公开(公告)号：US07392157B1

公开(公告)日：2008-06-24

申请号：US11591652

申请日：2006-10-31

申请人： Phillip Dennis Delurgio ,  Brian Frederick Babcock ,  Dirk Manfred Beyer

发明人： Phillip Dennis Delurgio ,  Brian Frederick Babcock ,  Dirk Manfred Beyer

IPC分类号： G06F17/00

CPC分类号： G06Q30/02 ,  G06Q10/04

摘要： Methods and apparatuses for updating a forecast model quantifying the marketing of to the demand for a product and/or service are described. An original forecast model created at a reference M is used. An error of the forecast model is determined based on data including an original data and an additional data. At least one parameter is identified to be changed in value in, added to, or removed from the original forecast model. The forecast model is then modified to reduce the error of the forecast model by changing the value of the identified parameter in the forecast model, adding the identified parameter to the original forecast model, and/or removing the identified parameter from the original forecast model.

摘要翻译： 描述用于更新量化对产品和/或服务的需求的营销的预测模型的方法和装置。 使用在参考M创建的原始预测模型。 基于包括原始数据和附加数据的数据来确定预测模型的误差。 至少一个参数被识别为在原始预测模型中的值，添加到或从其中删除的值。 然后修改预测模型，通过改变预测模型中识别的参数的值，将所识别的参数添加到原始预测模型，和/或从原始预测模型中删除所识别的参数，来减少预测模型的误差。

公开(公告)号：US07702615B1

公开(公告)日：2010-04-20

申请号：US11267613

申请日：2005-11-04

申请人： Phillip Dennis Delurgio ,  Chad William Whipkey ,  Michael A. Shwe ,  Steven John Peter Hillion ,  Brian Frederick Babcock

发明人： Phillip Dennis Delurgio ,  Chad William Whipkey ,  Michael A. Shwe ,  Steven John Peter Hillion ,  Brian Frederick Babcock

IPC分类号： G06F7/00 ,  G06F17/30

CPC分类号： G06F17/30592 ,  G06Q10/04

摘要： Methods and apparatuses for predicting set of multi-dimensional dependent data and non-measurable data from a set of multi-dimensional historical dependent and causal data are described. In one embodiment, the method comprises receiving input data that comprises multi-dimensional historical dependent data and causal data and anticipated activity data, determining a set of multi-dimensional predicted dependent data using a predictive model and the input data, creating non-measurable data based on the set of multi-dimensional predicted dependent data and the input data.

摘要翻译： 描述了用于从一组多维历史依赖和因果数据预测多维依赖数据集和不可测量数据的方法和装置。 在一个实施例中，该方法包括接收包括多维历史相关数据和因果数据和预期活动数据的输入数据，使用预测模型和输入数据确定一组多维预测依赖数据，创建不可测数据 基于多维预测依赖数据和输入数据的集合。

公开(公告)号：US07636707B2

公开(公告)日：2009-12-22

申请号：US10818730

申请日：2004-04-06

申请人： Surajit Chaudhuri ,  Brian Frederick Babcock

发明人： Surajit Chaudhuri ,  Brian Frederick Babcock

IPC分类号： G06F17/30

CPC分类号： G06F17/30469 ,  Y10S707/99932 ,  Y10S707/99933 ,  Y10S707/99945

摘要： Selectivity estimates are produced that meet a desired confidence threshold. To determine the confidence level of a given selectivity estimate for a query expression, the query expression is evaluated on a sample tuples. A probability density function is derived based on the number of tuples in the sample that satisfy the query expression. The cumulative distribution for the probability density function is solved for the given threshold to determine a selectivity estimate at the given confidence value.

摘要翻译： 产生满足期望置信阈值的选择性估计。 为了确定查询表达式的给定选择性估计的置信水平，查询表达式将在样本元组上进行求值。 基于满足查询表达式的样本中的元组数量导出概率密度函数。 为给定阈值求解概率密度函数的累积分布，以确定给定置信度值下的选择性估计。