ADAPTIVE FORECASTING MODELS
    1.
    发明申请

    公开(公告)号:US20220058668A1

    公开(公告)日:2022-02-24

    申请号:US16997275

    申请日:2020-08-19

    Applicant: eBay Inc.

    Abstract: Technologies are disclosed for implementing a time series forecasting model. A time series forecasting model comprising a periodic component is modified to include an event-based component comprising a long-term portion representing impacts greater than one period of the periodic component, and a short-term portion representing impacts shorter than one period of the periodic component. A forecast is output using the modified time series forecasting model.

    GRAPH ANALYSIS AND DATABASE FOR AGGREGATED DISTRIBUTED TRACE FLOWS

    公开(公告)号:US20230385175A1

    公开(公告)日:2023-11-30

    申请号:US18232525

    申请日:2023-08-10

    Applicant: eBay Inc.

    Abstract: Technologies are shown for generating process flow graphs from system trace data that involve obtaining raw distributed trace data for a system, aggregating the raw distributed trace data into aggregated distributed trace data, generating a plurality of process flow graphs from the aggregated distributed trace data, and storing the plurality of process flow graphs in a graphical store. A first critical path can be determined from the plurality of process flow graphs based on an infrastructure design for the system and a process flow graph corresponding to the first critical path provided for graphical display. Certain examples can determine a second critical path involving a selected element of the first critical path and provide the process flow graph for the second critical path for display. Some examples pre-process the aggregated distributed trace data to repair incorrect traces. Other examples merge included process flow graphs into longer graphs.

    GRAPH ANALYSIS AND DATABASE FOR AGGREGATED DISTRIBUTED TRACE FLOWS

    公开(公告)号:US20210294717A1

    公开(公告)日:2021-09-23

    申请号:US17209633

    申请日:2021-03-23

    Applicant: eBay Inc.

    Abstract: Technologies are shown for generating process flow graphs from system trace data that involve obtaining raw distributed trace data for a system, aggregating the raw distributed trace data into aggregated distributed trace data, generating a plurality of process flow graphs from the aggregated distributed trace data, and storing the plurality of process flow graphs in a graphical store. A first critical path can be determined from the plurality of process flow graphs based on an infrastructure design for the system and a process flow graph corresponding to the first critical path provided for graphical display. Certain examples can determine a second critical path involving a selected element of the first critical path and provide the process flow graph for the second critical path for display. Some examples pre-process the aggregated distributed trace data to repair incorrect traces. Other examples merge included process flow graphs into longer graphs.

    ENHANCEMENT OF MACHINE LEARNING-BASED ANOMALY DETECTION USING KNOWLEDGE GRAPHS

    公开(公告)号:US20200293917A1

    公开(公告)日:2020-09-17

    申请号:US16351453

    申请日:2019-03-12

    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.

Patent Agency Ranking