Applying decomposition strategies in large-scale process mining projects can have both benefits and challenges. Here are some of the main advantages and disadvantages:

**Benefits:**

1. **Improved scalability**: Decomposition helps to break down a large and complex process into smaller, more manageable sub-processes, making it easier to analyze and mine.
2. **Reduced computational complexity**: By focusing on smaller sub-processes, the computational complexity of process mining algorithms is decreased, making it possible to handle larger datasets.
3. **Enhanced understanding of local processes**: Decomposition enables a deeper understanding of local processes and their interactions, which can lead to more targeted improvements.
4. **Increased flexibility**: Decomposition allows for the application of different process mining techniques and algorithms to different sub-processes, increasing flexibility and adaptability.
5. **Better management of data quality issues**: Decomposition can help identify and isolate data quality issues, making it easier to clean and preprocess data.
6. **Facilitated communication and collaboration**: Decomposition can help stakeholders understand their specific roles and responsibilities within the larger process, promoting communication and collaboration.

**Challenges:**

1. **Increased complexity in model integration**: Decomposition can create multiple sub-process models, which can be challenging to integrate and align with each other.
2. **Loss of global process visibility**: Focusing on sub-processes may lead to a loss of visibility into the overall process, making it difficult to identify global patterns and trends.
3. **Difficulty in setting boundaries**: Identifying the correct boundaries for decomposition can be challenging, and incorrect boundaries may lead to sub-optimal results.
4. **Data consistency and integration issues**: Decomposition can create data consistency and integration issues, particularly if different sub-processes have different data formats or schemas.
5. **Higher demands on analytical resources**: Decomposition may require additional analytical resources, such as expertise and tools, to handle the complexity of sub-process analysis.
6. **reater need for coordination and governance**: Decomposition requires more coordination and governance to ensure that sub-processes are aligned with the overall process goals and objectives.
7. **Risk of sub-optimization**: Decomposition can lead to sub-optimization, where local improvements may not necessarily lead to global process improvements.
8. **Difficulty in handling dependencies and interactions**: Decomposition can make it challenging to model and analyze dependencies and interactions between sub-processes.

To overcome these challenges, it is essential to:

1. Establish clear goals and objectives for the decomposition strategy.
2. Define a structured approach to decomposition,