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    • 9. 发明授权
    • Anomaly detection in data perspectives
    • 数据透视异常检测
    • US07065534B2
    • 2006-06-20
    • US10874956
    • 2004-06-23
    • Allan FoltingBo ThiessonDavid E. HeckermanDavid M. ChickeringEric Barber Vigesaa
    • Allan FoltingBo ThiessonDavid E. HeckermanDavid M. ChickeringEric Barber Vigesaa
    • G06F7/00G06F17/00
    • G06F17/30592G06N7/00Y10S707/957Y10S707/958Y10S707/99943
    • The present invention leverages curve fitting data techniques to provide automatic detection of data anomalies in a “data tube” from a data perspective, allowing, for example, detection of data anomalies such as on-screen, drill down, and drill across data anomalies in, for example, pivot tables and/or OLAP cubes. It determines if data substantially deviates from a predicted value established by a curve fitting process such as, for example, a piece-wise linear function applied to the data tube. A threshold value can also be employed by the present invention to facilitate in determining a degree of deviation necessary before a data value is considered anomalous. The threshold value can be supplied dynamically and/or statically by a system and/or a user via a user interface. Additionally, the present invention provides an indication to a user of the type and location of a detected anomaly from a top level data perspective.
    • 本发明利用曲线拟合数据技术从数据角度提供“数据管”中的数据异常的自动检测,从而允许例如检测诸如屏幕上的数据异常,向下钻取和钻取数据异常的数据异常 例如,枢轴表和/或OLAP多维数据集。 它确定数据是否基本上偏离由曲线拟合处理(例如应用于数据管的分段线性函数)所建立的预测值。 本发明也可以采用阈值,以便在确定数据值被认为是异常之前确定所需的偏差程度。 阈值可以由系统和/或用户经由用户界面动态地和/或静态地提供。 另外,本发明从顶级数据的角度向用户提供了检测到的异常的类型和位置的指示。