The database query optimizer requires the estimation of the query selectivity to find the most efficient access plan. For queries referencing multiple attributes from the same relation, we need a multi-dimensional selectivity estimation technique when the attributes are dependent each other because the selectivity is determined by the joint data distribution of the attributes. Additionally, for multimedia databases, there are intrinsic requirements for the multi-dimensional selectivity estimation because feature vectors are stored in multi-dimensional indexing trees. In the 1-dimensional case, a histogram is practically the most preferable. In the multi-dimensional case, however, a histogram is not adequate because of high storage overhead and high error rates.
In this paper, we propose a novel approach for the multi-dimensional selectivity estimation. Compressed information from a large number of small-sized histogram buckets is maintained using the discrete cosine transform. This enables low error rates and low storage overheads even in high dimensions. In addition, this approach has the advantage of supporting dynamic data updates by eliminating the overhead for periodical reconstructions of the compressed information. Extensive experimental results show advantages of the proposed approach.
数据库查询优化器需要估计查询的选择性,以找到最有效的访问计划。对于引用相同关系的多个属性的查询,当属性相互依赖时,我们需要一种多维选择性估计技术,因为选择性是由属性的联合数据分布确定的。另外,对于多媒体数据库,由于特征向量存储在多维索引树中,因此对于多维选择性估计有内在的要求。在一维情况下,直方图实际上是最优选的。但是,在多维情况下,由于高存储开销和高错误率,直方图是不够的。 P>
在本文中,我们提出了一种新的多维选择性估计方法。使用离散余弦变换可维护来自大量小型直方图存储桶的压缩信息。即使在高尺寸的情况下,这也可以实现低错误率和低存储开销。另外,该方法的优点是,通过消除压缩信息定期重建的开销,可以支持动态数据更新。大量的实验结果证明了该方法的优越性。 P>
机译:主动和反应多维直方图维护,用于选择性估计
机译:带有任意存储桶布局的压缩直方图,用于选择性估计
机译:压缩的分层二进制直方图,用于汇总多维数据
机译:使用压缩直方图信息的多维选择性估计
机译:多维参数估计:通过预测带宽外推和快速算法进行二维锐化,用于三维自回归估计。
机译:基于异步复杂直方图的联合光纤非线性噪声估计OSNR估计和调制格式识别和数字相干接收器的深度学习
机译:使用压缩直方图信息的多维选择性估计