Abstract: Provided are techniques for a troubleshooting system. Selection of a trace is received. The trace is associated with raw log lines. A trace category of the trace is identified, where the trace category is associated with a common log lines template. Common log lines are generated by removing log lines from the raw log lines that do not appear in the common log lines template. One or more relevant trace categories and one or more relevant traces are identified. A problem is identified using the trace, the common log lines, the one or more relevant trace categories, and the one or more relevant traces. A solution for the problem is implemented.

Abstract: A computer-implemented method includes parsing, by a processor set, text comprised by an alert corresponding to an information technology (IT) abnormality incident, resulting in alert data. The processor set uses a generative machine learning (ML) model to generate a natural language summary of the incident. The natural language summary includes a symptom-resource pairing corresponding to the alert and is based on the alert data and on a topology of keywords comprised by the alert. In one or more embodiments, the computer-implemented method further comprises employing, by the processor set, graph connectivity distances between elements of the topology to verify the symptom-resource pairing.

Abstract: An approach is provided in which the approach trains a machine learning model using reference entries included in a reference dataset. During the training, the machine learning model learns a first set of unidirectional associations between the reference entries. The approach inputs a user dataset into the trained machine learning model and generates a second set of unidirectional associations between user dataset entries included in the user dataset. The approach builds a hierarchical relationship of the user dataset based on the second set of unidirectional associations and manages the user dataset based on the hierarchical relationship.

Abstract: An approach is provided in which the approach trains a machine learning model using reference entries included in a reference dataset. During the training, the machine learning model learns a first set of unidirectional associations between the reference entries. The approach inputs a user dataset into the trained machine learning model and generates a second set of unidirectional associations between user dataset entries included in the user dataset. The approach builds a hierarchical relationship of the user dataset based on the second set of unidirectional associations and manages the user dataset based on the hierarchical relationship.