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    Generating database cluster health alerts using machine learning
    3.
    发明授权
    Generating database cluster health alerts using machine learning 有权

    公开(公告)号：US10373065B2

    公开(公告)日：2019-08-06

    申请号：US13791669

    申请日：2013-03-08

    Applicant: Oracle International Corporation

    Inventor： Yaser I. Suleiman ,  Michael Zoll ,  Angelo Pruscino

    IPC: G06F11/00 ,  G06N20/00

    Abstract: A method, system, and computer program product for generating database cluster health alerts using machine learning. A first database cluster known to be operating normally is measured and modeled using machine learning techniques. A second database cluster is measured and compared to the learned model. More specifically, the method collects a first set of empirically-measured variables of a first database cluster, and using the first set of empirically-measured variables a mathematical behavior predictor model is generated. Then, after collecting a second set of empirically-measured variables of a second database cluster over a plurality of second time periods, the mathematical behavior predictor model classifies the observed behavior. The classified behavior might be deemed to be normal behavior, or some form of abnormal behavior. The method forms and report alerts when the classification deemed to be anomalous behavior, or fault behavior. A Bayesian belief network predicts the likelihood of continued anomalous behavior.

    Analyzing database cluster behavior by transforming discrete time series measurements
    5.
    发明授权
    Analyzing database cluster behavior by transforming discrete time series measurements 有权
    通过转换离散时间序列测量来分析数据库集群行为

    公开(公告)号：US09424288B2

    公开(公告)日：2016-08-23

    申请号：US13791651

    申请日：2013-03-08

    Applicant: Oracle International Corporation

    Inventor： Yaser I. Suleiman ,  Michael Zoll ,  Angelo Pruscino

    IPC: G06F17/30

    CPC classification number: G06F17/30306 ,  G06F17/30572 ,  G06F17/30716

    Abstract: A method, system, and computer program product for analyzing performance of a database cluster. Disclosed are techniques for analyzing performance of components of a database cluster by transforming many discrete event measurements into a time series to identify dominant signals. The method embodiment commences by sampling the database cluster to produce a set of timestamped events, then pre-processing the timestamped events by tagging at least some of the timestamped events with a semantic tag drawn from a semantic dictionary and formatting the set of timestamped events into a time series where a time series entry comprises a time indication and a plurality of values corresponding to signal state values. Further techniques are disclosed for identifying certain signals from the time series to which is applied various statistical measurement criteria in order to isolate a set of candidate signals which are then used to identify indicative causes of database cluster behavior.

    Abstract translation: 一种用于分析数据库集群性能的方法，系统和计算机程序产品。 公开的是通过将许多离散事件测量转换成时间序列来识别主要信号来分析数据库簇的组件的性能的技术。 该方法实施例开始于对数据库集群进行采样以产生一组时间戳事件，然后通过使用从语义字典中绘制的语义标签标记至少一些时间戳事件来预处理时间戳事件，并将该时间戳事件格式化 时间序列，其中时间序列条目包括对应于信号状态值的时间指示和多个值。 公开了用于从时间序列中识别某些信号的其它技术，其中应用了各种统计测量标准，以隔离一组候选信号，然后这些候选信号被用于识别数据库集群行为的指示性原因。

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