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The authors propose a novel gossip-based technique that allows each node in a system to estimate the distribution of values held by other nodes. They observe that the presence of duplicate values does not significantly affect the distribution of values in samples collected through gossip, and based on that explore different data synopsis techniques that optimize space and time while allowing nodes to accumulate information. Unlike previous aggregation schemes, the approach focuses on allowing all nodes in the system to compute an estimate of the entire distribution in a decentralized and efficient manner.
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