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    Exploratory data analysis system for generation of wildcards within log templates through log clustering and analysis thereof
    1.
    发明授权
    Exploratory data analysis system for generation of wildcards within log templates through log clustering and analysis thereof 有权

    公开(公告)号：US12182174B1

    公开(公告)日：2024-12-31

    申请号：US18147639

    申请日：2022-12-28

    Applicant: SPLUNK Inc.

    Inventor： Francis Beckert ,  Kristal Curtis ,  Om Rajyaguru ,  Abraham Starosta ,  Poonam Yadav

    IPC: G06F16/24 ,  G06F16/248 ,  G06F16/28 ,  G06F16/957

    Abstract: A search assistant engine is described that integrates with a data intake and query system and provides an intuitive user interface to assist a user in searching and evaluating indexed event data. Additionally, the search assistant engine provides logic to intelligently provide data to the user through the user interface such as determining fields of events likely to be of interest based on determining a mutual information score for each field and determining groups of related fields based on determining a mutual information score for each field grouping. Some implementations utilize machine learning techniques in certain analyses such as when clustering events and determining an event templates for each cluster. Additionally, the search assistant engine may import terms or characters from user interaction into predetermined search query templates to generate tailored search query for the user.

    Anomaly Detection System and Method for Implementing a Data Regularity Check and Adaptive Thresholding
    2.
    发明申请
    Anomaly Detection System and Method for Implementing a Data Regularity Check and Adaptive Thresholding 有权

    公开(公告)号：US20250028618A1

    公开(公告)日：2025-01-23

    申请号：US18222870

    申请日：2023-07-17

    Applicant: Splunk Inc.

    Inventor： Houwu Bai ,  Kristal Curtis ,  William Deaderick ,  Tanner Gilligan ,  Poonam Yadav ,  Om Rajyaguru

    IPC: G06F11/34 ,  G06F11/30 ,  G06F16/23 ,  G06F16/2458

    Abstract: Computerized methodologies are disclosed that are directed to detecting anomalies within a time-series data set. A first aspect of the anomaly detection process includes analyzing the regularity of the data points of the time-series data set and determining whether a data aggregation process is to be performed based on the regularity of the data points, which results in a time-series data set having data points occurring at regular intervals. A seasonality pattern may be determined for the time-series data set, where a silhouette score is computed to measure the quality of the fit of the seasonality pattern to the time-series data. The silhouette score may be compared to a threshold and based on the comparison, the seasonality pattern or a set of heuristics may be utilized in an anomaly detection process. When the seasonality pattern is utilized, the seasonality pattern may be utilized to generate thresholds indicating anomalous behavior.

    Anomaly Detection Through Clustering of Time-Series Data Subsequences and Determination of Adaptive Thresholding
    4.
    发明申请
    Anomaly Detection Through Clustering of Time-Series Data Subsequences and Determination of Adaptive Thresholding 有权

    公开(公告)号：US20250028737A1

    公开(公告)日：2025-01-23

    申请号：US18222863

    申请日：2023-07-17

    Applicant: Splunk Inc.

    Inventor： Houwu Bai ,  Kristal Curtis ,  William Deaderick ,  Tanner Gilligan ,  Poonam Yadav ,  Om Rajyaguru

    IPC: G06F16/28 ,  G06F16/2458

    Abstract: Computerized methodologies are disclosed that are directed to detecting anomalies within a time-series data set. An aspect of the anomaly detection process includes determining one or more seasonality patterns that correspond to a specific time-series data set by evaluating a set of candidate seasonality patterns (e.g., hourly, daily, weekly, day-start off-sets, etc.). The evaluation of a candidate seasonality pattern may include dividing the time-series data set into a collection of subsequences based on the particular candidate seasonality pattern. Further, the collection of subsequences may be divided into clusters and a silhouette score may be computed to measure the clustering quality of the candidate seasonality pattern. In some instances, the candidate seasonality pattern having the highest silhouette score is selected and utilized in anomaly detection process. In other instances, a plurality of seasonality patterns may be combined forming a time policy, where the time policy is utilized in anomaly detection process.

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