公开(公告)号：US12158880B1

公开(公告)日：2024-12-03

申请号：US17978153

申请日：2022-10-31

Applicant: SPLUNK, INC.

Inventor： Kristal Curtis ,  William Deaderick ,  Tanner Gilligan ,  Joseph Ross ,  Abraham Starosta ,  Sichen Zhong

IPC: G06F16/22 ,  G06F16/242 ,  G06F16/2458 ,  G06F16/28

Abstract: Implementations of this disclosure provide an anomaly detection system and methods of performing anomaly detection on a time-series dataset. The anomaly detection may include utilization of a forecasting machine learning algorithm to obtain a prediction of points of the dataset and comparing the predicted value of a point in the dataset with the actual value to determine an error value associated with that point. Additionally, the anomaly detection may include determination of a sensitivity threshold that impacts whether points within the dataset associated with certain error values are flagged as anomalies. The forecasting machine learning algorithm may implement a seasonality component determination process that accounts for seasonality or patterns in the dataset. A search query statement may be automatically generated through importing the sensitivity threshold into a predetermined search query statement that implements that forecasting machine learning algorithm.

公开(公告)号：US11516269B1

公开(公告)日：2022-11-29

申请号：US16835148

申请日：2020-03-30

Applicant: SPLUNK INC.

Inventor： Sonya Chang, Jr. ,  Maxime Petazzoni ,  Joseph Ross ,  Sahinaz Safari Sanjani

IPC: G06F15/16 ,  H04L65/65 ,  G06F16/9035 ,  G06F16/901 ,  G06F16/907 ,  H04L65/61

Abstract: A method of diagnosing anomalous patterns from metrics data associated with a microservices-based application comprises aggregating a plurality of ingested spans into a plurality of streams of metric data. The method also comprises performing computations on a stream of metric data from the plurality of streams of metric data to identify an anomalous pattern. Further, the method comprises generating an alert in response to the anomalous pattern and querying a data set using metadata associated with the alert to retrieve additional information pertaining to the anomalous pattern.