会员体验
专利管家(专利管理)
工作空间(专利管理)
风险监控(情报监控)
数据分析(专利分析)
侵权分析(诉讼无效)
联系我们
交流群
官方交流:
QQ群: 891211   
微信请扫码    >>>
现在联系顾问~
热词
    • 2. 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。
    • 7. 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。
    • 9. 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。
    • 10. 发明授权
    • Unstructured data in a mining model language
    • 挖掘模型语言中的非结构化数据
    • US07593927B2
    • 2009-09-22
    • US11373319
    • 2006-03-10
    • C. James MacLennanIoan Bogdan CrivatZhaoHui TangRaman S. Iyer
    • C. James MacLennanIoan Bogdan CrivatZhaoHui TangRaman S. Iyer
    • G06F7/00G06F17/30
    • G06F17/30943G06F2216/03Y10S707/99933
    • A standard mechanism for directly accessing unstructured data types (e.g., image, audio, video, gene sequencing and text data) in accordance with data mining operations is provided. The subject innovation can enable access to unstructured data directly from within the data mining engine or tool. Accordingly, the innovation enables multiple vendors to provide algorithms for mining unstructured data on a data mining platform (e.g., an SQL-brand server), thereby increasing adoption. As well, the subject innovation allows users to directly mine unstructured data that is not fixed-length, without pre-processing and tokenizing the data external to the data mining engine. In accordance therewith, the innovation can provide a mechanism to expand declarative language content types to include an “unstructured” data type thereby enabling a user and/or application to affirmatively designate mining data as an unstructured type.
    • 提供了一种用于根据数据挖掘操​​作直接访问非结构化数据类型(例如图像,音频,视频,基因排序和文本数据)的标准机制。 主题创新可以直接从数据挖掘引擎或工具中访问非结构化数据。 因此,该创新使得多个供应商能够提供用于在数据挖掘平台(例如,SQL品牌服务器)上挖掘非结构化数据的算法,从而增加采用。 此外,本创新允许用户直接挖掘不固定长度的非结构化数据,而不需要对数据挖掘引擎外部的数据进行预处理和标记。 根据此,创新可以提供一种机制来扩展声明性语言内容类型以包括“非结构化”数据类型,从而使得用户和/或应用程序肯定地将挖掘数据指定为非结构化类型。