Abstract: A method executes at a computing device having a display, processors, and memory. The device displays a user interface for a data preparation application, including icons in a flow element palette, each icon representing a parameterized operation that can be inserted into data preparation flows in a flow pane of the user interface. A user places icons into the flow pane, visually defining flow elements for a flow that extracts data from selected data sources, transforms the extracted data, and exports the transformed data. The device retrieves the version number of a corresponding server application running on a server. Using a feature matrix, the device determines which flow elements are not supported by the data prep server application according to the version number. When there are flow elements not supported by the data prep server application running on the server, the device indicates this to the user.

Abstract: Embodiments are directed to generating metrics based on visualizations. A dashboard that may be associated with source visualizations display a current value of metrics from source visualization models. A classifier may automatically use characteristics from the source visualizations to determine metrics for the source visualization. The source visualization models may be sample to provide values of the metrics across time, at a sampling rate determined by a metric profile. The sampled values may be stored with time values in a metric data store such that the time values may correspond to when the values sampled from the visualization. Metric visualizations may be generated based on the values and the time values such that the metric visualizations display previously sampled values of the metrics.

Abstract: Embodiments are directed to managing event information. A plurality of events associated with entities may be provided. A plurality of state types may be determined based on the plurality of events such that each state type is associated with a state. State keys associated with each entity may be determined based on events associated with each entity and the state types. A state stream for each entity may be provided based on their state keys such that each state stream may be an ordered sequence of the keys associated with each entity. In response to a query that includes a pattern filter, the pattern filter may be employed to determine a portion of the entities based on the state stream for each entity such that the pattern filter matches the state stream for each of the portion of the entities.

Abstract: A computer displays, in a user interface, a data field region and an object model visualization region that includes object model icons. The computer detects user input to join a first object class and a second object class and detects user selection of a first linking field and user selection of a second linking field. In response to receiving the user selection of the first linking field and the second linking field, the computer generates a relationship that connects the first object class and second object class according to shared data values of the first linking field and the second linking field. The computer also displays, in the data field region, information regarding cardinality of the relationship and information regarding referential integrity of the relationship. The computer also updates the object model visualization region to display a visual connection between the object icons representing the first and second object classes.

Abstract: A computing system receives from a messaging system a first input identifying a data source. The computing system receives from the messaging system a second input specifying a natural language command related to the data source. The system translates the second input into a set of parameters for building a first data visualization from the data source. The system calls one or more API functions provided by a data visualization server, and the computing system receives from the data visualization server a first data visualization that includes a first subset of data fields from the data source. The system generates a representation of the first data visualization and sends the representation to the messaging system, which in turn sends the representation to an electronic device for display on the device.

Abstract: At a computer system a graphical user interface is displayed that includes a schema region displaying data field icons, each data field icon representing a respective data field, and a canvas region including block cards. Each block card specifies a respective layout type that is one of: a row-based arrangement, a column-based arrangement, or an inline arrangement. Each block card is generated in response to user placement of one or more respective data field icons onto the canvas region. For each block card, a respective database query is generated and executed to retrieve tuples of data for data fields associated with the respective block card. For each retrieved tuple, a respective two-dimensional grid position, including a respective row index and a respective column index, is computed. A data visualization is generated and displayed. Each retrieved tuple is represented as a visual mark displayed at its respective computed grid position.

Abstract: At a computer system a number of interrelated block object specifications are generated according to user placement of data field icons on a canvas region. Each block object specification includes a layout type, a mark type, and one or more visual encodings. For each block object specification, a respective query is generated according to the user-selected data field icons that specify the visual encodings for the respective block object specification. Each query is executed to retrieve a respective set of tuples from the data source. Each tuple comprises data values for data fields corresponding to the user-selected data field icons. A data visualization is generated in accordance with the layout type, the mark type, and the visual encodings specified in each of the block object specifications, wherein each tuple in each of the respective set of tuples is represented as a visual mark in the data visualization.

Abstract: A method generates a data visualization dashboard using a natural language interface. The method is performed at a computing device having a display, one or more processors, and memory. A user specifies a natural language input directed to a data source, using a first region of a graphical user interface. In response, the computing device parses the natural language input to identify one or more database entities from the data source. For each of the database entities, the computing device determines one or more respective analytical operations for the respective database entity. The device then generates a dashboard, which includes a plurality of data visualizations corresponding to distinct combinations of the analytical operations. The first dashboard is displayed in a second region of the graphical user interface, distinct from the first region.

Abstract: A method processes natural language commands (e.g., for data visualization). The method includes obtaining word embeddings (e.g., word vectors) for a set of words (or phrases) of a natural language. In some implementations, the set of words includes high-frequency or common words of the natural language. The method also includes training a word similarity model to identify similar words based on the word embeddings and a synonym database (a large lexical database, such as a thesaurus). The method also includes generating semantic annotations for a published data source using the trained word similarity model, based on the synonym database, and the word embeddings. The method also includes generating a semantic interpretation for a natural language command based on the semantic annotations for the published data source. The method also includes querying the published data source based on the sematic interpretation, thereby retrieving a dataset.

Abstract: A method is performed at a computing device that includes a display, one or more processors, and memory. The device displays a data visualization based on a dataset retrieved from a database. It also displays one or more phrases in a first region. The phrases define the data visualization. The device receives user input in a second region to specify a natural language command related to the displayed data visualization. In response to the user input, the device extracts independent analytic phrases from the command, determines one or more proposed actions based on the extracted phrases, and displays the proposed actions. The device receives user selection of a first proposed action. In response, the device generates an updated data visualization. It displays the updated data visualization and displays one or more updated phrases in the first region. The updated phrases define the updated data visualization.

Abstract: A method for analyzing data from data sources includes receiving user input to specify a mathematical expression. The mathematical expression includes a first data field from a first object and a second data field from a second object. The first object and the second object are distinct objects of an object model that includes a tree in which each relation between objects represents a respective many-to-one relationship between respective objects. The method further includes generating a calculated data field based on the mathematical expression, and assigning the calculated data field as a member of a third object in the object model according to relations in the tree connecting the first object to the second object.

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 computing device displays a first data visualization that includes a first plurality of data marks. Each of the data marks corresponds to a respective distinct data value of a first data field from a dataset. In response to user selection of a first data mark that corresponds to a first data value of the first data field, the device displays a first data widget that includes one or more user-selectable affordances. In response to user selection of a first affordance of the affordances, the device displays a first drill down widget. The device receives user selection of a second data field from the dataset in the drill down widget. In response to the user selection, the device generates a second data visualization that includes a second plurality of data marks and displays the second data visualization.

Abstract: Embodiments are directed to managing data models. A data source that includes records may be provided. Source fields may be determined based on the records and the source fields may be displayed in a source panel. A data model that includes a source data object may be displayed. Relationships between the source fields may be determined based on values in the records. In response to providing a relationship between the source fields, a data object that includes a key field and one or more data fields that correspond to the relationship may be generated. The data model may be modified to include the data object and to remove the source fields that correspond to the data fields.

Abstract: Embodiments are directed to visualizing data. A graphical user interface (GUI) that includes a shelf panel and a display panel may be generated. Column fields may be generated and displayed in the shelf panel based on a data source or a table specification. The table specification may be employed to generate a table view that may be displayed in the display panel such that the table view includes columns that correspond to each column field. Data items may be determined based on the data source and the column fields such that each column field corresponds to a portion of the data items. Each portion of the data items may be displayed in a column of the table view that corresponds to the column field that corresponds to the portion of the data items.

Abstract: An electronic device has one or more processors, and memory. The memory stores one or more programs configured for execution by the one or more processors. The electronic device receives a request directed to a data source. The request includes one or more intent types and one or more predefined attributes associated with the intent types. Each of the predefined attributes limits a respective data analysis operation of a respective intent type. In response to the request, for each of the intent types, the electronic device formulates a respective intent specification according to the request, including determining respective properties of the respective intent specification. The electronic device also generates a respective output specification. The respective output specification includes a respective recommended data visualization type.

Abstract: Embodiments are directed to managing display hardware for visualizing data a network computer. A modeling engine may provide a data model includes a plurality of data objects and a display model that includes a plurality of display objects that may be based on the plurality of data objects. Action objects may be associated with display objects in the display model. A display engine may display the display model on a hardware display based on context information that includes performance characteristics of the hardware display. In response to a trigger events, action objects may be displayed on the hardware display based on the context information. In response to detecting input signals associated with the action objects the display model may be modified based on the action objects. The modified display model may be displayed on the hardware display based on the context information.

Abstract: Data items are aggregated in a quantile sketch according to a compactor hierarchy. A first number of data items are stored in a first buffer corresponding to a first (i.e., lowest) compaction level of the compactor hierarchy, and a top level number of data items are stored in a top buffer corresponding to a top compaction level of the compactor hierarchy. For a first subset of the first number of data items, each data item is already stored in the top buffer, and a weight of the respective data item stored in the top buffer is increased by 1. For a second subset of the first number of data items, each data item does not match any data item stored in the top buffer, and a second number of data items are selected from the second subset and stored in a second buffer of a second compaction level.

Abstract: Embodiments are directed to managing data visualizations. A primary visualization that is associated with a data model may be provided such that the primary visualization may be displayed in a display panel. Insight items may be generated based on the primary visualization and the data model such that the insight items may correspond to one or more visualizations that may share one or more portions of the data model and such that the insight items may be displayed in an insight panel. In response to an insight item being selected from the insight panel, additional actions may be performed, including: generating a visualization based on the insight item that is displayed in the display panel instead of the primary visualization; and generating a scratch item that includes a thumbnail view of the primary visualization such that the thumbnail view is displayed in a scratch panel.

Abstract: Embodiments are directed to managing visualizations of data. Visualizations that include one or more marks associated with values from a data source may be provided. A mark-of-interest may be determined from the one or more marks. Assessment models may be employed to generate assessment results for the assessment models based on the mark-of-interest and the data from the data source such that each assessment result may be associated with scores that may be based on a best fit to the data from the data source and a value of the mark-of-interest. The assessment results may be rank ordered based on their association with the scores. A report that includes the rank ordered list of the assessment results may be provided.