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    Multiple seasonality online data decomposition
    32.
    发明授权
    Multiple seasonality online data decomposition 有权

    公开(公告)号：US12079233B1

    公开(公告)日：2024-09-03

    申请号：US17246241

    申请日：2021-04-30

    Applicant: SPLUNK INC.

    Inventor： Abhinav Mishra ,  Ram Sriharsha ,  Sichen Zhong

    IPC: G06F16/2458

    CPC classification number: G06F16/2465

    Abstract: Embodiments described herein are directed to facilitating performing online data decomposition to identify multiple seasonal components. In accordance with aspects of the present disclosure, a first iterative process is performed to determine a first seasonal component associated with an incoming data point based on a set of previous data points of a time series data set and corresponding data components. In addition, a second iterative process is performed to determine a second seasonal component associated with the incoming data point based on previous data points of the time series data set and corresponding data components. The first seasonal component and the second seasonal component can then be provided for analysis of the incoming data point (e.g., for presentation, for use in determining trend and residual components, etc.).

    Systems and methods for DNS text classification
    33.
    发明授权
    Systems and methods for DNS text classification 有权

    公开(公告)号：US12056169B1

    公开(公告)日：2024-08-06

    申请号：US17513670

    申请日：2021-10-28

    Applicant: SPLUNK Inc.

    Inventor： Abhinav Mishra ,  Giovanni Mola ,  Ram Sriharsha ,  Abraham Starosta ,  Zhaohui Wang

    IPC: G06F7/00 ,  G06F16/33 ,  G06F16/35 ,  G06N20/00

    CPC classification number: G06F16/334 ,  G06F16/35 ,  G06N20/00

    Abstract: A computerized method is disclosed that includes operations of training a machine learning model using a labeled training set of data, wherein the machine learning model is configured to classify domain name server (DNS) records, obtaining DNS record data including at least a first DNS Txt record, applying the trained machine learning model to the first DNS Txt record to classify the first DNS Txt record and responsive to the classification of the first DNS Txt record, generating a flag for a system administrator. The trained machine learning model may classify the first DNS Txt record using logistic regression. In some instances, applying the trained machine learning model to the first DNS Txt record includes performing a tokenizing operation on the first DNS Txt record to generate a tokenized first DNS Txt record.

    Log sourcetype inference model training for a data intake and query system
    35.
    发明授权
    Log sourcetype inference model training for a data intake and query system 有权

    公开(公告)号：US11704490B2

    公开(公告)日：2023-07-18

    申请号：US16945448

    申请日：2020-07-31

    Applicant: Splunk Inc.

    Inventor： Ram Sriharsha ,  Zhaohui Wang ,  Kristal Curtis

    IPC: G06F40/284 ,  G06N20/00 ,  G06F40/242 ,  G06F16/33 ,  G06N5/04

    CPC classification number: G06F40/284 ,  G06F16/3347 ,  G06F40/242 ,  G06N5/04 ,  G06N20/00

    Abstract: Systems and methods are described for training an artificial intelligence model to infer a log sourcetype of a log. For example, logs may have different log sourcetypes, and logs having the same log sourcetypes may have different messagetypes. The artificial intelligence model may be a machine learning model, and can be trained using training data that includes logs with known log sourcetypes. Each log can be tokenized, filtered, converted into a vector, and applied to a machine learning model as an input to perform the training. The machine learning model may output an inferred log sourcetype, which can be compared with the known log sourcetype to update model parameters to improve the machine learning model accuracy. The trained machine learning model may be trained to infer a log sourcetype of a log regardless of the messagetype of the log.

    Data ingestion pipeline anomaly detection
    38.
    发明授权
    Data ingestion pipeline anomaly detection 有权

    公开(公告)号：US11620157B2

    公开(公告)日：2023-04-04

    申请号：US16670789

    申请日：2019-10-31

    Applicant: Splunk Inc.

    Inventor： Ram Sriharsha ,  Mark Huang ,  Abhinav Mishra ,  Harsha Wasalathanthrige Don

    IPC: G06F9/48 ,  G06F17/18 ,  G06F16/17 ,  G06F9/38

    Abstract: Systems and methods are described for processing ingested pipeline metrics and ingested logs in an asynchronous manner as the data is being ingested to explain anomalies detected in the pipeline metrics using the ingested logs. For example, one or more streaming data processors can convert data as the data is ingested into a comparable data structure, determine whether the comparable data structure should be assigned to an existing data pattern or a new data pattern, and determine whether the logs corresponding to the comparable data structure is anomalous. Separately, the streaming data processor(s) can perform an outlier detection on the pipeline metrics to detect outliers. The streaming data processor(s) can then window the anomalous logs and the pipeline metric outliers to surface explanations for the pipeline metric outliers using the anomalous logs.

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