Abstract: The present disclosure relates to comparisons of data over time and generating recommendations to create such comparisons. A visualization in an analytics application may include a plurality of data points and a user may select of one or more of the data points to configure the visualization. If the context of the visualization is based on time, the software may determine whether the one or more selected data points includes the current time period. If so, comparisons of the data over time may be included in the visualization and creation of these comparisons may be recommended to the user. One or more data comparisons may be determined by comparing each of the one or more selected data points to a corresponding data point in a previous time period. An updated visualization including the selected data points and the data comparisons may be provided.

Abstract: Embodiments relate to techniques for performing data blending operations across multiple different data sets comprising data structures with columns and rows. The data sets may be classified and displayed in a visualization (i.e., chart) in a client interface. Columns and rows from the blended data sets may be mapped together (i.e., linked). Updates to the visualization, including adding elements from the data sets, may trigger a data blending process on the backend server in communication with a database. The server may blend the specified data by generating a runtime artifact representing a calculation graph for the blend operation and query the database to retrieve a resulting data set. The data blending operation may comprise collapsing dimensions of a primary data set with linked dimensions of a secondary data sets into a blended column and aggregating values of measures in rows of the blended column of the resulting data structure.

Abstract: Embodiments relate to performing data blending operations across multiple different data sets comprising data structures with columns and rows. Columns of data sets to be blended may be linked together. Filters may be applied to data sets before the data blend operation is performed to specify which columns to be displayed in a visualization at a client interface. A direct filter may be applied to one of the data sets to obtain a filtered resulting data set. Data elements of the filtered resulting data set can be identified that correspond to the linked columns of the data sets to be blended. The results of applying the direct filter may then be used as the filtering criteria for an indirect filter to filter a second data set. The results of applying the direct and indirect filters may then be combined together in the data blending operation.

Abstract: Data blending operations are disclosed configured to blend data from a primary data set and a secondary data set. Primary and secondary data structure models are generated reflecting selected dimensions and measures of the primary and secondary data structure models upon which the data blending operation is to be performed. Each dimension and measure in the primary and secondary data structure models includes a local identifier. Unique global identifiers are assigned to each local identifier of the primary and secondary data structure models. Client queries are generated and communicated to a backend server to perform data blending on the selected dimensions and measures of the primary data set and the secondary data set. The client queries may comprise the global identifiers and mapping information between the global identifiers and local identifiers. The backend system retrieves a resulting data set in response to the data blending operation based on this mapping.

Abstract: Embodiments relate to techniques for performing data blending operations across multiple different data sets comprising data structures with columns and rows. The data sets may be classified and displayed in a visualization (i.e., chart) in a client interface. Columns and rows from the blended data sets may be mapped together (i.e., linked). Updates to the visualization, including adding elements from the data sets, may trigger a data blending process on the backend server in communication with a database. The server may blend the specified data by generating a runtime artifact representing a calculation graph for the blend operation and query the database to retrieve a resulting data set. The data blending operation may comprise collapsing dimensions of a primary data set with linked dimensions of a secondary data sets into a blended column and aggregating values of measures in rows of the blended column of the resulting data structure.

Abstract: Data blending operations are disclosed configured to blend data from a primary data set and a secondary data set. Primary and secondary data structure models are generated reflecting selected dimensions and measures of the primary and secondary data structure models upon which the data blending operation is to be performed. Each dimension and measure in the primary and secondary data structure models includes a local identifier. Unique global identifiers are assigned to each local identifier of the primary and secondary data structure models. Client queries are generated and communicated to a backend server to perform data blending on the selected dimensions and measures of the primary data set and the secondary data set. The client queries may comprise the global identifiers and mapping information between the global identifiers and local identifiers. The backend system retrieves a resulting data set in response to the data blending operation based on this mapping.

Abstract: Embodiments relate to performing data blending operations across multiple different data sets comprising data structures with columns and rows. Columns of data sets to be blended may be linked together. Filters may be applied to data sets before the data blend operation is performed to specify which columns to be displayed in a visualization at a client interface. A direct filter may be applied to one of the data sets to obtain a filtered resulting data set. Data elements of the filtered resulting data set can be identified that correspond to the linked columns of the data sets to be blended. The results of applying the direct filter may then be used as the filtering criteria for an indirect filter to filter a second data set. The results of applying the direct and indirect filters may then be combined together in the data blending operation.