Abstract: The present disclosure relates to computer-implemented methods, software, and systems for utilizing tools and techniques for identifying process rules for automated execution of instances of a process workflow. One example method includes extracting rules from a machine learning model for prediction of execution results of process workflow instances. Metrics defining coverage and accuracy of the rules are calculated. The rules are evaluated according to the metrics and are reduced to a first set of rules that are provided for further evaluation. A rule from the first set of rules is determined to be incorporated into process rules defined for the process workflow at a process execution engine. The process rules associated with execution of the process workflow are updated to include the first rule and to generate a process result automatically according to the first rule when the instance complies with prerequisites defined at the first rule.

Abstract: The present disclosure relates to computer-implemented methods, software, and systems for utilizing tools and techniques for identifying process rules for automated execution of instances of a process workflow. One example method includes extracting rules from a machine learning model for prediction of execution results of process workflow instances. Metrics defining coverage and accuracy of the rules are calculated. The rules are evaluated according to the metrics and are reduced to a first set of rules that are provided for further evaluation. A rule from the first set of rules is determined to be incorporated into process rules defined for the process workflow at a process execution engine. The process rules associated with execution of the process workflow are updated to include the first rule and to generate a process result automatically according to the first rule when the instance complies with prerequisites defined at the first rule.

Abstract: The present disclosure relates to computer-implemented methods, software, and systems for utilizing tools and techniques for providing data that can be used for prediction and automation of process execution. One example method includes that customer-specific data is joined with generic framework data based on identification of work item to create initial data. The generic framework data is for a generic workflow associated with multiple process scenarios. The initial data set is provided for predicting variable of a process scenario of the generic workflow. Machine learning prediction is performed for a instant process scenario execution at a customer environment. The initial data is adjusted based on provided data enhancement rules to generate an output data set. The output data set is provided for evaluation by an implementation of the machine learning algorithm to provide a prediction result for the process scenario execution.