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    Univariate anomaly detection in a sensor network
    11.
    发明授权
    Univariate anomaly detection in a sensor network 有权

    公开(公告)号：US11526790B2

    公开(公告)日：2022-12-13

    申请号：US16585764

    申请日：2019-09-27

    Applicant: Oracle International Corporation

    Inventor： Karthik Gvd ,  Utkarsh Milind Desai ,  Vijayalakshmi Krishnamurthy

    IPC: G01D3/08 ,  G06N7/00 ,  G06K9/62 ,  H04W84/18 ,  G06F17/13 ,  G01D21/00

    Abstract: Embodiments determine anomalies in sensor data generated by a sensor by receiving an evaluation time window of clean sensor data generated by the sensor. Embodiments receive a threshold value for determining anomalies. When the clean sensor data has a cyclic pattern, embodiments divide the evaluation time window into a plurality of segments of equal length, wherein each equal length comprises the cyclic pattern. When the clean sensor data does not have the cyclic pattern, embodiments divide the evaluation time window into a pre-defined number of plurality of segments of equal length. Embodiments convert the evaluation time window and each of the plurality of segments into corresponding curves using Kernel Density Estimation (“KDE”). For each of the plurality of segments, embodiments determine a Kullback-Leibler (“KL”) divergence value between corresponding curves of the segment and the evaluation time window to generate a plurality of KL divergence values.

    Automatic asset anomaly detection in a multi-sensor network
    12.
    发明授权
    Automatic asset anomaly detection in a multi-sensor network 有权

    公开(公告)号：US11216247B2

    公开(公告)日：2022-01-04

    申请号：US16806275

    申请日：2020-03-02

    Applicant: Oracle International Corporation

    Inventor： Amit Vaid ,  Karthik Gvd ,  Vijayalakshmi Krishnamurthy

    IPC: G06F7/02

    Abstract: Embodiments determine anomalies in sensor data generated by a plurality of sensors that correspond to a single asset. Embodiments receive a first time window of clean sensor input data generated by the sensors, the clean sensor data including anomaly free data comprised of clean data points. Embodiments divide the clean data points into training data points and evaluation data points, and divide the training data points into a pre-defined number of plurality of segments of equal length. Embodiments convert each of the plurality of segments into corresponding segment curves using Kernel Density Estimation (“KDE”) and determine a Jensen-Shannon (“JS”) divergence value for each of the plurality of segments using the segment curves to generate a plurality of JS divergence values. Embodiments then assign the maximum value of the plurality of JS divergence values as a threshold value and validate the threshold value using the evaluation data points.

    Univariate anomaly detection in a sensor network
    13.
    发明授权
    Univariate anomaly detection in a sensor network 有权

    公开(公告)号：US11060885B2

    公开(公告)日：2021-07-13

    申请号：US16587334

    申请日：2019-09-30

    Applicant: Oracle International Corporation

    Inventor： Karthik Gvd ,  Utkarsh Milind Desai ,  Vijayalakshmi Krishnamurthy ,  Goldee Udani

    IPC: G01D1/00 ,  G01D18/00

    Abstract: Embodiments determine anomalies in sensor data generated by a sensor. Embodiments receive a first time window of clean sensor data generated by the sensor, the clean sensor data including anomaly free data, and determine if the clean sensor data includes a cyclic pattern. When the clean sensor data has a cyclic pattern, embodiments divide the first time window into a plurality of segments of equal length, where each equal length includes the cyclic pattern. Embodiments convert the first time window and each of the plurality of segments into corresponding curves using Kernel Density Estimation (“KDE”). For each of the plurality of segments, embodiments determine a Kullback-Leibler (“KL”) divergence value between corresponding curves of the segment and the first time window to generate a plurality of KL divergence values.

    Integrating data quality analyses for modeling metrics
    14.
    发明授权
    Integrating data quality analyses for modeling metrics 有权

    公开(公告)号：US12050969B2

    公开(公告)日：2024-07-30

    申请号：US16921579

    申请日：2020-07-06

    Applicant: Oracle International Corporation

    Inventor： Amit Vaid ,  Vijayalakshmi Krishnamurthy

    IPC: G06N20/00 ,  G06F18/21 ,  G06F18/2113

    CPC classification number: G06N20/00 ,  G06F18/2113 ,  G06F18/2193

    Abstract: Techniques for generating a composite score for data quality are disclosed. Univariate analysis is performed on a plurality of data points corresponding to each of a first feature, a second feature, and a third feature of a data set. The univariate analysis includes at least a first type of analysis generating a first score having a first range of possible values, and a second type of analysis generating a second score having a second range of possible values. A first quality score is computed for the data values for the first, second, and third features based on a normalized first score and a normalized second score. Machine learning is performed on the data points corresponding to one or both of the first feature and the second feature having a first quality score above a threshold value to model the third feature.

    IDENTIFYING ROOT CAUSE ANOMALIES IN TIME SERIES
    15.
    发明公开
    IDENTIFYING ROOT CAUSE ANOMALIES IN TIME SERIES 审中-公开

    公开(公告)号：US20240118965A1

    公开(公告)日：2024-04-11

    申请号：US17962869

    申请日：2022-10-10

    Applicant: Oracle International Corporation

    Inventor： Shwan Ashrafi ,  Michal Piotr Prussak ,  Hariharan Balasubramanian ,  Vijayalakshmi Krishnamurthy

    IPC: G06F11/07

    CPC classification number: G06F11/079 ,  G06F11/0712

    Abstract: Techniques are described for identifying root cause anomalies in time series. Information to be used for root cause analysis (RCA) is obtained from a graph neural network (GNN) and is used to construct a dependency graph having nodes corresponding to each time series and directed edges corresponding to dependencies between the time series. Nodes corresponding to time series that do not contain anomalies may be removed from this dependency graph, as well as edges connected to these nodes. This edge and node removal may result in the creation of one or more sub-graphs from the dependency graph. A root cause analysis algorithm may be run on these one or more sub-graphs to create a root cause graph for each sub-graph. These root cause graphs may then be used to identify root cause anomalies within the multiple time series, as well as sequences of anomalies within the multiple time series.

    EXPLAINABILITY OF TIME SERIES PREDICTIONS MADE USING STATISTICAL MODELS
    17.
    发明申请
    EXPLAINABILITY OF TIME SERIES PREDICTIONS MADE USING STATISTICAL MODELS 有权

    公开(公告)号：US20230122150A1

    公开(公告)日：2023-04-20

    申请号：US17731147

    申请日：2022-04-27

    Applicant: Oracle International Corporation

    Inventor： Nitin Rawat ,  Lakshmi Sirisha Chodisetty ,  Samik Raychaudhuri ,  Vijayalakshmi Krishnamurthy

    IPC: G06N7/00 ,  G06K9/62

    Abstract: Techniques are described for providing explanation information for time series-based predictions made using statistical models, such as linear statistical models, examples of which include various Exponential Smoothing models, Autoregressive Integrated Moving Average (ARIMA) models, and others. For a forecast predicted by a statistical model that has been trained upon and/or fit to a set of historical times series data points, an explanation is generated for the forecast, where the explanation for the forecast includes information indicative of the importance or impact or influence of individual time series data points in the set on the forecast. The explanation for the forecast may be output to a user along with the forecast. This enables the user to have some visibility into why the particular forecast was predicted by the statistical model.

    Identifying root cause anomalies in time series
    20.
    发明授权
    Identifying root cause anomalies in time series 有权

    公开(公告)号：US12242332B2

    公开(公告)日：2025-03-04

    申请号：US17962869

    申请日：2022-10-10

    Applicant: Oracle International Corporation

    Inventor： Shwan Ashrafi ,  Michal Piotr Prussak ,  Hariharan Balasubramanian ,  Vijayalakshmi Krishnamurthy

    IPC: G06F11/07

    Abstract: Techniques are described for identifying root cause anomalies in time series. Information to be used for root cause analysis (RCA) is obtained from a graph neural network (GNN) and is used to construct a dependency graph having nodes corresponding to each time series and directed edges corresponding to dependencies between the time series. Nodes corresponding to time series that do not contain anomalies may be removed from this dependency graph, as well as edges connected to these nodes. This edge and node removal may result in the creation of one or more sub-graphs from the dependency graph. A root cause analysis algorithm may be run on these one or more sub-graphs to create a root cause graph for each sub-graph. These root cause graphs may then be used to identify root cause anomalies within the multiple time series, as well as sequences of anomalies within the multiple time series.

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