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    AUTOMATED RECOGNITION OF PROCESS MODELING SEMANTICS IN FLOW DIAGRAMS
    8.
    发明申请
    AUTOMATED RECOGNITION OF PROCESS MODELING SEMANTICS IN FLOW DIAGRAMS 有权
    流程图自动识别过程建模语言

    公开(公告)号：US20120062574A1

    公开(公告)日：2012-03-15

    申请号：US12881120

    申请日：2010-09-13

    申请人： Pankaj Dhoolia ,  Juhnyoung Lee ,  Debdoot Mukherjee ,  Aubrey J. Rembert

    发明人： Pankaj Dhoolia ,  Juhnyoung Lee ,  Debdoot Mukherjee ,  Aubrey J. Rembert

    IPC分类号： G06T1/20

    CPC分类号： G06K9/00476 ,  G06F8/10 ,  G06F8/20 ,  G06F8/30

    摘要： An example embodiment disclosed is a system for automated model extraction of documents containing flow diagrams. An extractor is configured to extract from the flow diagrams flow graphs. The extractor further extracts nodes and edges, and relational, geometric and textual features for the extracted nodes and edges. A classifier is configured to recognize process semantics based on the extracted nodes and edges, and the relational, geometric and textual features of the extracted nodes and edges. A process modeling language code is generated based on the recognized process semantics. Rules to recognize patterns in process diagrams may be determined using supervised learning and/or unsupervised learning. During supervised learning, an expert labels example flow diagrams so that a classifier can derive the classification rules. During unsupervised learning flow diagrams are clustered based on relational, geometric and textual features of nodes and edges.

    摘要翻译： 所公开的示例性实施例是用于自动模型提取包含流程图的文档的系统。 提取器被配置为从流程图流程图中提取。 提取器进一步提取节点和边缘，以及提取的节点和边缘的关系，几何和文本特征。 分类器被配置为基于提取的节点和边缘以及提取的节点和边缘的关系，几何和文本特征来识别进程语义。 基于识别的流程语义生成流程建模语言代码。 可以使用监督学习和/或无监督学习来确定在过程图中识别模式的规则。 在监督学习期间，专家标签示例流程图，使得分类器可以导出分类规则。 在无监督的学习流程图中，基于节点和边缘的关系，几何和文本特征进行聚类。

    Automated recognition of process modeling semantics in flow diagrams
    9.
    发明授权
    Automated recognition of process modeling semantics in flow diagrams 有权
    流程图中过程建模语义的自动识别

    公开(公告)号：US09087236B2

    公开(公告)日：2015-07-21

    申请号：US12881120

    申请日：2010-09-13

    申请人： Pankaj Dhoolia ,  Juhnyoung Lee ,  Debdoot Mukherjee ,  Aubrey J. Rembert

    发明人： Pankaj Dhoolia ,  Juhnyoung Lee ,  Debdoot Mukherjee ,  Aubrey J. Rembert

    IPC分类号： G06F9/44 ,  G06K9/00

    CPC分类号： G06K9/00476 ,  G06F8/10 ,  G06F8/20 ,  G06F8/30

    摘要： An example embodiment disclosed is a system for automated model extraction of documents containing flow diagrams. An extractor is configured to extract from the flow diagrams flow graphs. The extractor further extracts nodes and edges, and relational, geometric and textual features for the extracted nodes and edges. A classifier is configured to recognize process semantics based on the extracted nodes and edges, and the relational, geometric and textual features of the extracted nodes and edges. A process modeling language code is generated based on the recognized process semantics. Rules to recognize patterns in process diagrams may be determined using supervised learning and/or unsupervised learning. During supervised learning, an expert labels example flow diagrams so that a classifier can derive the classification rules. During unsupervised learning flow diagrams are clustered based on relational, geometric and textual features of nodes and edges.

    摘要翻译： 所公开的示例实施例是用于自动模型提取包含流程图的文档的系统。 提取器被配置为从流程图流程图中提取。 提取器进一步提取节点和边缘，以及提取的节点和边缘的关系，几何和文本特征。 分类器被配置为基于提取的节点和边缘以及提取的节点和边缘的关系，几何和文本特征来识别进程语义。 基于识别的流程语义生成流程建模语言代码。 可以使用监督学习和/或无监督学习来确定在过程图中识别模式的规则。 在监督学习期间，专家标签示例流程图，使得分类器可以导出分类规则。 在无监督的学习流程图中，基于节点和边缘的关系，几何和文本特征进行聚类。

    Method and Apparatus for Locating Input-Model Faults Using Dynamic Tainting
    10.
    发明申请
    Method and Apparatus for Locating Input-Model Faults Using Dynamic Tainting 审中-公开
    使用动态污染定位输入模型故障的方法和装置

    公开(公告)号：US20110314337A1

    公开(公告)日：2011-12-22

    申请号：US12818439

    申请日：2010-06-18

    申请人： Saurabh Sinha ,  Pankaj Dhoolia ,  Senthil Kk Mani ,  Vibha S. Sinha

    发明人： Saurabh Sinha ,  Pankaj Dhoolia ,  Senthil Kk Mani ,  Vibha S. Sinha

    IPC分类号： G06F11/07

    CPC分类号： G06F11/3624

    摘要： Approaches based on dynamic tainting to assist transform users in debugging input models. The approach instruments the transform code to associate taint marks with the input-model elements, and propagate the marks to the output text. The taint marks identify the input-model elements that either contribute to an output string, or cause potentially incorrect paths to be executed through the transform, which results in an incorrect or a missing string in the output. This approach can significantly reduce the fault search space and, in many cases, precisely identify the input-model faults. By way of a significant advantage, the approach automates, with a high degree of accuracy, a debugging task that can be tedious to perform manually.

    摘要翻译： 基于动态污染的方法来协助转换用户调试输入模型。 该方法将变换代码设置为将污染标记与输入模型元素相关联，并将标记传播到输出文本。 污点标识识别对输出字符串有贡献的输入模型元素，或者导致通过变换执行可能不正确的路径，这会导致输出中的错误或缺失的字符串。 这种方法可以显着减少故障搜索空间，并且在许多情况下可以精确地识别输入模型故障。 通过一个显着的优势，该方法可以高度准确地自动执行一个调试任务，这个调试任务可能很麻烦，可以手动执行。