JP7535171B2 - Method and user interface for generating level of detail calculations for data visualization - Patent Application 20070123633 - Google Patents

Description

Related Applications
[0001] This application is a continuation of U.S. patent application Ser. No. 17/095,696, entitled "Methods and User Interfaces for Generating Level of Detail Calculations for Data Visualizations," filed on November 11, 2020, which claims priority to U.S. Provisional Patent Application Ser. No. 63/087,862, entitled "Methods and User Interfaces for Generating Level of Detail Calculations for Data Visualizations," filed on October 5, 2020, each of which is incorporated by reference in its entirety.

[0002] This application is related to U.S. Provisional Patent Application No. 62/933,940, entitled "Methods and User Interfaces for Determining Level of Detail for Data Visualizations," filed November 11, 2019, and U.S. Provisional Patent Application No. 16/166,125, entitled "Determining Levels of Detail for Data Visualizations Using Natural Language Constructs," filed October 21, 2018, each of which is incorporated herein by reference in its entirety.

Technical Field
[0003] The disclosed implementations relate generally to data visualization, and specifically to systems, methods, and user interfaces that enable users to interact with data visualizations and analyze data by using drag-and-drop operations.

background
[0004] Data visualization applications allow users to visually understand a data set. Visual analysis of a data set, including distributions, trends, outliers, and other factors, is important for making business decisions. Some data sets are very large or complex and contain many data fields. Various tools can be used to help understand and analyze the data, including dashboards with multiple data visualizations and natural language interfaces that aid in visual analysis tasks. In particular, Level of Detail (LOD) representations are a powerful tool for aggregating data at various levels.

overview
[0005] There is a need for improved systems and methods to support interaction with visual analytics systems. This disclosure describes a method for resolving user input on a user interface to formal queries that may be executed against a visual analytics system (e.g., a data visualization application). The method further supports multiple aggregation levels in a single data visualization. Thus, the method and user interface reduce the cognitive load on the user and create a more efficient human-machine interface.

[0006] Level of Detail representations (also known as LOD representations) allow users to calculate values at the data source level and the visualization level. LOD representations may provide control over the level of granularity for the calculations. For example, LOD representations may be performed at a finer level of granularity (INCLUDE), at a greater level of granularity (EXCLUDE), or completely separate (FIXED). Some implementations allow users to define or create and/or modify such LOD representations via an intuitive graphic user interface.

[0007] According to some implementations, a method for generating a level of detail calculation for a data visualization is provided. The method is performed in a computing device having a display, one or more processors, and a memory storing one or more programs configured for execution by the one or more processors. The method includes receiving a user selection of a data source. The method also includes displaying a data visualization interface, the data visualization interface including: a data visualization area; a shelf area having a plurality of shelves, each shelf defining a respective characteristic of the data visualization based on an arrangement of data fields on each shelf; and a schema information area displaying a plurality of data objects, each data object having one or more selectable data fields, and each data field designated as a dimension or a measure. The method also includes receiving user input to select a measure data field and a dimension data field from the schema information area. The method also includes, in response to user input: generating a custom calculation that aggregates data of the measure data fields grouped by distinct data values of the dimension data fields; and storing the custom calculation as a new selectable data field associated with the data object corresponding to the dimension data field. The method also includes receiving a user selection of the new selectable data field and placement of the new selectable data field on a first shelf in the shelf area, the first shelf defining a first data visualization characteristic; and generating and displaying a data visualization in the data visualization area, the first data visualization characteristic of the data visualization being determined according to the data values of the custom calculation.

[0008] In some implementations, the user input is a drag-and-drop operation that includes dragging a measure data field and dropping the measure data field onto a dimension data field. In some implementations, the dimension data field is a primary or alternate key of a data object that corresponds to the dimension data field. In some implementations, the user input further includes: a user initiation of a context menu associated with the measure data field or the dimension data field; and selecting a context menu option to build a custom calculation. In some implementations, the method further includes, in response to a user selection of the context menu option: displaying a dialog window including the generated custom calculation; and detecting a second user input in the dialog window to edit the custom calculation, where storing the custom calculation as a new selectable data field is in response to detecting a user activation of a save affordance in the dialog window.

[0009] The examples shown below use a single dimension and a single measure data field, but the same techniques can be applied to additional fields. For example, a user may select two or more dimension fields and then drag a measure field onto any one of the dimension fields. In this case, the data visualization application generates an LOD representation with a combined grouping of all data fields in the specified dimension data field. For example, with two dimensions and one measure, the generated LOD representation is {FIXED[dimension 1], [dimension 2]: SUM([measure])}.

[0010] In some implementations, the custom calculation is of the form {FIXED[field1]:AGG([field2])}, where "field1" is the name of a dimension data field, "AGG" is the aggregation operator, and "field2" is the name of a measure data field. In some implementations, the aggregation operator is one of SUM, COUNT, AVERAGE, MIN, and MAX.

[0011] In some implementations, generating and displaying a data visualization in the data visualization area includes: generating one or more database queries directed to a data source according to a user placement of data fields from the schema information area onto shelves in the shelf area (including placement of a new selectable data field onto a first shelf); executing one or more database queries to retrieve one or more data sets from the data source (including aggregate data of measure data fields grouped according to dimension data fields); and generating and displaying a data visualization according to the retrieved data sets.

[0012] In some implementations, a computing device includes one or more processors, a memory, a display, and one or more programs stored in the memory. The programs are configured for execution by the one or more processors. The one or more programs include instructions for performing any of the methods described herein.

[0013] In some implementations, a non-transitory computer-readable storage medium stores one or more programs configured for execution by a computing device having one or more processors, a memory, and a display. The one or more programs include instructions for performing any of the methods described herein.

[0014] Accordingly, a method, system and graphical user interface are disclosed that allows a user to easily interact with data visualizations and analyze data by using natural language expressions.

BRIEF DESCRIPTION OF THE DRAWINGS
[0015] For a better understanding of the above-mentioned systems, methods and graphic user interfaces, as well as additional systems, methods and graphic user interfaces for providing data visualization analysis, reference should be made to the following description of implementations in conjunction with the following drawings, in which like reference characters refer to corresponding components throughout the accompanying drawings.

[0016] Figure 1 illustrates a graphic user interface used in some implementations. [0017] FIG. 1 is a block diagram of a computing device according to some implementations. [0018] FIG. 1 illustrates a graphic user interface for generating level of detail (LOD) representations according to some implementations. [0018] FIG. 1 illustrates a graphic user interface for generating level of detail (LOD) representations according to some implementations. [0019] FIG. 1 illustrates an example user interface for creating and/or visualizing an object model according to some implementations. [0019] FIG. 1 illustrates an example user interface for creating and/or visualizing an object model according to some implementations. [0019] FIG. 1 illustrates an example user interface for creating and/or visualizing an object model according to some implementations. [0020] FIG. 1 illustrates another way to generate an LOD representation by using a context menu according to some implementations. [0020] FIG. 1 illustrates another way to generate an LOD representation by using a context menu according to some implementations. [0020] FIG. 1 illustrates another way to generate an LOD representation by using a context menu according to some implementations. [0020] FIG. 1 illustrates another way to generate an LOD representation by using a context menu according to some implementations. [0021] FIG. 1 illustrates an example user interface for viewing and/or editing details of previously generated LOD calculations, according to some implementations. [0021] FIG. 1 illustrates an example user interface for viewing and/or editing details of previously generated LOD calculations, according to some implementations. [0021] FIG. 1 illustrates an example user interface for viewing and/or editing details of previously generated LOD calculations, according to some implementations. [0022] FIG. 1 illustrates a graphic user interface for generating level of detail (LOD) representations according to some implementations. [0023] A flowchart of a method for generating a level of detail representation for data visualization according to some implementations is provided.

[0024] Reference will now be made to implementations, examples of which are illustrated in the accompanying drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be apparent to one skilled in the art that the present invention may be practiced without the need for these specific details.

Implementation Description
[0025] Figure 1 illustrates a graphic user interface 100 for interactive data analysis. The user interface 100 includes a data tab 114 and an analysis tab 116 according to some implementations. When the data tab 114 is selected, the user interface 100 displays a schema information area 110 (also called a data pane). The schema information area 110 provides named data elements (e.g., field names) that can be selected and used to build a data visualization. In some implementations, the list of field names is separated into a set of dimensions (e.g., categorical data) and a set of measures (e.g., quantities). Some implementations also include a list of parameters. When the analysis tab 116 is selected, the user interface displays a list of analysis functions instead of data elements (not shown).

[0026] The graphic user interface 100 also includes a data visualization area 112. The data visualization area 112 includes multiple shelf areas, such as a column shelf area 120 and a row shelf area 122, which are also referred to as column shelves 120 and row shelves 122. As shown herein, the data visualization area 112 also has a large space for displaying visual graphics (also referred to herein as data visualization). The space is initially empty of visual graphics because no data elements have yet been selected. In some implementations, the data visualization area 112 has multiple layers, called sheets. In some implementations, the data visualization area 112 includes an area 126 for data visualization filters.

[0027] In some implementations, the graphic user interface 100 also includes a natural language input box 124 (also referred to as a command box) for receiving natural language commands. A user may interact with the command box to provide commands. For example, a user may provide natural language commands by typing commands in the box 124. In addition, a user may indirectly interact with the command box by speaking into the microphone 220 to provide commands. In some implementations, data elements are initially associated with the column shelf 120 and the row shelf 122 (e.g., by using a drag-and-drop operation from the schema information area 110 to the column shelf 120 and/or the row shelf 122). After the initial association, a user may use the natural language commands (e.g., in the natural language box 124) to further explore the display data visualization. In some cases, a user creates an initial association by using the natural language input box 124, which results in one or more data elements being placed in the column shelf 120 and the row shelf 122. For example, a user may provide an instruction to create a relationship between data element X and data element Y. In response to receiving the instruction, column shelf 120 and row shelf 122 may be populated with data elements (e.g., column shelf 120 may be populated with data element X and row shelf 122 may be populated with data element Y, or vice versa).

2 is a block diagram illustrating a computing device 200 that may display the graphical user interface 100 according to some implementations. Various examples of computing devices 200 include desktop computers, laptop computers, tablet computers, and other computing devices having a display and a processor capable of executing a data visualization application 230. The computing device 200 typically includes one or more processing units (processors or cores) 202, one or more network or other communication interfaces 204, memory 206, and one or more communication buses 208 for interconnecting these components. The communication bus 208 optionally includes circuitry (sometimes referred to as a chipset) that interconnects and controls communication between the system components. The computing device 200 includes a user interface 210. The user interface 210 typically includes a display device 212. In some implementations, the computing device 200 includes input devices such as a keyboard, a mouse, and/or other input buttons 216. Alternatively or additionally, in some implementations, the display device 212 includes a touch-sensitive surface 214, in which case the display device 212 is a touch-sensitive display. In some implementations, the touch-sensitive surface 214 is configured to detect various swipe gestures (e.g., vertical and/or horizontal continuous gestures) and/or other gestures (e.g., single/double taps). In computing devices with a touch-sensitive display 214, a physical keyboard is optional (e.g., a soft keyboard may be displayed when keyboard input is required). The user interface 210 also includes an audio output device 218 (such as a speaker, or an audio output connection connected to a speaker, earphones or headphones, etc.). In addition, some computing devices 200 use a microphone and voice recognition to supplement or replace a keyboard. Optionally, the computing device 200 includes an audio input device 220 (e.g., a microphone) to capture audio (e.g., speech from a user).

[0029] In some implementations, memory 206 includes high-speed random access memory, such as DRAM, SRAM, DDR RAM, or other random access solid-state memory devices; and may include non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid-state storage devices. In some implementations, memory 206 includes one or more storage devices located remotely from processor 202. Memory 206, or alternatively a non-volatile memory device within memory 206, includes a non-transitory computer-readable storage medium. In some implementations, memory 206 or the computer-readable storage medium of memory 206 stores the following programs, modules, and data structures, or a subset or superset thereof:
• An operating system 222 that contains procedures for handling various basic system services and for performing hardware dependent tasks;
• A communications module 224 used to connect computing device 200 with other computers and devices via one or more communications interfaces 204 (wired or wireless), such as the Internet, other wide area networks, local area networks, metropolitan area networks, etc.;
Optionally, a web browser 226 (or other application capable of displaying web pages) that allows the user to communicate over the network with remote computers or devices;
- Optionally, an audio input module 228 (e.g., a microphone module) for processing audio captured by the audio input device 220. The captured audio may be transmitted to a remote server and/or processed by an application (e.g., data visualization application 230) executing on the computing device 200;
A data visualization application 230 for generating data visualizations and associated features. In some implementations, the data visualization application 230 also includes:
A graphical user interface 100 for a user to construct visual graphics. In some implementations, the graphical user interface includes a user input module 234 for receiving user input via a natural language box 124 (FIG. 1). For example, a user enters natural language instructions or expressions into the natural language box 124 (which may be stored on the computing device 200 or may be stored remotely) that identify one or more data sources 242 and/or identify data fields from the data sources. In some implementations, the natural language expressions are speech utterances captured by the audio input device 220. The selected fields may be used to define the visual graphics. The data visualization application 230 then displays the generated visual graphics in the graphical user interface 100. In some implementations, the data visualization application 230 executes as a standalone application (e.g., a desktop application). In some implementations, the data visualization application 230 executes within the web browser 226 or another application by using a web page provided by a web server:
o A data visualization generation module 236 that automatically generates and displays corresponding visual graphics (also called "data visualizations" or "data viz") by using user input (e.g., natural language input);
Optionally, a natural language processing module 238 for processing (e.g., interpreting) the received natural language input (e.g., instructions) by using the natural language box 124. In some implementations, the natural language processing module 238 parses (e.g., into tokens) and converts the natural language instructions into an intermediate language (e.g., ArkLang). The natural language processing module 238 includes an analyzed representation 239 that is used by the natural language processing module 238 to form an intermediate representation of the natural language instructions. The natural language processing module 238 also converts (e.g., compiles) the intermediate representation into a database query by employing a visualization query language to issue queries to a database or data source 242 and to retrieve one or more data sets from the database or data source 242;
○ A visual specification 240 that is used to define the characteristics of the desired data visualization. In some implementations, information provided by a user (e.g., user input) is stored as a visual specification. In some implementations, the visual specification 240 includes previous natural language instructions received from a user or characteristics defined by a user via natural language instructions. In some implementations, the visual specification 240 includes two or more aggregations based on various levels of detail. More information regarding levels of detail can be found in U.S. Patent Application Serial No. 14/801,750, filed July 16, 2015, entitled "Systems and Methods for using Multiple Aggregation Levels in a Single Data Visualization," which is incorporated herein by reference in its entirety;
Zero or more databases or data sources 242 (e.g., a first data source 244 and a second data source 246) used by the data visualization application 230. In some implementations, the data sources are stored as spreadsheet files, CSV files, XML files, flat files, or JSON files, or are stored in a relational database. For example, a user selects one or more databases or data sources 242 (which may be stored on the computing device 200 or may be stored remotely), selects data fields from the data sources, and uses the selected fields to define a visual graphic;
zero or more object models 248 (e.g., a first object model 248-1 corresponding to data source 244, and a second object model corresponding to data source 246. The object models 248 represent the logical organization of tables (or objects) in the data sources according to some implementations. In some implementations, the object models 248 encode the relationships between the logical and/or physical tables of the data sources; and
• A custom calculation generation module 250 that generates and/or stores custom calculations 252 (eg, custom calculations 252-2, 252-4; sometimes referred to as level of detail (LOD) calculations) based on user selection of data fields (eg, dimensions, measures) or objects of a data source.

[0030] FIG. 2 illustrates a computing device 200, but is not intended as a structural schematic of the implementations described herein, but rather as a functional description of various features that may be present. In fact, and as will be recognized by those skilled in the art, items shown separately may be combined and some items may be separated.

[0031] Each of the identified executable modules, applications, or sets of procedures may be stored in one or more of the memory devices described above and correspond to a set of instructions for performing the functions described above. The identified modules or programs (i.e., sets of instructions) need not be implemented as separate software programs, procedures, or modules, and thus, in various implementations, various subsets of these modules may be combined or otherwise rearranged. In some implementations, memory 206 stores a subset of the identified modules and data structures. Additionally, memory 206 may store additional modules or data structures not described above.

Generating a level of detail for data visualization based on user input on a user interface
3A and 3B show a graphic user interface 100 for generating a level of detail (LOD) representation according to some implementations. Some implementations allow a user to generate an LOD representation by dragging a measure data field onto a dimension data field. For example, a user can drag a sales data field onto a product data field, and the system (e.g., the custom calculation generation module 250) automatically generates an LOD representation that aggregates sales per product. The LOD expression is {FIXED [product]: SUM ([sales])}. In some implementations, this calculation is assigned a name as a new calculation field, and the new calculation field appears in the schema information area 110 as a selectable field. In some implementations, after the new LOD calculation is generated, the LOD calculation can then be used like any other data field.

[0033] As an alternative to drag-and-drop operations, some implementations allow the user to select dimensions and measures and then invoke a context menu (e.g., by using a right-click from a dimension data field). In some implementations, this invokes a dialog window pre-populated with the generated LOD representation. The user may then modify the representation (if necessary) before saving it. In some implementations, the calculation is automatically saved as a new data field without invoking a dialog window.

3A and 3B, the price data field 302 (which is a measure from the version table 300) is dragged onto the dimension data field ISBN (Sales Q1+) 306, which is the primary key of the sales table 304. Some implementations create a copy of the price data field 302 in the context of the data field ISBN 306 to create a new LOD representation 308. Some implementations allow the user to create the LOD representation via a context menu (described above and below). Some implementations allow the user to create the LOD representation by dragging and dropping the data fields (e.g., by using command or control keys or similar gestures to create the data fields). Some implementations allow the user to create the measure in the context of the new data field.

3C, 3D, and 3E show an exemplary user interface 310 for creating and/or visualizing an object model according to some implementations. The user interface 310 includes a connection area 312 that displays data sources. The connection area 318 provides a connection to a database server that hosts a database (or data source). Each data source includes one or more tables of data 320 that can be selected and used to build an object model. In some implementations, the list of tables is grouped (e.g., according to the logical organization of the tables). The graphical user interface 310 also includes an object model visualization area 314. The object model visualization area 314 displays an object model (e.g., a tree or graph of data objects). The displayed object model includes one or more data object icons (e.g., icons 322-2, 322-4, 322-6, 322-8, 322-10, 322-12, 322-14). In Figure 3C, the object model shown in the object model visualization area 314 corresponds to the structure of the tables shown in Figures 3A and 3B. Each data object icon represents either a table (e.g., a physical table) or a logical combination of one or more tables. For example, icon 322-2 represents a books table, icon 322-4 represents an authors table, icon 322-6 represents an awards table, icon 322-8 represents an editions table 300, icon 322-10 represents a publishers table, icon 322-12 represents a sales table 304, and icon 322-14 represents a scores table. In some implementations, the interface 310 also includes a data grid area 306 that displays data fields of one or more data object icons displayed in the object model visualization area 314. In some implementations, the grid area 316 is updated or refreshed in response to detecting a user input in the object model visualization area 314. In FIG. 3C, the visualization area 314 shows a highlighted object icon 322-2, and the grid area 316 displays the details (e.g., data fields) of the book table that corresponds to the object icon 322-2. In some implementations, the grid area shows the first table (e.g., the logical table or the root of the object model tree) to start with (e.g., when an existing object model is loaded) without detecting user input. If the user navigates away and/or selects an alternative object icon (e.g., icon 322-4), the grid area updates to show details of the logical table (or physical table) that corresponds to the alternative object icon (e.g., details of the edition table 300). In some implementations, the user may double-click 324 on a logical table to view its physical table.

[0036] To further illustrate the process of generating LOD expressions, when a user drags a price data field 302 from the edition table 300 to the sales table 304, the system generates a measure that is row-level price information in the sales table 304. The user may then drag the generated measure (e.g., data field 308) up one level (e.g., for the edition table 300) or even multiple levels (e.g., up to two levels for the books table). By dragging and dropping a calculated measure, the user may generate a new measure at the correct level of detail for the context (e.g., table or data field). Thus, in addition to allowing a user to drag and drop existing measures to generate LOD calculations (sometimes called custom calculations), some implementations allow a user to drag custom calculations into the context to generate further calculations, processing the custom calculations similar to existing measures. This process of nesting custom calculations (including LOD expressions) inside other calculations may be extended to any depth as needed.

4A-4D show another way to generate an LOD representation by using a context menu according to some implementations. Before any measures are generated, as shown in FIG. 4A, if the user selects a data field (e.g., the series ID information field 400 in the book table), the system does not show any LOD calculations. Then, as shown in FIG. 4B, after a dimension and a measure are selected, if the user selects a data field (e.g., the book ID information field 404), followed by a right click on another data field (e.g., the expected revenue data field 406), the system shows a context menu 408 to generate an LOD calculation according to some implementations. If the user selects an LOD calculation as an option, the system shows a pop-up window 410 to generate an LOD calculation according to some implementations. This is shown in FIG. 4C. FIG. 4D shows an expanded view of the pop-up window 410. Some implementations allow the user to name a calculation 412. Some implementations allow the user to edit the LOD calculation 416. For example, the user may change SUM 414 to AVG or a similar calculation. Some implementations validate the LOD calculation and/or show the results of the validation (e.g., information 418). Some implementations show affordances (e.g., an Apply button 420 and an OK button 422) to apply the LOD calculation (e.g., to check the validity of the LOD calculation) and/or to accept the entries or close the pop-up window.

[0038] FIG. 5A shows an exemplary user interface for viewing and/or editing details of a previously generated LOD calculation according to some implementations. In the example shown, when the user selects LOD calculation 308 (generated as described above with reference to FIGS. 3A-3E), the system responds by presenting a drop-down menu 500 with options 502 for editing fields. Assuming the user selects to edit the fields, the system presents a pop-up window 504 (shown in FIG. 5B) for viewing and/or editing details of data fields 308. FIG. 5C shows an expanded view of pop-up window 504 according to some implementations. Similar to FIG. 4D, the user may edit the fields of LOD calculation 506 according to some implementations.

[0039] Some implementations track primary and foreign keys of tables. Suppose a user drags a measure onto a table (as opposed to a data field of the table), some implementations generate LOD calculations based on the primary key of the table. For example, as shown in FIG. 6, suppose a user drags a price data field from the edition table onto the sales table 304. In response, the system detects that the sales table 304 has a primary key and uses the primary key as a data field for generating LOD calculations. Some implementations allow a user to use multiple data fields for generating LOD calculations. For example, a user may drag a measure onto a first data field and then select a second data field, thereby generating an LOD calculation based on the first and second data fields.

[0040] FIG. 7 provides a flowchart of a method 700 for generating 702 a level of detail calculation for data visualization according to some implementations. The method 700 may also be referred to as a process.

[0041] The method 700 is performed (704) on a computing device 200 having a display 212, one or more processors 202, and a memory 206. The memory 206 stores (706) one or more programs configured for execution by the one or more processors 202. In some implementations, the operations performed by the computing device correspond to instructions stored in the memory 206 or other non-transitory computer-readable storage medium. The computer-readable storage medium may include magnetic or optical disk storage devices, solid-state storage devices such as flash memory, or other non-volatile memory device or devices. The instructions stored on the computer-readable storage medium may include one or more of source code, assembly language code, object code, or other instruction formats interpreted by one or more processors. Some operations in the method may be combined and/or the order of some operations may be changed.

[0042] The method includes receiving (708) a user selection of a data source. For example, in FIG. 3, the user selects the books data source. The method also includes displaying (710) a data visualization interface, the data visualization interface including a data visualization area 112; a shelf area having a number of shelves (e.g., shelves 120, 122), each shelf defining respective characteristics of the data visualization based on the placement of data fields on each shelf; and a schema information area 110 (sometimes called a table) displaying a number of data objects. Each data object has one or more selectable data fields, and each data field is designated as a dimension or measure.

[0043] The method also includes receiving (712) user input to select measure data fields and dimension data fields from the schema information area. Examples of such user input are described above with reference to Figures 3A-3E according to some implementations.

[0044] The method also includes, in response to user input: generating a custom calculation that aggregates data of the measure data fields grouped by distinct data values of the dimension data fields; and storing (714) the custom calculation as a new selectable data field associated with the data object corresponding to the dimension data field. Examples of generating a custom calculation and storing the custom calculation as a selectable data field are described above with reference to Figures 3A-3E according to several implementations.

[0045] The method also includes receiving (716) a user selection of a new selectable data field and placement of the new selectable data field on a first shelf in the shelf area, the first shelf defining a first data visualization characteristic; and generating and displaying (718) a data visualization in the data visualization area, the first data visualization characteristic of the data visualization being determined according to a data value of the custom calculation. Examples of user selection of a new data field, placement of a data field on a shelf area, and/or generating and displaying a data visualization are described above with reference to Figures 1, 2, 3A-3E, 4A-4D according to several implementations.

[0046] In some implementations, the user input is a drag-and-drop operation that includes dragging a measure data field and dropping the measure data field onto a dimension data field. In some implementations, the dimension data field is a primary or alternate key of a data object corresponding to the dimension data field. In some implementations, the user input further includes: a user initiation of a context menu associated with the measure data field or the dimension data field; and selecting a context menu option to build a custom calculation. In some implementations, the method further includes, in response to a user selection of the context menu option: displaying a dialog window including the generated custom calculation; and detecting a second user input in the dialog window to edit the custom calculation, and storing the custom calculation as a new selectable data field is in response to detecting a user activation of a save affordance in the dialog window.

[0047] In some implementations, custom calculations are of the form {FIXED[field1]:AGG([field2])}, where "field1" is the name of a dimension data field, "AGG" is the aggregation operator, and "field2" is the name of a measure data field. In some implementations, the aggregation operator is one of SUM, COUNT, AVERAGE, MIN, and MAX.

[0048] In some implementations, generating and displaying a data visualization in the data visualization area includes: generating one or more database queries directed to a data source according to a user placement of data fields from the schema information area onto shelves in the shelf area (including placement of a new selectable data field onto a first shelf); executing one or more database queries to retrieve one or more data sets from the data source (including aggregate data of measure data fields grouped according to dimension data fields); and generating and displaying a data visualization according to the retrieved data sets.

[0049] According to some implementations, a method for determining a level of detail for a data visualization is provided. The method is performed in a computing device having a display, one or more processors, and a memory storing one or more programs configured for execution by the one or more processors. The method includes displaying a data visualization interface on the display, receiving a user selection of a data source, and detecting an input to specify a type of level of detail representation directed to the data source. The method also includes, in response to detecting the input: determining (i) an aggregation type in a first aggregation, (ii) data fields aggregated for the first aggregation, and (iii) a grouping for the first aggregation based on the input; generating one or more database queries including the first aggregation according to the data source based on the aggregation type, data fields, and grouping; executing one or more database queries to retrieve one or more data sets from the data source aggregated according to the first aggregation; and generating and displaying an updated data visualization of the retrieved data sets.

[0050] In some implementations, the first aggregation is a measure and the data field is a dimension of the data source. In some implementations, the input is a drag-and-drop operation that includes dragging the first aggregation and dropping it onto the data field. In some implementations, the input is a right-click operation on the data field, and the method further includes displaying a context menu or dialog that allows a user to specify the measure or calculation, and determining the first aggregation based on the measure or calculation.

[0051] In some implementations, the input is a drag-and-drop operation that includes dragging and dropping the first aggregate onto a table, and the method further includes retrieving a primary key associated with the table and using the primary key as the data field.

[0052] In some implementations, the method further includes: displaying the first aggregate; detecting a second input to view details of the first aggregate; displaying a calculation or measure corresponding to the first aggregate in response to detecting the second input; detecting a third input to modify the calculation or measure; and updating the first aggregate in response to detecting the third input.

[0053] Some implementations allow users to define LOD by using drag and drop operations. Some implementations generate calculated fields in the schema viewer. Some implementations include a calculation dialog. Some implementations allow users to define ad-hoc calculations and/or other pills (e.g. pill context menu) for defining LOD. Some implementations provide user feedback to allow users to know if their calculations meet their expectations.

[0054] Some implementations allow the user to specify the type of LOD by using one of the following syntaxes: {FIXED dims:calcs} (FIXED, INCLUDE, or EXCLUDE), {calcs}, {INCLUDE:calcs} (INCLUDE, or EXCLUDE).

[0055] Some implementations allow the user to select different types of LOD (FIXED, INCLUDE, or EXCLUDE). Some implementations allow the user to drag a calculation onto a dimension or drag a set of dimensions onto a calculation. Some implementations assume that the interactions between two parts of an LOD calculation are equivalent and that the order is implicit. Some implementations assume a FIXED type of LOD by default and provide a context menu for the user to change the type.

[0056] Some implementations detect right-click drags and/or present options after the user releases the mouse to select different types of LOD.

[0057] Some implementations detect secondary drop targets after the initial drop, similar to how some data visualization platforms detect user input to refine an analysis (e.g., input provided within an analysis pane of a table, pane, or cell).

[0058] Some implementations detect user input to determine interactions between data fields.

[0059] Some implementations determine the defining dimensions of an object (e.g., the friendly field name of a primary key) based on user input and/or detecting metadata information related to the object.

[0060] Some implementations detect user input to determine interactions between dimensions and calculations or measures. Some implementations detect user input to determine interactions between dimensions or between dimensions and hierarchical database models.

[0061] Some implementations detect user input to determine the dimensions and/or categorize each dimension as a string, a datetime field, a set of bins, part of a combination field, or part of a hierarchy.

[0062] Some implementations detect user input to change the aggregation for a measure.

[0063] Some implementations detect user input to determine a particular type of LOD (e.g., {FIXED dim:COUNTD(dim2)}, {FIXED year(order date):SUM(sales)}). Some implementations detect user input to change the hierarchy within a dimension.

[0064] The terms used in the description of the invention herein are for the purpose of describing particular implementations only and are not intended to limit the invention. As used in the description of the invention and the appended claims, the singular definite and indefinite articles are intended to include the plural as well, unless the context clearly indicates otherwise. It will also be understood that the term "and/or" as used herein refers to and includes all possible combinations of one or more of the associated list items. It will also be further understood that the term "comprising," as used herein, specifies the presence of stated features, steps, operations, elements, and/or components, but does not exclude the presence or addition of one or more other features, steps, operations, elements, components, and/or groups thereof.

[0065] The foregoing description has been provided for purposes of explanation and has been made with reference to specific implementations. However, the illustrative discussion is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in light of the above teachings. The implementations have been chosen and described in order to best explain the principles and practical applications of the invention and to thereby enable others skilled in the art to best utilize the invention and its various implementations with various modifications as may be suited to the particular use contemplated.

Claims (20)

1. A method for generating a level of detail calculation for data visualization, comprising:
1. A computing device having a display, one or more processors, and a memory storing one or more programs configured for execution by the one or more processors, the one or more processors:
Receiving a user selection of a data source;
displaying a data visualization interface, the data visualization interface comprising:
Data visualization area,
displaying a shelf area having a plurality of shelves, each shelf defining a respective characteristic of a data visualization based on an arrangement of data fields on each shelf, and a schema information area displaying a plurality of data objects, each data object having one or more selectable data fields, each data field designated as a dimension or measure;
receiving user input to select measure data fields and dimension data fields from the schema information area;
generating a custom calculation in response to the user input that aggregates data in the measure data fields grouped by distinct data values of the dimensional data fields, and storing the custom calculation as a new selectable data field associated with a data object corresponding to the dimensional data field;
receiving a user selection of the new selectable data field and placement of the new selectable data field on a first shelf within the shelf area, the first shelf defining a first data visualization feature;
generating and displaying a data visualization within the data visualization area, the first data visualization characteristic of the data visualization being determined according to a data value of the custom calculation;
The method includes: The method of claim 1, wherein the user input is a drag-and-drop operation that includes dragging the measure data field and dropping the measure data field onto the dimension data field. The method of claim 2, wherein the dimensional data field is a primary key or an alternate key of the data object that corresponds to the dimensional data field. The user input further comprises:
The method of claim 1 , comprising: a user initiation of a context menu associated with the measure data field or the dimension data field; and a selection of a context menu option to construct the custom calculation. in response to the user selection of the context menu option;
displaying a dialog window including the generated custom calculation; and detecting a second user input in the dialog window to edit the custom calculation,
The method of claim 4 , wherein storing the custom calculation as a new selectable data field occurs in response to detecting user activation of a save affordance in the dialog window. The method of claim 1, wherein the custom calculation is of the form {FIXED[field1]:AGG([field2])}, where "field1" is the name of the dimension data field, "AGG" is an aggregation operator, and "field2" is the name of the measure data field. The method of claim 6, wherein the aggregation operator is one of SUM, COUNT, AVERAGE, MIN, and MAX. Generating and displaying the data visualization in the data visualization area includes:
generating one or more database queries against the data source according to a user arrangement of data fields from the schema information area onto shelves in the shelf area, including arrangement of the new selectable data field onto the first shelf;
2. The method of claim 1 , comprising: executing the one or more database queries to retrieve one or more data sets from the data sources, the data sets comprising aggregated data of the measure data fields grouped according to the dimensional data fields; and generating and displaying the data visualization according to the retrieved data sets. 1. A computing device comprising: one or more processors; and a memory coupled to the one or more processors, the memory storing one or more programs configured to be executed by the one or more processors, the one or more programs comprising:
Receiving a user selection of a data source;
displaying a data visualization interface, the data visualization interface comprising:
Data visualization area,
displaying a shelf area having a plurality of shelves, each shelf defining a respective characteristic of a data visualization based on an arrangement of data fields on each shelf, and a schema information area displaying a plurality of data objects, each data object having one or more selectable data fields, each data field designated as a dimension or measure;
receiving user input to select measure data fields and dimension data fields from the schema information area;
generating a custom calculation in response to the user input that aggregates data in the measure data fields grouped by distinct data values of the dimensional data fields, and storing the custom calculation as a new selectable data field associated with a data object corresponding to the dimensional data field;
receiving a user selection of the new selectable data field and placement of the new selectable data field on a first shelf within the shelf area, the first shelf defining a first data visualization feature;
generating and displaying a data visualization within the data visualization area, the first data visualization characteristic of the data visualization being determined according to a data value of the custom calculation;
A computing device that contains instructions to perform the steps described above. The computing device of claim 9, wherein the user input is a drag-and-drop operation that includes dragging the measure data field and dropping the measure data field onto the dimension data field. The computing device of claim 10, wherein the dimensional data field is a primary key or an alternate key of the data object corresponding to the dimensional data field. The user input further comprises:
The computing device of claim 9 , further comprising: a user initiation of a context menu associated with the measure data field or the dimension data field; and a selection of a context menu option to construct the custom calculation. The one or more programs further comprise:
displaying a dialog window including the generated custom calculation; and detecting a second user input in the dialog window to edit the custom calculation.
instructions for responding to the user selection of the context menu option by
The computing device of claim 12 , wherein storing the custom calculation as a new selectable data field occurs in response to detecting user activation of a save affordance in the dialog window. The computing device of claim 9, wherein the custom calculation is of the form {FIXED[field1]:AGG([field2])}, where "field1" is the name of the dimension data field, "AGG" is an aggregation operator, and "field2" is the name of the measure data field. The computing device of claim 14, wherein the aggregation operator is one of SUM, COUNT, AVERAGE, MIN, and MAX. The instructions for generating and displaying the data visualization in the data visualization area further include:
generating one or more database queries against the data source according to a user arrangement of data fields from the schema information area onto shelves in the shelf area, including arrangement of the new selectable data field onto the first shelf;
executing the one or more database queries to retrieve one or more data sets from the data sources, the data sets including aggregated data of the measure data fields grouped according to the dimension data fields; and generating and displaying the data visualization according to the retrieved data sets.
10. The computing device of claim 9, further comprising instructions for: 1. A non-transitory computer-readable storage medium storing one or more programs configured for execution by a computer system having one or more processors and a memory, the one or more programs comprising:
Receiving a user selection of a data source;
displaying a data visualization interface, the data visualization interface comprising:
Data visualization area,
displaying a shelf area having a plurality of shelves, each shelf defining a respective characteristic of a data visualization based on an arrangement of data fields on each shelf, and a schema information area displaying a plurality of data objects, each data object having one or more selectable data fields, each data field designated as a dimension or measure;
receiving user input to select measure data fields and dimension data fields from the schema information area;
generating a custom calculation in response to the user input that aggregates data in the measure data fields grouped by distinct data values of the dimensional data fields, and storing the custom calculation as a new selectable data field associated with a data object corresponding to the dimensional data field;
receiving a user selection of the new selectable data field and placement of the new selectable data field on a first shelf within the shelf area, the first shelf defining a first data visualization feature;
generating and displaying a data visualization within the data visualization area, the first data visualization characteristic of the data visualization being determined according to a data value of the custom calculation;
A non-transitory computer-readable storage medium comprising instructions for performing the steps of: The computer-readable storage medium of claim 17, wherein the user input is a drag-and-drop operation including dragging the measure data field and dropping the measure data field onto the dimension data field. The user input further comprises:
20. The computer-readable storage medium of claim 17, further comprising: a user initiation of a context menu associated with the measure data field or the dimension data field; and a selection of a context menu option to construct the custom calculation. The one or more programs further comprise:
displaying a dialog window including the generated custom calculation; and detecting a second user input in the dialog window to edit the custom calculation.
instructions for responding to the user selection of the context menu option by
20. The computer-readable storage medium of claim 19, wherein storing the custom calculation as a new selectable data field occurs in response to detecting user activation of a save affordance in the dialog window.