Tableau Software, Inc.

Microsoft Technology Licensing, LLC

Softview, L.l.c.

Other

Scott A Sherman

Gary B Rohrabaugh

Matthew Jay Kotler

Ilan Berker

Janet L Schorr

Vidya Raghavan Setlur

Christopher Richard Stolte

Matthew Michael Brehmer

Karen K Wong

Stephen Thomas Wells

Thomas Charles Underhill

Dawn Marie Wright

Richard J Wolf

Brent Gilbert

Gary Allan Pritting

Cynthia Christine Shelly

Kim Tapia St Amant

Jeffrey Jon Weir

Jun Ho Kim

Andrew C Beers

Daniel Cory

Crystal L Hoyer

Ryan Andrew Atallah

James Baker

Austin David Dahl

Jason Churchill Costa

Justin Frederick Rockwood

David M Gerton

Stephen Howard Pellegrin

Anna Dayen

Lu Sien Tan

Jason Scott King

Keywon Chung

Eleanor Page Darby Fields

Stephen T Wells

Thomas C Underhill

A computing device receives a natural language command directed to a data source. The device applies one or more respective rules to determine whether the natural language command comprises an ambiguous and/or underspecified request. In accordance with a determination that the natural language command comprises an ambiguous and/or underspecified request, the device infers information to resolve the ambiguous and/or underspecified request according to (i) the one or more respective rules, (ii) metadata for the data source, and (iii) metadata for one or more data fields specified in the natural language command. The device updates a respective intent specification based on the inferred information, retrieving data sets from the data source according to the respective intent specification, and generates and displays a data visualization based on the retrieved data sets.

Applying a visual analytics intent language to generate data visualizations

A method of interactive visual analysis is performed at a computer. The computer generates a data visualization by executing a data visualization data flow graph comprising a directed graph having a plurality of nodes. Each of the nodes specifies either a data retrieval operation or a data transformation operation and the data visualization comprises visual marks having a first set of characteristics, including a first mark type and one or more first visual mark encodings. A user specifies a second mark type and/or one or more second visual mark encodings. In response, the computer updates a portion, less than all, of the data flow graph according to the second mark type and/or the one or more second visual mark encodings, and generates an updated data visualization by executing updated nodes of the data flow graph without executing nodes not updated according to the user input.

Interactive visual analysis of datasets using a specialized virtual machine

An electronic device has a first device type. The 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 one or more intent types. The device formulates, for each of the intent types, a respective intent specification according to the request. In some implementations, the device determines respective properties of the respective intent specification according to the respective predefined attributes and according to data fields, from the data source, specified in the request. The device generates, for each of the intent types, a respective output specification that specifies a respective recommended data visualization type. The device renders data analysis responsive to the request in accordance with the electronic device having the first device type.

Embodiments are directed to visualizing data using a graphical user interface (GUI) that may include a graph panel and a visualization panel arranged to receive inputs or interactions. A data model may be provided and displayed in the visualization panel. Input information that specifies portions of the data model may be provided to the visualization panel. Transform models may be determined based on the specified portions of the data model such that the determined transform models include a model interface that accepts the input information. The transform models may be employed to generate graph objects based on the data model, the input information such that the graph objects may be included in a graph model. Queries based on the graph model may be executed to provide results from the data model such that the results may be displayed in a visualization.

Dynamic graph generation for interactive data analysis

A method generates data visualizations. A computing device retrieves a set of tuples from a database according to user selection. Each tuple has the same set of fields. The device identifies a relation between tuples. The relation is a non-empty set of ordered pairs of tuples from the set of tuples. A user selects a base tuple from the set of tuples and the device forms a filtered subset of tuples consisting of the selected base tuple and those tuples that are connected to the selected base tuple by a sequence of tuples that are related by the relation. The user selects an aggregation level, which consisting of fields from the set of fields. The device generates and displays a data visualization by aggregating the filtered subset of tuples at the selected aggregation level to form a set of aggregated tuples, and displaying each aggregated tuple as a visible mark.

Data analysis platform utilizing database relationships to visualize data

Embodiments are directed to a display engine that provides a graphical user interface (GUI) that includes a graph panel and a visualization panel may be generated. The graph panel may receive graph related inputs or interactions. Graph information based on the graph related inputs or interactions and a data source may be generated. A graph model based on the graph information and the data source may be generated. Elements that correspond to objects in the graph model or relationships in the graph model may be displayed in the graph panel. The graph information and the graph model may be employed to execute a query that provides a data model based on results of the query. The data model may be employed to provide a visualization that is displayed in the display panel.

Interactive data visualization interface for data and graph models

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 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 device formulates a respective intent specification according to the request. The device also applies one or more respective rules corresponding to the respective intent type to determine whether the respective intent type is ambiguous and/or underspecified. When the respective intent type is ambiguous and/or underspecified, the device infers information to resolve the ambiguous and/or underspecified intent.

Applying a visual analytics intent language to infer ambiguous or underspecified intent

An electronic device running a first application has one or more processors and memory. The memory stores one or more programs configured for execution by the one or more processors. The device downloads, from a server system, a visual analytics intent library, which is configured to execute across a plurality of applications, including the first application. The device configures the library to execute on the device based on the device running the first application. After the configuring, the 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 one or more intent types. In accordance with the request, for each of the intent types, the device formulates a respective intent specification according to the library. It also determines one or more respective properties of the respective intent specification according to the respective predefined attributes.

Implementing a visual analytics intent language across multiple devices

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. The respective output specification also includes respective data visualization parameters that specify how to render a data visualization of the respective recommended data visualization type.

A method implements a virtual machine for interactive visual analysis. The method receives a data visualization data flow graph, which is a directed graph including data nodes and transform nodes. Each transform node specifies a set of inputs for retrieval, where each input corresponds to a data node. Each transform node also specifies a transform operator, which identifies an operation to be performed on the inputs. Some transform nodes specify (a) a set of outputs corresponding to respective data nodes and (b) a function for use in performing the operation of the transform node. The method traverses the data flow graph according to directions of arcs between nodes in the data flow graph, thereby retrieving data corresponding to each data node and executing the respective transformation operator specified for each of the transform nodes. This generates a data visualization according to transform nodes that specify graphical rendering.

A method generates a graphical representation of a data source. The method generates and displays a graphical user interface on a computer display. The graphical user interface includes a schema information region and a data visualization region. The schema information region includes: (i) multiple field names, each field name associated with a data field, and (ii) one or more relationship names, each relationship name associated with a relation between rows of the data source. The data visualization region includes a connector shelf. The method detects user input to associate a field name or relationship name from the schema information region with the connector shelf. The method then generates and displays, in the data visualization region, a visual graphic including visual marks corresponding to rows from the data source and connectors between the visual marks according to field names and relationship names associated with the connector shelf.

Data analysis platform for visualizing data according to relationships

A method sorts data in data visualizations. The method is performed at a computing device having one or more processors and memory. The method retrieves a set of tuples from a database according to user selection. Each tuple includes a same set of fields. The method identifies a relation between tuples. The relation is a non-empty set of ordered pairs of tuples from the set of tuples. The method receives user selection of the relation to specify a dimensional position of visual marks corresponding to the tuples. The dimensional position is either the x-position of the y-position. The method generates and displays a data visualization with each tuple represented by a visible mark. The dimensional position of each displayed visual mark is based on a network traversal of the tuples using the relation.

Systems and methods of arranging displayed elements in data visualizations that use relationships

A method implements a virtual machine for interactive visual analysis. The method receives a data visualization data flow graph, which is a directed graph including data nodes and transform nodes. Each transform node specifies a set of inputs for retrieval, where each input corresponds to a data node. Each transform node also specifies a transform operator that identifies an operation to be performed on the inputs. Some transform nodes specify (a) a set of outputs corresponding to respective data nodes and (b) a function for use in performing the operation of the transform node. The method traverses the data flow graph according to directions of arcs between nodes in the data flow graph, thereby retrieving data corresponding to each data node and executing the respective transformation operator specified for each of the transform nodes. This generates a data visualization according to transform nodes that specify graphical rendering.

Systems and methods for interactive visual analysis using a specialized virtual machine

Mobile devices enabled to support resolution-independent scalable display of Internet (Web) content to allow Web pages to be scaled (zoomed) and panned for better viewing on smaller screen sizes. The mobile devices employ software-based processing of original Web content, including HTML-based content, XML, cascade style sheets, etc. to generate scalable content. The scalable content and/or data derived therefrom are then employed to enable the Web content to be rapidly rendered, zoomed, and panned. Display lists may also be employed to provide further enhancements in rendering speed. Context zooms, including tap-based zooms on columns, images, and paragraphs are also enabled.

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Bellingham, WA, United States of America

Scott A Sherman