Patent Publication Number: US-2016232692-A1

Title: Displaying multivariate data in multiple dimensions

Description:
BACKGROUND 
     Data, such as values of qualitative and/or quantitative variables, belonging to a set of items, can be visually represented in a structure, often tabular, a tree, and/or a graph structure. Information and/or knowledge can be derived from visual representations of data. For instance, users can identify efficiencies, inefficiencies, areas for improvement, patterns, and outliers to direct actions. Analysis and visual representation of data can be simple or complex, and in many examples can include a collection of datasets so large and complex that it is difficult to process such data using traditional data processing applications. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIGS. 1A-1B  illustrate an example construction of a multivariate and multidimensional (MVMD) graph according to the present disclosure. 
         FIGS. 2A-2B  illustrate examples of systems according to the present disclosure. 
         FIGS. 3A-3C  illustrate examples of modifications of a visualization effect of an MVMD graph according to the present disclosure. 
         FIGS. 4A-4B  illustrate examples of modifications of an MVMD graph according to the present disclosure. 
         FIG. 5  illustrates a flow chart of an example method for displaying multivariate data in multiple dimensions according to the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Several data analytics tools exist that enable users to inspect, clean, transform, and model data with the goal of discovering useful information. Data analysis encompasses diverse techniques, and each technique provides different advantages to a user. Furthermore, the utility of data analytics techniques has increased, and more advanced data mining, data analytics, and data modeling tools have been developed that are capable of compiling and analyzing large amounts of data, and more particularly, multivariate data (e.g., data having a large number of distinct, although not necessarily independent, variables). However, with an increase in the complexity and diversity in data analytics and modeling tools, it has become increasingly difficult to extract meaning from data which is presented in different formats. For instance, it may be desirable to find and analyze correlations between two or more different graphs of the same data, each graph plotting different variables associated with the data. Finding and visualizing correlations between the two can be difficult and time intensive. 
     Some systems enable two-dimensional (2D) visualization of complex datasets. In one such system, termed “parallel coordinates”, one dimension is used to show common data points between a plurality of datasets. That is, a plurality of datasets can be combined to generate a composite graph illustrating the common points between the datasets. Such systems attempt to exploit spatial properties to display multivariate data, but the representations of the datasets themselves are abstracted down to a single linear axis and a number of familiar and effective visualization techniques cannot be used (maps, for example). A development of this technique, three-dimensional (3D) parallel coordinates, extends this technique to 2D scatterplots arranged parallel to each other in a third dimension (e.g., the 3D visualization of gene inter-relationships in  Drosophila melanogaster  cells). However, this system only compares the distribution points in scatterplot graphs to identify characteristic relationships between particular genes, and does not encompass other types of 2D visualization. 
     In contrast, in accordance with examples of the present disclosure, multivariate data can be presented in a multidimensional graph. The presentation of data in a multivariate and multidimensional (MVMD) graph can simultaneously present a number of 2D visualizations (e.g., on layers and/or planes), which describe different aspects and/or facets of the data, and/or illustrate interconnections between the number of 2D visualizations in the third dimension to further illustrate how the data is connected between all of the views. Furthermore, the presentation of data in an MVMD graph enables a user to interact with the data to further manipulate and explore the data. As used herein, an MVMD graph includes a visual representation of a plurality of planes, wherein each plane includes a 2D graph, displayed with a depth perspective illustrating multiple dimensions, and connected in a manner to illustrate correlations. An MVMD graph can be presented as an orthographic or perspective projection (e.g., a representation of a three-dimensional object in 2D), as well as a three-dimensional projection (e.g., on a stereographic 3D display) of a three-dimensional object. As used herein, a perspective projection refers to a method for creating a 2D rendering of a 3D object based on projecting points in the object through a common viewpoint onto an image plane. 
     In the following detailed description of the present disclosure, reference is made to the accompanying drawings that form a part hereof, and in which is shown by way of illustration how examples of the disclosure may be practiced. These examples are described in sufficient detail to enable those of ordinary skill in the art to practice the examples of this disclosure, and it is to be understood that other examples may be used and the process, electrical, and/or structural changes may be made without departing from the scope of the present disclosure. 
     The figures herein follow a numbering convention in which the first digit or digits correspond to the drawing figure number and the remaining digits identify an element or component in the drawing. Elements shown in the various examples herein can be added, exchanged, and/or eliminated so as to provide a number of additional examples of the present disclosure. 
     In addition, the proportion and the relative scale of the elements provided in the figures are intended to illustrate the examples of the present disclosure, and should not be taken in a limiting sense. As used herein, “a number of” an element and/or feature can refer to one or more of such elements and/or features. For example, “a number of planes” can include one or more planes. Also, as used herein, the phrase “substantially similar” refers to a plurality of items having a quality and/or quantity within a specified threshold. 
       FIGS. 1A and 1B  illustrate an example construction of a multivariate and multidimensional (MVMD) graph. As used herein, an MVMD graph can include a number of individual graphical visualizations, combined into a multidimensional visualization, and connected to illustrate common data elements. As used herein, multivariate data includes data having a number of distinct, although not necessarily independent, variables. Common data elements can include a number of values, parameters, metadata, and/or qualities that are associated with data. In some examples, data elements can be considered common if they are associated with the same transaction. For instance, a particular financial transaction can include a number of data elements describing the transaction, such as the sale price, the date and/or time of the transaction, the profit margin realized from the transaction and/or the geographic location of the transaction, among other values, parameters, metadata, and/or qualities. Each of the data elements associated with the transaction can be considered a common data element, and can be connected on an MVMD graph. Furthermore, each of the individual graphical visualizations included in an MVMD graph can include multivariate data. 
       FIG. 1A  illustrates a plurality of individual graphical visualizations (e.g., 2D graphs) which can be combined to create an MVMD graph  106  shown in  FIG. 1B . Each of the plurality of 2D graphs can be displayed on a separate plane (e.g., first plane  101 - 1 , second plane  101 - 2 , third plane  101 - 3 , and fourth plane  101 - 4 ), wherein each plane as two vertical edges and two horizontal edges. In a number of examples, each of the 2D graphs can be of a different type, and/or a number of the 2D graphs can be of the same type. For instance, the first plane  101 - 1  can contain a RadViz plot (e.g., a radial, force-driven point layout), whereas the second plane  101 - 2  and the third plane  101 - 3  can contain scatterplots, and the fourth plane  101 - 4  can contain a geographical data visualization. Examples in accordance with the present disclosure are not so limited, however, and each of the 2D graphs can be of any type of graphical visualization that represents data elements as discrete paints. 
     In a number of examples, a plurality of planes containing 2D graphs (e.g., first plane  101 - 1 , . . . , fourth plane  101 - 4 , herein referred to as “the plurality of planes  101 ”) can be selected for constructing an MVMD graph  106 . For instance, the plurality of planes  101  can be selected from a database of existing planes containing 2D graphs. In some examples, prior to constructing the MVMD graph  106 , multivariate data can be analyzed and a plurality planes  101  can be constructed to represent the analyzed multivariate data, wherein each of the plurality of planes  101  includes a graphical surface having a first vertical edge (e.g., vertical edge  103 - 1 , vertical edge  103 - 2 , vertical edge  103 - 2 , vertical edge  103 - 4 ) and a second vertical edge (e.g., vertical edge  106 - 1 , vertical edge  106 - 2 , vertical edge  106 - 3 , vertical edge  106 - 4 ), and a first horizontal edge (e.g., horizontal edge  104 - 1 , horizontal edge  104 - 2 , horizontal edge  104 - 3 , horizontal edge  104 - 4 ) and a second horizontal edge (e.g., horizontal edge  107 - 1 , horizontal edge  107 - 1  horizontal edge  107 - 3 , horizontal edge  107 - 4 ). For instance, analytics can be run on raw multivariate data to identify clusters, outliers, correlations, and/or trends, which can be presented in a 2D graph (e.g., on first plane  101 - 1 ) as a RadViz plot having two vertical edges (e.g., first vertical edge  103 - 1  and second vertical edge  106 - 1 ) and two horizontal edges (e.g., horizontal edge  104 - 1  and horizontal edge  107 - 1 ). In another example, the results of analytics can be presented in a scatterplot, for example, and presented in a 2D graph on the second plane  101 - 2 , which similarly has two vertical edges (e.g., vertical edge  103 - 2  and vertical edge  106 - 2 ) and two horizontal edges (e.g., horizontal edge  104 - 2  and horizontal edge  107 - 2 ). As used herein, a “vertical edge” and a “horizontal edge” of a plane refers to an edge, margin, border, rim and/or side of a plane extending in a vertical and horizontal direction, respectively. 
       FIG. 1B  illustrates an example of a constructed MVMD graph  106  according to the present disclosure. A plurality of planes  101  can be combined to form an MVMD graph  106 . For example, a user can select a first plane  101 - 1  of a first graph type (e.g., a RadViz Plot) and a second plane  101 - 2  of a second graph type (e.g., scatterplot), wherein the 2D graph on the first plane  101 - 1  includes a first set of data points and the 2D graph on the second plane  101 - 2  includes a second set of data points. However, examples are not so limited, and the plurality of planes  101  car be selected for constructing the MVMD graph  106  by a non-transitory computer-readable medium storing a set of instructions executable by the processing resource, as discussed further herein. 
     As illustrated in  FIG. 1B , a constructed MVMD graph  106  can include a plurality of planes  101  displayed in a multidimensional (e.g., 3D) visualization. For instance, the first plane  101 - 1  containing a first 2D graph, can have a first vertical edge  103 - 1 , second vertical edge  106 - 1 , a first horizontal edge  104 - 1  and a second horizontal edge  107 - 1 , and a second plane  101 - 2  containing a second 2D graph, can have a first vertical edge  103 - 2 , a second vertical edge  106 - 2 , a first horizontal edge  104 - 2  and a second horizontal edge  107 - 2 . Similarly, as illustrated in  FIG. 1B , third plane  101 - 3  and fourth plane  101 - 4  can contain a third 2D graph and a fourth 2D graph, respectively, and each have two vertical edges and two horizontal edges. 
     The plurality of planes  101  can be displayed in a parallel orientation such that the first vertical edge of the second plane overlays the first vertical edge of the first plane, and the overlaid 2D graphs can be rotated about a vertical axis of the MVMD graph such that the first vertical edge of the second plane and the first vertical edge of the first plane are closer to a viewer than the second vertical edge of the second plane and the second vertical edge of the first plane. The plurality of planes  101  can be spaced apart such that one plane (e.g., the second plane  101 - 2 ) does not occlude part of another plane (e.g., the first plane  101 - 1 ), and the rotation can be such that the first vertical edge of each plane can be located to the left of the second vertical edge of each plane. For example, the second plane  101 - 2  can overlay (e.g., transposed on top of) the first plane  101 - 1 , such that the vertical edges (e.g., first vertical edge  103 - 2  and second vertical edge  106 - 2 ) of the second plane  101 - 2  are parallel to the vertical edges (e.g., first vertical edge  103 - 1  and second vertical edge  106 - 1 ) of the first plane  101 - 1 , and the horizontal edges (e.g., first horizontal edge  104 - 2  and second horizontal edge  107 - 2 ) of the second plane  101 - 2  are parallel to the horizontal edges (e.g., first horizontal edge  104 - 1  and second horizontal edge  107 - 1 ) of the first plane  101 - 1 . The overlaid planes (e.g., second plane  101 - 2  and first plane  101 - 1 ) can be rotated about a vertical axis (not shown in  FIG. 1B ) such that the plurality of planes  101  appear to be stacked in the third dimension. As illustrated in  FIG. 1B , first vertical edges  103 - 1 ,  103 - 2 ,  103 - 3 , and  103 - 4  appear closer to a viewer than second vertical edges  106 - 1 ,  106 - 2 ,  106 - 3 , and  106 - 4 , while all vertical edges remain parallel to one another. 
     In a number of examples, a common data element can be identified among a plurality of planes  101  comprising the MVMD graph  106 . As previously discussed, a common data element can include a number of values, parameters, metadata, and/or qualities that are associated with the data. For instance, a data point from a first set of data (e.g., first data point  102 - 1 ) on the first plane  101 - 1  and a data point from a second set of data (e.g., second data point  102 - 2 ) on the second plane  101 - 2  can be identified as being related to a common data element. Similarly, a data point from a third set of data (e.g., third data point  102 - 3 ) on third plane  101 - 3  and a data point from a fourth set of data (e.g., fourth data point  102 - 4 ) on fourth plane  101 - 4  can be identified as sharing the common data element identified between the first data point  102 - 1  and the second data point  102 - 2 . While  FIG. 1B  illustrates four planes included in the MVMD graph  106  and displayed in a multidimensional graph, examples are not so limited, and the MVMD graph  106  can include more or less planes than illustrated. 
     A common data element identified between a first plane  101 - 1  and a second plane  101 - 2  can be connected to visually depict a correlation between the two planes  101 - 1 ,  101 - 2 . For example, first data point  102 - 1  on the first plane  101 - 1  can be connected to second data point  102 - 1  on the second plane  101 - 2  by a polyline (e.g., polyline  105 ) representing the common data element between the two data points. As used herein, a polyline refers to a line composed of one or more line segments. Similarly, a data point on the third plane  101 - 3  (e.g., third data point  102 - 3 ) and a data point on the fourth plane  101 - 4  (e.g., fourth data point  102 - 4 ) can be connected by polyline  105 , representing a common data element between the data points  102 - 1 ,  102 - 2 ,  102 - 3 , and  102 - 4 . Examples are not so limited, however, and a polyline can include curved lines, among other line types. 
     In some examples, the MVMD graph  106  can include a plurality of polylines representing a plurality of common data elements between various data points on the plurality of planes  101 . Similarly, a data point on a particular plane can be connected to a plurality of polylines. For instance, first data point  102 - 1  can be connected to polyline  105 , representing a first common data element, and can be connected to a second polyline (not illustrated in  FIG. 1B ) representing a second common data element. Additionally, polyline  105  can represent a common data element among a portion of the MVMD graph (e.g., connecting less than all of the plurality of planes  101 ). For instance, polyline  105  can connect second data point  102 - 2  and third data point  102 - 3 , but not first data point  101 - 1  and fourth data point  102 - 4 . As discussed further herein, the MVMD graph  106  can be manipulated in a number of ways to illustrate and/or emphasize portions of the MVMD graph  106 . 
       FIGS. 2A-2B  illustrate examples of systems  210 ,  218  according to the present disclosure.  FIG. 2A  illustrates a diagram of an example of a system  210  for displaying and interacting with multivariate data in multiple dimensions according to the present disclosure. The system  210  can include a data store  211 , a subsystem  212 , and/or a number of engines  213 ,  214 ,  215 ,  216 ,  217 . The subsystem  212  can include the number of engines (e.g., data compilation engine  213 , common data element engine  214 , visualization engine  215 , data mapping engine  216 , and/or interaction engine  217 ) and can be in communication with the data store  211  via a communication link. The system  210  can include additional or fewer engines than illustrated to perform the various functions described herein. 
     The number of engines can include a combination of hardware and programming that is configured to perform a number of functions described herein (e.g., identify a common data element among a plurality of planes selected for an MVMD graph). The programming can include program instructions (e.g., software, firmware, etc,) stored in a memory resource (e..g., computer readable medium, machine readable medium, etc.) as well as hard-wired program (e.g., logic). 
     The data compilation engine  213  can include hardware and/or a combination of hardware and programming to compile raw data to create a plurality of planes, wherein each of the plurality of planes contains a 2D graph and includes a graphical surface having a first and a second vertical edge, and a first and a second horizontal edge. For example, in accordance with some examples of the present disclosure, analytics can be run on raw data to identify clusters, outliers, correlations, and/or trends, among other qualitative data analysis determinations. The results of analytics can be presented on a plurality of graphs on a plurality of planes. Additionally, raw data can be presented in a plurality of planes, wherein each of the plurality of planes contains a graph including a different set of related data and/or a different set of variables. 
     The common data element engine  214  can include hardware and/or a combination of hardware and programming to identify a common data element among a plurality of planes selected for an MVMD graph. A common data element can be identified among a plurality of planes using a number of means. For example, the common data element engine  214  can identify that a data point in a first plane is associated a particular business unit within an organization, based on metadata associated with the graph contained in the first plane. Similarly, the common data element engine  214  can identify that a data point in a second plane is associated with the same business unit, based on metadata associated with the graph contained in the second plane. Examples are not so limited, however, and the common data element, engine  214  can identify a common data element among a plurality of planes using including data mining took, statistical analysis tools, and/or raw data comparison tools, among others. 
     The visualization engine  215  can include hardware and/or a combination of hardware and programming to display the MVMD graph, including the plurality of planes, in an orientation wherein the plurality of planes are parallel to one another and rotated about a vertical axis such that the first vertical edge of each of the plurality of planes is closer to a viewer than the second vertical edge of each of the plurality of planes. A plurality of planes, selected for inclusion in the MVMD graph and/or constructed for inclusion in the MVMD graph, can be displayed in a parallel orientation, as discussed in relation to  FIG. 1B . As discussed further in relation to  FIGS. 1A and 1B , each plane can include two vertical edges (e.g., vertical edges) as well as two horizontal edges (e.g., horizontal edges). 
     The data mapping engine  216  can include hardware and/or a combination of hardware and programming to display a polyline on the MVMD graph, wherein the polyline connects data points on each of the plurality of planes and represents the common data element among the plurality of planes. In a number of examples, a polyline that passes through parts of select planes can be turned off, far instance, to allow a user to more effectively analyze a portion of the MVMD graph. In some examples, the data mapping engine  216  can display a plurality of polylines on the MVMD graph and the interaction engine  217  can enhance a particular polyline selected from the plurality of polylines (e.g., as discussed further herein). 
     The interaction engine  217  can include hardware and/or a combination of hardware and programming to display a modification of the MVMD graph in response to a user input. A modification can include any change in the visual presentation of the MVMD graph in response to a user input. For example, a user can provide input requesting various cluster selection techniques be implemented to explore a particular data set cluster selection techniques can include lassoing, or otherwise selecting, a region in a graph on a plane, or selecting regions in a particular graph on a plane for analysis. 
     Polylines passing through a selected region can be highlighted to enable visual exploration of data elements common to a selected region. In some examples, a user can select a single polyline and can alter the visual effects of the selected polyline. Additionally, some planes can contain 2D graphs that employ colored regions to visually distinguish different characteristics of the graph contained therein. In some examples, a user can provide input selecting such a plane in the MVMD graph, and can cause all the polylines passing through the selected plane to be colored according to the area of the graph that they pass through. In some examples, a plurality of colors can be assigned to different polylines within the MVMD graph to visually distinguish different characteristics. 
     In a number of examples, the interaction engine  217  can collapse selected planes from the MVMD graph about a vertical axis such that the graphical surface of the selected planes are not visible to the user, in response to user input. For example, in response to user input, interaction engine  217  can collapse a selected plane about a vertical axis of the plane such that only the vertical edge of the plane is visible. This allows more screen space to be devoted to the remaining planes. As discussed further herein, collapsing a plane does not change the number of polylines connecting it to its neighboring planes. 
     The interaction engine  217  in various examples can modify a visualization effect of a plane in the MVMD graph, in response to user input. For example, modifying the visualization effect can include changing spatial orientation of the plane such that the graphical surface of the plane does not visually obstruct the graphical surface of a second plane in the MVMD graph. In a number of examples, modifying the visualization effect can include modifying the opacity of a selected plane. In some examples, modifying the visualization effect can include changing the degree of rotation of the plurality of planes about the vertical axis. 
     In some examples, the number of data points in an MVMD graph can become large enough that visually marking each data point and polyline becomes impractical. In such examples, the plurality of planes can be changed to indicate (e.g., by means of color) the density of data points in a region of a 2D graph. For instance, a high density area of data points in a region of a 2D graph can be indicated with a red color and a low density area of data points in a region of a 2D graph can be indicated with a green color. The polylines associated with high or low density areas of a selected 2D graph can be similarly colored. In some examples, the polylines connecting data points can be rendered with an opacity that depends on the local density of polylines in that region of the MVMD graph. 
       FIG. 2B  illustrates a diagram of an example computing device  218  according to the present disclosure. The computing device  218  can utilize software, hardware, firmware, and/or logic to perform a number of functions described herein. 
     The computing device  218  can be any combination of hardware and program instructions configured to share information. The hardware, for example can include a processing resource  220  and/or a memory resource  222  (e.g., computer-readable medium (CRM) machine readable medium (MRM), database, etc.) A processing resource  220 , as used herein, can include any number of processors capable of executing instructions stored by a memory resource  222 . Processing resource  220  may be integrated in a single device or distributed across multiple devices. The program instructions (e.g., computer-readable instructions (CRI)) can include instructions stored on the memory resource  222  and executable by the processing resource  220  to implement a desired function (e.g., modify a visualization effect of a plane in the MVMD graph, in response to user input). 
     The memory resource  222  can be in communication with a processing resource  220 . A memory resource  222 , as used herein, can include any number of memory components capable of storing instructions that can be executed by processing resource  220 . Such memory resource  222  can be a non-transitory CRM or MRM. Memory resource  222  may be integrated in a single device or distributed across multiple devices. Further, memory resource  222  may be fully or partially integrated in the same device as processing resource  220  or it may be separate but accessible to that device and processing resource  220 . Thus, it is noted that the computing device  218  may be implemented on a participant device, on a server device, on a collection of server devices, and/or a combination of the user device and the server device. 
     The memory resource  222  can be in communication with the processing resource  220  via a communication link (e.g., a path)  221 . The communication link  221  can be local or remote to a machine (e.g., a computing device) associated with the processing resource  220 . Examples of a local communication link  221  can include an electronic bus internal to a machine (e.g., a computing device) where the memory resource  222  is one of volatile, non-volatile, fixed, and/or removable storage medium in communication with the processing resource  220  via the electronic bus. 
     A number of modules  223 ,  224 ,  225 ,  226 ,  227  can include CRI that when executed by the processing resource  220  can perform a number of functions. The number of modules  223 ,  224 ,  225 ,  226 ,  227  can be sub-modules of other modules. For example, the data compilation module  223  and the common data element module  224  can be sub-modules and/or contained within the same computing device. In another example, the number of modules  223 ,  224 ,  225 ,  226 ,  227  can comprise individual modules at separate and distinct locations (e.g., CRM, etc.). 
     Each of the number of modules  223 ,  224 ,  225 ,  226 ,  227  can include instructions that when executed by the processing resource  220  can function as a corresponding engine as described herein. For example, the data compilation module  223  can include instructions that when executed by the processing resource  220  can function as the data compilation engine  213 . In another example, the common data element module  224  can include instructions that when executed by the processing resource  220  can function as the common data element engine  214 . 
       FIGS. 3A-3C  illustrate alternative methods for visually representing an MVMD graph according to the present disclosure. For example, as illustrated in the top views of  FIGS. 3A and 3B , a plurality of planes  301  can be oriented in a parallel overlay to create a multidimensional graph. Each of the planes can include a graphical surface that displays the graphical data (e.g., graphical surface  330 - 1 ,  330 - 2 ,  330 - 3 ,  330 - 4 ). The planes (as illustrated in  FIG. 3A ) can be visually represented as an orthographic projection (e.g., a representation of a three-dimensional object in two dimensions that does not use perspective projection). This results in the individual planes being displayed with substantially similar size, aspect ratio (e.g., proportional relationship between width and height) and/or spacing. However, such visual presentation can diminish depth cues due to the lack of perspective for each of the planes. As used herein depth cue refers to perceptual cues in an image (e.g., relative sizes due to perspective effects, occlusion, shadows, and/or stereo disparity) that provide an indication of an object&#39;s distance from the viewer. The depth perception of the graph on first plane  301 - 1  may be diminished due to the lack of perspective of foreshortening the first plane  301 - 1 . The resulting image, as shown in  FIG. 3A , can include a plurality of planes  301  lacking in depth perspective. A usable stereographic rendering would not be possible using orthographic projection. 
     As illustrated in  FIG. 3B , when the MVMD graph is rendered using a perspective projection, then depth cues due to perspective effects are present, but the various planes have inconsistent areas and spacing. The inconsistent spacing can result in the planes overlapping as shown in  FIG. 3B , which diminishes the clarity of the displayed area.  FIG. 3C  illustrates an arrangement where the planes are arranged so as to appear turned at the same angle  331 - 1  to the viewpoint  332 - 1  and with equal separation (e.g., distance  333 ). As with the scheme illustrated in  FIG. 3B , the use of a perspective projection preserves perspective depth cues in 2D, and allows for the generation of usable stereographic 3D images. In this case however, the graph sizes, aspect ratios and spacing are roughly equal and overlaps can be avoided. 
     In the arrangements of  FIGS. 3A and 3B , a user can navigate around the MVMD graph by moving their viewpoint. For example, moving viewpoint  332 - 1  to the right (e.g., panning to the right) can expose more of the graphical surface of each of the plurality of planes  301 . Additionally, rotating the MVMD graph about a central axis (not illustrated in  FIGS. 3A-3C ) can allow polylines on the MVMD graph to be explored from different angles. In  FIG. 3C , a visual effect similar to panning can be achieved by rotating each of the planes about its first vertical edge by an equal angle (e.g., angles  331 - 2 ,  331 - 3 ,  331 - 4 ,  331 - 5 ). Additionally, instead of rotating the viewpoint  332 - 2  around the plurality of planes  301 , each of the plurality of planes  301  can be rotated about the central axis (e.g.,  334 - 1 ,  334 - 2 ,  334 - 3 ,  334 - 4 ) of each plane. The resulting image in  FIG. 3C  can include each of the plurality of planes  301  rendered without overlapping, and each of the plurality of planes  301  presented with depth perspective. User-configurable settings and/or modifications can change the opacity and/or crispness of each of the plurality of planes  301 . For instance, the opacity of the plurality of planes  301  can be increased and/or decreased to enhance visual effects. 
       FIGS. 3A-3C  illustrate modifications of the visual effects of the MVMD graph, wherein the visual effects of the plurality of planes  301  are changed simultaneously. Examples are not so limited, however, and the visual effects of a single plane can be changed independent of the rest of the MVMD graph. For instance, the opacity of the first plane  301 - 1  can be changed, while the opacity of the second plane  301 - 2 , the third plane  301 - 3 , and the fourth plane  301 - 4  remain unchanged. Similarly, individual polylines (not shown in  FIGS. 3A-3C ) can be displayed with low or high opacity, and the build-up in opacity due to multiple lines overlapping can reveal denser structures within the MVMD graph. 
       FIGS. 4A-4B  illustrate examples of modifications of an MVMD graph according to the present disclosure. In a number of examples, data within the MVMD graph can be explored by visually manipulating components of the MVMD graph. In some examples, large numbers of planes can be displayed at the same time, and screen space may not be available to display all of the planes with sufficient detail. Portions of the MVMD graph can be collapsed, and/or various portions of the data in the MVMD graph can be selected and compared against the remaining planes. For instance, as illustrated in  FIG. 4A , a number of planes can be collapsed about an axis, such as the first plane  401 - 1  and the third plane  401 - 3 . When collapsed, planes can present a simple edge-on view of their data points, and/or the linear representation can be processed to optimally distinguish clusters present in the 2D view. 
     As illustrated in  FIG. 4A , a particular cluster of data can be selected and can be compared across the plurality of planes. By selecting a particular cluster  432  for analysis, data in the plurality of planes  401  can be identified as correlating to the particular cluster  432 , and the polylines connecting the correlating data in the plurality of planes  401  can be emphasized. For instance, the polylines associated with cluster  432  (e.g., selected polylines) can be visually depicted in a different color, opacity, line weight, and/or line pattern compared to polylines in the MVMD graph that are not associated with cluster  432  (e.g., unselected polylines). In denser MVMD graphs, the unselected polylines can be rendered with a lower opacity than the selected polylines so as not to interfere with visualizing the selected structure. Similarly, selected polyline segments can be treated as edges to construct and render a bounding volume between each plane. 
       FIG. 4B  further illustrates examples of modifications of an MVMD graph, according to the present disclosure. In a number of examples, a timeline graph (e.g., fifth plane  401 - 5 ) can be included in the MVMD graph, and can be utilized to implement a time-varying analysis on the MVMD graph. For example, a user can select a particular point in time (e.g., time point  432 ) on plane  401 - 5 , and identify data in the plurality of planes  401  that are associated with the time point  432 . Data associated with time point  432  can be highlighted and/or otherwise emphasized for visual acuity. In a number of examples, the time point  432  can be dynamically modified (e.g., moved within fifth plane  401 - 5 ), and the changes in the polylines correlating to the modified time point (not shown in  FIG. 4B ) can be illustrated. 
       FIG. 5  illustrates a flow chart of an example method  540  for displaying multivariate data in multiple dimensions according to the present disclosure. At  541 , the method  540  can include analyzing multivariate data and constructing a plurality of planes representing the analyzed multivariate data, wherein each of the planes includes a graphical surface having a first and a second vertical edge, and a first and a second horizontal edge. In a number of examples, analyzing can include identifying a cluster of data for representation in a graph on a first plane among the plurality of planes. 
     A number of different types of graphical visualizations can be selected for the 2D graphs. A type of graphical visualization for a particular 2D graph can be selected by a user and/or chosen by a computing system based on the type of data to be presented and/or the type of analysis generating the data. In some examples, each 2D graph can be of a different type, selected from the group including: a RadViz plot, a scatterplot, a geographical data visualization, and a timeline graph. 
     At  542 , the method  540  can include identifying a common data element among the plurality of planes. At  543 , the method  540  can include connecting the plurality of planes with a polyline representing the common data element, wherein the polyline connects a data point on a first plane with a data point on a second plane. As discussed further herein, and as illustrated in  FIGS. 1A-4B , a plurality of polylines can be illustrated on a plurality of planes. 
     At  544 , the method  540  can include displaying an MVMD graph, including the plurality of planes and the polyline, wherein the plurality of planes are overlaid in a parallel orientation and rotated about a vertical axis such that the graphical surface of each of the plurality of planes is visible to a user. In some examples, a select plane can be added and/or removed from the MVMD graph. Similarly, various planes from within the MVMD graph can be cut, copied and/or pasted from within the MVMD graph to alter the overall visual presentation of the MVMD graph. 
     In some examples, the order of layering of planes in an MVMD graph can be defined by a user. The order of layering can also be defined by a computing system based on common data elements identified between planes, and/or detected by previous analytics steps. In some instances, a particular plane can be repeated and/or displayed more than one time within the MVMD graph. 
     At  545  the method  540  can include modifying the MVMD graph in response to user input. In some examples, modifying the MVMD graph can include selecting a cluster or other segment of data for comparison against the plurality of planes. In other examples, modifying the MVMD graph can include time-varying the MVMD graph, wherein time-varying includes linking a data point on a first plane in the MVMD graph with a particular time and identifying data points on the remaining planes corresponding with the particular time. As discussed in relation to  FIG. 4B , time-varying can be dynamic. 
     In some examples, an overall viewpoint of the MVMD graph can be changed. For example, a constructed MVMD graph can be rotated at a 45 degree angle relative to a viewer such that all planes (e.g., layers) are visible. In response to user input to modify the angle of rotation, the MVMD graph can be rotated to a different angle, such as 30 degrees relative to a viewer. Similarly, in response to user input, a select portion and/or the entire MVMD graph can be zoomed into such that the select portion and/or the entire MVMD graph appears larger to a viewer. In some examples, a number of the planes can be oriented in a flat visualization (e.g., traditional 2D visualization) to provide a more conventional face-on view. Additionally, a number of controls can be provided that allow a user to add, save, delete, cut, copy, and/or past a number of planes within the MVMD graph and/or the entire MVMD graph. 
     The specification examples provide a description of the applications and use of the system and method of the present disclosure. Since many examples can be made without departing from the spirit and scope of the system and method of the present disclosure, this specification sets forth some of the many possible example configurations and implementation.