公开(公告)号：US09378079B2

公开(公告)日：2016-06-28

申请号：US14475550

申请日：2014-09-02

Applicant: Microsoft Corporation

Inventor： Olga Ivanova ,  Smita Ojha ,  Alexandre de Baynast ,  Markus Cozowicz ,  Ulrich Pinsdorf ,  Yi Wang ,  Philipp Kranen ,  Venkat Narayanan

IPC: G06F11/00 ,  G06F11/07 ,  G06F11/30 ,  G06N99/00

CPC classification number: G06F11/0751 ,  G06F11/008 ,  G06F11/0709 ,  G06F11/3072 ,  G06F11/3438 ,  G06F2201/81 ,  G06F2201/88 ,  G06N7/005 ,  G06N99/005

Abstract: Anomalies detection in error signals of a cloud based service is provided. An application such as an analysis application identifies a machine learning algorithm that matches error signals of components of a cloud based service. A periodic pattern from the error signals is removed with the machine learning algorithm to filter the periodic pattern from an error count in the error signals. The error signals are processed with the machine learning algorithm to detect one or more anomalies with the components. The machine learning algorithm is updated while processing new data to detect new patterns.

Abstract translation: 提供了基于云服务的错误信号中的异常检测。 诸如分析应用程序的应用程序识别与基于云的服务的组件的错误信号相匹配的机器学习算法。 使用机器学习算法去除误差信号的周期性模式，以从误差信号中的误差计数中滤除周期性模式。 误差信号用机器学习算法进行处理，以检测组件的一个或多个异常。 在处理新数据以检测新模式时更新机器学习算法。

公开(公告)号：US20160062815A1

公开(公告)日：2016-03-03

申请号：US14475550

申请日：2014-09-02

Applicant: Microsoft Corporation

Inventor： Olga Ivanova ,  Smita Ojha ,  Alexandre de Baynast ,  Markus Cozowicz ,  Ulrich Pinsdorf ,  Yi Wang ,  Philipp Kranen ,  Venkat Narayanan

IPC: G06F11/07 ,  G06N99/00 ,  G06F11/30

CPC classification number: G06F11/0751 ,  G06F11/008 ,  G06F11/0709 ,  G06F11/3072 ,  G06F11/3438 ,  G06F2201/81 ,  G06F2201/88 ,  G06N7/005 ,  G06N99/005

Abstract: Anomalies detection in error signals of a cloud based service is provided. An application such as an analysis application identifies a machine learning algorithm that matches error signals of components of a cloud based service. A periodic pattern from the error signals is removed with the machine learning algorithm to filter the periodic pattern from an error count in the error signals. The error signals are processed with the machine learning algorithm to detect one or more anomalies with the components. The machine learning algorithm is updated while processing new data to detect new patterns.

Abstract translation: 提供了基于云服务的错误信号中的异常检测。 诸如分析应用程序的应用程序识别与基于云的服务的组件的错误信号相匹配的机器学习算法。 使用机器学习算法去除误差信号的周期性模式，以从误差信号中的误差计数中滤除周期性模式。 误差信号用机器学习算法进行处理，以检测组件的一个或多个异常。 在处理新数据以检测新模式时更新机器学习算法。