Abstract: A user selects a first group of one or more dimension fields that specifies a primary aggregation for a data visualization, where the one or more dimension fields are in a first data source. When one or more first dimension fields in the first group are not dimension fields in a second data source, a client device joins the first data source with the second data source at a first level of detail that is more granular than the primary aggregation to form a single combined data set that includes the one or more dimension fields specified in the first group and one or more measure data fields aggregated according to the first group. The client device rolls up the combined data set, including the one or more measure data fields, to form a final data set and displays the data visualization using the data from the final data.

Abstract: A method dynamically combines multiple data sources. A user selects a first group of fields that specify primary aggregation for a data visualization. When a field in the first group is not a data field in one of the data sources, the process creates a second group of fields that specifies an intermediate aggregation for joining data sets that is more granular than the primary aggregation. For each data source, the process aggregates measure fields according to the second group to form a respective intermediate data set. The process forms a single combined data set from the intermediate data sets, including the fields specified in the first group, and aggregates each measure field in the combined data set using the primary aggregation to form a final data set based on the fields from the first group. The process then displays the data visualization using the final data set.

Abstract: A method dynamically combines data from multiple data sources. A visual specification for a data visualization requires data from a primary data source and one or more secondary data sources. The primary data source is independent of the secondary data sources. The visual specification includes a first set of dimension fields that specify a hierarchical level for the data visualization. A second set of dimension fields specify a hierarchical level for joining data sets from the data sources. The hierarchical level for joining data sets is more granular than the hierarchical level for the data visualization. For each data source, a query is generated, and a data set is retrieved from the data source using the query. A single combined data set is formed that includes the dimension fields in the first set. The combined data set is rolled-up and a data visualization is displayed using the rolled-up data.

Abstract: A system and method to update a patch index such that one or more properties in the patch index are associated with the patch, wherein the patch includes one or more components for the target software system; updating a class path at run-time for the target software system such that the patch associated with each one of the one or more properties supersedes prior patches for the same property; and wherein a component can include a class.

Abstract: A system and method for an extendable application framework, comprising a user interface, at least one service, at least one extension, wherein one of the at least one extensions can provide access to functionality in the user interface, and wherein one of the at least one services can provide access to functionality in one of the at least one extensions.

Abstract: A method of satsfying a database query includes evaluating certain joins on a per-segment basis. An expression tree is produced for the query, and the expression tree is evaluated to identify joins whose operands are two instances of the same relation and whose join predicate conjunctively includes an equality comparison between two instances of the same column. When such a join is identified, it may be evaluated by segmenting the operand relation according to the columns that are compared for equality in the predicate. The join is then evaluated by performing the join operation separately on each segment. Segments may be spooled separately, thereby exploiting the efficiencies obtained by spooling even where the entire relation is too large to fit in the spool. Execution iterators are provided for spooling successive segments and for applying the join to the spooled segment.