中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

4 records (took 0.01 seconds)

    Log Event Summarization for Distributed Server System
    1.
    发明申请
    Log Event Summarization for Distributed Server System 审中-公开

    公开(公告)号：US20170220663A1

    公开(公告)日：2017-08-03

    申请号：US15011022

    申请日：2016-01-29

    Applicant: AppDynamics, Inc.

    Inventor： Yuchen Zhao ,  Arjun Iyer

    IPC: G06F17/30

    CPC classification number: G06F17/30598 ,  G06F11/30 ,  G06F17/30368 ,  G06F17/30705

    Abstract: Clusters of log lines are identified based on log line templates. The log line templates are based on a punctuality pattern for a log line. Clusters of log lines that match each punctuality pattern can be identified based on comparisons between the log lines. The comparison may determine the similarity of the log lines and ultimately identify whether the log lines are close enough to be clustered. The comparison may be based on generated n-grams for the log lines and performing a hash on the n-grams. The resulting cluster information may be communicated to a user in an interface.

    Time series metric data modeling and prediction
    2.
    发明授权
    Time series metric data modeling and prediction 有权
    时间序列量度数据建模与预测

    公开(公告)号：US09323599B1

    公开(公告)日：2016-04-26

    申请号：US14814815

    申请日：2015-07-31

    Applicant: AppDynamics, Inc.

    Inventor： Arjun Iyer ,  Yuchen Zhao

    IPC: G06F11/00 ,  G06F11/07

    CPC classification number: G06F11/079 ,  G06F9/45508 ,  G06F11/0706 ,  G06F11/0751 ,  G06F11/30 ,  G06F11/3452 ,  G06F2201/81 ,  G06F2201/875 ,  G06N7/005

    Abstract: A system that utilizes a plurality of time series of metric data to more accurately detect anomalies and model and predict metric values. Streams of time series metric data are processed to generate a set of independent metrics. In some instances, the present system may automatically analyze thousands of real-time streams. Advanced machine learning and statistical techniques are used to automatically find anomalies and outliers from the independent metrics by learning latent and hidden patterns in the metrics. The trends of each metric may also be analyzed and the trends for each characteristic may be learned. The system can automatically detect latent and hidden patterns of metrics including weekly, daily, holiday and other application specific patterns. Anomaly detection is important to maintaining system health and predicted values are important for customers to monitor and make planning and decisions in a principled and quantitative way.

    Abstract translation: 利用多个时间序列度量数据来更精确地检测异常并建模和预测度量值的系统。 处理时间序列量度数据流以生成一组独立度量。 在某些情况下，本系统可以自动分析数千个实时流。 高级机器学习和统计技术用于通过学习潜在和隐藏的度量标准，从独立度量中自动找出异常值和异常值。 还可以分析每个度量的趋势，并且可以了解每个特征的趋势。 该系统可以自动检测包括每周，每日，假期和其他应用程序特定模式的潜在和隐藏的度量模式。 异常检测对维护系统健康至关重要，预测值对客户来说是重要的，以原则和定量的方式监控和制定规划和决策。

    TIME SERIES METRIC DATA MODELING AND PREDICTION
    4.
    发明申请
    TIME SERIES METRIC DATA MODELING AND PREDICTION 审中-公开
    时间系列公制数据建模与预测

    公开(公告)号：US20170031744A1

    公开(公告)日：2017-02-02

    申请号：US15134263

    申请日：2016-04-20

    Applicant: AppDynamics, Inc.

    Inventor： Arjun Iyer ,  Yuchen Zhao

    IPC: G06F11/07 ,  G06N7/00 ,  G06F9/455

    CPC classification number: G06F11/079 ,  G06F9/45508 ,  G06F11/0706 ,  G06F11/0751 ,  G06F11/30 ,  G06F11/3452 ,  G06F2201/81 ,  G06F2201/875 ,  G06N7/005

    Abstract: A system that utilizes a plurality of time series of metric data to more accurately detect anomalies and model and predict metric values. Streams of time series metric data are processed to generate a set of independent metrics. In some instances, the present system may automatically analyze thousands of real-time streams. Advanced machine learning and statistical techniques are used to automatically find anomalies and outliers from the independent metrics by learning latent and hidden patterns in the metrics. The trends of each metric may also be analyzed and the trends for each characteristic may be learned. The system can automatically detect latent and hidden patterns of metrics including weekly, daily, holiday and other application specific patterns. Anomaly detection is important to maintaining system health and predicted values are important for customers to monitor and make planning and decisions in a principled and quantitative way.

    Abstract translation: 利用多个时间序列度量数据来更精确地检测异常并建模和预测度量值的系统。 处理时间序列量度数据流以生成一组独立度量。 在某些情况下，本系统可以自动分析数千个实时流。 高级机器学习和统计技术用于通过学习潜在和隐藏的度量标准，从独立度量中自动找出异常值和异常值。 还可以分析每个度量的趋势，并且可以了解每个特征的趋势。 该系统可以自动检测包括每周，每日，假期和其他应用程序特定模式的潜在和隐藏的度量模式。 异常检测对维护系统健康至关重要，预测值对客户来说是重要的，以原则和定量的方式监控和制定规划和决策。

Patent Agency Ranking