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We present a scalable approach for identifying moving flock patterns in large trajectory databases. A moving flock pattern refers to a group of entities that move closely together within a defined spatial radius for a minimum time interval. We focus on improving the state-of-the-art sequential algorithms, which suffer from high computational costs when dealing with large datasets. By leveraging distributed frameworks and utilizing spatial partitioning, the proposed solution aims to significantly reduce the time required to detect moving flock patterns. We highlight the bottlenecks of the sequential approaches and offer optimizations like partition-based parallelism and strategies for managing flock patterns that span multiple partitions. An experimental evaluation using synthetic trajectory datasets, demonstrates that the proposed methods substantially improve scalability and performance compared to existing sequential algorithms.