Faster compressed quadtrees

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摘要

Real-world point sets tend to be clustered, so using a machine word for each point is wasteful. In this paper we first show how a compact representation of quadtrees using O(1) bits per node can break this bound on clustered point sets, while offering efficient range searches. We then describe a new compact quadtree representation based on heavy-path decompositions, which supports queries faster than previous compact structures. We present experimental evidence showing that our structure is competitive in practice.

论文关键词:Compact data structures,Quadtrees,Heavy-path decomposition,Range queries,Clustered points

论文评审过程:Received 2 December 2020, Revised 9 December 2021, Accepted 5 September 2022, Available online 14 September 2022, Version of Record 22 September 2022.

论文官网地址:https://doi.org/10.1016/j.jcss.2022.09.001