Abstract: A computer implemented method identifies a root cause of a problem. A number of processor units uses a machine learning model to predict a set of initial log files for review to identify the root cause of the problem. The number of processor units displays the set of initial log files predicted by the machine learning model on a graphical user interface. The number of processor units predicts a set of next log files for review to identify the root cause of the problem using the machine learning model and user behavior data related to the graphical user interface in response to a user input to the graphical user interface. The number of processor units displays a recommendation to review the set of next log files predicted by the machine learning model on the graphical user interface.

Abstract: An embodiment detects, by an outlier detector, a first potential outlier in a data structure. The embodiment determines, by the outlier detector, whether the first potential outlier is a first outlier based on a first threshold. The embodiment applies, by an outlier corrector, responsive to determining the first potential outlier is a first outlier, a data quality rule to the first outlier. The embodiment detects, by the outlier detector, a second potential outlier in the data structure. The embodiment decreases, by the outlier detector, the first threshold to a second threshold. The embodiment determines, by the outlier detector, whether the second potential outlier is a second outlier based on the second threshold. The embodiment applies, by the outlier corrector, responsive to determining the second potential outlier is a second outlier, the data quality rule to the second outlier.

Abstract: A method, computer system, and a computer program product for storing a graph is provided. The present invention may include providing a key-value store. The present invention may include storing a graph in the key-value store, wherein storing a specific node of the graph in the key-value store comprises: storing, as a key, identifiers of the specific node and of k-hop neighborhood of the specific node, and as value of the key, attribute values of the specific node, wherein the k-hop neighborhood of the specific node comprises a set of neighboring nodes that are reachable from the specific node within k edges.

Abstract: Inference results of a machine learning model and associated inputs are collected. An inference request is received. A determination is made whether a request input of the inference request matches at least one collected input of a set of collected inputs. In response to determining that the request input matches at least one collected input in the set of collected inputs, an inference result is determined using one or more collected inference results associated with said one or more matching inputs in the set of collected inputs.