Cigna Intellectual Property, Inc.

Mahsa Shateri

Steven Conforti

Methods and systems for anomaly detection in relational databases are provided. The methods and systems perform operations comprising: selecting a first data point representing a first resource utilization of a server at a starting point of a first time interval; selecting a first data point representing a first resource utilization of a server at a starting point of a first time interval; selecting a second data point representing a second resource utilization of the server during a second time interval; computing an angle representing a resource utilization growth rate between the first data point and the second data point; and in response to determining that the angle representing the resource utilization growth rate transgresses a threshold, triggering an anomaly detection operation.

Anomaly detection in relational databases

Methods and systems for anomaly detection in relational databases. The methods and systems perform operations comprising: accessing a data object including a data set that has been collected over a given time interval; selecting a reference data point from the data set; computing an angle representing a data growth rate of the data set at least within the given time interval relative to the reference data point; determining that the angle representing the data growth rate of the data set within the given time interval exceeds a specified threshold; and in response to determining that the angle representing the data growth rate of the data set within the given time interval exceeds the specified threshold, triggering an anomaly detection operation.

Methods and systems for performing operations comprising: accessing one or more data objects including a data set that has been collected over a given span of time, the data set representing a plurality of parameters corresponding to resource utilization of a given server; computing first and second statistical measures based on the plurality of parameters; obtaining current resource utilization corresponding to at least a subset of the plurality of parameters; determining a first condition in which values of the current resource utilization exceed a first threshold associated with the first statistical measure; determining a second condition in which values of the data set corresponding to a time period associated with the current resource utilization exceed a second threshold associated with the second statistical measure; and triggering an anomaly detection operation in response to determining the first and second conditions.

Anomaly detection for multiple parameters

Methods and systems for performing operations comprising: accessing one or more data objects including a data set that has been collected over a given span of time, the data set representing resource utilization of a given server; generating a first model based on a moving average of a subset of data points in the data set; generating a second model based on a historical value of a given data point in the data set, the given data point having been collected on a prior time period that corresponds to the given time period; combining the first and second models into a third model that provides an expected measure of resource utilization of the given server; and triggering an anomaly detection operation in response to determining that current resource utilization of the given server fails to correspond to the expected measure of resource utilization of the given server.

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Erie, PA, United States of America

Mahsa Shateri