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公开(公告)号：US10740310B2
公开(公告)日：2020-08-11
申请号：US15925427
申请日：2018-03-19
Applicant: Oracle International Corporation
Inventor： Dieter Gawlick , Kenny C. Gross , Zhen Hua Liu , Adel Ghoneimy
IPC: G06F17/00 , G06F16/22 , G06N5/04 , G06N20/00 , G06F16/174 , G06F16/23 , G06F17/40 , G06F11/30 , G06F11/00
Abstract: The disclosed embodiments relate to a system that preprocesses sensor data to facilitate prognostic-surveillance operations. During operation, the system obtains training data from sensors in a monitored system during operation of the monitored system, wherein the training data comprises time-series data sampled from signals produced by the sensors. The system also obtains functional requirements for the prognostic-surveillance operations. Next, the system performs the prognostic-surveillance operations on the training data and determines whether the prognostic-surveillance operations meet the functional requirements when tested on non-training data. If the prognostic-surveillance operations do not meet the functional requirements, the system iteratively applies one or more preprocessing operations to the training data in order of increasing computational cost until the functional requirements are met.
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公开(公告)号：US20190286725A1
公开(公告)日：2019-09-19
申请号：US15925427
申请日：2018-03-19
Applicant: Oracle International Corporation
Inventor： Dieter Gawlick , Kenny C. Gross , Zhen Hua Liu , Adel Ghoneimy
Abstract: The disclosed embodiments relate to a system that preprocesses sensor data to facilitate prognostic-surveillance operations. During operation, the system obtains training data from sensors in a monitored system during operation of the monitored system, wherein the training data comprises time-series data sampled from signals produced by the sensors. The system also obtains functional requirements for the prognostic-surveillance operations. Next, the system performs the prognostic-surveillance operations on the training data and determines whether the prognostic-surveillance operations meet the functional requirements when tested on non-training data. If the prognostic-surveillance operations do not meet the functional requirements, the system iteratively applies one or more preprocessing operations to the training data in order of increasing computational cost until the functional requirements are met.
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公开(公告)号：US10740358B2
公开(公告)日：2020-08-11
申请号：US14665171
申请日：2015-03-23
Applicant: Oracle International Corporation
Inventor： Eric S. Chan , Dieter Gawlick , Adel Ghoneimy , Zhen Hua Liu
Abstract: Embodiments of the invention provide systems and methods for managing and processing large amounts of complex and high-velocity data by capturing and extracting high-value data from low value data using big data and related technologies. Illustrative database systems described herein may collect and process data while extracting or generating high-value data. The high-value data may be handled by databases providing functions such as multi-temporality, provenance, flashback, and registered queries. In some examples, computing models and system may be implemented to combine knowledge and process management aspects with the near real-time data processing frameworks in a data-driven situation aware computing system.
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4.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
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。
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5.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
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。
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Patent Agency Ranking
6.发明申请KNOWLEDGE INTENSIVE DATA MANAGEMENT SYSTEM FOR BUSINESS PROCESS AND CASE MANAGEMENT 有权了解企业流程和案例管理的强化数据管理系统公开(公告)号：US20140310285A1
公开(公告)日：2014-10-16
申请号：US14109651
申请日：2013-12-17
Applicant: Oracle International Corporation
Inventor： Eric S. Chan , Dieter Gawlick , Adel Ghoneimy , Zhen Hua Liu
IPC: G06F17/30
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。
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公开(公告)号：US11468098B2
公开(公告)日：2022-10-11
申请号：US16917468
申请日：2020-06-30
Applicant: Oracle International Corporation
Inventor： Eric S. Chan , Dieter Gawlick , Adel Ghoneimy , Zhen Hua Liu
Abstract: Embodiments of the invention provide systems and methods for managing and processing large amounts of complex and high-velocity data by capturing and extracting high-value data from low value data using big data and related technologies. Illustrative database systems described herein may collect and process data while extracting or generating high-value data. The high-value data may be handled by databases providing functions such as multi-temporality, provenance, flashback, and registered queries. In some examples, computing models and system may be implemented to combine knowledge and process management aspects with the near real-time data processing frameworks in a data-driven situation aware computing system.
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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.
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9.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
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。
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10.发明授权Knowledge intensive data management system for business process and case management 有权知识密集型数据管理系统，用于业务流程和案例管理公开(公告)号：US09330119B2
公开(公告)日：2016-05-03
申请号：US14109651
申请日：2013-12-17
Applicant: Oracle International Corporation
Inventor： Eric S. Chan , Dieter Gawlick , Adel Ghoneimy , Zhen Hua Liu
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。
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