Enhancement Of Machine Learning-Based Anomaly Detection Using Knowledge Graphs

    公开(公告)号:US20230048212A1

    公开(公告)日:2023-02-16

    申请号:US17977240

    申请日:2022-10-31

    Applicant: eBay Inc.

    Abstract: Technologies are disclosed herein for enhancing machine learning (“ML”) -based anomaly detection systems using knowledge graphs. The disclosed technologies generate a connected graph that defines a topology of infrastructure components along with associated alarms generated by a ML component. The ML component generates the alarms by applying ML techniques to real-time data metrics generated by the infrastructure components. Scores are computed for the infrastructure components based upon the connected graph. A root cause of an anomaly affecting infrastructure components can then be identified based upon the scores, and remedial action can be taken to address the root cause of the anomaly. A user interface is also provided for visualizing aspects of the connected graph.

    Enhancement of machine learning-based anomaly detection using knowledge graphs

    公开(公告)号:US12131265B2

    公开(公告)日:2024-10-29

    申请号:US17977240

    申请日:2022-10-31

    Applicant: eBay Inc.

    CPC classification number: G06N5/04 G06F16/9024 G06N20/00

    Abstract: Technologies are disclosed herein for enhancing machine learning (“ML”)-based anomaly detection systems using knowledge graphs. The disclosed technologies generate a connected graph that defines a topology of infrastructure components along with associated alarms generated by a ML component. The ML component generates the alarms by applying ML techniques to real-time data metrics generated by the infrastructure components. Scores are computed for the infrastructure components based upon the connected graph. A root cause of an anomaly affecting infrastructure components can then be identified based upon the scores, and remedial action can be taken to address the root cause of the anomaly. A user interface is also provided for visualizing aspects of the connected graph.

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