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1. 发明申请

WO2004010305A2 SYSTEM AND METHOD FOR CACHING DATA FOR A MOBILE APPLICATION 审中-公开
标题翻译：用于为移动应用程序缓存数据的系统和方法
公开(公告)号：WO2004010305A2
公开(公告)日：2004-01-29
申请号：PCT/US2003/020050
申请日：2003-06-25
申请人： ORACLE INTERNATIONAL CORPORATION
发明人： AHAD, Rafiul , CHIANG, Jerry , KIBIREV, Oleg , PRAKASH, Ravindra , REHMAN, Samuelson
IPC分类号： G06F12/08
CPC分类号： G06F17/3048 , Y10S707/99942 , Y10S707/99945 , Y10S707/99953
摘要： A cache table comprises a set of access parameters and a set of data columns. One or more instances of a cache table are stored on a mobile computing device. Each instance includes an argument (a unique set of values for the access parameters) and a result set (a set of values for the data columns). Thus, each result in a result set comprises the argument and corresponding column values. Cached result sets have specified periods of validity, and may or may not be usable after becoming invalid. Valid cached data may be used regardless of whether a connection is available to a data source (e.g., data or application server). Invalid data may be used for a period of time if no connection is available to the data source. Data in a cache table may be selectively updated from a data source without synchronizing the entire local database.
摘要翻译：缓存表包含一组访问参数和一组数据列。高速缓存表的一个或多个实例存储在移动计算设备上。每个实例都包含一个参数（一组唯一的访问参数值）和一个结果集（数据列的一组值）。因此，结果集中的每个结果都包含参数和相应的列值。缓存的结果集具有指定的有效期，并且在无效后可能可用也可能不可用。无论连接是否可用于数据源（例如数据或应用程序服务器），都可以使用有效的缓存数据。如果没有连接可用于数据源，则可能会在一段时间内使用无效数据。高速缓存表中的数据可以选择性地从数据源更新，而无需同步整个本地数据库。

2. 发明申请

WO2014168981A1 SEASONAL TRENDING, FORECASTING, ANOMALY DETECTION, AND ENDPOINT PREDICTION OF JAVA HEAP USAGE 审中-公开
标题翻译： 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：WO2014168981A1
公开(公告)日：2014-10-16
申请号：PCT/US2014/033385
申请日：2014-04-08
申请人： ORACLE INTERNATIONAL CORPORATION
发明人： CHAN, Eric S. , AHAD, Rafiul , GHONEIMY, Adel , SANTOS, Adriano Covello
IPC分类号： G06F11/00 , G06F11/07
CPC分类号： 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
摘要： 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.
摘要翻译：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归类为产生信息的事实，如记忆泄漏，车队效应或其他问题。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。

3. 发明申请

WO2014169056A1 PREDICTIVE DIAGNOSIS OF SLA VIOLATIONS IN CLOUD SERVICES BY SEASONAL TRENDING AND FORECASTING WITH THREAD INTENSITY ANALYTICS 审中-公开
标题翻译：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：WO2014169056A1
公开(公告)日：2014-10-16
申请号：PCT/US2014/033530
申请日：2014-04-09
申请人： ORACLE INTERNATIONAL CORPORATION
发明人： CHAN, Eric S. , AHAD, Rafiul , GHONEIMY, Adel , SANTOS, Adriano Covello
IPC分类号： G06F11/00 , G06F11/07
CPC分类号： 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
摘要： 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.
摘要翻译：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。

4. 发明申请

WO2016191639A1 AUTOMATIC ANOMALY DETECTION AND RESOLUTION SYSTEM 审中-公开
标题翻译：自动异常检测和分辨率系统
公开(公告)号：WO2016191639A1
公开(公告)日：2016-12-01
申请号：PCT/US2016/034536
申请日：2016-05-27
申请人： ORACLE INTERNATIONAL CORPORATION
发明人： AHAD, Rafiul
IPC分类号： G06F11/34 , G06F11/30 , G06F11/07
CPC分类号： G06F11/0793 , G06F11/0709 , G06F11/0751 , G06F11/079 , G06F11/301 , G06F11/3048 , G06F11/3072 , G06F11/3409 , G06F11/3495 , H04L41/5003 , H04L41/5038 , H04L67/02
摘要： An anomaly detection and resolution system (ADRS) is disclosed for detecting and resolving anomalies in computing environments. The ADRS may be implemented using an anomaly classification system defining different types of anomalies (e.g., a defined anomaly and an undefined anomaly). A defined anomaly may be based on bounds (fixed or seasonal) on any metric to be monitored. An anomaly detection and resolution component (ADRC) may be implemented in each component defining a service in a computing system. An ADRC may be configured to detect and attempt to resolve an anomaly locally. If the anomaly event for an anomaly can be resolved in the component, the ADRC may communicate the anomaly event to an ADRC of a parent component, if one exists. Each ADRC in a component may be configured to locally handle specific types of anomalies to reduce communication time and resource usage for resolving anomalies.
摘要翻译：公开了一种异常检测和解决系统（ADRS），用于检测和解决计算环境中的异常。可以使用定义不同类型的异常（例如，定义的异常和未定义的异常）的异常分类系统来实现ADRS。定义的异常可以基于要监视的任何度量的边界（固定或季节性）。可以在定义计算系统中的服务的每个组件中实现异常检测和解决部件（ADRC）。 ADRC可能被配置为检测并尝试在本地解决异常。如果异常异常事件可以在组件中解决，则ADRC可以将异常事件传达给父组件的ADRC（如果存在）。组件中的每个ADRC可以被配置为本地处理特定类型的异常，以减少通信时间和资源使用以解决异常。

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