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 method uses natural language for visual analysis of a dataset. The method displays a data visualization based on a first dataset. The method then extracts analytic phrases from a first natural language command related to the data visualization. The method computes conversation centers associated with the first natural language command based on the analytic phrases and computes analytical functions for the conversation centers, thereby creating functional phrases. The method updates the data visualization based on the functional phrases. The method extracts new analytic phrases from a second natural language command related to the updated data visualization and computes a temporary conversation centers associated with the second natural language command based on the new analytic phrases. The method derives new conversation centers from the original conversation centers and the temporary conversation centers using transitional rules.

Abstract: A method includes displaying at least a first portion of a chart at a first magnification, the first portion containing a plurality of data marks. The method also includes detecting a first input at a location that corresponds to the first portion of the chart and, in response, zooming in to display a second portion of the chart at a second magnification, the second portion including a first data mark in the plurality of data marks. The method further includes detecting a second touch input at a location that corresponds to the second portion of the chart, and, in response: if one or more predefined data-mark-information-display criteria are not met, zooming in to display a third portion of the chart at a third magnification, the third portion including the first data mark; and, if the one or more predefined data-mark-information-display criteria are met, displaying information about the first data mark.

Abstract: A method visualizes data sources. A user selects a data source, and the computer system displays a first data visualization according to placement of data fields in shelves of the user interface. The data visualization comprises visual data marks representing the data source. A user selects some of the data marks. In response, the system displays a metric window including a data metric object preview, a summary of the selected data marks, and setting controls. The user provides input to create the data metric object. In response, the system creates the data metric object, including: configuration parameters derived from the first data visualization; an initial extract from the data source according to the configuration parameters; and a schedule for recurring retrieval of data from the data source to update the extract. The system then displays a second data visualization according to the configuration parameters and the extract.

Abstract: A method uses natural language for visual analysis of a dataset. A data visualization is displayed based on a first dataset. The method then extracts analytic phrases from a natural language command related to the data visualization. The method computes conversation centers based on the analytic phrases and computes analytical functions associated with the conversation centers, thereby creating functional phrases. The method updates the data visualization according to the functional phrases. The method then extracts analytic phrases from a second natural language command related to the updated data visualization, and computes temporary conversation centers from these analytic phrases. The method then computes cohesion between the first analytic phrases and the second analytic phrases to build a set of conversation centers, and computes analytical functions from this set of conversation centers, thereby creating functional phrases.

Abstract: A method prepares data for subsequent analysis. The method displays a user interface having a data flow pane, including a flow diagram having a plurality of nodes. Each node specifies one or more operations. The method receives user input to select an existing node in the flow diagram, receives user input to insert a new node into the flow diagram, and receives user input to select a first set of two or more columns of data values from a data set corresponding to the existing node. The method pivots the selected columns to form a modified data set having new rows and new columns. The user selects two or more additional columns, and the method pivots on the additional columns, resulting in a second new column added to the modified data set. Each data value from the additional columns is added to the second new column in a respective row.

Abstract: A method visualizes object models for data sources is performed at an electronic device. The device displays, in an object model visualization region, a first visualization of a tree of data object icons, each data object icon representing a logical combination of one or more tables. While concurrently displaying the first visualization in the object model visualization region, the device detects, in the object model visualization region, a first input on a first data object icon of the tree of data object icons. In response to detecting the first input on the first data object icon, the device displays a second visualization of the tree of the data object icons in a first portion of the object model visualization region and displays a third visualization of information related to the first data object icon in a second portion of the object model visualization region.

Abstract: A method is performed at a computing device having a display, one or more processors, and memory. The device displays a graphical user interface on the display. A user provides a natural language input, which specifies a filter, including a first data field, a relation, and a first comparison value. The user then replaces the first data field in the filter with a second data field. In response to replacing the first data field, the device automatically replaces the first comparison value with a second comparison value. The second comparison value corresponds to the data type of the second data field. In some instances, the device also updates the relation based on the data type of the second data field. The device then displays an updated data visualization according to the updated filter.

Abstract: A database engine receives a human-readable database query that includes a plurality of conditional expressions. The database engine parses the database query to build an operator tree that includes a subtree corresponding to each of the conditional expressions. The database engine identifies a subexpression that appears in two or more of the conditional expressions. The subexpression is executed conditionally. The database engine hoists the subexpression outside of the conditional expression so that it is executed unconditionally. The database engine modifies the operator tree to specify computation of a value for the subexpression a first time and to reuse the computed value when the subexpression is subsequently encountered. The database engine executes the modified operator tree to form a result set corresponding to the database query, thereby evaluating the subexpression only a single time for each row of input data and returns the result set.

Abstract: A method displays a chart that includes visual marks representing a data set, displayed according to contents of displayed shelf regions, which determine characteristics of the chart. The method detects selection of a plurality of visual marks, and visually emphasizes the selected plurality of visual marks. The method also detects a first input on one of the selected marks, and displays a moveable icon corresponding to the selected visual marks while maintaining display of the visual marks. The method detects a second input on the moveable icon, and moves the moveable icon over a first shelf region. Upon ceasing to detect the input, the method updates the content of the first shelf region based on the selected visual marks, and updates the chart in accordance with updated content of the first shelf region.

Abstract: A method uses natural language processing for visual analysis of a dataset by a computer. The computer displays a data visualization based on a dataset retrieved from a database using a first set of database queries. The computer receives user input (e.g., keyboard or voice) to specify a natural language command related to the displayed data visualization. Based on the displayed data visualization, the computer extracts one or more cue phrases from the natural language command. The computer computes analytical intent (e.g., visualization state intent and/or transitional intent) of the user based on the one or more cue phrases. The computer then derives visualization states based on the analytical intent. The computer subsequently computes one or more analytical functions associated with the visualization states, thereby creating one or more functional phrases. The computer then updates the data visualization based on the one or more functional phrases.

Abstract: A user selects a dataset and a first interaction profile. There are multiple interaction profiles, each specifying a corresponding user interface and a corresponding decision tree for forming data visualizations according to user-selected data fields. A first data visualization user interface is displayed corresponding to the first interaction profile. Using the first data visualization user interface, the user selects data fields from the dataset and uses them to specify data visualization characteristics. The method displays a first data visualization according to the user-selected data fields and the decision tree for the first interaction profile. When the user selects a second interaction profile, the method displays a second data visualization according to the second interaction profile. The second data visualization uses at least some of the user-selected data fields.

Abstract: A method displays a chart including visual marks representing a dataset. The chart is displayed according to contents of multiple shelf regions. The shelf regions determine characteristics of the chart. The method generates a visual analytic object by applying an analytic operation to the set of data represented by the visual marks, and displays the visual analytic object superimposed over the chart. The method detects user input to select the displayed visual analytic object and place an icon representing the visual analytic object onto a first shelf region. In response, the method updates the content of the first shelf region to associate it with the analytic operation corresponding to the visual analytic object and updates the chart according to the updated content of the first shelf region. This includes updating the chart based on values of the dataset represented by the visual marks relative to the analytic operation.

Abstract: This application is directed to modifying display of data visualizations. A computer system visualizes on its display a set of data in a first data space. A focus point is identified in the first data space and used as a starting point for constructing a set of geodesics, each of which is configured to connect the focus point to a respective data point in the first data space. A plurality of primary eigenvectors are determined in the first data space and they are used to create a second data space having fewer dimensions than the first data space. For each data point, the computer system determines a plurality of projection values associated with the primary eigenvectors. The set of data is re-visualized in the second data space created using the projection values of each data point.

Abstract: A method compares data sets in a data preparation application. The method displays a user interface including a data flow pane and a profile pane. The data flow pane displays a flow diagram with nodes, each node corresponding to a data set. When a user selects a first node, the method displays distributions of data values for data fields from a first data set for the first node. When a user selects a second node, the method: (1) forms a composite data set comprising a union of (i) the first data set and (ii) a second data set for the second node; (2) groups data values for each data field to form sets of bins; and (3) displays data value distributions for each data field using the bins. Each bin depicts counts of data values in the bin originating from the first data set and the second data set.

Abstract: A user interface that includes a data flow pane and a profile pane. The data flow pane displays a flow diagram that identifies a data source. For each of multiple queries against the data source, the process issues the query against the data source asynchronously with an initial block size. Upon retrieval of the initial set of rows, the process repeats the query asynchronously with an updated block size until all of the rows have been retrieved. Periodically the process determines a high water mark for rows from the data source that have been retrieved for all of the queries. When the water mark changes, the process updates the profile pane to display data value histograms for multiple data fields in the data source. Each bar in each data value histogram counts the rows below the water mark that have a single specific data value or range of data values.

Abstract: A method implements data visualization collaboration. The method receives, from a first user, a first comment that refers to a data visualization. The comment includes text, a first thumbnail image of the data visualization, and a first visual specification to build the data visualization. The method displays, to a second user, an interface with a comment pane that displays the first comment text and the first thumbnail image. The method detects input from the second user to select the first thumbnail image. In response, the method generates and displays the data visualization. The method receives, from the second user, input modifying the data visualization, and input specifying a second comment that refers to the data visualization. In response, the method generates a second thumbnail image and a second visual specification corresponding to the modified data visualization, and transmits that information along with the second comment text to the first user.

Abstract: A computer displays a graphical user interface, which has a data visualization region displaying a primary data visualization, including a plurality of visual marks. The primary data visualization represents a first set of data fields from a dataset. A user uses the graphical user interface to select a visual mark (e.g., by hovering or clicking). In response to the user input, the computer generates a secondary data visualization, for a second set of data fields, having visual marks highlighted only when their corresponding data values for a shared data field between the first and second sets matches a data value of the selected visual mark in the primary data visualization. The computer then displays the generated secondary data visualization in a tooltip overlaying a portion of the primary data visualization.

Abstract: A method identifies relevant data sources in a data visualization user interface. The process constructs an interaction matrix X whose rows correspond to users and whose columns correspond to data sources. Each entry measures the extent to which a user interacts with a data source according to user interactions with data visualizations generated from the data source. The method factors X into a product of a user matrix U times a source matrix ST using iterative refinement to minimize the least squares difference between X and U·ST. A user connects to a data server. The method computes affinity scores between the user and data sources. Each affinity score is a dot product of a row of U (a user) and a row of S (a data source). The method selects some data sources corresponding to high computed affinity scores and returns the selected data sources to the user.

Abstract: A device concurrently graphs a first data visualization and a second data visualization on a display. The first data visualization and the second data visualization share a common axis corresponding to a shared data field from a data source. The first data visualization comprises graphical marks corresponding to data values of a first data field from the data source and the second data visualization comprises graphical marks corresponding to data values of a second data field. A user moves a first graphical mark from a first location corresponding to an actual data value of the first data field to a second location, creating a first hypothetical value of the first data field. The device moves a second graphical mark in the second data visualization to an adjusted location corresponding to a computed hypothetical value for the second data field according to the first hypothetical value of the first data field.