Oracle International Corporation

Sujay Sarkhel

Nickolas Kavantzas

Vijayalakshmi Krishnamurthy

Mohan Kamath

Sachin Bhatkar

Sunil Kunisetty

Sergey Troshin

Shivakumar Subramanian Govindarajapuram

Joseph Marc Posner

Amit Vaid

Vidya Mani

Suresh Kumar Golconda

Neelesh Kumar Shukla

Someshwar Maroti Kale

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.

Dynamic performance tuning based on implied data characteristics

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

A model analyzer may receive a representative data set as input and select one of a plurality of analytic models to perform the analysis. Before deciding which model to use the model may be trained, and the trained model evaluated for accuracy. However, some models are known to behave poorly when the training data is distributed in a particular way. Thus, the cost of training a model and evaluating the trained model can be avoided by first analyzing the distribution of the representative data. Identifying the representative data distribution allows ruling out use of models for which the distribution of the representative data is unsuitable. Only models that may be compatible with the distribution of the representative data may be trained and evaluated for accuracy. The most accurate trained model whose accuracy meets an accuracy threshold may be selected to analyze subsequently received data related to the representative data.

Selecting an algorithm for analyzing a data set based on the distribution of the data set

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San Jose, CA, United States of America

Sujay Sarkhel