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    PREDICTIVE DIAGNOSIS OF SLA VIOLATIONS IN CLOUD SERVICES BY SEASONAL TRENDING AND FORECASTING WITH THREAD INTENSITY ANALYTICS
    3.
    发明申请
    PREDICTIVE DIAGNOSIS OF SLA VIOLATIONS IN CLOUD SERVICES BY SEASONAL TRENDING AND FORECASTING WITH THREAD INTENSITY ANALYTICS 有权
    通过季节性变化预测云服务中的SLA违规和预测强度分析的预测性诊断

    公开(公告)号：US20170012834A1

    公开(公告)日：2017-01-12

    申请号：US15275035

    申请日：2016-09-23

    Applicant: ORACLE INTERNATIONAL CORPORATION

    Inventor： Eric S. Chan ,  Rafiul Ahad ,  Adel Ghoneimy ,  Adriano Covello Santos

    IPC: H04L12/24 ,  G06F17/30 ,  G06F9/48

    CPC classification number: H04L41/5009 ,  G06F9/4881 ,  G06F11/008 ,  G06F11/0715 ,  G06F11/0718 ,  G06F11/0751 ,  G06F11/0778 ,  G06F11/079 ,  G06F11/3003 ,  G06F11/3037 ,  G06F11/34 ,  G06F11/3409 ,  G06F11/3419 ,  G06F11/3452 ,  G06F11/3471 ,  G06F17/30289 ,  G06F17/30598 ,  G06F2201/865 ,  G06F2201/88

    Abstract: Data can be categorized into facts, information, hypothesis, and directives. Activities that generate certain categories of data based on other categories of data through the application of knowledge which can be categorized into classifications, assessments, resolutions, and enactments. Activities can be driven by a Classification-Assessment-Resolution-Enactment (CARE) control engine. The CARE control and these categorizations can be used to enhance a multitude of systems, for example diagnostic system, such as through historical record keeping, machine learning, and automation. Such a diagnostic system can include a system that forecasts computing system failures based on the application of knowledge to system vital signs such as thread or stack segment intensity and memory heap usage. These vital signs are facts that can be classified to produce information such as memory leaks, convoy effects, or other problems. Classification can involve the automatic generation of classes, states, observations, predictions, norms, objectives, and the processing of sample intervals having irregular durations.

    Abstract translation: 数据可以分为事实，信息，假设和指令。 通过应用可分类到分类，评估，决议和制定的知识，基于其他类别的数据生成某些类别的数据的活动。 活动可以通过分类评估 - 分配制度（CARE）控制引擎来驱动。 CARE控制和这些分类可用于增强大量系统，例如诊断系统，例如通过历史记录保存，机器学习和自动化。 这样的诊断系统可以包括基于将知识应用于诸如线程或堆栈段强度和内存堆使用的系统生命体征来预测计算系统故障的系统。 这些生命体征是可以分类以产生诸如记忆泄漏，车队效应或其他问题的信息的事实。 分类可以涉及自动生成类，状态，观察，预测，规范，目标以及具有不规则持续时间的采样间隔的处理。

    SEASONAL TRENDING, FORECASTING, ANOMALY DETECTION, AND ENDPOINT PREDICTION OF JAVA HEAP USAGE
    4.
    发明申请
    SEASONAL TRENDING, FORECASTING, ANOMALY DETECTION, AND ENDPOINT PREDICTION OF JAVA HEAP USAGE 审中-公开
    JAVA HEAP使用的季节性变化，预测，异常检测和端点预测

    公开(公告)号：US20150234869A1

    公开(公告)日：2015-08-20

    申请号：US14705304

    申请日：2015-05-06

    Applicant: Oracle International Corporation

    Inventor： Eric S. Chan ,  Rafiul Ahad ,  Adel Ghoneimy ,  Adriano Covello Santos

    IPC: G06F17/30 ,  G06F9/455

    CPC classification number: H04L41/5009 ,  G06F9/45533 ,  G06F9/4881 ,  G06F11/008 ,  G06F11/0715 ,  G06F11/0718 ,  G06F11/0751 ,  G06F11/0778 ,  G06F11/079 ,  G06F11/3003 ,  G06F11/3037 ,  G06F11/34 ,  G06F11/3409 ,  G06F11/3419 ,  G06F11/3452 ,  G06F11/3471 ,  G06F16/1824 ,  G06F16/21 ,  G06F16/22 ,  G06F16/285 ,  G06F2201/865 ,  G06F2201/88

    Abstract: Data can be categorized into facts, information, hypothesis, and directives. Activities that generate certain categories of data based on other categories of data through the application of knowledge which can be categorized into classifications, assessments, resolutions, and enactments. Activities can be driven by a Classification-Assessment-Resolution-Enactment (CARE) control engine. The CARE control and these categorizations can be used to enhance a multitude of systems, for example diagnostic system, such as through historical record keeping, machine learning, and automation. Such a diagnostic system can include a system that forecasts computing system failures based on the application of knowledge to system vital signs such as thread or stack segment intensity and memory heap usage. These vital signs are facts that can be classified to produce information such as memory leaks, convoy effects, or other problems. Classification can involve the automatic generation of classes, states, observations, predictions, norms, objectives, and the processing of sample intervals having irregular durations.

    Abstract translation: 数据可以分为事实，信息，假设和指令。 通过应用可分类到分类，评估，决议和制定的知识，基于其他类别的数据生成某些类别的数据的活动。 活动可以通过分类评估 - 分配制度（CARE）控制引擎来驱动。 CARE控制和这些分类可用于增强大量系统，例如诊断系统，例如通过历史记录保存，机器学习和自动化。 这样的诊断系统可以包括基于将知识应用于诸如线程或堆栈段强度和内存堆使用的系统生命体征来预测计算系统故障的系统。 这些生命体征是可以分类以产生诸如记忆泄漏，车队效应或其他问题的信息的事实。 分类可以涉及自动生成类，状态，观察，预测，规范，目标以及具有不规则持续时间的采样间隔的处理。

    Seasonal trending, forecasting, anomaly detection, and endpoint prediction of thread intensity statistics
    5.
    发明授权
    Seasonal trending, forecasting, anomaly detection, and endpoint prediction of thread intensity statistics 有权

    公开(公告)号：US10333798B2

    公开(公告)日：2019-06-25

    申请号：US14705304

    申请日：2015-05-06

    Applicant: Oracle International Corporation

    Inventor： Eric S. Chan ,  Rafiul Ahad ,  Adel Ghoneimy ,  Adriano Covello Santos

    IPC: G06F11/00 ,  H04L12/24 ,  G06F16/21 ,  G06F16/22 ,  G06F16/28 ,  G06F16/182 ,  G06F9/48 ,  G06F11/07 ,  G06F11/34 ,  G06F11/30 ,  G06F9/455

    Abstract: Data can be categorized into facts, information, hypothesis, and directives. Activities that generate certain categories of data based on other categories of data through the application of knowledge which can be categorized into classifications, assessments, resolutions, and enactments. Activities can be driven by a Classification-Assessment-Resolution-Enactment (CARE) control engine. The CARE control and these categorizations can be used to enhance a multitude of systems, for example diagnostic system, such as through historical record keeping, machine learning, and automation. Such a diagnostic system can include a system that forecasts computing system failures based on the application of knowledge to system vital signs such as thread or stack segment intensity and memory heap usage. These vital signs are facts that can be classified to produce information such as memory leaks, convoy effects, or other problems. Classification can involve the automatic generation of classes, states, observations, predictions, norms, objectives, and the processing of sample intervals having irregular durations.

    Predictive diagnosis of SLA violations in cloud services by seasonal trending and forecasting with thread intensity analytics
    6.
    发明授权
    Predictive diagnosis of SLA violations in cloud services by seasonal trending and forecasting with thread intensity analytics 有权
    通过线性强度分析，通过季节性趋势和预测，预测SLA违规云服务

    公开(公告)号：US09495395B2

    公开(公告)日：2016-11-15

    申请号：US14109578

    申请日：2013-12-17

    Applicant: Oracle International Corporation

    Inventor： Eric S. Chan ,  Rafiul Ahad ,  Adel Ghoneimy ,  Adriano Covello Santos

    IPC: G06F9/46 ,  G06F17/30 ,  G06F9/48 ,  G06F11/00 ,  G06F11/07 ,  G06F11/34

    CPC classification number: H04L41/5009 ,  G06F9/4881 ,  G06F11/008 ,  G06F11/0715 ,  G06F11/0718 ,  G06F11/0751 ,  G06F11/0778 ,  G06F11/079 ,  G06F11/3003 ,  G06F11/3037 ,  G06F11/34 ,  G06F11/3409 ,  G06F11/3419 ,  G06F11/3452 ,  G06F11/3471 ,  G06F17/30289 ,  G06F17/30598 ,  G06F2201/865 ,  G06F2201/88

    Abstract: Data can be categorized into facts, information, hypothesis, and directives. Activities that generate certain categories of data based on other categories of data through the application of knowledge which can be categorized into classifications, assessments, resolutions, and enactments. Activities can be driven by a Classification-Assessment-Resolution-Enactment (CARE) control engine. The CARE control and these categorizations can be used to enhance a multitude of systems, for example diagnostic system, such as through historical record keeping, machine learning, and automation. Such a diagnostic system can include a system that forecasts computing system failures based on the application of knowledge to system vital signs such as thread or stack segment intensity and memory heap usage. These vital signs are facts that can be classified to produce information such as memory leaks, convoy effects, or other problems. Classification can involve the automatic generation of classes, states, observations, predictions, norms, objectives, and the processing of sample intervals having irregular durations.

    Abstract translation: 数据可以分为事实，信息，假设和指令。 通过应用可分类到分类，评估，决议和制定的知识，基于其他类别的数据生成某些类别的数据的活动。 活动可以通过分类评估 - 分配制度（CARE）控制引擎来驱动。 CARE控制和这些分类可用于增强大量系统，例如诊断系统，例如通过历史记录保存，机器学习和自动化。 这样的诊断系统可以包括基于将知识应用于诸如线程或堆栈段强度和内存堆使用的系统生命体征来预测计算系统故障的系统。 这些生命体征是可以分类以产生诸如记忆泄漏，车队效应或其他问题的信息的事实。 分类可以涉及自动生成类，状态，观察，预测，规范，目标以及具有不规则持续时间的采样间隔的处理。

    PREDICTIVE DIAGNOSIS OF SLA VIOLATIONS IN CLOUD SERVICES BY SEASONAL TRENDING AND FORECASTING WITH THREAD INTENSITY ANALYTICS
    8.
    发明申请
    PREDICTIVE DIAGNOSIS OF SLA VIOLATIONS IN CLOUD SERVICES BY SEASONAL TRENDING AND FORECASTING WITH THREAD INTENSITY ANALYTICS 有权
    通过季节性变化预测云服务中的SLA违规和预测强度分析的预测性诊断

    公开(公告)号：US20140310714A1

    公开(公告)日：2014-10-16

    申请号：US14109578

    申请日：2013-12-17

    Applicant: Oracle International Corporation

    Inventor： Eric S. Chan ,  Rafiul Ahad ,  Adel Choneimy ,  Adriano Covello Santos

    IPC: G06F9/48

    CPC classification number: H04L41/5009 ,  G06F9/4881 ,  G06F11/008 ,  G06F11/0715 ,  G06F11/0718 ,  G06F11/0751 ,  G06F11/0778 ,  G06F11/079 ,  G06F11/3003 ,  G06F11/3037 ,  G06F11/34 ,  G06F11/3409 ,  G06F11/3419 ,  G06F11/3452 ,  G06F11/3471 ,  G06F17/30289 ,  G06F17/30598 ,  G06F2201/865 ,  G06F2201/88

    Abstract: Data can be categorized into facts, information, hypothesis, and directives. Activities that generate certain categories of data based on other categories of data through the application of knowledge which can be categorized into classifications, assessments, resolutions, and enactments. Activities can be driven by a Classification-Assessment-Resolution-Enactment (CARE) control engine. The CARE control and these categorizations can be used to enhance a multitude of systems, for example diagnostic system, such as through historical record keeping, machine learning, and automation. Such a diagnostic system can include a system that forecasts computing system failures based on the application of knowledge to system vital signs such as thread or stack segment intensity and memory heap usage. These vital signs are facts that can be classified to produce information such as memory leaks, convoy effects, or other problems. Classification can involve the automatic generation of classes, states, observations, predictions, norms, objectives, and the processing of sample intervals having irregular durations.

    Abstract translation: 数据可以分为事实，信息，假设和指令。 通过应用可分类到分类，评估，决议和制定的知识，基于其他类别的数据生成某些类别的数据的活动。 活动可以通过分类评估 - 分配制度（CARE）控制引擎来驱动。 CARE控制和这些分类可用于增强大量系统，例如诊断系统，例如通过历史记录保存，机器学习和自动化。 这样的诊断系统可以包括基于将知识应用于诸如线程或堆栈段强度和内存堆使用的系统生命体征来预测计算系统故障的系统。 这些生命体征是可以分类以产生诸如记忆泄漏，车队效应或其他问题的信息的事实。 分类可以涉及自动生成类，状态，观察，预测，规范，目标以及具有不规则持续时间的采样间隔的处理。

    Seasonal trending, forecasting, anomaly detection, and endpoint prediction of java heap usage
    9.
    发明授权
    Seasonal trending, forecasting, anomaly detection, and endpoint prediction of java heap usage 有权

    公开(公告)号：US10205640B2

    公开(公告)日：2019-02-12

    申请号：US14109546

    申请日：2013-12-17

    Applicant: Oracle International Corporation

    Inventor： Eric S. Chan ,  Rafiul Ahad ,  Adel Ghoneimy ,  Adriano Covello Santos

    IPC: G06F17/30 ,  H04L12/24 ,  G06F9/48 ,  G06F11/00 ,  G06F11/07 ,  G06F11/34 ,  G06F11/30

    Abstract: Data can be categorized into facts, information, hypothesis, and directives. Activities that generate certain categories of data based on other categories of data through the application of knowledge which can be categorized into classifications, assessments, resolutions, and enactments. Activities can be driven by a Classification-Assessment-Resolution-Enactment (CARE) control engine. The CARE control and these categorizations can be used to enhance a multitude of systems, for example diagnostic system, such as through historical record keeping, machine learning, and automation. Such a diagnostic system can include a system that forecasts computing system failures based on the application of knowledge to system vital signs such as thread or stack segment intensity and memory heap usage. These vital signs are facts that can be classified to produce information such as memory leaks, convoy effects, or other problems. Classification can involve the automatic generation of classes, states, observations, predictions, norms, objectives, and the processing of sample intervals having irregular durations.

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