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    METHODS, COMPUTER-ACCESIBLE MEDIUM AND SYSTEMS FOR FACILITATING DATA ANALYSIS AND REASONING ABOUT TOKEN/SINGULAR CAUSALITY
    1.
    发明申请
    METHODS, COMPUTER-ACCESIBLE MEDIUM AND SYSTEMS FOR FACILITATING DATA ANALYSIS AND REASONING ABOUT TOKEN/SINGULAR CAUSALITY 审中-公开
    方法，用于促进数据分析的计算机可访问的媒体和系统和关于TOKEN / SINGULAR CAUSALITY的原因

    公开(公告)号：US20130218826A1

    公开(公告)日：2013-08-22

    申请号：US13580180

    申请日：2011-02-20

    申请人： Samantha Kleinberg ,  Bhubaneswar Mishra

    发明人： Samantha Kleinberg ,  Bhubaneswar Mishra

    IPC分类号： G06N5/04

    CPC分类号： G06N5/048 ,  G06N5/045

    摘要： Exemplary embodiments of exemplary methods, procedures, computer-accessible medium and systems according to the present disclosure can be provided which can be used for determining token causality. For example, data which comprises token-level time course data and type-level causal relationships can be obtained. In addition, a determination can be made as to whether the type-level causal relationships are instantiated in the token-level time course data, and using a computing arrangement. Further, exemplary significance scores for the causal relationships can be determined based on the determination procedure. It is also possible to determine probabilities associated with the type-level causal relationships using the token-level time course data and a probabilistic temporal model and/or type-level time course data when at least one of the type-level causal relationships have indeterminate truth values. The exemplary determination of the probabilities can be performed using a prior causal information inference procedure.

    摘要翻译： 可以提供可用于确定令牌因果关系的根据本公开的示例性方法，过程，计算机可访问介质和系统的示例性实施例。 例如，可以获得包括令牌级时间过程数据和类型因果关系的数据。 此外，可以确定在令牌级时间过程数据中是否实例化了类型级因果关系，并且使用计算机构。 此外，可以基于确定过程来确定因果关系的示例性重要性得分。 当类型级别因果关系中的至少一个具有不确定性时，还可以使用令牌级时间过程数据和概率时间模型和/或类型级时间过程数据来确定与类型级因果关系相关联的概率 真值。 概率的示例性确定可以使用先验因果信息推理过程来执行。

    Method, system, and computer-accessible medium for inferring and/or determining causation in time course data with temporal logic
    2.
    发明授权
    Method, system, and computer-accessible medium for inferring and/or determining causation in time course data with temporal logic 有权
    方法，系统和计算机可访问介质，用于推断和/或确定具有时间逻辑的时间过程数据中的因果关系

    公开(公告)号：US08762319B2

    公开(公告)日：2014-06-24

    申请号：US12994122

    申请日：2009-05-21

    申请人： Samantha Kleinberg ,  Bhubaneswar Mishra

    发明人： Samantha Kleinberg ,  Bhubaneswar Mishra

    IPC分类号： G06N5/00

    CPC分类号： G06N5/04

    摘要： Time-course data with an underlying causal structure may appear in a variety of domains, including, e.g., neural spike trains, stock price movements, and gene expression levels. Provided and described herein are methods, procedures, systems, and computer-accessible medium for inferring and/or determining causation in time course data based on temporal logic and algorithms for model checking. For example, according to one exemplary embodiment, the exemplary method can include receiving data associated with particular causal relationships, for each causal relationship, determining average characteristics associated with cause and effects of the causal relationships, and identifying the causal relationships that meet predetermined requirement(s) as a function of the average characteristics so as to generate a causal relationship. The exemplary characteristics associated with cause and effects of the causal relationships can include an associated average difference that a cause can make to an effect in relation to each other cause of that effect.

    摘要翻译： 具有潜在因果结构的时间过程数据可能出现在各种领域，包括例如神经尖峰训练，股票价格变动和基因表达水平。 本文提供和描述的是用于基于用于模型检查的时间逻辑和算法来推断和/或确定时间过程数据中的因果关系的方法，过程，系统和计算机可访问介质。 例如，根据一个示例性实施例，示例性方法可以包括接收与特定因果关系相关联的数据，用于每个因果关系，确定与因果关系的原因和影响相关联的平均特征，以及识别满足预定要求的因果关系（ s）作为平均特征的函数，以产生因果关系。 与因果关系的原因和影响相关联的示例性特征可以包括相关联的平均差异，其原因可以相对于该效应的彼此原因而产生影响。

    Method, system, computer-accessible medium and software arrangement for organization and analysis of multiple sets of data
    3.
    发明授权
    Method, system, computer-accessible medium and software arrangement for organization and analysis of multiple sets of data 有权
    方法，系统，计算机可访问的介质和软件安排，用于组织和分析多组数据

    公开(公告)号：US08090747B2

    公开(公告)日：2012-01-03

    申请号：US12124753

    申请日：2008-05-21

    申请人： Samantha Kleinberg ,  Bhubaneswar Mishra

    发明人： Samantha Kleinberg ,  Bhubaneswar Mishra

    IPC分类号： G06F17/30

    CPC分类号： G06F19/24 ,  G06F19/22 ,  G06F19/26 ,  Y10S707/941 ,  Y10S707/944 ,  Y10S707/95

    摘要： Exemplary embodiments of system, computer-accessible medium and method can be provided for organizing or analyzing at least two sets of data. The sets of data can be organized and/or analyzed by generating a data structure for the sets of data and comparing the data structure for the at least two sets of data. The data structure can be in the form of a phylogenetic-type tree, and at least one of the sets of the data can include time series data.

    摘要翻译： 可以提供系统，计算机可访问介质和方法的示例性实施例，用于组织或分析至少两组数据。 可以通过为数据集合生成数据结构并比较至少两组数据的数据结构来组织和/或分析数据集。 数据结构可以是系统发生型树的形式，并且数据集合中的至少一个可以包括时间序列数据。

    METHOD, SYSTEM, AND COMPUTER-ACCESSIBLE MEDIUM FOR INFERRING AND/OR DETERMINING CAUSATION IN TIME COURSE DATA WITH TEMPORAL LOGIC
    4.
    发明申请
    METHOD, SYSTEM, AND COMPUTER-ACCESSIBLE MEDIUM FOR INFERRING AND/OR DETERMINING CAUSATION IN TIME COURSE DATA WITH TEMPORAL LOGIC 有权
    方法，系统和计算机可访问媒体，用于传播和/或确定具有时间逻辑的时间课程数据中的起因

    公开(公告)号：US20110167031A1

    公开(公告)日：2011-07-07

    申请号：US12994122

    申请日：2009-05-21

    申请人： Samantha Kleinberg ,  Bhubaneswar Mishra

    发明人： Samantha Kleinberg ,  Bhubaneswar Mishra

    IPC分类号： G06N5/02

    CPC分类号： G06N5/04

    摘要： Time-course data with an underlying causal structure may appear in a variety of domains, including, e.g., neural spike trains, stock price movements, and gene expression levels. Provided and described herein are methods, procedures, systems, and computer-accessible medium for inferring and/or determining causation in time course data based on temporal logic and algorithms for model checking. For example, according to one exemplary embodiment, the exemplary method can include receiving data associated with particular causal relationships, for each causal relationship, determining average characteristics associated with cause and effects of the causal relationships, and identifying the causal relationships that meet predetermined requirement(s) as a function of the average characteristics so as to generate a causal relationship. The exemplary characteristics associated with cause and effects of the causal relationships can include an associated average difference that a cause can make to an effect in relation to each other cause of that effect.

    摘要翻译： 具有潜在因果结构的时间过程数据可能出现在各种领域，包括例如神经尖峰训练，股票价格变动和基因表达水平。 本文提供和描述的是用于基于用于模型检查的时间逻辑和算法来推断和/或确定时间过程数据中的因果关系的方法，过程，系统和计算机可访问介质。 例如，根据一个示例性实施例，示例性方法可以包括接收与特定因果关系相关联的数据，用于每个因果关系，确定与因果关系的原因和影响相关联的平均特征，以及识别满足预定要求的因果关系（ s）作为平均特征的函数，以产生因果关系。 与因果关系的原因和影响相关联的示例性特征可以包括相关联的平均差异，其原因可以相对于该效应的彼此原因而产生影响。