Abstract: Solutions for identifying and optimizing batch processes are provided herein. A task monitor identifies a plurality of processes in an event log. The processes identified by the task monitor are used by a batch processing engine to identify batches from the processes. The batch processing engine compares each of the identified batches to a set of policies or with other identified batches. The batch processing engine provides a recommendation to user to optimize a particular batch, for example, based on the comparing. Identifying batches and providing recommendations to optimize the batches enables processes associated with the batches to be optimized upon implementation of the recommendation.

Abstract: Methods, systems and computer program products are provided for performing root cause analysis (RCA) in process mining. Root causes of labeled values may be determined and visualized for user interpretation. RCA performance may be improved by selecting and/or excluding features based on at least one of correlation, granularity, and/or relevance thresholds. An RCA engine may perform RCA on a set of features to generate a set of RCA rules. A visualizer may generate a visualization of the RCA rules. Visualizations may be interactive. For example, users may select a condition to view actions triggered by the condition. Rules may be developed by modifying feature values (e.g., using LIME, SHAP). Users may provide target goals to improve a process, for example, before and/or after visualization. The RCA engine may perform an RCA based on modified feature values to achieve the target goal and present (e.g., visualize) target modifications.

Abstract: The disclosure herein describes creating and managing an editable rules-based model. A model is trained to generate output values for data sources based on input feature data of the data sources. Using the trained model, results are generated that include feature-output value pairs and associated confidence values for each data source. The confidence values are aggregated for each feature-output value pair of the generated results, and rules of a rules-based model are built using the feature-output value pairs and the respective aggregated confidence values of the generated results. A user is provided access to the rules of the rules-based model via a model management interface. The rules-based model is updated based on rule change instructions received from the user via the model management interface.

Abstract: The disclosure herein describes creating and managing an editable rules-based model. A model is trained to generate output values for data sources based on input feature data of the data sources. Using the trained model, results are generated that include feature-output value pairs and associated confidence values for each data source. The confidence values are aggregated for each feature-output value pair of the generated results, and rules of a rules-based model are built using the feature-output value pairs and the respective aggregated confidence values of the generated results. A user is provided access to the rules of the rules-based model via a model management interface. The rules-based model is updated based on rule change instructions received from the user via the model management interface.