Supplementing extraction rules based on event clustering

    公开(公告)号:US12099517B1

    公开(公告)日:2024-09-24

    申请号:US18300936

    申请日:2023-04-14

    Applicant: Splunk Inc.

    CPC classification number: G06F16/26

    Abstract: Systems and methods include causing presentation of a first cluster in association with an event of the first cluster, the first cluster from a first set of clusters of events. Each event includes a time stamp and event data. Based on the presentation of the first cluster, an extraction rule corresponding to the event of the first cluster is received from a user. Similarities in the event data between the events are determined based on the received extraction rule. The events are grouped into a second set of clusters based on the determined similarities. Presentation is caused of a second cluster in association with an event of the second cluster, where the second cluster is from the second set of clusters.

    Event forecasting
    49.
    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.

    AUTOMATIC GENERATION OF DATA ANALYSIS QUERIES

    公开(公告)号:US20210192395A1

    公开(公告)日:2021-06-24

    申请号:US17190751

    申请日:2021-03-03

    Applicant: Splunk Inc.

    Abstract: Disclosed herein is a computer-implemented tool that facilitates data analysis by use of machine learning (ML) techniques. The tool cooperates with a data intake and query system and provides a graphical user interface (GUI) that enables a user to train and apply a variety of different ML models on user-selected datasets of stored machine data. The tool can provide active guidance to the user, to help the user choose data analysis paths that are likely to produce useful results and to avoid data analysis paths that are less likely to produce useful results.

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