, allowing users to zoom in or out as needed, depending on their specific requirements and level of detail desired.

4. Model generalization: By abstracting low-level events, the resulting process model becomes more general and less dependent on the specific implementation details or system-level events. This can facilitate the transfer of process knowledge and models across different systems or organizations within the same domain.

5. Conformance checking and diagnostics: Event abstraction can improve the accuracy and interpretability of conformance checking and diagnostics techniques. By working with higher-level activities, deviations from the expected process behavior can be more easily identified and explained, making it easier to pinpoint and address potential issues or inefficiencies.

To effectively utilize event abstraction, several techniques can be employed, such as domain knowledge elicitation, ontology-based event mapping, and machine learning-based event clustering. Additionally, the choice of abstraction levels and the specific mapping of low-level events to higher-level activities should be guided by the specific goals and requirements of the process analysis task at hand.

It is important to note that event abstraction should be applied judiciously, as over-abstraction can lead to a loss of important details, while under-abstraction can result in models that are still too complex and difficult to comprehend. Finding the right balance between abstraction and detail is crucial for achieving effective process model readability and manageability.