P. Agarwal, G. Cormode, Z. Huang, J. Phillips, Z. Wei, and K. Yi. Mergeable coresets. In Third Workshop on Massive Data Algorithmics (MASSIVE), 2011.

We study the mergeability of data summaries. Informally speaking, mergeability requires that, given two summaries on two data sets, there is a way to merge the two summaries into a summary on the two data sets combined together, while preserving the error and size guarantees. This property means that the summary can be treated like other algebraic objects such as sum and max, which is especially useful for computing summaries on massive distributed data. Many data summaries are trivially mergeable by construction, most notably those based on linear transformations. But some other fundamental ones like those for heavy hitters and quantiles, are not (known to be) mergeable. In this paper, we demonstrate that these summaries are indeed mergeable or can be made mergeable after appropriate modifications. Specifically, we show that for ε-approximate heavy hitters, there is a deterministic mergeable summary of size O(1/ε); for ε-approximate quantiles, there is a deterministic summary of size O((1)/(ε) log(εn)) that has a restricted form of mergeability, and a randomized one of size O((1)/(ε) log3/2(1)/(ε)) with full mergeability. We also extend our results to geometric summaries such as ε-approximations and ε-kernels.

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