Splunk Inc.

Sichen Zhong

Kristal Curtis

William Deaderick

Tanner Gilligan

Ram Sriharsha

Abhinav Mishra

Chandrima Sarkar

Ralph Donald Thompson, Iii

Poonam Yadav

Wei Jie Gao

Joseph Ari Ross

Abraham Starosta

Alexander Stojanovic

Aleksander Stojanovic

Systems and methods are disclosed that are directed to improving the prioritization, display, and viewing of system alerts through the use of machine learning techniques to group the alerts and further to prioritize the groupings. Additionally, a graphical user interface is generated that illustrates the prioritized listing of the plurality of groupings. Thus, a system administrator or other user receives an improved experience as the number of notifications provided to the system administrator are reduced due to the grouping of individual alerts into related groupings and further due to the prioritization of the groupings. Previously, or in current technology, system alerts may be automatically generated and provided immediately to a system administrator. In some instances, any advantage of detecting system errors or system monitoring provided by the alerts is negated by the vast number of alerts and provision of minimally important alerts in a manner that concealed more important alerts.

Machine-learning based prioritization of alert groupings

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.

Hyperparameter tuning for anomaly detection service implementing machine learning forecasting

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.).

Multiple seasonality online data decomposition

Embodiments of the present disclosure are directed to facilitating performing online data forecasting. In operation, data decomposition of an incoming data point is performed to determine a trend component associated with the incoming data point. Such a trend component, and previous trend components, can be used to determine a trend component expected for a data point subsequent to the incoming data point. A seasonality component expected for the data point subsequent to the incoming data point can be identified, for example, based on a seasonality component associated with a previous corresponding data point. Thereafter, the expected trend and seasonality components can be used to predict the data point subsequent to the incoming data point. Such a data prediction can be performed in an online processing manner such that a subsequent data point is not used to decompose the incoming data point or forecast the data point.

Online data forecasting

A computerized method is disclosed for generating a prioritized listing of alerts based on scoring by a machine learning model and retraining the model based on user feedback. Operations of the method include receiving a plurality of alerts, generating a score for each of the plurality of alerts through evaluation of each of the plurality of alerts by a machine learning model, generating a prioritized listing of the plurality of alerts based on the generated scores, receiving user feedback on the prioritized listing, retraining the machine learning model based on the user feedback by generating a set of labeled alert pairs, wherein a labeled alert pair includes a first alert, a second alert, and an indication as to which of the first alert or the second alert is a higher priority in accordance with the user feedback, and evaluating subsequently received alerts with the retrained machine learning model.

System and method for machine-learning based alert prioritization

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San Francisco, CA, United States of America

Sichen Zhong