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Abstract

The declustering problem is to allocate given data on parallel working storage devices in such a manner that typical requests find their data evenly distributed on the devices. Using deep results from discrepancy theory, we improve previous work of several authors concerning range queries to higher-dimensional data. We give a declustering scheme with an additive error of Od(logd-1M) independent of the data size, where d is the dimension, M the number of storage devices and d-1 does not exceed the smallest prime power in the canonical decomposition of M into prime powers. In particular, our schemes work for arbitrary M in dimensions two and three. For general d, they work for all Md-1 that are powers of two. Concerning lower bounds, we show that a recent proof of a Ωd(log(d-1)/2M) bound contains an error. We close the gap in the proof and thus establish the bound.