An adaptive load shedding approach for windowed stream joins was developed to improve processing efficiency. Instead of dropping data, the method selectively processes tuples within windows to reduce CPU load. By dynamically adjusting the subset of tuples used for join operations based on input stream rates, time correlation, and join directions, the approach significantly outperformed traditional methods in terms of output rate.