Abstract: Some embodiments provide a non-transitory machine-readable medium that stores a program executable by a device. The program identifies a set of visualizations associated with the user. The program further determines, for each visualization in the set of visualizations, a score associated with changes in the visualization. The program also determines a subset of the set of visualizations based on the set of scores. The program further provides to the user notifications associated with the subset of the set of visualizations.

Abstract: A question library aids in intuitive analysis of stored data. The question library comprises: 1) a plurality of text questions, 2) a numerical representation (e.g., a vector) of each text question, and 3) a corresponding query in a query language. A numerical vector is generated for a question posed to a database. If a matching library question (based upon vector similarity) is not found, the user receives the original answer. If a matching library question based upon vector similarity is found, the user receives the answer to that library question (with potential modifications). Embodiments may determine similarity by calculating Pearson's coefficient, Spearman's rho, or Kendall's tau. Embodiments may parse the first query to identify constituent elements (measures, dimensions, filters). These entities are extracted and compared to elements of the second question matched within the library, to allow modification of the library query to align with the initial query.

Abstract: Some embodiments provide a non-transitory machine-readable medium that stores a program executable by a device. The program identifies a set of visualizations associated with the user. The program further determines, for each visualization in the set of visualizations, a score associated with changes in the visualization. The program also determines a subset of the set of visualizations based on the set of scores. The program further provides to the user notifications associated with the subset of the set of visualizations.

Abstract: Some embodiments provide a method for identifying and pruning breakpoints in time series data. The method receives time series data. The method then generates a plurality of piecewise linear regression models that fit the time series data. The plurality of piecewise linear regression models may have differing numbers of breakpoints. The method further calculates an information criterion for each of the plurality of piecewise linear regression models. Next, the method selects one of the plurality of piecewise linear regression models having a lowest information criterion. Additionally, the method determines, for each breakpoint in the selected model, whether the prune each breakpoint. The method prunes one or more breakpoints in the set of breakpoints that are determined to be pruned.

Abstract: A question library aids in intuitive analysis of stored data. The question library comprises: 1) a plurality of text questions, 2) a numerical representation (e.g., a vector) of each text question, and 3) a corresponding query in a query language. A numerical vector is generated for a question posed to a database. If a matching library question (based upon vector similarity) is not found, the user receives the original answer. If a matching library question based upon vector similarity is found, the user receives the answer to that library question (with potential modifications). Embodiments may determine similarity by calculating Pearson's coefficient, Spearman's rho, or Kendall's tau. Embodiments may parse the first query to identify constituent elements (measures, dimensions, filters). These entities are extracted and compared to elements of the second question matched within the library, to allow modification of the library query to align with the initial query.