Abstract: A method of visualizing data is performed at a computing device. A user selects a field in a data structure. The field has a set of field values and an associated field name. Each field value corresponds to a record in the data structure. The field values are words in a natural language. For each field value, the process builds a term set of base terms including the field value and the field name. The process retrieves a set of images from an image corpus according to the term sets. The process then selects an image from each retrieved set of images and displays data from the data structure in a data visualization. Each record in the data structure is displayed using the corresponding selected image. Each selected image is displayed, instead of the field value, at a location in the data visualization according to data in the respective record.

Abstract: A method compares text strings having Unicode encoding. The method receives a first string S=s1s2 . . . sn and a second string T=t1t2 . . . tm, where s1, s2, . . . , sn and t1, t2, . . . , tm are Unicode characters. The method computes a first string weight for the first string S according to a weight function ƒ. When S consists of ASCII characters, ƒ(S)=S. when S includes one or more non-replaceable non-ASCII characters, the first string weight ƒ(S) is a concatenation of an ASCII weight prefix ƒA(S) and a Unicode weight suffix ƒU(S). The method also computes a second string weight for the second text string T. Equality of the strings is tested using the string weights.

Abstract: A computer receives a visual specification, which specifies a data source, visual variables, and data fields from the data source. Each visual variable is associated with either data fields or filters. The computer obtains a data model encoding the data source as a tree of related logical tables. Each logical table includes logical fields that corresponds to either a data field or a calculation that spans logical tables. The computer constructs a query corresponding to the visual specification. The query references two logical tables connected via multiple linking fields. To generate the query, the computer generates a first left-outer join of the two tables, forms a hidden join key expression using a coalesce, according to the data model, and generates another left-outer join using the hidden join key expression. The computer executes the query and displays a data visualization according to the results of the query.

Abstract: A user interface 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 a block size. Upon retrieval of the initial set of rows, the process repeats the query asynchronously 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 statistical distributions of data values for multiple data fields in the data source. Each distribution element in each statistical distribution counts the rows below the water mark that have a single specific data value or range of data values.

Abstract: A computing device displays a primary data visualization in a data visualization workbook that includes a second worksheet having a secondary data visualization. The primary data visualization includes a plurality of visual marks representing a first set of data fields from a dataset. A first user input in a tooltip definition refers to the secondary data visualization. A second user input selects a first visual mark. In response to the second user input, the device generates a filtered version of the secondary data visualization, for a second set of data fields from the dataset, specified in the second worksheet, based on the selected first visual mark. The device displays the generated secondary data visualization in a tooltip. Sometimes the device receives a third user input selecting a second visual mark in the plurality of visual marks, and dynamically updates the displayed secondary data visualization based on the second visual mark.

Abstract: The various embodiments described herein include methods and devices for interactive data visualization. In one aspect, a method is performed at a device with a touch-sensitive surface and a display. The method includes displaying a first chart with one or more visual marks corresponding to a first dimension and a first measure. The method further includes determining whether a detected touch input is in a first region corresponding to the first dimension or in a second region corresponding to the first measure. If the touch input is in the first region, the method displays a plurality of dimension options, and, in response to selection of a respective dimension option, adds a second dimension through an animated transition to display a second chart. At least one visual mark in the first chart is split into a plurality of visual marks in the second chart through the animated transition.

Abstract: A method executes at a computing device that includes a display, one or more processors, and memory. The device displays a user interface that includes a data pane and a calculation pane. The data pane comprises data rows and data columns. Each of the data columns corresponds to a data field from a data source. The device receives a first user input to specify a grouping on a first data field. The device receives a second user input to specify an aggregation function on a second data field. In response to the first and second user inputs, for each distinct value of the first data field, the device aggregates corresponding values of the second data field according to the aggregation function. A plurality of first data rows are displayed in the calculation pane. The device saves the rows of data displayed in the calculation pane as a new data source.

Abstract: A process builds a view of a dataset. The process displays a graphical user interface, including: a fields region; a visualization region; and a shelf region that includes multiple shelves. The process detects user input that moves a data field icon from the fields region into the visualization region. Upon ceasing to detect the user input, the process associates the data field icon with a first shelf according to its characteristics and characteristics corresponding to other data field icons, if any, previously associated with the shelves. The method further includes: (i) determining one or more chart types for visualizing a portion of the dataset based, at least in part, on the data field icon and its characteristics, and (ii) generating a graphical representation of the portion of the dataset in the visualization region using a first chart type selected from the one or more determined chart types.

Abstract: A method is executed at a computer system to retrieve data from a database. Upon receiving a database query, a database engine of the computer system parses the query to form an operator tree including a plurality of join operators. For each of the plurality of clauses, the database engine adds to the operator tree a respective node that specifies a mark join operator, a single join operator, an inner join operator, or an outer join operator. Specifically, the database engine adds the mark join operator when the respective clause includes one of a predetermined set of predicate subqueries, and adds the single join operator when the respective clause includes a scalar subquery. The database engine performs one or more optimization passes on the operator tree to form an optimized execution plan, and executes the optimized execution plan to retrieve a result set from the database.

Abstract: An exemplary method includes displaying a UI that includes a data flow pane and a data grid pane, and in response to user selection of a node, displaying, in the data grid pane, a data grid corresponding to the user-selected node. The method further comprises receiving user input on a first cell in the data grid that changes a first data value v1 for the first cell (intersection between a first column and a first row of the data grid) to a first modified data value w1. The method further comprises determining a formula f(v1)=w1 corresponding to the input, inserting a new node into the flow diagram immediately after the user-selected node, and applying the formula f to the first column for each of the plurality of rows in the data grid, including changing a second data value v2 to a second modified data value w2, where v2?v1 and w2?w1.

Abstract: A method estimates counts of distinct items. A system allocates a matrix B in memory and receives a data stream including many data items. Each item includes a set of query fields and a set of identifier fields. For each item, the system computes a row for the data item by hashing data values of the identifier fields. The system also computes a column for the data item by hashing a combination of data values of the query fields and the row. The system computes a bin value for the item by hashing data values of the identifier fields. The system updates the matrix B by computing B[r,c]=max(v, B[r,c]). Subsequently, the system receives a query key value. For each row in the matrix B, the system computes a column, and estimates the number of data items from the data stream matching the query key.

Abstract: A method prepares data for analysis. The method displays a user interface including a data flow pane. The data flow pane includes a flow diagram having a plurality of nodes, each node having an intermediate set and specifying a primary operation. When a user selects a node, the user interface displays a change list pane with an ordered list of secondary operations performed at the node and a data pane with rows from an intermediate dataset of the node. When the user provides input to perform an additional secondary operation at the node, the user interface (i) performs the additional secondary operation, (ii) determines a logical ordering for the additional secondary operation based on the primary operation of the node, (iii) adds the additional secondary operation to the change list pane according to the logical ordering, and (iv) updates the data pane in accordance with the additional secondary operation.

Abstract: 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.

Abstract: A computing device displays a data visualization interface and receives user selection of a data source and a natural language command directed to the data source. The device forms an intermediate expression according to a context-free grammar and a semantic model of data fields in the data source. It identifies in the natural language command (i) a first term that specifies an aggregation type in a first aggregation, (ii) a second term that specifies a data field, in the semantic model, to be aggregated for the first aggregation, and (iii) terms that specify data fields, in the semantic model, to determine grouping for the first aggregation. It translates the intermediate expression into database queries, executes the database queries to retrieve one or more data sets from the data source, aggregated according to the first aggregation, then generates and displays an updated data visualization of the retrieved data sets.

Abstract: A database engine receives a database query that specifies retrieving data from a data source. The database engine parses the query to build an operator tree that includes a TableScan operator configured to scan a table from the data source to produce outputs corresponding to a single data field from the table, and includes a GroupBy operator that groups rows of the table according to the data field. The database engine generates and executes code corresponding to the operator tree to retrieve a result set. When the TableScan operator is a child of the GroupBy operator and the outputs are independent of duplicate input rows from the table, execution of the TableScan operator comprises, for each storage block of rows from the table: determining a storage compression scheme for encoding the data field and, for certain encodings, using the encoding to produce the outputs without duplication of rows.

Abstract: A method executes at a computer system to retrieve data from a database. Upon receiving a database query, the computer system translates the query into an intermediate representation, and estimates a compilation time to compile the intermediate representation into machine executable code. The query execution time to retrieve a result set is also estimated. In accordance with a determination that the query execution time and compilation time satisfy an interpretation criterion, the computer system invokes a byte code interpreter to interpret the intermediate representation and retrieve the result set from the database. In accordance with a determination that the query execution and compilation times satisfy one of a plurality of compilation criteria, the computer system compiles the intermediate representation to form machine code and executes the machine code to retrieve the result set from the database. In some cases, the query intermediate representation is optimized prior to compilation.

Abstract: A method is performed at a computing device coupled with a display. The method includes displaying a graphical user interface on the display and analyzing a natural language input, received from a user, to identify a portion of the natural language input corresponding to a first phrase that includes a first term. The method further includes receiving, from the user, a second input that modifies the first term in the first phrase. In response to receiving the second input, the computing device updates a second phrase based on the second input. In response to updating the second phrase based on the second input, the computing device displays, on the graphical user interface, an updated natural language expression that comprises the modified first phrase and the updated second phrase and displays an updated data visualization representing the updated natural language expression.

Abstract: 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.

Abstract: A computing device displays a data visualization interface and receives user selection of a data source and a natural language command directed to the data source. The device identifies a first keyword and second keyword(s) in the natural language command. The second keyword(s) include adjectives that modify the first keyword. The device generates a visual specification that specifies the data source, a plurality of visual variables, and a plurality of data fields from the data source. Each of the visual variables is associated with a respective data field of the plurality of data fields. The first keyword corresponds to one or more first data fields of the plurality of data fields, and the one or more visual variables are associated with the one or more first data fields according to the one or more second keywords. The device generates and displays a data visualization based on the visual specification.

Abstract: A method displays a user interface for a graphical view application. The user interface includes shelves for specifying graphical views of a dataset. A user associates first and second fields of the dataset with first and second shelves. In response, the method displays graphical views in the user interface according to the first and second fields. Each of the graphical views corresponds to a respective distinct value of the second field and each of the graphical views includes a set of graphical marks. When the first shelf defines color encoding, each graphical mark has a respective color that corresponds to a respective data value associated with the first field. When the first shelf defines size encoding, each graphical mark has a respective size that corresponds to a respective data value associated with the first field.