Abstract: A system obtains a bundle of insights generated based on insight templates and provides the bundle of insights as input to a machine learning model. The system then generates a summary of the bundle of insights using the machine learning model.

Abstract: A method generates data visualizations for interactive recommender systems for analytic assets. The method obtains recommendations to destination nodes for a source node of an input graph, which includes nodes including the source node and a destination node. Each node stores metadata for a respective analytic asset. The input graph encodes asset lineage that captures relationships between the analytic assets. The method also generates a data visualization for the recommendations. The data visualization includes (i) a summary of the recommendations, (ii) a comparison of the destination nodes, and (iii) a set of factors that contributed to one or more recommendations. The method also includes displaying the data visualization using a graphical user interface. The graphical user interface includes a data region that includes the summary, a recommendation overview region that includes the comparison, and a recommendation detail region that includes the set of factors.

Abstract: A method generates analytic asset recommendations using graph neural networks. The method obtains a data graph that includes a plurality of nodes. Each node stores metadata for a respective analytic asset of a plurality of analytic assets. The data graph encodes relationships between the plurality of analytic assets. The method extracts a set of features for each node of the data graph. Each node has the same features as other nodes. The method derives corresponding node embeddings for two nodes of the data graph using a two-layer graph neural network based on the data graph and the set of features. The method predicts a link between the two nodes of the data graph based on the corresponding node embeddings. The method also generates a recommendation for an analytic asset when the probability for the link is above a predetermined threshold.

Abstract: Embodiments are directed to visualizations. A visualization may be provided based on data from a data source such that the visualization includes marks that are associated with from the data source. A mark-of-interest may be determined from the marks based on characteristics of the marks or the visualization. A snapshot of the data may be generated from the data source that may be associated with the visualization and a time that the mark-of-interest is determined. Mark evaluators may be employed to generate evaluation results based on the mark-of-interest and the snapshot data such that the evaluation results may include an explanation narrative or an explanation visualization and such that each evaluation result may be associated with scores that may be based on the evaluation. Evaluation results may be ordered based on their association with the scores. A report that includes the evaluation results may be provided.

Abstract: Embodiments are directed to visualization data. A visualization may be provided based on data from a data source such that the visualization includes marks that are associated with from the data source. A mark-of-interest may be determined from the marks based on characteristics of the marks or the visualization. A snapshot of the data may be generated from the data source that may be associated with the visualization and a time that the mark-of-interest is determined. Mark evaluators may be employed to generate evaluation results based on the mark-of-interest and the snapshot data such that the evaluation results may include an explanation narrative or an explanation visualization and such that each evaluation result may be associated with scores that may be based on the evaluation. Evaluation results may be ordered based on their association with the scores. A report that includes the evaluation results may be provided.