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1.发明授权公开(公告)号：US07809660B2
公开(公告)日：2010-10-05
申请号：US11542397
申请日：2006-10-03
IPC分类号： G06N5/00
摘要： A computer implemented method, apparatus, and computer usable program code for automatically selecting an optimal control cohort. Attributes are selected based on patient data. Treatment cohort records are clustered to form clustered treatment cohorts. Control cohort records are scored to form potential control cohort members. The optimal control cohort is selected by minimizing differences between the potential control cohort members and the clustered treatment cohorts.
摘要翻译： 一种用于自动选择最优控制队列的计算机实现的方法，装置和计算机可用程序代码。 属性是根据患者数据选择的。 治疗队列记录聚集形成聚类治疗队列。 控制队列记录得分，形成潜在的控制队列成员。 通过最小化潜在对照队列成员与群集治疗队列之间的差异来选择最优控制队列。
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2.发明申请公开(公告)号：US20080082356A1
公开(公告)日：2008-04-03
申请号：US11542397
申请日：2006-10-03
摘要： A computer implemented method, apparatus, and computer usable program code for automatically selecting an optimal control cohort. Attributes are selected based on patient data. Treatment cohort records are clustered to form clustered treatment cohorts. Control cohort records are scored to form potential control cohort members. The optimal control cohort is selected by minimizing differences between the potential control cohort members and the clustered treatment cohorts.
摘要翻译： 一种用于自动选择最优控制队列的计算机实现的方法，装置和计算机可用程序代码。 属性是根据患者数据选择的。 治疗队列记录聚集形成聚类治疗队列。 控制队列记录得分，形成潜在的控制队列成员。 通过最小化潜在对照队列成员与群集治疗队列之间的差异来选择最优控制队列。
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3.发明申请公开(公告)号：US20080249971A1
公开(公告)日：2008-10-09
申请号：US12139649
申请日：2008-06-16
IPC分类号： G06N5/02
CPC分类号： G06Q30/02 , G06Q30/0207 , G06Q30/0244 , G06Q30/0251
摘要： Method, system, and article of manufacture for selecting prospects for a product promotion though data mining. An initial set of prospects in a customer database is identified, by data mining, as initially identified prospects based on predetermined selection criteria. The number of initially identified prospects is compared to a target number of prospects. When the number of initially identified prospects matches the target number of prospects, the initially identified prospects are utilized as the final selection of prospects. When the number of initially identified prospects mismatches the target number of prospects, the final selection of prospects is determined by performing a culling process or an augmenting process to reduce or increase, respectively, the initial set of prospects using a heuristic measure H, until the number of prospects in the initial set of prospects matches the target number of prospects.
摘要翻译： 通过数据挖掘选择产品推广前景的方法，系统和制造。 通过数据挖掘，根据预先确定的选择标准，将最初确定的前景识别出客户数据库中的初始集合。 将最初确定的前景数量与目标数量的前景进行比较。 当初始确定的前景数量与目标数量匹配时，初步确定的前景被用作最终的前景选择。 当初始确定的前景数量与目标数量的前景不匹配时，前景的最终选择是通过执行剔除过程或增加过程来确定的，以分别使用启发式测量H来减少或增加初始集合的前景，直到 初步前景数量与目标数量相符。
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4.发明授权Method and system for direct linkage of a relational database table for data preparation 有权用于数据准备的关系数据库表的直接链接的方法和系统公开(公告)号：US07228308B2
公开(公告)日：2007-06-05
申请号：US10706731
申请日：2003-11-12
申请人： John Baxter Rollins
发明人： John Baxter Rollins
CPC分类号： G06F17/30539 , G06F17/30501 , Y10S707/99942 , Y10S707/99943
摘要： A method, apparatus, and computer instructions for direct linkage of relational database table to a data preparation tool for data preparation. In a preferred embodiment, the mechanism of the present invention allows data to be read directly from one or more relational database tables to a data preparation tool into datasets without generating output flat files. Multiple datasets from different relational database table are merged into one dataset if more than one relational database table is read. Upon completion of necessary data preparation on the dataset by the data preparation tool, the present invention creates a new relational database table and loads resulting data from the prepared dataset into the new relational database table.
摘要翻译： 用于将关系数据库表直接链接到用于数据准备的数据准备工具的方法，装置和计算机指令。 在优选实施例中，本发明的机制允许将数据从一个或多个关系数据库表直接读取到数据准备工具到数据集中而不产生输出平面文件。 如果读取了多个关系数据库表，则来自不同关系数据库表的多个数据集将合并到一个数据集中。 在由数据准备工具完成数据集上的必要数据准备之后，本发明创建一个新的关系数据库表，并将得到的数据从准备的数据集加载到新的关系数据库表中。
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5.发明授权公开(公告)号：US08036931B2
公开(公告)日：2011-10-11
申请号：US10742099
申请日：2003-12-19
IPC分类号： G06Q30/00
CPC分类号： G06Q30/02 , G06Q30/0207 , G06Q30/0244 , G06Q30/0251
摘要： An initial set of prospects in a customer database is identified, by data mining, as initially identified prospects based on predetermined selection criteria. The number of initially identified prospects is compared to a target number of prospects. When the number of initially identified prospects matches the target number of prospects, the initially identified prospects are utilized as the final selection of prospects. When the number of initially identified prospects mismatches the target number of prospects, the final selection of prospects is determined by performing a culling process or an augmenting process to reduce or increase, respectively, the initial set of prospects using a heuristic measure H, until the number of prospects in the initial set of prospects matches the target number of prospects.
摘要翻译： 通过数据挖掘，根据预先确定的选择标准，将最初确定的前景识别出客户数据库中的初始集合。 将最初确定的前景数量与目标数量的前景进行比较。 当初始确定的前景数量与目标数量匹配时，初步确定的前景被用作最终的前景选择。 当初始确定的前景数量与目标数量的前景不匹配时，前景的最终选择是通过执行剔除过程或增加过程来确定的，以分别使用启发式测量H来减少或增加初始集合的前景，直到 初步前景数量与目标数量相符。
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6.发明授权公开(公告)号：US07725415B2
公开(公告)日：2010-05-25
申请号：US12139649
申请日：2008-06-16
CPC分类号： G06Q30/02 , G06Q30/0207 , G06Q30/0244 , G06Q30/0251
摘要： Method, system, and article of manufacture for selecting prospects for a product promotion though data mining. An initial set of prospects in a customer database is identified, by data mining, as initially identified prospects based on predetermined selection criteria. The number of initially identified prospects is compared to a target number of prospects. When the number of initially identified prospects matches the target number of prospects, the initially identified prospects are utilized as the final selection of prospects. When the number of initially identified prospects mismatches the target number of prospects, the final selection of prospects is determined by performing a culling process or an augmenting process to reduce or increase, respectively, the initial set of prospects using a heuristic measure H, until the number of prospects in the initial set of prospects matches the target number of prospects.
摘要翻译： 通过数据挖掘选择产品推广前景的方法，系统和制造。 通过数据挖掘，根据预先确定的选择标准，将最初确定的前景识别出客户数据库中的初始集合。 将最初确定的前景数量与目标数量的前景进行比较。 当初始确定的前景数量与目标数量匹配时，初步确定的前景被用作最终的前景选择。 当初始确定的前景数量与目标数量的前景不一致时，通过执行剔除过程或扩充过程来确定前景的最终选择，以分别使用启发式测量H来减少或增加初始集合的前景，直到 初步前景数量与目标数量相符。
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公开(公告)号：US07912812B2
公开(公告)日：2011-03-22
申请号：US11970337
申请日：2008-01-07
IPC分类号： G06F17/00
CPC分类号： G06F17/30457 , G06F17/3048
摘要： Methods and apparatus, including computer program products, implementing and using techniques for populating a data cache on a server. Data requests received by the server are collected in a repository. A data mining algorithm is applied to the collected data requests to predict a set of data that is likely to be requested during an upcoming time period. It is determined whether the complete set of predicted data exists in the data cache. If the complete set of predicted data does not exist in the data cache, the missing data is retrieved from a database and added to the data cache.
摘要翻译： 方法和装置，包括计算机程序产品，用于在服务器上填充数据高速缓存的实现和使用技术。 服务器收到的数据请求将收集到存储库中。 数据挖掘算法应用于收集的数据请求以预测在即将到来的时间段内可能请求的一组数据。 确定数据高速缓存中是否存在完整的预测数据集。 如果数据高速缓存中不存在完整的预测数据集，则从数据库中检索丢失的数据，并将其添加到数据高速缓存。
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公开(公告)号：US20090177667A1
公开(公告)日：2009-07-09
申请号：US11970337
申请日：2008-01-07
IPC分类号： G06F17/30
CPC分类号： G06F17/30457 , G06F17/3048
摘要： Methods and apparatus, including computer program products, implementing and using techniques for populating a data cache on a server. Data requests received by the server are collected in a repository. A data mining algorithm is applied to the collected data requests to predict a set of data that is likely to be requested during an upcoming time period. It is determined whether the complete set of predicted data exists in the data cache. If the complete set of predicted data does not exist in the data cache, the missing data is retrieved from a database and added to the data cache.
摘要翻译： 方法和装置，包括计算机程序产品，用于在服务器上填充数据高速缓存的实现和使用技术。 服务器收到的数据请求将收集到存储库中。 数据挖掘算法应用于收集的数据请求以预测在即将到来的时间段内可能请求的一组数据。 确定数据高速缓存中是否存在完整的预测数据集。 如果数据高速缓存中不存在完整的预测数据集，则从数据库中检索丢失的数据，并将其添加到数据高速缓存。
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