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Parallel Skyline Queries

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In this paper, we design and analyze parallel algorithms for skyline queries. The skyline of a multidimensional set consists of the points for which no other point exists that is at least as good along every dimension. As a framework for parallel computation, we use both the MP model proposed in Koutris and Suciu (2011), which requires that the data is perfectly load-balanced, and a variation of the model in Afrati and Ullman (2010), the GMP model, which demands weaker load balancing constraints. In addition to load balancing, we want to minimize the number of blocking steps, where all processors must wait and synchronize. We propose a 2-step algorithm in the MP model for any dimension of the dataset, as well a 1-step algorithm for the case of 2 and 3 dimensions. Finally, we present a 1-step algorithm in the GMP model for any number of dimensions and a 1-step algorithm in the MP model for uniform distributions of data points.

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  1. Throughout this paper, we will assume set (and not bag) semantics.

  2. If the Conjunctive Query has k variables, then ε is at most 1/k.

  3. In [13], the size of the broadcast data was required to be O(n ε), for some ε<1. In this paper we impose a stricter bound, by requiring it to be independent on n.

  4. It will be one of p, p logp or p 1/(d−1).


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Correspondence to Paraschos Koutris.

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Afrati, F.N., Koutris, P., Suciu, D. et al. Parallel Skyline Queries. Theory Comput Syst 57, 1008–1037 (2015).

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