EVENT FORECASTING
    23.
    发明申请
    EVENT FORECASTING 审中-公开

    公开(公告)号:US20180218269A1

    公开(公告)日:2018-08-02

    申请号:US15419918

    申请日:2017-01-30

    Applicant: SPLUNK INC.

    CPC classification number: G06N5/04 G06F16/2465 G06F16/26 G06N20/00

    Abstract: Embodiments of the present invention are directed to facilitating event forecasting. In accordance with aspects of the present disclosure, a set of events determined from raw machine data is obtained. The events are analyzed to identify leading indicators that indicate a future occurrence of a target event, wherein the leading indicators occur during a search period of time the precedes a warning period of time, thereby providing time for an action to be performed prior to an occurrence of a predicted target event. At least one of the leading indicators is used to predict a target event. An event notification is provided indicating the prediction of the target event.

    Providing completion recommendation variations for a partial natural language request

    公开(公告)号:US12282500B1

    公开(公告)日:2025-04-22

    申请号:US17964808

    申请日:2022-10-12

    Applicant: SPLUNK INC.

    Abstract: In various embodiments, a natural language (NL) application receives a first incomplete natural language (NL) request, and generates one or more request completion recommendations based on at least the first incomplete NL request and a first recommendation model, where the first recommendation model is generated via a machine learning algorithm applied to a first data dependency model and a first request completion model. The NL application receives a selection of a first request completion recommendation included in the one or more request completion recommendations, generates a complete request based on the first incomplete NL request and the first request completion recommendation, and causes the complete request to be applied to the data storage system.

    Generating trending natural language request recommendations

    公开(公告)号:US11288319B1

    公开(公告)日:2022-03-29

    申请号:US16147435

    申请日:2018-09-28

    Applicant: Splunk Inc.

    Abstract: In various embodiments, a natural language (NL) application implements functionality for recommending trending NL requests to users of the application. The functionality includes generating rating data associated with a plurality of natural language (NL) requests and one or more intents corresponding to the plurality of NL requests, wherein the rating data indicates a preference of at least one user for using at least one of the plurality of NL request to access data, training a trends recommendation model based on the rating data associated with the plurality of NL requests, generating a set of NL request recommendations based on the trends recommendation model, and causing the set of NL request recommendations to be presented in a query recommendation interface.

    Event forecasting
    29.
    发明授权

    公开(公告)号:US11093837B2

    公开(公告)日:2021-08-17

    申请号:US15419918

    申请日:2017-01-30

    Applicant: SPLUNK INC.

    Abstract: Embodiments of the present invention are directed to facilitating event forecasting. In accordance with aspects of the present disclosure, a set of events determined from raw machine data is obtained. The events are analyzed to identify leading indicators that indicate a future occurrence of a target event, wherein the leading indicators occur during a search period of time the precedes a warning period of time, thereby providing time for an action to be performed prior to an occurrence of a predicted target event. At least one of the leading indicators is used to predict a target event. An event notification is provided indicating the prediction of the target event.

    Predicting follow-on requests to a natural language request received by a natural language processing system

    公开(公告)号:US11017764B1

    公开(公告)日:2021-05-25

    申请号:US16147426

    申请日:2018-09-28

    Applicant: Splunk Inc.

    Abstract: In various embodiments, a natural language (NL) application receives a partial NL request associated with a first context, and determining that the partial NL request corresponds to at least a portion of a first next NL request prediction included in one or more next NL request predictions generated based on a first natural language (NL) request, the first context associated with the first NL request, and a first sequence prediction model, where the first sequence prediction model is generated via a machine learning algorithm applied to a first data dependency model and a first request prediction model. In response to determining that the partial NL request corresponds to at least the portion of the first next NL request prediction, the NL application generates a complete NL request based on the first NL request and the partial NL request, and causes the complete NL request to be applied to a data storage system.

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