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    KNOWLEDGE-INTENSIVE DATA PROCESSING SYSTEM
    11.
    发明申请
    KNOWLEDGE-INTENSIVE DATA PROCESSING SYSTEM 审中-公开
    知识密集型数据处理系统

    公开(公告)号：US20150254330A1

    公开(公告)日：2015-09-10

    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

    IPC: G06F17/30

    CPC classification number: G06F16/285 ,  G06F11/3006 ,  G06F11/3442 ,  G06F11/3452 ,  G06F11/3466 ,  G06F16/219 ,  G06F16/273 ,  G06F16/9535 ,  G06F2201/84 ,  G06F2201/865

    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.

    Abstract translation: 本发明的实施例提供了通过使用大数据和相关技术从低值数据中捕获和提取高值数据来管理和处理大量复杂和高速数据的系统和方法。 本文描述的说明性数据库系统可以在提取或生成高价值数据的同时收集和处理数据。 高价值数据可以由提供多时间，来源，闪回和注册查询等功能的数据库来处理。 在一些示例中，可以实现计算模型和系统以将知识和过程管理方面与数据驱动情境感知计算系统中的近实时数据处理框架相结合。

    Seasonal trending, forecasting, anomaly detection, and endpoint prediction of java heap usage
    12.
    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.

    SEASONAL TRENDING, FORECASTING, ANOMALY DETECTION, AND ENDPOINT PREDICTION OF JAVA HEAP USAGE
    14.
    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：US20140310235A1

    公开(公告)日：2014-10-16

    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

    CPC classification number: G06F17/30312 ,  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 ,  G06F17/30197 ,  G06F17/30289 ,  G06F17/30598 ,  G06F2201/865 ,  G06F2201/88 ,  H04L41/5009

    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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