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What is Optimal Quantile Approximation in Streams about?

This paper presents an optimal quantile sketching algorithm for streaming data, achieving a space complexity of O((1/ε) log log(1/δ)) for approximating quantiles with a specified error and failure probability. The authors introduce a novel compaction technique that improves upon previous methods, demonstrating a clear gap between randomized and deterministic approaches. This work resolves a long-standing problem in streaming algorithms and has implications for other sketching objectives and geometric corese

Author

alekspichler1904

Language

EN