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    • 2. 发明申请
    • Automatic discovery and maintenance of business processes in web services and enterprise development environments
    • 在Web服务和企业开发环境中自动发现和维护业务流程
    • US20060229925A1
    • 2006-10-12
    • US11102023
    • 2005-04-08
    • Nanchariah ChalasaniMandar JogNeeraj JoshiBalan Subramanian
    • Nanchariah ChalasaniMandar JogNeeraj JoshiBalan Subramanian
    • G05B19/418
    • G06Q10/06G06Q10/063
    • A method, an apparatus, and computer instructions are provided for automatic discovery and maintenance of business processes in Web services and enterprise development environments. A set of agents are deployed to a set of enterprise containers to collect user function calls and report events to a central business workflow language generator. An event handler collects the events reported and an event grouper groups the events into a single workflow activity. An event correlation engine correlates activities to business workflow language constructions by using a temporal condition handler and a collection counting handler. The temporal condition handler maps activities that are in sequence, in parallel, and are repetitive. The collection counting handler counts the number of times an event is handled. Events and Rules for these events are written on the system based on a variety of business constructs that are particular to given business process definition language. A business workflow language generator then generates a business workflow and a workflow presents the business workflow in a user interface.
    • 提供了一种方法,设备和计算机指令,用于在Web服务和企业开发环境中自动发现和维护业务流程。 一组代理被部署到一组企业容器以收集用户函数调用并将事件报告给中央业务工作流语言生成器。 事件处理程序收集所报告的事件,事件分组器将事件分组为单个工作流活动。 事件关联引擎通过使用时间条件处理程序和收集计数处理程序将活动与业务工作流语言结构相关联。 时间条件处理程序按顺序并行地映射活动,并且是重复的。 收集计数处理程序计算事件处理的次数。 这些事件的事件和规则基于特定于业务流程定义语言的各种业务结构写在系统上。 业务工作流语言生成器然后生成业务工作流,并且工作流在用户界面中呈现业务工作流。
    • 3. 发明申请
    • Causal ladder mechanism for proactive problem determination, avoidance and recovery
    • 主动问题确定,避免和恢复的因果梯度机制
    • US20080091384A1
    • 2008-04-17
    • US11894889
    • 2007-08-21
    • Balan SubramanianNanchariah ChalasaniJaved RahmanAjamu Wesley
    • Balan SubramanianNanchariah ChalasaniJaved RahmanAjamu Wesley
    • G06F15/00
    • G06F11/008
    • A plurality of causal ladder is assembled in advance from component system events taken from previous system failures. The ladders classify the various transitions the system goes through from one set of observed states to another in multiple stages representing issues of differing urgency, importance and need for remediation. These stages are used at runtime to determine the criticality of any abnormal system activity and to accurately predict the component failure prior to the system crashing. Each ladder comprises a plurality of elevated stages representing criticality of the problem. At runtime, the causal ladder engine correlates real-time events received from the system to stages of one or more pre-constructed causal ladders and identifies a probable problem (and/or the faulty component) from the corresponding causal ladder. The causal ladder engine also determines the stage of the problem from event occurrences. At each stage, a different potential solution is identified for the problem.
    • 从先前的系统故障中提取的组件系统事件提前组装了多个因果梯。 梯子将系统所经历的各种过渡从多个观察状态分类到另一个阶段,代表不同紧迫性,重要性和需要进行修复的问题。 这些阶段在运行时用于确定任何异常系统活动的关键性,并在系统崩溃之前准确预测组件故障。 每个梯子包括代表问题的关键性的多个提升阶段。 在运行时,因果梯形图引擎将从系统接收的实时事件与一个或多个预构造因子梯级的阶段相关联,并从相应的因果梯度中识别可能的问题(和/或有缺陷的组件)。 因果梯形图引擎还确定事件发生时的问题的阶段。 在每个阶段,针对该问题确定了不同的潜在解决方案。
    • 4. 发明申请
    • Using stochastic models to diagnose and predict complex system problems
    • 使用随机模型来诊断和预测复杂的系统问题
    • US20070265811A1
    • 2007-11-15
    • US11433822
    • 2006-05-12
    • Nanchariah ChalasaniAjamu WesleyJaved RahmanBalan Subramanian
    • Nanchariah ChalasaniAjamu WesleyJaved RahmanBalan Subramanian
    • G06F17/10
    • G06Q10/04
    • A plurality of stochastic models is built that predict the probabilities of state transitions for components in a complex system. The models are trained using output observations from the system at runtime. The overall state and health of the system can be determined at runtime by analyzing the distribution of current component states among the possible states. Subsequent to a low level component failure, the state transition probability stochastic model for the failed component can be analyzed by uncovering the previous states at N time intervals prior to the failure. The resulting state transition path for the component can be analyzed for the causes of the failure. Additionally, component failures resulting from the failure, or worsening state transition, in other components can be diagnosed by uncovering the previous states at the N times prior to the failure for multiple components in the system and then analyzing the state transition paths for correlations to the failed component. Additionally, transitions to worsening states can be predicted using an action matrix. The action matrix is created beforehand using state information and transition probabilities derived from a component's stochastic model. The action matrix is populated probabilities of state transitions at a current state for given actions. At runtime, when an action is requested of a component, the probability of the component transitioning to a worsening state by performing the action can be assessed from the action matrix by using the current state of the component (available from the stochastic model).
    • 建立了多个随机模型来预测复杂系统中组件的状态转换概率。 在运行时,使用系统的输出观测来训练模型。 通过分析可能状态之间的当前组件状态的分布,可以在运行时确定系统的总体状态和健康状况。 在低级组件故障之后,可以通过在故障之前以N个时间间隔发现先前的状态来分析故障组件的状态转移概率随机模型。 可以分析组件的结果状态转换路径的故障原因。 另外,可以通过在系统中的多个组件的故障之前的N次发现先前的状态来诊断其他组件中的故障或状态转换的恶化,然后分析与状态转换路径相关的状态转换路径 组件失效 另外,可以使用动作矩阵来预测到恶化状态的转变。 使用从组件的随机模型导出的状态信息和转移概率预先创建动作矩阵。 动作矩阵是针对给定动作的当前状态的状态转换的填充概率。 在运行时,当请求一个组件的动作时,可以通过使用组件的当前状态(随机模型可用)从动作矩阵来评估组件转变到恶化状态的可能性。