Oracle International Corporation

Amit Vaid

Vijayalakshmi Krishnamurthy

Karthik Gvd

Vidya Mani

Nickolas Kavantzas

Utkarsh Milind Desai

Mohan Kamath

Sujay Sarkhel

Goldee Udani

Suresh Kumar Golconda

Neelesh Kumar Shukla

Neha Tomar

Someshwar Maroti Kale

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.

Action determination using recommendations based on prediction of sensor-based systems

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.

Hyper parameter tuning for machine learning models

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.

Integrating data quality analyses for modeling metrics

Embodiments match sensor data output by a sensor to a trained pattern. Embodiments form a plurality of windows of an identified pattern from the sensor data, each of the plurality of windows having a substantially equal window length to a length of the trained pattern. For each of the windows, embodiments generate a corresponding first Symbolic Aggregate approximation ('SAX') word, determine a Hamming distance between the first SAX word and a second SAX word corresponding to the trained pattern, and determine a final distance score based on coefficients between the first SAX word and the second SAX word. For each of the windows, embodiments determine a number of positions in the first SAX word that do not contribute to the final distance score, update the Hamming distance after eliminating the number of positions and determine an average distance based on the final distance score and the updated Hamming distance.

Adaptive pattern recognition for a sensor network

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.

Identifying and ranking anomalous measurements to identify faulty data sources in a multi-source environment

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 (&#x201c;CDF&#x201d;) using the PDF. For each asset, embodiments determine an optimized time to failure (&#x201c;TTF&#x201d;) using the CDF. Embodiments then create the schedule for each level that includes a minimum TTF for the assets at each level.

Sensor network for optimized maintenance schedule

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.

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Bengaluru, India

Amit Vaid