Ca, Inc.

Michael J Cohen

Daniel David Sill

William J Mcallister

Techniques are disclosed relating to increasing the amount of training data available to machine learning algorithms. A computer system may access an initial set of training data that specifies a plurality of sequences, each of which may define a set of data values. The computer system may amplify the initial set of training data to create a revised set of training data. The amplifying may include identifying sub-sequences of data values in ones of the plurality of sequences in the initial set of training data and using an inheritance algorithm to create a set of additional sequences of data values, where each one of the set of additional sequences may include sub-sequences of data values from at least two different sequences in the initial set of training data. The computer system may process the set of additional sequences using the machine learning algorithm to train a machine learning model.

Amplification of initial training data

A method includes determining a service task output definition corresponding to a first property within a first scope of a first service task in a compiled code file, and determining a service task input definition corresponding to a second property within a second scope of a second service task in the compiled code file. The method also includes formatting for display the service task output definition, the service task input definition, and a workflow variable. The method further includes determining a data-flow definition for a workflow process, which includes receiving a first workflow mapping between the service task output definition and the service task input definition, and receiving a second workflow mapping between an output of the second service task and the workflow variable. The method additionally includes determining a value of the workflow variable after execution of the first service task and the second service task.

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Flushing, NY, United States of America

Michael J Cohen