Applying decomposition strategies can be very beneficial for handling large-scale process mining projects, but it also comes with its own set of challenges. Here are some main advantages and disadvantages:

**Benefits:**

1. **Scalability**: Decomposition allows you to break down a large process into smaller, manageable subprocesses or event logs. This makes it possible to handle vast amounts of data that would otherwise be too complex to analyze.

2. **Reduced Complexity**: Smaller subprocesses often have simpler structures, making it easier to understand the behavior within each subprocess and identify patterns.

3. **Improved Performance**: Analyzing smaller datasets requires fewer computational resources, leading to faster processing times and better performance of process mining algorithms.

4. **Focused Analysis**: Decomposition enables you to focus on specific parts of a process, allowing for more detailed analysis and targeted improvement efforts.

5. **Parallel Processing**: Smaller subprocesses can be analyzed simultaneously, reducing the overall project time.

**Challenges:**

1. **Identifying Appropriate Points of Decomposition**: It's crucial to find the right balance between too many (resulting in overly complex sub-processes) and too few (losing the benefits of decomposition) subprocesses. This requires a deep understanding of the process being mined.

2. **Data Consistency**: When decomposing, it's essential to ensure that data consistency is maintained across boundaries. Data must be properly aligned and synchronized between subprocesses to avoid inaccuracies in the analysis.

3. **Edge Cases and Exceptions**: Real-world processes often have edge cases or exceptions that may not fit neatly into the decomposed subprocesses. Handling these can lead to additional complexity.

4. **Information Loss**: Decomposition might lead to loss of information about dependencies or interactions between subprocesses, especially when using a divide-and-conquer approach.

5. **Validation and Integration**: After analyzing each subprocess separately, results must be validated and integrated to ensure they hold true for the overall process. This can be challenging and time-consuming.

6. **Scalability in Reverse**: While decomposition improves scalability during analysis, it can make reverse engineering (i.e., creating a model from an event log) more complex, as relationships between subprocesses need to be taken into account.

7. **Communication Complexity**: Stakeholders might find it challenging to understand and engage with results when they are presented in terms of multiple subprocesses rather than the overall process.