计算在希尔伯特R树中使用的点的希尔伯特值？

问题描述

我有一个应用程序，希尔伯特R树 似乎是一个适当的数据结构。具体来说，它需要对经历大量更新的数据集进行相当快速的空间查询。

然而，就我所见，没有一个描述这种数据结构的算法即使提到如何实际计算必要的希尔伯特值;这是的距离。

所以有任何关于如何计算这些的建议？

解决方案

有趣的问题！

我做了一些谷歌搜索，好消息是，我发现了希尔伯特价值的实现。

可能的坏消息是，它在Haskell ...

它还提出了一个Lebesgue距离度量，您可以更轻松地进行计算。

I have an application where a Hilbert R-Tree (wikipedia) (citeseer) would seem to be an appropriate data structure. Specifically, it requires reasonably fast spatial queries over a data set that will experience a lot of updates.

However, as far as I can see, none of the descriptions of the algorithms for this data structure even mention how to actually calculate the requisite Hilbert Value; which is the distance along a Hilbert Curve to the point.

So any suggestions for how to go about calculating this?

解决方案

Fun question!

I did a bit of googling, and the good news is, I've found an implementation of Hilbert Value.

The potentially bad news is, it's in Haskell...

http://www.serpentine.com/blog/2007/01/11/two-dimensional-spatial-hashing-with-space-filling-curves/

It also proposes a Lebesgue distance metric you might be able to compute more easily.

这篇关于计算在希尔伯特R树中使用的点的希尔伯特值？的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！