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    • 2. 发明授权
    • Using a rowset as a query parameter
    • 使用行集作为查询参数
    • US07451137B2
    • 2008-11-11
    • US11069121
    • 2005-02-28
    • Ioan Bogdan CrivatC James MacLennanRaman S IyerMarius Dumitru
    • Ioan Bogdan CrivatC James MacLennanRaman S IyerMarius Dumitru
    • G06F17/30
    • G06F17/30539G06F17/30421G06F17/30595Y10S707/99932Y10S707/99934Y10S707/99944
    • Architecture that facilitates syntax processing for data mining statements. The system includes a syntax engine that receives as an input a query statement which, for example, is a data mining request. The statement can be generated from many different sources, e.g., a client application and/or a server application, and requests query processing of a data source (e.g., a relational database) to return a result set. The syntax engine includes a binding component that converts the query statement into an encapsulated statement in accordance with a predefined grammar. The encapsulated statement includes both data and data operations to be performed on the data of the data source, and which is understood by the data source. An execution component processes the encapsulated statement against the data source to return the desired result set.
    • 促进数据挖掘语句的语法处理的架构。 该系统包括语法引擎,其作为输入接收诸如数据挖掘请求的查询语句。 语句可以从许多不同的来源(例如客户端应用程序和/或服务器应用程序)生成,并且请求数据源(例如,关系数据库)的查询处理以返回结果集。 语法引擎包括一个绑定组件,它根据预定义的语法将查询语句转换成封装语句。 封装语句包括要对数据源的数据执行的数据和数据操作,数据源可以理解。 执行组件根据数据源处理封装语句以返回所需的结果集。
    • 5. 发明申请
    • DYNAMICALLY DETECTING EXCEPTIONS BASED ON DATA CHANGES
    • 基于数据变化动态检测异常
    • US20080189639A1
    • 2008-08-07
    • US11670783
    • 2007-02-02
    • Raman S. IyerC. James MacLennanIoan Bogdan Crivat
    • Raman S. IyerC. James MacLennanIoan Bogdan Crivat
    • G06F17/30G06F3/048
    • G06F17/245
    • Fields contained in data expressed as tabular data having columns and rows can initially be marked as exceptions, wherein a column within a row can be the potential cause of the exception. A user configurable parameter can be utilized to change the sensitivity or allowable exceptions for each row and/or column, to increase or decrease the number of exceptions detected. As data within each field are modified, added or deleted, or when the configurable parameter is changed, the exceptions marked can be automatically updated. Such updated exceptions can be the same or different from the initially marked exceptions. As such, a user can evaluate data and determine whether various changes within the data will change various outcomes.
    • 包含在以列和行表格数据表示的数据中的字段最初可以被标记为异常,其中行内的列可能是异常的潜在原因。 可以使用用户可配置参数来改变每行和/或列的灵敏度或允许的异常,以增加或减少检测到的异常数量。 由于每个字段中的数据被修改,添加或删除,或者当可配置参数被更改时,可以自动更新标记的异常。 这种更新的异常可以与初始标记的异常相同或不同。 因此,用户可以评估数据并确定数据内的各种变化是否会改变各种结果。
    • 8. 发明授权
    • Dynamically detecting exceptions based on data changes
    • 基于数据更改动态检测异常
    • US07797356B2
    • 2010-09-14
    • US11670783
    • 2007-02-02
    • Raman S. IyerC. James MacLennanIoan Bogdan Crivat
    • Raman S. IyerC. James MacLennanIoan Bogdan Crivat
    • G06F7/00
    • G06F17/245
    • Fields contained in data expressed as tabular data having columns and rows can initially be marked as exceptions, wherein a column within a row can be the potential cause of the exception. A user configurable parameter can be utilized to change the sensitivity or allowable exceptions for each row and/or column, to increase or decrease the number of exceptions detected. As data within each field are modified, added or deleted, or when the configurable parameter is changed, the exceptions marked can be automatically updated. Such updated exceptions can be the same or different from the initially marked exceptions. As such, a user can evaluate data and determine whether various changes within the data will change various outcomes.
    • 包含在以列和行表格数据表示的数据中的字段最初可以被标记为异常,其中行内的列可能是异常的潜在原因。 可以使用用户可配置参数来改变每行和/或列的灵敏度或允许的异常,以增加或减少检测到的异常数量。 由于每个字段中的数据被修改,添加或删除,或者当可配置参数被更改时,可以自动更新标记的异常。 这种更新的异常可以与初始标记的异常相同或不同。 因此,用户可以评估数据并确定数据内的各种变化是否会改变各种结果。
    • 10. 发明授权
    • Partitioning of data mining training set
    • 数据挖掘训练集分区
    • US07756881B2
    • 2010-07-13
    • US11371477
    • 2006-03-09
    • Ioan Bogdan CrivatRaman S. IyerC. James MacLennan
    • Ioan Bogdan CrivatRaman S. IyerC. James MacLennan
    • G06F7/00G06F17/30
    • G06F17/30539
    • A system that effectuates fetching a complete set of relational data into a mining services server and subsequently defining desired partitions upon the fetched data is provided. In accordance with the innovation, the data can be locally cached and partitioned therefrom. Accordingly, upon the same mining structure (e.g., cache) that has been partitioned, the novel innovation can build mining models for each partition. In other words, the innovation can employ the concept of mining structure as a data cache while manipulating only partitions of this cache in certain operations. The innovation can be employed in scenarios where a user wants to train a mining model using only data points that satisfy a particular Boolean condition, a user wants to split the training set into multiple partitions (e.g., training/testing) and/or a user wants to perform a data mining procedure known as “N-fold cross validation.”
    • 提供了一种能够将完整的关系数据集提取到采矿服务服务器中并随后在获取的数据上定义所需分区的系统。 根据创新,数据可以被本地缓存并从中分割。 因此,在已经被划分的相同挖掘结构(例如,高速缓存)上,新颖的创新可以为每个分区建立挖掘模型。 换句话说,创新可以采用挖掘结构的概念作为数据高速缓存,同时在某些操作中仅操纵该高速缓存的分区。 该创新可以在用户想要仅使用满足特定布尔条件的数据点来训练挖掘模型的情况下使用,用户希望将训练集合分成多个分区(例如,训练/测试)和/或用户 想要执行称为“N-fold交叉验证”的数据挖掘过程。