Histograms are widely used in selectivity estimation for one-dimensional data. Using the one-dimensional histograms to estimate the selectivity of the multidimensional queries will result in a high estimation error, unless the assumption of attribute independence is true. Constructing a multidimensional histogram also brings great challenges. The storage of a multidimensional histogram exponentially increases with the number of dimensions. In this paper, we propose a density-model-based multidimensional histogram. It uses a lightweight density model to predict the densities of a large number of regions instead of storing too many buckets. The experimental results indicate that our method can provide highly accurate selectivity estimations while occupying little space. In addition, the superiority of our method is more evident in high-dimensional data.

直方图被广泛用于一维数据的选择性估计中。除非属性独立性的假设为真，否则使用一维直方图估计多维查询的选择性将导致较高的估计误差。构建多维直方图也带来了巨大的挑战。多维直方图的存储量随维数呈指数增长。在本文中，我们提出了一种基于密度模型的多维直方图。它使用轻量级密度模型来预测大量区域的密度，而不是存储太多的存储桶。实验结果表明我们的方法可以在不占用空间的情况下提供高度准确的选择性估计。此外，