Process mining is a discipline that deals with extracting insights and knowledge from event logs recorded by information systems during the execution of business processes. It consists of three main techniques: process discovery, conformance checking, and process enhancement.

1. Process Discovery:
Process discovery is the technique of constructing a process model from an event log. It involves analyzing the sequences of activities recorded in the log and generating a graphical representation (e.g., a process model or a Petri net) that accurately depicts the underlying process. Process discovery aims to discover the real-life execution paths and control flow of a process, including parallel branches, loops, and decision points.

2. Conformance Checking:
Conformance checking is the technique of comparing the recorded event log with an existing process model to identify deviations or conformance violations. It assesses the extent to which the real-life process execution conforms to the prescribed process model. Conformance checking can detect instances where the actual execution deviates from the expected behavior defined in the model, such as activities executed out of order, missing activities, or additional activities performed.

3. Process Enhancement:
Process enhancement, also known as process improvement or process extension, is the technique of using the insights gained from process discovery and conformance checking to improve or extend the existing process model. It involves analyzing the event log and the discovered process model to identify potential improvements, optimizations, or extensions that can be applied to the process.

Process enhancement can take various forms, such as:

a. Repair: Modifying the process model to better reflect the real-life execution paths observed in the event log. This may involve adding or removing activities, adjusting control flow, or resolving conflicts between the model and the log.

b. Extension: Extending the process model with additional perspectives or information derived from the event log. For example, incorporating data attributes, performance indicators, or resource assignments into the process model.

c. Prediction: Using process mining techniques to predict the outcome or performance of running instances based on historical data from the event log.

d. Recommendation: Providing recommendations for process optimization, bottleneck identification, resource allocation, or decision-making based on the analysis of the event log and process model.

The main difference between process enhancement and process discovery/conformance checking is that process enhancement focuses on actively improving or extending the process model based on the insights gained from the other two techniques. Process discovery and conformance checking are more analytical in nature, aimed at understanding the current state of the process, while process enhancement seeks to leverage that understanding to optimize or enhance the process itself.