Abstract: Systems and methods for data anomaly detection include recommending one or more algorithms from a set of algorithms to process received time series data, wherein the one or more algorithms are recommended based at least in part on a type of workload for processing the received time series data. Assisted parameter tuning is provided for a detected anomaly alert and calibration, and the received time series data is processed based on a user selected algorithm that is parameter tuned, thereby resulting in more efficient and reliable anomaly detection.

Abstract: Systems and methods for data anomaly detection include recommending one or more algorithms from a set of algorithms to process received time series data, wherein the one or more algorithms are recommended based at least in part on a type of workload for processing the received time series data. Assisted parameter tuning is provided for a detected anomaly alert and calibration, and the received time series data is processed based on a user selected algorithm that is parameter tuned.

Abstract: Methods, computer readable media, and devices for quantifying an infrastructure service health as a score and optimizing performance of the infrastructure service based on benchmarks of dynamically identified control groups are disclosed. One method may include determining, for an infrastructure service of an organization, a metric health score for one or more metrics and an overall health score for the organization, creating, for at least one of the metrics, a number of control groups based on a timeframe criteria and including a set of organizations having a metric health score for the timeframe criteria similar to the organization, and maximizing performance of the infrastructure service using machine learning to compare, for at least one metric, performance impacts to the organization based on service changes with the number of control groups for the at least one metric.

Abstract: Systems and methods for data anomaly detection include recommending one or more algorithms from a set of algorithms to process received time series data, wherein the one or more algorithms are recommended based at least in part on a type of workload for processing the received time series data. Assisted parameter tuning is provided for a detected anomaly alert and calibration, and the received time series data is processed based on a user selected algorithm that is parameter tuned, thereby resulting in more efficient and reliable anomaly detection.

Abstract: Systems and methods for data anomaly detection include recommending one or more algorithms from a set of algorithms to process received time series data, wherein the one or more algorithms are recommended based at least in part on a type of workload for processing the received time series data. Assisted parameter tuning is provided for a detected anomaly alert and calibration, and the received time series data is processed based on a user selected algorithm that is parameter tuned.

Abstract: A method of data processing includes identifying a segment of entity identifiers that are associated with a target tenant and correspond to a set of clients that are to receive at least one content object via a first channel of a plurality of supported channels. The method includes modifying a feature associated with communication of content for a test subset of the segment relative to a control subset of the segment, determining a first metric corresponding to the control subset and the test subset in association with the communication of the content via the first channel and a second metric associated with the target tenant over a second channel of the plurality of channels. The method includes comparing the second metric to a metric associated with a peer group of tenants, and adjusting subsequent communications for the target based at least in part on the comparing and the first metric.

Abstract: Systems and methods for data anomaly detection include recommending one or more algorithms from a set of algorithms to process received time series data, wherein the one or more algorithms are recommended based at least in part on a type of workload for processing the received time series data. Assisted parameter tuning is provided for a detected anomaly alert and calibration, and the received time series data is processed based on a user selected algorithm that is parameter tuned, thereby resulting in more efficient and reliable anomaly detection.