Patent Application
20060082592
The present invention relates generally to processing data for visual presentation, wherein the processing includes the creation or manipulation of graphic objects prior to presenting the processed data on a specific display system. More particularly, the present invention includes subject matter wherein the processed data is displayed to a user as one or more nested treemaps showing a relationship between two or more variables. The present invention further includes color information processing wherein the color and shading in the treemap is calculated to visually distinguish a nested treemap from the parent treemap or other nested treemaps.
Conceptually, much of the world's information can be organized as a hierarchy. Brian Johnson & Ben Shneiderman, Tree-Maps: A Space-Filling Approach to the Visualization of Hierarchical Information Structures (April 1991), at ftp://ftp.cs.umd.edu/pub/hcil/Reports-Abstracts-Bibliography/91-06html/91-06.html (last visited Oct. 8, 2004) (incorporated herein by reference). For many years, the most common methods for presenting hierarchical information were listings, outlines, and tree diagrams. Id. A tree diagram is a widely-used computer data structure that emulates a tree structure with a set of linked “nodes.” As used herein, the term “node” includes any data structure unit having either data or a link to another data structure unit. Each node may have one or more “child nodes,” which are below it in the tree. Naturally, the node of which a node is a child is called its “parent node.” A child node has at most one parent node. A child node without a parent node is called the “root node.” Generally, a tree diagram has only one root node. A node having only data and no child node is called a “leaf node,” while any node that is neither a root node nor a child node is referred to generally as an “interior” node. See, generally, Wikipedia, at http://en.wikipedia.org/wiki/Tree data structure (last visited Oct. 13, 2004) (incorporated herein by reference); Ben Shneiderman, Tree visualization with Tree-maps: A 2-D space filling approach (Jun. 18, 1991), ftp://ftp.cs.umd.edu/pub/hcil/Reports-Abstracts-Bibliography/91-03html/91-03.html (last visited Oct. 8, 2004) (incorporated herein by reference) [hereinafter Shneiderman I]. The amount of space required to display listings, outlines, and tree diagrams, though, is directly proportional to the amount of information displayed. Thus, the conventional rooted display methods generally make poor use of display space or hide vast amounts of information from users. See Johnson & Shneiderman, supra.
“Treemaps” first appeared in the early 1990's as an alternative to the conventional presentation methods. See, e.g., Ben Shneiderman, Treemaps for space-constrained visualization of hierarchies (Dec. 26, 1998), at http://www.cs.umd.edu/hcil/treemap-history/ (last updated May 18, 2004) (last visited Oct. 8, 2004) (incorporated herein by reference) [hereinafter Shneiderman II]; Shneiderman I, supra; Johnson & Shneiderman, supra. A treemap “makes 100% use of the available display space, mapping the full hierarchy onto a rectangular region in a space-filling manner. This efficient use of space allows very large hierarchies to be displayed in their entirety and facilitates the presentation of semantic information.” Johnson & Shneiderman, supra. As Johnson & Shneiderman explain, treemaps “partition the display space into a collection of rectangular bounding boxes representing the tree structure. The drawing of nodes within their bounding boxes is entirely dependent on the content of the nodes, and can be interactively controlled. Since the display size is user controlled, the drawing size of each node varies inversely with the size of the tree (i.e., # of nodes). Trees with many nodes (1000 or more) can be displayed and manipulated in a fixed display space.” Id. Thus, treemaps provide access to detail while keeping the global context. Harsha Kumar et al., Visual Information Management for Network Configuration (June 1994), at ftp://ftp.cs.umd.edu/pub/hcil/Reports-Abstracts-Bibliography/94-07html/94-07.html (last visited Oct. 8, 2004). Screen space utilization is maximized, and scrolling and panning are not required. Id. The number of nodes that can be displayed in a treemap is an order of magnitude greater than that by a traditional tree diagram. Id.
Originally developed to visualize large directory structures on a hard disk, see Shneiderman I, supra; Johnson & Shneiderman, supra, treemaps have evolved considerably, and now include rich feature sets that support flexible hierarchies, color binning, improved color setting, and aggregation. Shneiderman I, supra. Several treemap variants also have evolved to address some of the shortcomings of the original implementation. Two popular variants are the “nested” (or “clustered”) and “squarified” treemap, both of which employ an algorithm for minimizing aspect ratios of each bounding box displayed in a treemap. See, e.g., Benjamin B. Bederson et al., Ordered and Quantum Treemaps: Making Effective Use of 2D Space to Display Hierarchies (October 2002), at ftp://ftp.cs.umd.edu/pub/hcil/Reports-Abstracts-Bibliography/2001-18html/2001-18.pdf (last visited Oct. 8, 2004) (incorporated herein by reference). Treemaps have been used to provide an efficient visual presentation of a diverse variety of information, ranging from financial analysis to sports reporting. Id. See also Shneiderman I, supra; Map of the Market, http://www.smartmoney.com/marketmap (last visited Oct. 13, 2004). In an enterprise computing system, system administrators also need to monitor large collections of data about the performance of an entire network topology. Much like tree data structures, a network topology generally consists of a root node and one or more linked nodes. Thus, treemaps are ideal for depicting network performance data. Typically, such performance data consists of five to ten properties (such as central processing unit (CPU) usage, goal attainment, name, and number of deployed applications) for each node in the topology. The number of nodes in any given topology can vary, but the number typically ranges from ten to one thousand.
Hierarchical information comprises both structural (also referred to as “organizational”) information associated with the hierarchy, and content information associated with each node in the hierarchy. Johnson & Shneiderman, supra. Generally, treemaps rely heavily on visual cues such as color and size to present content information to a user. Id. A color gradient often represents a given property of a given node, while bounding box size represents another property of the node. Thus, in the treemap of the exemplary network topology of
The invention described in detail below addresses this shortcoming in the art. In particular, it is an object of the present invention to improve processing of color information and shading in a treemap so that a nested treemap is visually distinguishable from a parent treemap or other nested treemaps. This and other objects of the invention will be apparent to those skilled in the art from the following detailed description of a preferred embodiment of the invention.
The inventive process described below comprises an improved process for displaying hierarchical information in a treemap by associating a different color with each nested treemap in a parent treemap, and generating a gradient for each color to preserve the representative value of varying shades.
In general, a system administrator or other user divides the hierarchical information into clusters of nodes, designates a primary weight and a secondary weight for each cluster, and designates a base color for each cluster. The inventive process comprises dividing the range of each cluster's secondary weight into bins, adjusting each cluster's base color to create a distinguishing gradient of the base color, assigning a distinguishing gradient to each bin, and drawing a nested treemap for each cluster so that each nested treemap has the cluster's base color and each node in the cluster is represented by a bounding box having a distinct gradient of the cluster's base color.
The novel features believed characteristic of the invention are set forth in the appended claims. The invention itself, however, as well as a preferred mode of use, further objectives and advantages thereof, will be understood best by reference to the following detailed description of an illustrative embodiment when read in conjunction with the accompanying drawings, wherein:
The principles of the present invention are applicable to a variety of computer hardware and software configurations. The term “computer hardware” or “hardware,” as used herein, refers to any machine or apparatus that is capable of accepting, performing logic operations on, storing, or displaying data, and includes without limitation processors and memory; the term “computer software” or “software,” refers to any set of instructions operable to cause computer hardware to perform an operation. A “computer,” as that term is used herein, includes without limitation any useful combination of hardware and software, and a “computer program” or “program” includes without limitation any software operable to cause computer hardware to accept, perform logic operations on, store, or display data. A computer program may, and often is, comprised of a plurality of smaller programming units, including without limitation subroutines, modules, functions, methods, and procedures. Thus, the functions of the present invention may be distributed among a plurality of computers and computer programs. The invention is described best, though, as a single computer program that configures and enables one or more general-purpose computers to implement the novel aspects of the invention. For illustrative purposes, the inventive computer program will be referred to below as the “TreeMap Program.”
Additionally, the TreeMap Program is described below with reference to an exemplary network of hardware devices, as depicted in
TreeMap Program (TMP) 300 typically is stored in a memory, represented schematically as memory 320 in
In such an embodiment, administration program 330 provides an interface through which a system administrator can configure and monitor network nodes. In particular, administration program 330 allows a system administrator to monitor the performance of any network node. Typically, administration program 330 collects data such as CPU usage, goal attainment, name, and number of deployed applications for each network node. For illustrative purposes, the following discussion assumes that administration program 330 directly measures the performance of network nodes, but as noted above, such a function easily could be delegated to a more specialized programming unit.
Administration program 330 also allows the system administrator to choose a color for each cluster of nodes (420), which is stored in memory 320 as user data 395. In one embodiment, the system administrator designates a single “base” color, which identifies the “middle” of a desired range of color gradients for a node cluster. White and black then become the extremes of the range. Alternatively, the system administrator chooses a “low” color and a “high” color, which represent the extremes of the desired color gradient for each cluster. In two-color embodiment, black is the middle color. In either embodiment, though, the system administrator or administration program 330 identifies each color as a triplet in a Red, Green, Blue (RGB) color model. As used herein, an RGB color model is any additive color model in which red, green, and blue light are combined in various ways to create other colors. In practice, every color in an RGB color model is identified as a triplet of numbers. Each value in an RGB triplet is a number between 0 and 255, representing the intensity of the primary red, green, and blue colors, respectively, in a given color. Although many color models are available to choose from, the RGB color model is a convenient and commonly used model with which most administrators should be familiar.
When a system administrator or other user invokes TMP 300 from administration program 330, administration program 330 creates an instance (“instantiates”) of TreeMapNode 340 for each node cluster. Each instance of TreeMapNode 340 then loads the preferred color for its node from user data 395, instantiates TreeMap 350 for each internal node, and TreeMapWeight 360 for each leaf node.
Although the RGB color model is familiar to administrators, color gradients are more readily manipulated when modeled as a combination of Hue, Saturation, and Lightness (HSL). Thus, in one embodiment, TMP 300 converts each RGB triplet to an equivalent HSL triplet for easier manipulation (430). As used herein, “hue” refers to a particular color within the visible spectrum, as defined by its dominant wavelength. See, generally, Color Theory, httn://www.colorcube.com/articles/theory/theory.htm (2000) (last visited Oct. 12, 2004) (incorporated herein by reference). In short, hue distinguishes red from green from blue. “Lightness” (also sometimes referred to as “luminance” or “luminescence”) indicates the intensity of light per unit area of its source. See id. In general, “saturation” is the intensity of a color at any given lightness. Id. In an HSL color model, saturation is defined mathematically as the difference between the maximum of the equivalent RGB values and the minimum of the equivalent RGB values, or
saturation=max(R,G,B)−min(R,G,B).
See, e.g., TheFreeDictionary.com, http://encyclopedia.thefreedictionary.com/HLS%20color%20space (last visited Oct. 12, 2004) (incorporated herein by reference). Similarly, lightness is defined as the average of the sum of the minimum and maximum, or
lightness=(max(R,G,B)+min(R,G,B))/2
Id. Algorithms for converting RGB triples to HSL triples and vice versa are common and, thus, not discussed in detail here. See, e.g., The World Wide Web Consortium, CSS3 Color Module: W3C Candidate Recommendation (Tantek Celic & Chris Lilley eds., May 14, 2003), http://www.w3.org/TR/css3-color/#hsl-color (last visited Oct. 12, 2004) (incorporated herein by reference).
After converting each RGB triple to an equivalent HSL triplet, TMP 300 then adjusts each saturation value to approximately 0.5 (or 50%), which provides a normalized, softer look to the color (440). Next, administration program 330 generates a dataset comprised of the network node properties specified by the system administrator, grouped hierarchically by cluster. TMP 300 then determines the maximum and minimum values of each property in the dataset, and divides the secondary weight range into bins. As used herein, the term “bin” refers to any discrete interval within the range of secondary weight values of any given cluster. In the embodiment described herein, each bin represents a percentage interval (e.g. 20%-30%) of the secondary weight range for a cluster of nodes. The system administrator may specify the number of bins through administrator program 330, and save the number as user data 395.
TMP 300 then generates color gradients at “waypoints” for the secondary weight range, so that the middle color is limited to a reasonable range. The function getGradientWayPoints 370 in
After generating the color gradient for each waypoint, TMP 300 generates an array of color gradients for each remaining bin in the secondary weight range (450). The function generateGradient 380 in
Finally, TMP 300 generates an index into the array of gradients that associates the secondary weight of each node with a color gradient in the array, calculates the appropriate size of bounding box to represent the primary weight of each node (460), and renders the treemap for the generated dataset (470).
A preferred form of the invention has been shown in the drawings and described above, but variant in the preferred form will be apparent to those skilled in the art. The preceding description is for illustration purposes only, and the invention should not be construed as limited to the specific form shown and described. The scope of the invention should be limited only by the language of the following claims.
