公开(公告)号：US20140067732A1

公开(公告)日：2014-03-06

申请号：US13605723

申请日：2012-09-06

申请人： YUDAER NEZIHI DOGANATA ,  GEETIKA TEWARI LAKSHMANAN ,  MERVE UNUVAR

发明人： YUDAER NEZIHI DOGANATA ,  GEETIKA TEWARI LAKSHMANAN ,  MERVE UNUVAR

IPC分类号： G06F15/18

CPC分类号： G06Q10/06375

摘要： A method for training a machine learning tool to generate a prediction in a business process includes receiving a business process model corresponding to the business process, the business process model including a plurality of tasks, identifying a cycling set at a decision point in the business process model, wherein the cycling set comprises at least one task that the business process model iterates through, and building a training table by determining a total number of sub-traces and a total number of variables from a plurality of execution traces of the business process model based on the cycling set identified at the decision point, wherein a new row of the training table is created for each of the sub-traces and a new column of the training table is created for each of the variables.

摘要翻译： 一种用于训练机器学习工具以在业务过程中生成预测的方法包括：接收与业务过程相对应的业务流程模型，业务流程模型包括多个任务，在业务流程中的决策点识别循环组 模型，其中所述循环组包括所述业务过程模型迭代的至少一个任务，以及通过从所述业务过程模型的多个执行跟踪中确定子跟踪的总数和总数来构建训练表 基于在决策点识别的循环组，其中为每个子轨迹创建训练表的新行，并为每个变量创建训练表的新列。

公开(公告)号：US20130103441A1

公开(公告)日：2013-04-25

申请号：US13279067

申请日：2011-10-21

申请人： Yurdaer N. Doganata ,  Rania Yousef Khalaf ,  Geetika T. Lakshmanan ,  Merve Unuvar

发明人： Yurdaer N. Doganata ,  Rania Yousef Khalaf ,  Geetika T. Lakshmanan ,  Merve Unuvar

IPC分类号： G06Q10/06

CPC分类号： G06Q10/06

摘要： A method for generating predictions includes dividing a business process model into fragments, wherein the business process model includes task nodes and at least one decision node, determining the decision node in at least one of the fragments, determining a decision tree for each decision node, determining a probability for reaching a terminal node in each fragment, and merging the probabilities obtained from the fragments to find a probability of a future task.

摘要翻译： 一种用于产生预测的方法包括将业务流程模型划分为分段，其中业务流程模型包括任务节点和至少一个决策节点，确定至少一个分段中的决策节点，确定每个决策节点的决策树， 确定在每个片段中到达终端节点的概率，并且合并从片段获得的概率以找到未来任务的概率。

公开(公告)号：US20140067446A1

公开(公告)日：2014-03-06

申请号：US13598185

申请日：2012-08-29

申请人： YURDAER N. DOGANATA ,  GEETIKA TEWARI LAKSHMANAN ,  MERVE UNUVAR

发明人： YURDAER N. DOGANATA ,  GEETIKA TEWARI LAKSHMANAN ,  MERVE UNUVAR

IPC分类号： G06Q10/06

CPC分类号： G06Q10/06375

摘要： A method for training a machine learning tool to generate a prediction in a business process includes receiving a business process model corresponding to the business process, the business process model including a plurality of tasks, identifying a cycling set at a decision point in the business process model, wherein the cycling set comprises at least one task that the business process model iterates through, and building a training table by determining a total number of sub-traces and a total number of variables from a plurality of execution traces of the business process model based on the cycling set identified at the decision point, wherein a new row of the training table is created for each of the sub-traces and a new column of the training table is created for each of the variables.

摘要翻译： 一种用于训练机器学习工具以在业务过程中生成预测的方法包括：接收与业务过程相对应的业务流程模型，业务流程模型包括多个任务，在业务流程中的决策点识别循环组 模型，其中所述循环组包括所述业务过程模型迭代的至少一个任务，以及通过从所述业务过程模型的多个执行跟踪中确定子跟踪的总数和总数来构建训练表 基于在决策点识别的循环组，其中为每个子轨迹创建训练表的新行，并为每个变量创建训练表的新列。