Abstract: The method receives a visual specification, which specifies a data source, visual variables, and data fields from the data source. Each visual variable is associated with data fields and each data field is either a dimension or a measure. From an object model of the data source, the method identifies a minimal subtree that includes all of the dimension data fields and constructs a query from the minimal subtree. The method executes the query against the data source to retrieve a set of tuples, each tuple comprising a unique ordered combination of data values for the dimension data fields. For each tuple, the method forms an extended tuple by appending aggregated data values corresponding to each measure data field. The method then builds and displays a data visualization according to the data fields in the extended tuples and according to the visual variables to which the data fields are associated.

Abstract: A computer system displays a data visualization in a data visualization user interface. In response to detecting a user input that selects a subset of visual data marks from the data visualization, the computer system displays a summary of the selected subset of visual data marks in a summary tab of a view data window. When the computer system determines that the selected subset of visual marks (1) corresponds to a plurality of data fields and (2) references a row-level calculation that uses logical fields from at least two logical tables of a plurality of logical tables of a data model, the computer system (i) generates a representation of the row-level calculation and (ii) displays the representation of the row-level calculation as a first tab in the view data window, distinct from the summary tab.

Abstract: A user selects a data source, and a computer displays a data visualization in a data visualization user interface according to the data source. The data visualization includes visual data marks representing data from the data source. The user selects a subset of the visual data marks. In response, the computer displays a view data window having a summary of the selected data marks. The computer obtains a data model encoding the data source as a tree of logical tables. The computer identifies aggregate measures corresponding to the selected data marks, where each aggregate measure is aggregated from logical tables of the data model. The computer displays each aggregate measure in the view data window. The computer also displays, in the view data window, one or more level of detail calculations referenced in the selected subset of visual data marks.

Abstract: The process receives a visual specification, which specifies data sources, visual variables, and data fields from the data sources. Each visual variable is associated with one or more data fields and each data field is either a dimension or a measure. For each measure m, the process identifies a set R(m) consisting of dimensions that are reachable from the measure by a sequence of many-to-one relationships in an object model for the data sources. For each distinct set R, the process forms a data field set S, consisting of each dimension in R and each measure m for which R(m)=R. For each set S and for each measure in the set S, the process aggregates values of the measure according to the dimensions in S. The process builds a data visualization according to the data fields in the set S and the visual variables they are associated with.

Abstract: The method receives a visual specification, which specifies a data source, visual variables, and data fields from the data source. Each visual variable is associated with data fields and each data field is a dimension or a measure. The method forms dimension tuples comprising distinct ordered combinations of data values for the dimensions D. For each measure, the method: forms a set S of the dimensions D plus dimensions from a primary key corresponding to the measure; retrieves intermediate tuples containing the fields in S and the measure, without aggregation; and aggregates the intermediate tuples according to the dimensions D. For each dimension tuple, the method forms an extended tuple by appending the aggregated data values corresponding to each measure field. The method then builds and displays a data visualization according to the extended tuples and the visual variables to which the data fields are associated.

Abstract: A method generates a data source. A user selects a first data set from a displayed object model of a database. The object model includes data sets linked visually by many-to-one relationships to form a tree. The user selects a data field in the first data set and specifies a filter condition. The system identifies a second data set in the tree, and joins the first and second data sets. When the second data set is related to the first data set via one or more many-to-one relationships, rows of the second data set are joined to rows of the first data set that satisfy the first filter condition. When the second data does not satisfy the condition, the second data set is joined to rows of the first data set that satisfy the first filter condition, and all rows of the second data set are included in the generation.

Abstract: A data visualization application receives user actions to (i) associate a first data field with a first shelf and (ii) associate second and third data fields with a second shelf. The method receives an additional user action to specify a nested sort according to data for the first and third data fields. The method generates and displays a data visualization in accordance with the received actions, including: (i) partitioning retrieved data into groups of tuples, each tuple including data for the data fields, and each group of tuples corresponding to a distinct value of the second data field, (ii) forming panes, each pane corresponding to a respective group of tuples, and (iii) within each pane, displaying data marks, each data mark corresponding to a respective tuple.

Abstract: An exemplary method receives user actions to (i) associate a first field with a first shelf and (ii) associate second and third fields with a second shelf. The second field is positioned at an outer-most position on the second shelf and the third field is positioned at an inner-most position on the second shelf. The method receives an additional user action to specify a nested sort according to data for the first and third fields. The method generates and displays a data visualization in accordance with the received actions, including: (i) partitioning retrieved data into groups of tuples, each tuple including data for the fields, and each group of tuples corresponding to a distinct value of the second field, (ii) forming panes, each pane corresponding to a respective group of tuples, and (iii) within each pane, displaying data marks, each data mark corresponding to a respective tuple.

Abstract: A computing device displays a data visualization interface and receives user selection of a data source. The computing device also receives user input to select a measure data field and a dimension data field. In response to the user input, the computing device: generates a custom calculation that aggregates data for the measure data field, grouped by distinct data values of the dimension data field; and stores the custom calculation as a new selectable data field, associated with a data object corresponding to the dimension data field. The computing device also receives user selection of the new selectable data field and placement of the new selectable data field onto a first shelf in a shelf region. The first shelf defines a first data visualization characteristic determined according to data values of the custom calculation. The computing device generates and displays a data visualization based on the first data visualization characteristic.

Abstract: A user selects a data source, and a computer displays a data visualization in a data visualization user interface according to the data source. The data visualization includes visual data marks representing data from the data source. The user selects a subset of the visual data marks. In response, the computer displays a view data window having a summary of the selected data marks. The computer obtains a data model encoding the data source as a tree of logical tables. The computer identifies aggregate measures corresponding to the selected data marks, where each aggregate measure is aggregated from logical tables of the data model. The computer displays each aggregate measure in the view data window. The computer also displays, in the view data window, one or more level of detail calculations referenced in the selected subset of visual data marks.

Abstract: A user selects a data source, and a computer displays a data visualization in a data visualization user interface according to the data source. The data visualization includes visual data marks representing data from the data source. The user selects a subset of the visual data marks. In response, the computer displaying a view data window having a summary of the selected data marks. The computer obtains a data model encoding the data source as a tree of logical tables, each including one or more logical fields. Each logical field corresponds to either a data field or a calculation that spans one or more logical tables. Each edge of the tree connects two logical tables that are related. The computer identifies aggregate measures corresponding to the selected data marks, and displays each aggregate measure in the view data window.

Abstract: A user selects a data source, and a computer displays a data visualization in a data visualization user interface according to the data source. The data visualization includes visual data marks representing data from the data source. The user selects a subset of the visual data marks. In response, the computer displaying a view data window having a summary of the selected data marks. The computer obtains a data model encoding the data source as a tree of logical tables, each including one or more logical fields. Each logical field corresponds to either a data field or a calculation that spans one or more logical tables. Each edge of the tree connects two logical tables that are related. The computer identifies aggregate measures corresponding to the selected data marks, and displays each aggregate measure in the view data window.

Abstract: An exemplary method receives user actions to (i) associate a first field with a first shelf and (ii) associate second and third fields with a second shelf. The second field is positioned at an outer-most position on the second shelf and the third field is positioned at an inner-most position on the second shelf. The method receives an additional user action to specify a nested sort according to data for the first and third fields. The method generates and displays a data visualization in accordance with the received actions, including: (i) partitioning retrieved data into groups of tuples, each tuple including data for the fields, and each group of tuples corresponding to a distinct value of the second field, (ii) forming panes, each pane corresponding to a respective group of tuples, and (iii) within each pane, displaying data marks, each data mark corresponding to a respective tuple.

Abstract: An exemplary method receives user actions to (i) associate a first field with a first shelf and (ii) associate second and third fields with a second shelf. The second field is positioned at an outer-most position on the second shelf and the third field is positioned at an inner-most position on the second shelf. The method receives an additional user action to specify a nested sort according to data for the first and third fields. The method generates and displays a data visualization in accordance with the received actions, including: (i) partitioning retrieved data into groups of tuples, each tuple including data for the fields, and each group of tuples corresponds to a distinct value of the second field, (ii) forming panes, each pane corresponding to a respective group of tuples, and (iii) within each pane, displaying data marks, each data mark corresponding to a respective tuple.

Abstract: The method receives a visual specification, which specifies a data source, visual variables, and data fields from the data source. Each visual variable is associated with data fields and each data field is a dimension or a measure. The method forms dimension tuples comprising distinct ordered combinations of data values for the dimensions D. For each measure, the method: forms a set S of the dimensions D plus dimensions from a primary key corresponding to the measure; retrieves intermediate tuples containing the fields in S and the measure, without aggregation; and aggregates the intermediate tuples according to the dimensions D. For each dimension tuple, the method forms an extended tuple by appending the aggregated data values corresponding to each measure field. The method then builds and displays a data visualization according to the extended tuples and the visual variables to which the data fields are associated.

Abstract: The method receives a visual specification, which specifies a data source, visual variables, and data fields from the data source. Each visual variable is associated with data fields and each data field is either a dimension or a measure. From an object model of the data source, the method identifies a minimal subtree that includes all of the dimension data fields and constructs a query from the minimal subtree. The method executes the query against the data source to retrieve a set of tuples, each tuple comprising a unique ordered combination of data values for the dimension data fields. For each tuple, the method forms an extended tuple by appending aggregated data values corresponding to each measure data field. The method then builds and displays a data visualization according to the data fields in the extended tuples and according to the visual variables to which the data fields are associated.

Abstract: The process receives a visual specification, which specifies data sources, visual variables, and data fields from the data sources. Each visual variable is associated with one or more data fields and each data field is either a dimension or a measure. For each measure m, the process identifies a set R(m) consisting of dimensions that are reachable from the measure by a sequence of many-to-one relationships in an object model for the data sources. For each distinct set R, the process forms a data field set S, consisting of each dimension in R and each measure m for which R(m)=R. For each set S and for each measure in the set S, the process aggregates values of the measure according to the dimensions in S. The process builds a data visualization according to the data fields in the set S and the visual variables they are associated with.

Abstract: In one embodiment of the present invention, a GPU contains an authentication module at the front end, and a memory security engine and graphic memory interface at the backend. In one embodiment of the present invention, the memory security engine provides a privilege table. The programmable privilege table maps memory address ranges, and user IDs to privileges for accessing the memory address ranges. In one embodiment of the present invention, the memory security engine receives a memory access command along with an associated authenticated user ID. In one embodiment of the present invention, the memory security engine checks the authenticated user ID and address range against the privilege table. In one embodiment of the present invention, if the table indicates that the user has authorization for the particular read or write transaction to the graphic memory, the instruction is executed by the graphic memory interface.

Abstract: A chart is presented in a host application. The chart is managed by communicating data corresponding to the chart to a separate application and providing functionality for editing the data in the separate application. The presentation of the data in the separate application is distinct from the presentation of the chart in the host application. Changes to the data are communicated the host application when the data is edited in the separate application. The communicated changes to the data are reflected in the presentation of the chart in the host application.

Abstract: A chart is copied from a first application and pasted to a second application. A determination is made for which copy and paste option is selected for the chart. The chart is communicated to the second application using a clipboard application when the copy and paste operation corresponds to providing a link for the chart that is arranged communicating updated data of the chart. The chart is also communicated to the second application using a clipboard application when the copy and paste operation corresponds to representing the chart as a picture of assembled shapes. The chart is communicated to the second application using a separate data transfer interface when the copy and paste operation corresponds to embedding the chart in the second application. Furthermore, the formatting of the chart may be selected according to source formatting or destination formatting depending on the type of copy and paste operation selected.