Abstract: Aspects of the present disclosure provide systems, methods, apparatus, and computer-readable storage media that support automated action recommendation for structured processes. Aspects described herein leverage trained machine learning (ML) models to assign features extracted from historical event data into multiple clusters using unsupervised learning. In some implementations, current event data of a structured process is received, and extracted features assigned to one of the multiple clusters by the ML models. Candidate event sequences are generated based on members of the assigned cluster and are filtered based on corresponding association rule scores. Multiple incremental candidate sub-sequences are generated from the remaining candidate event sequences, and these are filtered based on a current event level and corresponding association rule scores.

Abstract: A system for issue prediction based on multidimensional data analysis includes a model generator that receives a resolved data item relating to a service issue. The resolved data item includes different attributes corresponding to multiple data dimensions and adjusts a population of attributes based on a statistical data model and a deep learning data model operating independent of each other. The statistical data model operates on the attributes for providing a predictive feature and the deep learning data model operates on the attributes for providing a predictive label based on performance metrics related to the data dimensions. The predictive feature and the predictive label collectively define training data. The model generator also trains a classification model based on the training data for predicting a potential issue related to an unresolved data item. The trained data model provides a trigger based on the potential issue being related to the performance metrics.

Abstract: A system for detecting and preventing an imminent failure in a target system includes an interface, a processor in communication with the interface, and non-transitory computer readable media in communication with the processor. The interface receives training data items. Each item corresponds to either a service ticket or a machine-generated log that specifies an issue and a resolution to the issue. The instruction code is executed by the processor and causes the processor to group the training data items according to different categories. For each group, the processor trains a model to match the issue of each item of the group with the corresponding resolution and associates a model configuration of the trained model with the category of the group. The processor receives a sequence of new data items that include service tickets or machine generated logs for which a resolution is unknown. The processor groups the new data items according to one or more categories.

Abstract: A system for detecting and preventing an imminent failure in a target system includes an interface, a processor in communication with the interface, and non-transitory computer readable media in communication with the processor. The interface receives training data items. Each item corresponds to either a service ticket or a machine-generated log that specifies an issue and a resolution to the issue. The instruction code is executed by the processor and causes the processor to group the training data items according to different categories. For each group, the processor trains a model to match the issue of each item of the group with the corresponding resolution and associates a model configuration of the trained model with the category of the group. The processor receives a sequence of new data items that include service tickets or machine generated logs for which a resolution is unknown. The processor groups the new data items according to one or more categories.