公开(公告)号：US06542886B1

公开(公告)日：2003-04-01

申请号：US09268275

申请日：1999-03-15

Applicant: Surajit Chaudhuri ,  Rajeev Motwani ,  Vivek Narasayya

Inventor： Surajit Chaudhuri ,  Rajeev Motwani ,  Vivek Narasayya

IPC: G06F1730

CPC classification number: G06F17/3061 ,  G06F17/30498 ,  G06F17/30536 ,  G06F2216/03 ,  Y10S707/99932 ,  Y10S707/99937

Abstract: A database server supports weighted and unweighted sampling of records or tuples in accordance with desired sampling semantics such as with replacement (WR), without replacement (WoR), or independent coin flips (CF) semantics, for example. The database server may perform such sampling sequentially not only to sample non-materialized records such as those produced as a stream by a pipeline in a query tree for example, but also to sample records, whether materialized or not, in a single pass. The database server also supports sampling over a join of two relations of records or tuples without requiring the computation of the full join and without requiring the materialization of both relations and/or indexes on the join attribute values of both relations.

Abstract translation: 数据库服务器根据期望的抽样语义（例如替换（WR），无替换（WoR）或独立硬币翻转（CF））语义支持对记录或元组进行加权和未加权采样。 数据库服务器可以顺序地执行这样的采样，以便例如在查询树中通过流水线生成的诸如作为流生成的非物化记录，而且在单次通过中对采样记录（无论是否具体化）进行采样。 数据库服务器还支持对两个记录或元组关系的连接进行抽样，而不需要计算完整连接，而不需要在关系的连接属性值上实现关系和/或索引。

公开(公告)号：US06278989B1

公开(公告)日：2001-08-21

申请号：US09139835

申请日：1998-08-25

Applicant: Surajit Chaudhuri ,  Rajeev Motwani ,  Vivek Narasayya

Inventor： Surajit Chaudhuri ,  Rajeev Motwani ,  Vivek Narasayya

IPC: G06F1730

CPC classification number: G06F17/30463 ,  G06F17/30536 ,  Y10S707/99932 ,  Y10S707/99933 ,  Y10S707/99942

Abstract: Using adaptive random sampling with cross-validation helps determine when enough data of a database has been sampled to construct histograms on one or more columns of one or more tables of the database within a desired or predetermined degree of accuracy. An adaptive random sampling histogram construction tool constructs an approximate equi-height k-histogram using an initial sample of data values from the database and iteratively updates the histogram using an additional sample of data values from the database until the histogram is within the desired degree of accuracy. The accuracy of the histogram is cross-validated against the additional sample at each iteration, and the additional sample is used to update the histogram to help improve its accuracy. The accuracy of the histogram may be measured by an error in distribution of the additional sample over the histogram as compared to a threshold error using a suitable error metric. By attempting to sample only the number of data values necessary to construct the histogram within the desired degree of accuracy, the adaptive random sampling histogram construction tool attempts to avoid any cost increases in time and memory from sampling too many data values.

Abstract translation: 使用具有交叉验证的自适应随机抽样有助于确定在数据库的足够数据被采样以在期望的或预定的准确度内在数据库的一个或多个表的一个或多个列上构造直方图。 自适应随机抽样直方图构造工具使用来自数据库的数据值的初始样本构建近似等高k直方图，并使用来自数据库的附加数据值样本迭代地更新直方图，直到直方图在所需的程度 准确性。 在每次迭代时，直方图的精度与附加样本进行交叉验证，并且附加样本用于更新直方图以帮助提高其准确性。 与使用合适的误差度量的阈值误差相比，可以通过直方图上的附加样本的分布误差来测量直方图的精度。 通过尝试仅在所需精度范围内仅采样构建直方图所需的数据值的数量，自适应随机抽样直方图构造工具尝试避免在采样太多数据值时的时间和内存中的任何成本增加。