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    DATA INSIGHTS FOR PERFORMANCE ANALYTICS
    3.
    发明申请
    DATA INSIGHTS FOR PERFORMANCE ANALYTICS 审中-公开

    公开(公告)号：US20190104041A1

    公开(公告)日：2019-04-04

    申请号：US15905696

    申请日：2018-02-26

    Applicant: ServiceNow, Inc.

    Inventor： Aida Rikovic Tabak ,  Shayan Shahand ,  Dileeshvar Radhakrishnan ,  Seth Tucker Stafford ,  Sumana Ravikrishnan ,  Abhijith Thette Nagarajan ,  Robert Andrew Ninness ,  Prabhakaran Subramani Thandayuthapani ,  Marta Penzo

    IPC: H04L12/26

    Abstract: An enterprise management platform is configured to host a respective instance for multiple client networks. The enterprise management platform receives incoming data including one or more metrics being tracked in the incoming data by the enterprise management platform. The enterprise management platform determines that a condition is reached based on the one or more metrics and selects at least a portion of the incoming data for a dashboard of the enterprise management platform responsive to determining that the incoming data is indicative of the condition being reached. The enterprise management platform provides, to a client device associated with one of the plurality of client networks, a representation of a graphical user interface displaying a data insights widget of the dashboard that displays the data indicative of the condition being reached without displaying other data in the incoming data that has not reached a level indicative of other conditions.

    Time series data forecasting
    4.
    发明授权
    Time series data forecasting 有权

    公开(公告)号：US11163747B2

    公开(公告)日：2021-11-02

    申请号：US15588088

    申请日：2017-05-05

    Applicant: ServiceNow, Inc.

    Inventor： Shayan Shahand ,  Aida Rikovic Tabak ,  Robert Ninness ,  Abhijith Thette Nagarajan ,  Prabhakaran Subramani Thandayuthapani

    IPC: G06F16/23 ,  G06Q30/00 ,  G06Q30/02 ,  G06F11/30 ,  G06Q10/06 ,  G06F17/18 ,  G06Q10/04 ,  G06N20/00

    Abstract: Time series data is generated and forecasted with a selected forecasting mechanism. Time series data to forecast including a plurality of data points is received. A count of the plurality of data points is determined to meet a threshold. Responsive to that determination, a plurality of test forecasts are generated with respective forecasting mechanisms of a plurality of forecasting mechanisms using a first subset of the plurality of data points. Errors are then determined for the respective forecasting mechanisms, such as based on comparisons of corresponding ones of the plurality of test forecasts and a second subset of the plurality of data points. One of the plurality of forecasting mechanisms is selected based on the errors. An output forecast is then generated with the selected forecasting mechanism using the first and second subsets of the plurality of data points.

    TIME SERIES DATA FORECASTING
    9.
    发明申请
    TIME SERIES DATA FORECASTING 审中-公开

    公开(公告)号：US20180322400A1

    公开(公告)日：2018-11-08

    申请号：US15588088

    申请日：2017-05-05

    Applicant: ServiceNow, Inc.

    Inventor： Shayan Shahand ,  Aida Rikovic Tabak ,  Robert Ninness ,  Abhijith Thette Nagarajan ,  Prabhakaran Subramani Thandayuthapani

    IPC: G06N5/04 ,  G06F17/30

    CPC classification number: G06N5/04 ,  G06F17/30002

    Abstract: Time series data is generated and forecasted with a selected forecasting mechanism. Time series data to forecast including a plurality of data points is received. A count of the plurality of data points is determined to meet a threshold. Responsive to that determination, a plurality of test forecasts are generated with respective forecasting mechanisms of a plurality of forecasting mechanisms using a first subset of the plurality of data points. Errors are then determined for the respective forecasting mechanisms, such as based on comparisons of corresponding ones of the plurality of test forecasts and a second subset of the plurality of data points. One of the plurality of forecasting mechanisms is selected based on the errors. An output forecast is then generated with the selected forecasting mechanism using the first and second subsets of the plurality of data points.

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