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公开(公告)号：US20220004822A1
公开(公告)日：2022-01-06
申请号：US16921579
申请日：2020-07-06
Applicant: Oracle International Corporation
Inventor： Amit Vaid , Vijayalakshmi Krishnamurthy
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.
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公开(公告)号：US20210097416A1
公开(公告)日：2021-04-01
申请号：US16585764
申请日：2019-09-27
Applicant: Oracle International Corporation
Inventor： Karthik GVD , Utkarsh Milind DESAI , Vijayalakshmi Krishnamurthy
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.
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公开(公告)号：US12223397B2
公开(公告)日：2025-02-11
申请号：US17007601
申请日：2020-08-31
Applicant: Oracle International Corporation
Inventor： Amit Vaid , Vijayalakshmi Krishnamurthy
IPC: G06N5/022 , G06F16/28 , G06F18/214 , G06F40/143 , G06F40/35 , G06F40/58 , G06N3/042 , G06N20/00
Abstract: Techniques for providing actionable recommendations for configuring system parameters are disclosed. A set of environmental constraints and a set of values for a set of parameters for a target device is applied to a machine learning model to predict a first performance value of the target device. Candidate values for the set of parameters are identified that are within a threshold range from the first set of values in a multi-dimensional space. For each particular candidate set of values of the candidate sets of values the machine learning model to predicts a performance value of the target device and identifies a subset of the candidate sets of values with corresponding performance values that meet a performance criteria. A subset of candidate sets of values that meets performance criteria is provided as a recommendation.
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公开(公告)号：US12182123B2
公开(公告)日：2024-12-31
申请号：US18149576
申请日：2023-01-03
Applicant: Oracle International Corporation
Inventor： Joseph Marc Posner , Sunil Kumar Kunisetty , Mohan Kamath , Nickolas Kavantzas , Sachin Bhatkar , Sergey Troshin , Sujay Sarkhel , Shivakumar Subramanian Govindarajapuram , Vijayalakshmi Krishnamurthy
IPC: G06F16/00 , G06F11/34 , G06F16/21 , G06F16/22 , G06F16/23 , G06F16/2453 , G06F16/28 , G06N20/00 , G06N20/10 , G06N20/20
Abstract: Techniques for improving system performance based on data characteristics are disclosed. A system may receive updates to a first data set at a first frequency. The system selects a first storage configuration, from a plurality of storage configurations, for storing the first data set based on the first frequency, and stores the first data set in accordance with the first storage configuration. The system may further receive updates to a second data set at a second frequency. The system selects a second storage configuration, from the plurality of storage configurations, for storing the second data set based on the second frequency, and stores the second data set in accordance with the second storage configuration. The second storage configuration is different than the first storage configuration.
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公开(公告)号：US20220067572A1
公开(公告)日：2022-03-03
申请号：US17007601
申请日：2020-08-31
Applicant: Oracle International Corporation
Inventor： Amit Vaid , Vijayalakshmi Krishnamurthy
Abstract: Techniques for providing actionable recommendations for configuring system parameters are disclosed. A set of environmental constraints and a set of values for a set of parameters for a target device is applied to a machine learning model to predict a first performance value of the target device. Candidate values for the set of parameters are identified that are within a threshold range from the first set of values in a multi-dimensional space. For each particular candidate set of values of the candidate sets of values the machine learning model to predicts a performance value of the target device and identifies a subset of the candidate sets of values with corresponding performance values that meet a performance criteria. A subset of candidate sets of values that meets performance criteria is provided as a recommendation.
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Patent Agency Ranking
公开(公告)号：US20210406110A1
公开(公告)日：2021-12-30
申请号：US17147737
申请日：2021-01-13
Applicant: Oracle International Corporation
Inventor： Amit Vaid , Karthik Gvd , Vijayalakshmi Krishnamurthy , Vidya Mani
Abstract: Techniques for identifying anomalous multi-source data points and ranking the contributions of measurement sources of the multi-source data points are disclosed. A system obtains a data point including a plurality of measurements from a plurality of sources. The system determines that the data point is an anomalous data point based on a deviation of the data point from a plurality of additional data points. The system determines a contribution of two or more measurements, from the plurality of measurements, to the deviation of the data point from the plurality of additional data points. The system ranks the at least the two or more measurements, from the plurality of measurements, based on the respective contribution of each of the two or more measurements to the deviation of the anomalous data point from the plurality of prior data points.
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公开(公告)号：US20200372030A1
公开(公告)日：2020-11-26
申请号：US16992819
申请日：2020-08-13
Applicant: Oracle International Corporation
Inventor： Joseph Marc Posner , Sunil Kumar Kunisetty , Mohan Kamath , Nickolas Kavantzas , Sachin Bhatkar , Sergey Troshin , Sujay Sarkhel , Shivakumar Subramanian Govindarajapuram , Vijayalakshmi Krishnamurthy
Abstract: Techniques for improving system performance based on data characteristics are disclosed. A system may receive updates to a first data set at a first frequency. The system selects a first storage configuration, from a plurality of storage configurations, for storing the first data set based on the first frequency, and stores the first data set in accordance with the first storage configuration. The system may further receive updates to a second data set at a second frequency. The system selects a second storage configuration, from the plurality of storage configurations, for storing the second data set based on the second frequency, and stores the second data set in accordance with the second storage configuration. The second storage configuration is different than the first storage configuration.
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公开(公告)号：US12086725B2
公开(公告)日：2024-09-10
申请号：US16987148
申请日：2020-08-06
Applicant: Oracle International Corporation
Inventor： Suresh Kumar Golconda , Vijayalakshmi Krishnamurthy , Someshwar Maroti Kale , Sujay Sarkhel , Nickolas Kavantzas , Mohan U. Kamath , Neelesh Kumar Shukla , Vidya Mani , Amit Vaid
Abstract: Techniques for selecting universal hyper parameters for use in a set of machine learning models across multiple computing environments include detection of a triggering condition for tuning a set of universal hyper parameters. The set of universal hyper parameters dictate configuration of the set of machine learning models that are independently executing, respectively, in the multiple computing environments. Based on the detected triggering condition, a first subset of universal hyper parameters from the set of universal hyper parameters are altered to generate a second set of universal hyper parameters. The second set of universal hyper parameters are applied to the set of machine learning models across the multiple computing environments.
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公开(公告)号：US11676071B2
公开(公告)日：2023-06-13
申请号：US17147737
申请日：2021-01-13
Applicant: Oracle International Corporation
Inventor： Amit Vaid , Karthik Gvd , Vijayalakshmi Krishnamurthy , Vidya Mani
CPC classification number: G06N20/00 , G06F11/0754 , G06F11/0766 , G06F18/2135 , G06F18/22 , G06F18/251
Abstract: Techniques for identifying anomalous multi-source data points and ranking the contributions of measurement sources of the multi-source data points are disclosed. A system obtains a data point including a plurality of measurements from a plurality of sources. The system determines that the data point is an anomalous data point based on a deviation of the data point from a plurality of additional data points. The system determines a contribution of two or more measurements, from the plurality of measurements, to the deviation of the data point from the plurality of additional data points. The system ranks the at least the two or more measurements, from the plurality of measurements, based on the respective contribution of each of the two or more measurements to the deviation of the anomalous data point from the plurality of prior data points.
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公开(公告)号：US11651627B2
公开(公告)日：2023-05-16
申请号：US16699023
申请日：2019-11-28
Applicant: Oracle International Corporation
Inventor： Amit Vaid , Neha Tomar , Utkarsh Milind Desai , Vijayalakshmi Krishnamurthy , Goldee Udani
IPC: G07C5/00 , G05B23/02 , G06Q10/0635 , G06Q10/20 , G07C5/08
CPC classification number: G07C5/006 , G05B23/024 , G05B23/0283 , G06Q10/0635 , G06Q10/20 , G07C5/0808
Abstract: Embodiments determine an optimized maintenance schedule for a maintenance program that includes multiple levels, each level including at least one asset (i.e., asset type) and at least one of the levels including a plurality of assets. Embodiments receive historical failure data for each of the assets, the historical failure data generated at least in part by a sensor network. For each asset, embodiments generate a probability density function (“PDF”) using kernel density estimation (“KDE”). For each asset, based on a reliability rate threshold, embodiments determine a cumulative density function (“CDF”) using the PDF. For each asset, embodiments determine an optimized time to failure (“TTF”) using the CDF. Embodiments then create the schedule for each level that includes a minimum TTF for the assets at each level.
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