Hierarchical anomaly localization and prioritization
    45.
    发明授权
    Hierarchical anomaly localization and prioritization 有权
    分层异常定位和优先级排序

    公开(公告)号:US09264331B2

    公开(公告)日:2016-02-16

    申请号:US14562234

    申请日:2014-12-05

    CPC classification number: H04L43/0823 H04L41/0618 H04L41/0677 H04L43/0864

    Abstract: Example methods disclosed herein to localize anomalies in a communication network include identifying a first set of abnormal nodes in the communication network, and including respective ones of the first set of abnormal nodes having a number of normal direct descendent nodes that is less than a combined number of abnormal direct descendent nodes and indeterminate direct descendent nodes in a set of candidate nodes. Such disclosed example methods also include iteratively selecting ones of the set of candidate nodes to include in a set of root cause abnormal nodes representing sources of the anomalies in the communication network. In such disclosed example methods, the ones of the set of candidate nodes are selected based on sizes of respective subsets of the abnormal nodes from the first set of abnormal nodes covered by the candidate nodes.

    Abstract translation: 本文公开的用于定位通信网络中的异常的示例方法包括识别通信网络中的第一组异常节点,并且包括具有小于组合数的正常直接后代节点数量的第一组异常节点中的相应组 在一组候选节点中的异常直接后代节点和不确定的直接后代节点。 这样公开的示例方法还包括迭代地选择候选节点集合中的一个,以包括在一组根部中,从而导致表示通信网络中的异常源的异常节点。 在这样公开的示例性方法中,基于来自候选节点所覆盖的第一组异常节点的异常节点的各个子集的大小来选择候选节点集合中的节点。

    HIERARCHICAL ANOMALY LOCALIZATION AND PRIORITIZATION
    46.
    发明申请
    HIERARCHICAL ANOMALY LOCALIZATION AND PRIORITIZATION 有权
    分层异常定位与优化

    公开(公告)号:US20150085675A1

    公开(公告)日:2015-03-26

    申请号:US14562234

    申请日:2014-12-05

    CPC classification number: H04L43/0823 H04L41/0618 H04L41/0677 H04L43/0864

    Abstract: Example methods disclosed herein to localize anomalies in a communication network include identifying a first set of abnormal nodes in the communication network, and including respective ones of the first set of abnormal nodes having a number of normal direct descendent nodes that is less than a combined number of abnormal direct descendent nodes and indeterminate direct descendent nodes in a set of candidate nodes. Such disclosed example methods also include iteratively selecting ones of the set of candidate nodes to include in a set of root cause abnormal nodes representing sources of the anomalies in the communication network. In such disclosed example methods, the ones of the set of candidate nodes are selected based on sizes of respective subsets of the abnormal nodes from the first set of abnormal nodes covered by the candidate nodes.

    Abstract translation: 本文公开的用于定位通信网络中的异常的示例方法包括识别通信网络中的第一组异常节点,并且包括具有小于组合数的正常直接后代节点数量的第一组异常节点中的相应组 在一组候选节点中的异常直接后代节点和不确定的直接后代节点。 这样公开的示例方法还包括迭代地选择候选节点集合中的一个,以包括在一组根部中,从而导致表示通信网络中的异常源的异常节点。 在这样公开的示例性方法中,基于来自候选节点所覆盖的第一组异常节点的异常节点的各个子集的大小来选择候选节点集合中的节点。

    PERFORMANCE-DRIVEN NETWORK PARAMETER CHANGES IN A COMMUNICATION NETWORK

    公开(公告)号:US20240224072A1

    公开(公告)日:2024-07-04

    申请号:US18090784

    申请日:2022-12-29

    CPC classification number: H04W24/02 H04L41/16 H04W24/10

    Abstract: A processing system may obtain a data set with records of network parameter changes, each record including at least one network parameter change and at least one attribute associated with a first aspect of a communication network, and a corresponding network performance indicator change. A first record may include a plurality of network parameter change groups. The processing system may next perform a de-confusion process by identifying a second record comprising a single network parameter change group, determining that a corresponding network performance indicator change is different from that of the at least the first record, and updating the data set to replace the first record with at least two replacement records. The processing system may apply at least one of the network parameter change groups to a second aspect of the communication network based upon a decision output of a classifier that is trained using the updated data set.

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