Process mining is a discipline at the intersection of business process management (BPM), data science, and complex systems analysis that focuses on discovering, monitoring, and enhancing business processes based on event logs. The goal of process mining is to gain insights into how processes actually operate and to use these insights for continuous improvement. Here are the three key components you mentioned:

1. **Process Discovery**:
   Process discovery involves analyzing event logs to uncover the actual process models that existed within an organization during the time period covered by the logs. The output of this phase is a model that represents the "as-is" process. It answers the question, "What are the processes actually being executed?" Key techniques include:
   
   - **Minority Analysis**: Focuses on finding the majority pattern and considers variations as exceptions.
   - **Frequent Pattern Mining**: Looks for frequent sequences of activities in the logs.
   - **Heuristic Miner (alpha-miner, beta-miner)**: Uses heuristics to mine different types of process models from event logs.
   
   The result is often a high-level representation of the process, which can be visualized as a flowchart or workflow diagram. This can help organizations understand their current processes and identify bottlenecks or deviations from standard procedures.

2. **Process Enhancement (also known as Process Improvement or Process Optimization)**:
   Once the "as-is" process model has been discovered and understood, process enhancement comes into play. This phase is about improving the efficiency and effectiveness of business processes based on the insights gained from the event logs and additional analysis. The focus here is not just on making changes for their own sake but on optimizing processes to achieve better performance outcomes. Key activities include:
   
   - **Performance Analysis**: Assessing how well the process conforms to the expected model or standards.
   - **Root Cause Analysis**: Identifying why certain issues are occurring in the process.
   - **What-if Analysis and Simulation**: Predicting the impact of changes before they are implemented.
   
   Process enhancement leverages advanced analytics, machine learning, and optimization techniques to propose improvements. It can involve redesigning processes, automating tasks, streamlining decision points, or implementing best practices based on industry benchmarks.

3. **Conformance Checking**:
   Conformance checking is the process of determining whether the actual behavior recorded in event logs adheres to a given process model. This is important for ensuring that the processes are being executed as intended and for identifying any anomalies or deviations. Key techniques include:
   
   - **Fitness Fairness**: Measures how often a process instance follows the model's behavior versus its variability.
   - **Enrichments**: Provides context to events in the logs, which can be used to check conformance more accurately.
   
   Conformance checking helps organizations ensure that their processes are being executed correctly and identifies areas where deviations may be occurring. This can lead to corrective actions, better adherence to regulations, or improved performance through more accurate process modeling.

In summary, while **process discovery** is about understanding the "as-is" state of a business process by creating models from event logs, **process enhancement** builds on this foundation to improve processes for better efficiency and effectiveness, and **conformance checking** ensures that the processes are being executed as modeled. All three are interrelated and support each other in the journey towards process excellence. Process mining tools often integrate these components into a single platform, allowing analysts to seamlessly move from one phase to another as they work to optimize business operations.