Benefits of applying decomposition strategies in large-scale process mining projects include:

1. Improved Performance: Decomposition strategies can significantly reduce the computational complexity of process discovery algorithms, making it possible to analyze large-scale event logs that would otherwise be infeasible.

2. Increased Accuracy: By dividing the event log into smaller, more manageable subsets, decomposition strategies can help to increase the accuracy of process models by reducing the impact of noise and outliers.

3. Better Understanding: Decomposing a complex process into smaller, more manageable parts can make it easier to understand and analyze, providing insights into specific aspects of the process and making it easier to identify areas for improvement.

Challenges of applying decomposition strategies in large-scale process mining projects include:

1. Compositional Analysis: One of the main challenges is to ensure that the decomposition results can be recomposed to provide a complete and accurate view of the overall process. This requires careful consideration of how the event log is partitioned and how the results are integrated.

2. Loss of Information: Decomposition strategies can result in the loss of information about the relationships between different parts of the process. This can make it difficult to understand how different activities are related and how changes in one part of the process might affect other parts.

3. Complexity: Decomposition can also add complexity to the analysis process, as it requires additional steps and decisions about how to partition the event log and how to interpret the results. This can make it more difficult to communicate findings and recommendations to stakeholders.