Oracle International Corporation

Klaus Thielen

Angelo Pruscino

Michael Zoll

Subhransu Basu

Yaser I Suleiman

Wataru Miyoshi

Thomas Herter

Sahil Kumar

Wolfgang Lohwasser

Thomas Breidt

Described is an approach that provides an adaptive solution to missing data for machine learning systems. A gradient solution is provided that is attentive to imputation needs at each of several missingness levels. This multilevel approach treats data missingness at any of multiple severity levels while utilizing, as much as possible, the actual observed data.

Method and system for adaptively imputing sparse and missing data for predictive models

Described is an improved approach to remove data outliers by filtering out data correlated to detrimental events within a system. One or more detrimental even conditions are defined to identify and handle abnormal transient states from collected data for a monitored system.

Method and system for adaptively removing outliers from data used in training of predictive models

Described is an approach for performing context-aware prognoses in machine learning systems. The approach harnesses streams of detailed data collected from a monitored target to create a context, in parallel to ongoing model operations, for the model outcomes. The context is then probed to identify the particular elements associated with the model findings.

Method and system for performing context-aware prognoses for health analysis of monitored systems

Described is an improved approach to implement selection of training data for machine learning, by presenting a designated set of specific data indicators where these data indicators correspond to metrics that end users are familiar with and are easily understood by ordinary users and DBAs within their knowledge domain. Selection of these indicators would correlate automatically to selection of a corresponding set of other metrics/signals that are less understandable to an ordinary user. Additional analysis of the selected data can then be performed to identify and correct any statistical problems with the selected training data.

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Munich, Germany

Klaus Thielen