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    Systems and methods for detecting long term seasons
    21.
    发明授权
    Systems and methods for detecting long term seasons 有权

    公开(公告)号：US12001926B2

    公开(公告)日：2024-06-04

    申请号：US16168377

    申请日：2018-10-23

    Applicant: Oracle International Corporation

    Inventor： Dustin Garvey ,  Sampanna Shahaji Salunke ,  Uri Shaft ,  Sumathi Gopalakrishnan

    IPC: G06N20/00 ,  G06N5/02

    CPC classification number: G06N20/00 ,  G06N5/02

    Abstract: Techniques for machine-learning of long-term seasonal patterns are disclosed. In some embodiments, a network service receives a set of time-series data that tracks metric values of at least one computing resource over time. Responsive to receiving the time-series data, the network service detects a subset of metric values that are outliers and associated with a plurality of timestamps. The network service maps the plurality of timestamps to one or more encodings of at least one encoding space that defines a plurality of encodings for different seasonal patterns. Based on the mapped encodings, the network service generates a representation of a seasonal pattern. Based on the representation of the seasonal pattern, the network service may perform one or more operations in association with the at least one computing resource.

    Systems and methods for multivariate anomaly detection in software monitoring
    24.
    发明授权
    Systems and methods for multivariate anomaly detection in software monitoring 有权

    公开(公告)号：US11533326B2

    公开(公告)日：2022-12-20

    申请号：US16400392

    申请日：2019-05-01

    Applicant: Oracle International Corporation

    Inventor： Sampanna Shahaji Salunke ,  Dario Bahena Tapia ,  Dustin Garvey ,  Sumathi Gopalakrishnan ,  Neil Goodman

    IPC: H04L29/06 ,  H04L9/40 ,  G06N20/10 ,  G06K9/62

    Abstract: Techniques are disclosed for summarizing, diagnosing, and correcting the cause of anomalous behavior in computing systems. In some embodiments, a system identifies a plurality of time series that track different metrics over time for a set of one or more computing resources. The system detects a first set of anomalies in a first time series that tracks a first metric and assigns a different respective range of time to each anomaly. The system determines whether the respective range of time assigned to an anomaly overlaps with timestamps or ranges of time associated with anomalies from one or more other time series. The system generates at least one cluster that groups metrics based on how many anomalies have respective ranges of time and/or timestamps that overlap. The system may preform, based on the cluster, one or more automated actions for diagnosing or correcting a cause of anomalous behavior.

    Technique for incremental and flexible detection and modeling of patterns in time series data
    29.
    发明授权
    Technique for incremental and flexible detection and modeling of patterns in time series data 有权

    公开(公告)号：US11023350B2

    公开(公告)日：2021-06-01

    申请号：US16256228

    申请日：2019-01-24

    Applicant: Oracle International Corporation

    Inventor： Sampanna Shahaji Salunke ,  Dustin Garvey ,  Sumathi Gopalakrishnan

    IPC: G06F11/34

    Abstract: The present disclosure describes a flexible technique to learn patterns in time series data that recur over time. The patterns may be used for simulation, predicting future behavior, or detecting anomalies in a system in which the data is collected. The technique incrementally detects daily, weekly, monthly, and yearly patterns. Each pattern is built over time instead of requiring all the data to be available at the beginning of the analysis. Instead of modeling each pattern explicitly, each pattern is described in the context of a day and formed based on time series data collected over an entire day. An example use of the technique is detecting load patterns in a computer system. A metric of system load such as CPU utilization may be collected periodically over a day. The techniques presented herein capture multiple daily models, each representing a different load pattern.

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