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    ADDING PROTOTYPE INFORMATION INTO PROBABILISTIC MODELS
    11.
    发明申请
    ADDING PROTOTYPE INFORMATION INTO PROBABILISTIC MODELS 有权
    将原型信息添加到概率模型中

    公开(公告)号：US20090076794A1

    公开(公告)日：2009-03-19

    申请号：US11855099

    申请日：2007-09-13

    申请人： Kannan Achan ,  Moises Goldszmidt ,  Lev Ratinov

    发明人： Kannan Achan ,  Moises Goldszmidt ,  Lev Ratinov

    IPC分类号： G06F17/27 ,  G10L15/14 ,  G10L15/18

    CPC分类号： G10L15/142 ,  G06F17/2715 ,  G06K9/6297

    摘要： Mechanisms are disclosed for incorporating prototype information into probabilistic models for automated information processing, mining, and knowledge discovery. Examples of these models include Hidden Markov Models (HMMs), Latent Dirichlet Allocation (LDA) models, and the like. The prototype information injects prior knowledge to such models, thereby rendering them more accurate, effective, and efficient. For instance, in the context of automated word labeling, additional knowledge is encoded into the models by providing a small set of prototypical words for each possible label. The net result is that words in a given corpus are labeled and are therefore in condition to be summarized, identified, classified, clustered, and the like.

    摘要翻译： 公开了将原型信息并入用于自动化信息处理，挖掘和知识发现的概率模型中的机制。 这些模型的示例包括隐马尔可夫模型（HMM），潜在狄利克雷分配（LDA）模型等。 原型信息将先前的知识注入到这些模型中，从而使它们更准确，有效和高效。 例如，在自动化字标识的上下文中，通过为每个可能的标签提供一小组原型字来将附加知识编码到模型中。 最终的结果是，给定语料库中的单词被标记，因此在其中被概括，识别，分类，聚类等等。

    DISTRIBUTED DETECTION WITH DIAGNOSIS
    12.
    发明申请
    DISTRIBUTED DETECTION WITH DIAGNOSIS 审中-公开
    分诊检测与诊断

    公开(公告)号：US20080103729A1

    公开(公告)日：2008-05-01

    申请号：US11554980

    申请日：2006-10-31

    申请人： Paul Barham ,  Richard Black ,  Moises Goldszmidt ,  Rebecca Isaacs ,  John MacCormick ,  Richard Mortier

    发明人： Paul Barham ,  Richard Black ,  Moises Goldszmidt ,  Rebecca Isaacs ,  John MacCormick ,  Richard Mortier

    IPC分类号： G06F19/00 ,  G06F17/40 ,  G06F11/30

    CPC分类号： H04L41/06 ,  H04L41/147 ,  H04L41/16 ,  H04L43/0811

    摘要： Activity models are maintained on a plurality of computers on a network. When a user or a particular activity model at a computer discovers an error, it may query its own activity model to determine a possible source of the error. If it is determined to not be the likely source of the error, the activity model queries the activity models of those computers on the network that it depends on. These activity models may then query the activity models of the computers that their particular host computer depends on and so forth. Ultimately the results of these activity model queries may be used to diagnose the likely source of the error and may be presented to the requesting user as a report.

    摘要翻译： 在网络上的多台计算机上维护活动模型。 当用户或计算机上的特定活动模型发现错误时，它可以查询其自己的活动模型以确定错误的可能来源。 如果确定不是错误的可能来源，则活动模型会查询网络上依赖的那些计算机的活动模型。 然后，这些活动模型可以查询其特定主机依赖的计算机的活动模型等等。 最终，这些活动模型查询的结果可以用于诊断错误的可能来源，并且可以作为报告呈现给请求用户。

    Dynamic activity model of network services
    14.
    发明授权
    Dynamic activity model of network services 有权
    网络服务动态活动模型

    公开(公告)号：US07949745B2

    公开(公告)日：2011-05-24

    申请号：US11554935

    申请日：2006-10-31

    申请人： Paul Barham ,  Richard Black ,  Moises Goldszmidt ,  Rebecca Isaacs ,  John MacCormick ,  Richard Mortier

    发明人： Paul Barham ,  Richard Black ,  Moises Goldszmidt ,  Rebecca Isaacs ,  John MacCormick ,  Richard Mortier

    IPC分类号： G06F15/173

    CPC分类号： H04L41/0645 ,  H04L41/142 ,  H04L41/16

    摘要： An activity model is generated at a computer. The activity model may be generated by monitoring incoming and outgoing data in the computer. The collected data is analyzed to form a graph that describes and predicts what output is generated in response to received input. Later, a window of input and output data is collected from the computer. This collected window of data is used to query the activity model. The graph in the activity model is then used to give the probability that the collected window of data was collected from the computer used to generate the activity model. A high probability indicates that the computer is performing normally, while a low probability indicates that the computer may behaving erratically and there may be a problem with the computer.

    摘要翻译： 在计算机上生成活动模型。 可以通过监视计算机中的传入和传出数据来生成活动模型。 分析所收集的数据以形成描述并预测响应于接收到的输入产生什么输出的图。 之后，从计算机收集输入和输出数据的窗口。 该收集的数据窗口用于查询活动模型。 然后使用活动模型中的图表给出从用于生成活动模型的计算机收集数据收集窗口的概率。 高概率表示计算机正常运行，而低概率表示计算机可能运行不规律，并且计算机可能存在问题。

    Time modulated generative probabilistic models for automated causal discovery that monitors times of packets
    15.
    发明授权
    Time modulated generative probabilistic models for automated causal discovery that monitors times of packets 有权
    用于自动病因发现的时间调制生成概率模型，用于监视数据包的时间

    公开(公告)号：US07895146B2

    公开(公告)日：2011-02-22

    申请号：US11949061

    申请日：2007-12-03

    申请人： Aleksandr Simma ,  Moises Goldszmidt

    发明人： Aleksandr Simma ,  Moises Goldszmidt

    IPC分类号： G06F17/00

    CPC分类号： H04L41/0663 ,  H04L41/142 ,  H04L41/145

    摘要： Dependencies between different channels or different services in a client or server may be determined from the observation of the times of the incoming and outgoing of the packets constituting those channels or services. A probabilistic model may be used to formally characterize these dependencies. The probabilistic model may be used to list the dependencies between input packets and output packets of various channels or services, and may be used to establish the expected strength of the causal relationship between the different events surrounding those channels or services. Parameters of the probabilistic model may be either based on prior knowledge, or may be fit using statistical techniques based on observations about the times of the events of interest. Expected times of occurrence between events may be observed, and dependencies may be determined in accordance with the probabilistic model.

    摘要翻译： 客户端或服务器中的不同信道或不同业务之间的依赖关系可以从对构成这些信道或业务的分组的进入和传出的时间的观察来确定。 概率模型可用于正式表征这些依赖性。 概率模型可以用于列出输入分组和各种信道或服务的输出分组之间的依赖性，并且可以用于建立围绕这些信道或服务的不同事件之间的因果关系的预期强度。 概率模型的参数可以基于现有知识，或者可以使用基于关于感兴趣事件的时间的观察的统计技术来拟合。 可以观察事件之间的预期发生时间，并且依赖性可以根据概率模型来确定。

    Mining Web Logs to Debug Wide-Area Connectivity Problems
    17.
    发明申请
    Mining Web Logs to Debug Wide-Area Connectivity Problems 审中-公开
    挖掘Web日志以调试广域连接问题

    公开(公告)号：US20080209030A1

    公开(公告)日：2008-08-28

    申请号：US11680483

    申请日：2007-02-28

    申请人： Moises Goldszmidt ,  Emre M. Kiciman ,  David A. Maltz ,  John C. Platt

    发明人： Moises Goldszmidt ,  Emre M. Kiciman ,  David A. Maltz ,  John C. Platt

    IPC分类号： G06F15/173

    CPC分类号： H04L41/069 ,  H04L29/06 ,  H04L41/064 ,  H04L41/5035 ,  H04L41/5067 ,  H04L41/5074 ,  H04L43/0811

    摘要： Internet service providers and their clients communicate by transmitting messages across one or more networks and infrastructure components. At various points between the service provider and the clients, inclusively, records may be created of each messages occurrence and status. These records may be read and analyzed to determine the effects of the networks and infrastructure components on the provided quality of service. User-effecting incidents (e.g., failures) occurring at networks may also be identified and described.

    摘要翻译： 互联网服务提供商及其客户通过在一个或多个网络和基础设施组件上发送消息进行通信。 在服务提供商和客户端之间的不同点，包括每个消息发生和状态可以创建记录。 可以读取和分析这些记录，以确定网络和基础设施组件对提供的服务质量的影响。 还可以识别和描述在网络处发生的用户影响事件（例如，故障）。

    DYNAMIC ACTIVITY MODEL OF NETWORK SERVICES
    18.
    发明申请
    DYNAMIC ACTIVITY MODEL OF NETWORK SERVICES 有权
    网络服务动态活动模型

    公开(公告)号：US20080101352A1

    公开(公告)日：2008-05-01

    申请号：US11554935

    申请日：2006-10-31

    申请人： Paul Barham ,  Richard Black ,  Moises Goldszmidt ,  Rebecca Isaacs ,  John MacCormick ,  Richard Mortier

    发明人： Paul Barham ,  Richard Black ,  Moises Goldszmidt ,  Rebecca Isaacs ,  John MacCormick ,  Richard Mortier

    IPC分类号： H04L12/56

    CPC分类号： H04L41/0645 ,  H04L41/142 ,  H04L41/16

    摘要： An activity model is generated at a computer. The activity model may be generated by monitoring incoming and outgoing data in the computer. The collected data is analyzed to form a graph that describes and predicts what output is generated in response to received input. Later, a window of input and output data is collected from the computer. This collected window of data is used to query the activity model. The graph in the activity model is then used to give the probability that the collected window of data was collected from the computer used to generate the activity model. A high probability indicates that the computer is performing normally, while a low probability indicates that the computer may behaving erratically and there may be a problem with the computer.

    摘要翻译： 在计算机上生成活动模型。 可以通过监视计算机中的传入和传出数据来生成活动模型。 分析所收集的数据以形成描述并预测响应于接收到的输入产生什么输出的图。 之后，从计算机收集输入和输出数据的窗口。 该收集的数据窗口用于查询活动模型。 然后使用活动模型中的图表给出从用于生成活动模型的计算机收集数据收集窗口的概率。 高概率表示计算机正常运行，而低概率表示计算机可能运行不规律，并且计算机可能存在问题。

    Repair-policy refinement in distributed systems
    19.
    发明授权
    Repair-policy refinement in distributed systems 有权
    分布式系统中修复策略的细化

    公开(公告)号：US08504874B2

    公开(公告)日：2013-08-06

    申请号：US12886566

    申请日：2010-09-21

    申请人： Moises Goldszmidt ,  Mihai Budiu ,  Yue Zhang ,  Michael Pechuk

    发明人： Moises Goldszmidt ,  Mihai Budiu ,  Yue Zhang ,  Michael Pechuk

    IPC分类号： G06F11/00

    CPC分类号： G06F11/0793 ,  G06F11/3006 ,  G06F11/3055

    摘要： In a distributed system a plurality of devices (including computing units, storage and communication units) are monitored by an automated repair service that uses sensors and performs one or more repair actions on computing devices that are found to fail according to repair policies. The repair actions include automated repair actions and non-automated repair actions. The health of the computing devices is recorded in the form of states along with the repair actions that were performed on the computing devices and the times at which the repair actions were performed, and events generated by both sensors and the devices themselves. After some period of the time, the history of states of each device, the events, and the repair actions performed on the computing devices are analyzed to determine the effectiveness of the repair actions. A statistical analysis is performed based on the cost of each repair action and the determined effectiveness of each repair action, and one or more of the policies may be adjusted, as well as determining from the signals and events from the sensors whether the sensors themselves require adjustment.

    摘要翻译： 在分布式系统中，多个设备（包括计算单元，存储和通信单元）由使用传感器的自动修复服务来监视，并且对根据修复策略发现失败的计算设备执行一个或多个修复动作。 修复操作包括自动修复操作和非自动修复操作。 以状态的形式记录计算设备的健康状况以及在计算设备上执行的修复动作以及执行修复动作的时间以及由传感器和设备本身产生的事件。 在一段时间之后，分析每个设备的状态历史，事件和在计算设备上执行的修复动作，以确定修复动作的有效性。 基于每个修复动作的成本和确定的每个修复动作的有效性进行统计分析，并且可以调整一个或多个策略，以及根据来自传感器的信号和事件确定传感器本身是否需要 调整。

    TIME MODULATED GENERATIVE PROBABILISTIC MODELS FOR AUTOMATED CAUSAL DISCOVERY
    20.
    发明申请
    TIME MODULATED GENERATIVE PROBABILISTIC MODELS FOR AUTOMATED CAUSAL DISCOVERY 审中-公开
    用于自动发现的时间调制生成概率模型

    公开(公告)号：US20110209001A1

    公开(公告)日：2011-08-25

    申请号：US13100412

    申请日：2011-05-04

    申请人： Aleksandr Simma ,  Moises Goldszmidt

    发明人： Aleksandr Simma ,  Moises Goldszmidt

    IPC分类号： G06F11/07 ,  G06F15/16 ,  G06N5/02

    CPC分类号： H04L41/0663 ,  H04L41/142 ,  H04L41/145

    摘要： Dependencies between different channels or different services in a client or server may be determined from the observation of the times of the incoming and outgoing of the packets constituting those channels or services. A probabilistic model may be used to formally characterize these dependencies. The probabilistic model may be used to list the dependencies between input packets and output packets of various channels or services, and may be used to establish the expected strength of the causal relationship between the different events surrounding those channels or services. Parameters of the probabilistic model may be either based on prior knowledge, or may be fit using statistical techniques based on observations about the times of the events of interest. Expected times of occurrence between events may be observed, and dependencies may be determined in accordance with the probabilistic model.

    摘要翻译： 客户端或服务器中的不同信道或不同业务之间的依赖关系可以从对构成这些信道或业务的分组的进入和传出的时间的观察来确定。 概率模型可用于正式表征这些依赖性。 概率模型可以用于列出输入分组和各种信道或服务的输出分组之间的依赖性，并且可以用于建立围绕这些信道或服务的不同事件之间的因果关系的预期强度。 概率模型的参数可以基于现有知识，或者可以使用基于关于感兴趣事件的时间的观察的统计技术来拟合。 可以观察事件之间的预期发生时间，并且依赖性可以根据概率模型来确定。