Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Streaming approximation of quantiles is a widely performed operation and, as a result, a ll studied problem. The goal is to construct a data structure for approximately ansring quantile queries in one pass under strict memory constraints. For $\varepsilon$ approximating a single quantile query with probability $1-\delta$  obtain a $O((1/\varepsilon)\log \log (1/\delta))$ space algorithm and a matching lor bound.  also provide a $O((1/\varepsilon)\log^2 \log (1/\delta))$ sketch which is fully mergeable. These give non-mergeable and mergeable summaries for simultaneously approximating all quantiles in space $O((1/\varepsilon)\log \log (1/\varepsilon\delta))$ and $O((1/\varepsilon)\log^2 \log (1/\varepsilon\delta))$ respectively. The algorithms operate in the comparison model and are simple to implement.
['Zohar Shay Karnin', 'Kevin J. Lang', 'Edo Liberty']
I am writing a research paper on a idea Almost Optimal Streaming Quantiles Algorithms
[{"from": "human", "value": "I am writing a research paper on a idea Almost Optimal Streaming Quantiles Algorithms"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Streaming approximation of quantiles is a widely performed operation and, as a result, a ll studied problem. The goal is to construct a data structure for approximately ansring quantile queries in one pass under strict memory constraints. For $\\varepsilon$ approximating a single quantile query with probability $1-\\delta$  obtain a $O((1/\\varepsilon)\\log \\log (1/\\delta))$ space algorithm and a matching lor bound.  also provide a $O((1/\\varepsilon)\\log^2 \\log (1/\\delta))$ sketch which is fully mergeable. These give non-mergeable and mergeable summaries for simultaneously approximating all quantiles in space $O((1/\\varepsilon)\\log \\log (1/\\varepsilon\\delta))$ and $O((1/\\varepsilon)\\log^2 \\log (1/\\varepsilon\\delta))$ respectively. The algorithms operate in the comparison model and are simple to implement."}]