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The Swinging Door Trending algorithm and the Douglas-Peucker algorithm are both staple lossy compression algorithms. The former one is widely used in real-time database software of industry, while the latter one is more popular for spatial data processing. In this paper, these two algorithms are compared first to summarize their advantages and disadvantages. And then, an optimized lossy compression algorithm for real-time data as well as a variant of it under certain constraints is proposed with the strategy “searching the farthest feasible point”. The experimental results show that the new algorithm is better than the conventional Swinging Door Trending algorithm and Douglas-Peucker algorithm at compression rate, overall error, and efficiency.