AUTOMATED LEARNING OF DATA AGGREGATION FOR ANALYTICS

    公开(公告)号:US20180374104A1

    公开(公告)日:2018-12-27

    申请号:US15633401

    申请日:2017-06-26

    Applicant: SAP SE

    Abstract: Methods, systems, and computer-readable storage media for automatically providing a predictive model for an asset made up of multiple sub-assets with actions including receiving asset data including data values associated with the asset and at least one of sub-asset of the multiple assets, providing, by the one or more processors, a set of features based on the asset data, and executing an iterative feature selection and supervised learning process, including, for each iteration: selecting a sub-set of features from the set of features, performing supervised learning over the sub-set of features to provide a predictive model, and determining an accuracy of the predictive model, the iterations are performed until the accuracy of the predictive model exceeds a threshold accuracy.

    Sensor data anomaly detection
    2.
    发明授权

    公开(公告)号:US10915391B2

    公开(公告)日:2021-02-09

    申请号:US16455186

    申请日:2019-06-27

    Applicant: SAP SE

    Abstract: Some embodiments include reception of a time-series of a respective data value generated by each of a plurality of sensors, calculation of a regression associated with a first sensor of the plurality of sensors based on the received plurality of time-series, the regression being a function of the respective data values of the others of the plurality of data sources, reception of respective data values associated with a time from and generated by each the plurality of respective sensors, determination of a predicted value associated with the time for the first sensor based on the regression associated with the first sensor and on the respective data values associated with the time, comparison of the predicted value with the received value associated with the time and generated by the first sensor, and determination of a value indicating a likelihood of an anomaly based on the comparison.

    MULTIVARIATE NONLINEAR AUTOREGRESSION FOR OUTLIER DETECTION

    公开(公告)号:US20200033831A1

    公开(公告)日:2020-01-30

    申请号:US16049287

    申请日:2018-07-30

    Applicant: SAP SE

    Abstract: Methods, systems, and computer-readable storage media for receiving a time-series of data values associated with a plurality of sensors, each sensor generating at least a portion of the time-series of a respective data value, providing a plurality of auto-regression models, each auto-regression model being provided based on a respective first sub-set of the time-series of data values used as input, and a respective second sub-set of the time-series of data values used as training data during a training process, receiving respective data values associated with a time from and generated by each of the plurality of sensors, determining respective predicted values for each of the auto-regression models, and selectively indicating that an anomaly is present in the system based on respective predicted values for each of the auto-regression models, and the respective data values associated with a time.

    SENSOR DATA ANOMALY DETECTION
    7.
    发明申请

    公开(公告)号:US20190317848A1

    公开(公告)日:2019-10-17

    申请号:US16455186

    申请日:2019-06-27

    Applicant: SAP SE

    Abstract: Some embodiments include reception of a time-series of a respective data value generated by each of a plurality of sensors, calculation of a regression associated with a first sensor of the plurality of sensors based on the received plurality of time-series, the regression being a function of the respective data values of the others of the plurality of data sources, reception of respective data values associated with a time from and generated by each the plurality of respective sensors, determination of a predicted value associated with the time for the first sensor based on the regression associated with the first sensor and on the respective data values associated with the time, comparison of the predicted value with the received value associated with the time and generated by the first sensor, and determination of a value indicating a likelihood of an anomaly based on the comparison.

    Sensor data anomaly detection
    8.
    发明授权

    公开(公告)号:US10379933B2

    公开(公告)日:2019-08-13

    申请号:US15463601

    申请日:2017-03-20

    Applicant: SAP SE

    Abstract: Some embodiments include reception of a time-series of a respective data value generated by each of a plurality of sensors, calculation of a regression associated with a first sensor of the plurality of sensors based on the received plurality of time-series, the regression being a function of the respective data values of the others of the plurality of data sources, reception of respective data values associated with a time from and generated by each the plurality of respective sensors, determination of a predicted value associated with the time for the first sensor based on the regression associated with the first sensor and on the respective data values associated with the time, comparison of the predicted value with the received value associated with the time and generated by the first sensor, and determination of a value indicating a likelihood of an anomaly based on the comparison.

    Multivariate nonlinear autoregression for outlier detection

    公开(公告)号:US11579588B2

    公开(公告)日:2023-02-14

    申请号:US16049287

    申请日:2018-07-30

    Applicant: SAP SE

    Abstract: Methods, systems, and computer-readable storage media for receiving a time-series of data values associated with a plurality of sensors, each sensor generating at least a portion of the time-series of a respective data value, providing a plurality of auto-regression models, each auto-regression model being provided based on a respective first sub-set of the time-series of data values used as input, and a respective second sub-set of the time-series of data values used as training data during a training process, receiving respective data values associated with a time from and generated by each of the plurality of sensors, determining respective predicted values for each of the auto-regression models, and selectively indicating that an anomaly is present in the system based on respective predicted values for each of the auto-regression models, and the respective data values associated with a time.

    COMPARING UNSUPERVISED ALGORITHMS FOR ANOMALY DETECTION

    公开(公告)号:US20190007432A1

    公开(公告)日:2019-01-03

    申请号:US15637471

    申请日:2017-06-29

    Applicant: SAP SE

    Abstract: Methods, systems, and computer-readable storage media for ranking anomaly detection algorithms, including operations of receiving a set of unlabeled data from one or more sensors in a plurality of sensors of an internet of things, generating a plurality of data distributions corresponding to the set of unlabeled data by using a plurality of anomaly detection algorithms, and ranking the plurality of anomaly detection algorithms relative to the set of unlabeled data based on a distance between a first quantile and a second quantile of each of the plurality of data distributions.

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