WO2011142720A1

WO2011142720A1 - A self-organizing and contextual graphical user interface

Info

Publication number: WO2011142720A1
Application number: PCT/SG2010/000181
Authority: WO
Inventors: Gabriyel Wong Chee Kien; Terence Ng Min Chiak
Original assignee: Gabriyel Wong Chee Kien; Terence Ng Min Chiak
Priority date: 2010-05-14
Filing date: 2010-05-14
Publication date: 2011-11-17

Abstract

A method and system for providing a self-organizing contextual graphical user interface from a plurality of nodes. The method comprises the steps of clustering of the plurality of nodes into a plurality of groups, abstracting of the plurality of nodes and the plurality of groups, organizing the plurality of nodes in a group and rendering a visualisation of the plurality of groups in a graphical format. The system comprising of a memory storage medium for loading the plurality of nodes; a processor; and a processor-readable storage medium in communication with the processor, wherein the processor-readable storage medium contains a plurality of programming instructions for providing the self-organizing contextual graphical user interface.

Description

A SELF-ORGANIZING CONTEXTUAL

GRAPHICAL USER INTERFACE

Field of the Invention

The present invention relates to the field of graphical user interfaces for electronic systems and devices and more particularly to a user interface method which utilizes the hierarchical graph data structure as the basis for organizing data, storage, retrieval and visualization of information.

Background

A graph is an abstract representation of a set of objects where some pairs of the objects are connected by links. The interconnected objects are typically represented by graphical elements such as vertices or nodes, and the links that connect some pairs of vertices are called edges. For user interface systems that use the graph, the visual presentation of such a data structure is commonly associated to a surface. This virtual surface which is the area on which the graph is made visible and where the interface system functions is known as the canvas.

United States Patent No. 6,888,554 ('554) taught two methods of organizing a group of nodes in the form of a graphs while United States Patent No. 7,036,093 (Ό93) is an extension of ('554) by the same inventor where Ό93 provides augmenting mechanisms (parameters and frameworks) that (1 ) deal with management of node display size and (2) the way a large of number nodes may be structured in terms of presentation.The scalability of graph-based user interfaces that handles arbitrary large-sized data, automated organization of these data sources and clarity in visual presentation of contextually related information is an important aspect of a user interface system and this is not addressed in '554 and'093. Moreover, in many situations, data sources imported into an electronic system may not be organized in an adequate or meaningful manner as in the case for '554 and Ό93. Both ('554) and (Ό93) neither demonstrates how the pre-organization of the graph came into place nor the existence of such a functionality. Abstraction is the process of generalization by reducing superfluous information surrounding a concept or subject of interest such that only relevant information remains. The abstraction ability is also a drawback of '554 and Ό93.

The design of many graph-based applications often overlooks a very important human factor consideration when it pertains to data organization. In the context when graph data structures are related to graphical user interface, the requirement for the graph elements to be easily remembered in terms of their visual appearance has proven to be critical in many circumstances. For example, if the nodes in a graph provide some form of interactivity with the user, it would be extremely disruptive if the locations of these nodes were to change frequently. This is analogous to changing the layout of the computer keyboard every time it is being used. Furthermore, this problem would be accentuated if a parent node has numerous children nodes at a particular level in the hierarchy as this will pose great difficulty to the user for finding and selecting a node. The common and often unaddressed problem that exists in both '554 and '093 is the overwhelming user experience when a group node is expanded and with a sudden display of numerous children nodes that overloads the display system.

It is an object of the present invention to provide a self-organizing and contextual graphical user interface method and system, wherein the limitation of the display device in showing the total number of visible nodes by introducing the concept of a virtual canvas and a virtual camera system. While the canvas is a visual element that is being displayed, it is a virtual element whose visible limits may not be bounded by the physical limits of the display device. This property when coupled with a virtual camera system that supports zooming (or commonly known as magnification) will allow the user to navigate and view an arbitrary large number of nodes which is subjected only to the hardware memory constrain of the computer device.

It is a further object of the present invention to provide a self-organizing and contextual graphical user interface method and system, wherein this invention provides the user with a framework that spans the information aggregation stage which is important but often ignored, to the visual presentation and data manipulation stage.

It is yet a further object of the present invention to provide a self-organizing and contextual graphical user interface method and system, wherein the clustering or classification functionality in the present invention is founded on a weighted computation process that operates on a list of selected properties from the data nodes, which form a basis of classification.

It is also an object of the present invention to provide a self-organizing and contextual graphical user interface method and system wherein there is an abstraction capability built into the invention.

It is yet a further object of the present invention to provide a self-organizing and contextual graphical user interface method and system wherein there is a mnemonics-centric and modulo classification features that preserve the order of organization in the graph data structure.

Other objects and advantages of the present invention will become apparent from the following description, taken in connection with the accompanying drawings, wherein by way of illustration and example, an embodiment of the present invention is disclosed.

Summary of the invention

In accordance with the first aspect of the invention, there is disclosed a method for providing a self-organizing contextual graphical user interface from a plurality of nodes. The method comprises the steps of: clustering of the plurality of nodes into a plurality of groups; abstracting of the plurality of nodes and the plurality of groups; organizing the plurality of nodes in a group; and rendering a visualisation of the plurality of groups in a graphical format.

In accordance with the second aspect of the invention, there is disclosed a system for providing a self-organizing contextual graphical user interface. The system comprises: a memory storage medium for loading the plurality of nodes; a processor; and a processor- readable storage medium in communication with the processor, wherein the processor- readable storage medium contains a plurality of programming instructions for providing the self-organizing contextual graphical user interface. Brief description of the drawings

The embodiments of the present invention will be discussed hereinafter in detail with reference to the accompanying in-line drawings. In addition, the general principles defined herein may be applied to other embodiments and applications without moving away from the spirit and scope of the invention. Consequently, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and featured disclosed herein.

By way of example / illustration only, an embodiment of the invention is described more fully hereinafter with reference to the accompanying drawings, in which:-

FIG. 1 illustrates the state at which data sources are imported into the system and data objects are represented by nodes. It is important to note that there is observably no relational information among the nodes at this juncture since the nodes are not grouped in any manner. These nodes are displayed at a certain size relative to the full estate of a display device.

FIG. 2 illustrates the scenario when all nodes are depicted at the minimum possible size, which is equivalent to the pixel size (dot pitch) of the display device.

FIG. 3 illustrates the visual properties of a node which is being selected (in focus).

FIG. 4 illustrates the distance between two nodes in their vicinity.

FIG. 5 illustrates the display of properties associated to a data object and the process of selecting these properties for the clustering function.

FIG. 6 illustrates the automated layout of nodes when the clustering function is applied. FIG. 7 illustrates the classification of the nodes according to the preference of the user. FIG. 8 illustrates an example of a full expanded four-level graph. FIG. 9a, FIG. 9b, FIG. 9c, FIG. 9d, illustrates the automated abstraction process in an animated sequence starting from the diagram at the left-most and ending at the diagram at the right-most.

FIG. 10 illustrates the modulo node group structure in which nodes that do not qualify a certain prioritized order are further classified into a child group.

FIG. 11 illustrates the expanded layout of a modulo graph structure as the user traverses the first two levels of the graph.

Detailed Description

This section details the operational aspects of the present invention by its computation processes and the described framework. It is exemplified by explanations and diagrams and is not in any way limited in implementation by the specific visual elements. Furthermore, the data sources described in the present invention are cited as examples and they do not limit the scope by which data may be brought into the graphical user interface system of the present invention. While some visual elements and effects are cited as part of the description of the present invention, they do not limit the scope and usage of other possible means whether in software or hardware that can realize the same goals, based on the same underlying principles that vary in size, color, line and other visual properties. The present invention pertains to graphical user interface system and is not limited to any implementation on a particular hardware or operating system or itself being developed in any particular programming language.

Formatted data structures in computer systems play a pivotal role in our daily lives. From health care records and student academic performance data to enterprise business transactions and military intelligence information, our efficiency in managing data can lead to serious consequences in communication, learning and decision-making processes. The present invention describes a graphical user interface that exploits the graph data structure for storage, organization and retrieval of information sources via its novel and intelligent processing algorithms. It addresses the key issues in data organization and presentation which prior art misses and offers to the user an expanded scope in the deployment of this technology. A graph is an abstract representation of a set of objects where some pairs of the objects are connected by links. The interconnected objects are typically represented by graphical elements such as vertices or nodes, and the links that connect some pairs of vertices are called edges. For graphical user interfaces that harness the graph, the visual presentation of such a data structure is commonly associated to a surface. This virtual surface which is the area on which the graph is made visible and where the interface system functions is known as the canvas. With reference to Figure 1 , in the context of the present invention, several nodes are created and laid out on a canvas when a data source (such as a database or a file system) is being imported into a computer program. A first Node 110 represents a data object (such as a record in a database or a file) just as all other nodes depicted in the diagram and all the nodes have no apparent visible correlation at this juncture. The data object may contain one or more discrete data or information and may also come from a number of different data sources such as personal data sources, economic data sources, financial data sources and the like regardless of the data format.

The present invention circumvents problems related to displaying arbitrary large number of nodes which other stated inventions fail to address. It effectively removes the limitation of the display device in showing the total number of visible nodes by introducing the concept of a virtual canvas and a virtual camera system. It is noteworthy that while the canvas is a visual element that is being displayed, it is a virtual element whose visible limits may not bounded by the physical limits of the display device. This property when coupled with a virtual camera system that supports zooming (or commonly known as magnification) will allow the user to navigate and view an arbitrary large number of nodes which is subjected only to the hardware memory constrain of the computer device.

With reference to Figure 2, a scenario where the total number of nodes to be shown is larger than the screen space available 240. The present invention allows each node represented visually to be reduced to one pixel size via the zoom function of the virtual camera system. A second Node 210 is a node taking the space of one pixel size in a "sea of nodes". Given the condition that all nodes are packed at one pixel distance from one another, the total number of visible nodes at any time is limited by the full resolution of the display device. As shown in Figure 2, the present invention scales the graphical user interface beyond this limitation - the virtual canvas 230 supports theoretically infinite numbers of nodes and its data holding limit is only subjected to the size of the memory hardware. A third Node 220 is a node on the virtual canvas but is not visible because it is excluded by the view space of the virtual camera which has been optimized to display the maximum number of nodes based on the highest resolution of the display device. Further on, the third Node 220 will be made visible when the virtual camera moves towards this node and when it falls within the view space of the virtual camera.

With reference to Figure 3, the magnified impression of a node is presented. The magnification functionality of the camera system allows nodes to be displayed at various sizes according to user preferences, usage conditions and display estate limitations. When a fourth Node 330 is selected (in focus) 320, such as the fourth Node 330, it is magnified 310 to a scale comfortable for viewing, even though in its prior state it may be rendered in a much smaller size. When the fourth Node 330 is deselected (not in focus), the magnification process reverses and the node is reduced back to its initial size. The visual magnification processes are automated often with accompanying visual effects such as enhanced line weight and highlight such that information pertaining to each node is always intuitively and clearly presented. It is also worthy to note that a node may be augmented by other forms of meta-information presented visually such as the usage of text label as in Figure 3, for the fourth Node 330.

The present invention optimizes display space usage through the employment of physics- based computation for the layout of the nodes. With reference to Figure 4, when a node is created on the canvas, it is subjected to attraction and repulsion forces (as taught in Gansner, E. R. and North, S. C. 1998, "Improved Force-Directed Layouts", In Proceedings of the 6th international Symposium on Graph Drawing, S. Whitesides, Ed. Lecture Notes in Computer Science, vol. 1547. Springer- Verlag, London, 364-373) that act on it from the surrounding nodes. To illustrate, a fifth Node 410 and a sixth Node 420 remain at their squilibrium positions as a consequence of the resultant forces that act on them. The distance 130, d between them is computed based on the relative strengths of their attraction and ^■epulsion factors. The present invention of using physics-based force-directed graph offers wo important benefits to the user. First, this type of graph in conjunction with given virtual Physical boundary constrains will ensure that all nodes will be laid out in a non-overlapping nanner. This offers superior clarity to the user in terms of visual understanding and pattern jxtraction especially under the circumstance when there are numerous visible nodes to be jonsidered. Second, the nodes when subjected to inter-nodal forces and the boundary :onstrains will also fill surrounding empty space thus ensuring optimal space usage and hence providing the form of local organization capability. The stated forces herein are also known as physical forces.

Repulsion forces between two nodes are applied only to visible nodes and if they are within a user-defined distance between them. Two types of attraction forces are applied - attraction forces are applied on nodes with their parent nodes and the amongst the children nodes of the same hierarchy. The attraction forces between the children nodes and the parent node is for the purpose of keeping them equidistant from the parent node, hence giving them a form of order. However, it is necessary to note that .the final location of a child node is subjected to the repulsion forces generated by nodes in the vicinity around the child node as well.

With reference to Figure 5, the user first selects a visible seventh Node 510 to set the focus on it. The present invention will allow the user to view a list of properties 520 which are common to all nodes that exist in the computer program. The user then selects from the aforementioned list the set of properties which mayform the basis for clustering. In Figure 5, the list of selected properties is depicted as the drop-down list items with a small tick beside it. With the selected properties in place, the forces applied to all nodes are recomputedaccording to the clustering algorithm described below

The clustering algorithm works as follows: for every visible node which is not grouped, compute its new position at every frame given the inter-nodal forces which are exerted by other visible nodes around it and within the user-defined vicinity where forces will act. The determinants for the computation consist of three components. They are the attraction and repulsion forces attributed by nodes in the neighborhood of the node in consideration and the Clustering Multiplier. The Clustering Multiplier is computed as follows: for every selected property, compute the average of the assigned numerical value of the property for the selected node and one other visible node. This computation is executed for other node-pairs until all valid nodes have been considered. This computation process will generate a new position for the subject node which is the consequence of the influence from all other nodes around it. The clustering algorithm that determines each node's position during every update frame is given by the following expression:

Π(Χ', Τ) = Q(X, Y) -ψν, x ξχ (w₂ x a + w₃ x r)]

where X, Y, X' and Y' are positions in a Cartesian coordinate system, Π is the new position of a node, Ω is the previous position of a node, ξ is the cluster multiplier, a is an attraction force, r is a repulsion force and w, , w₂ and w₃ are numerical constants. With reference to Figure 6, a typical scenario is illustrated with three visible clusters, 610, 620 and 630 formed as a result of applying the clustering algorithm. Three Nodes 612, 622 and 632 are found to be in close proximity with their respective groups of nodes. Possible groupings are highlighted by dotted lines in Figure 6. Given such a setting, the user can create the groupings intuitively as shown in Figure 7. An added benefit of this design is that the output of the clustering process is never restrictive, that is, the user is still given the freedom to include and exclude specific nodes in the choice of groupings such as those illustrated as the outliers (cluster 610 and cluster 620) and the residual nodes (ungrouped cluster 630) in Figure 7. The clustering of the nodes result in an organized manner, hence providing the form of global organization capability for the nodes.The groups of nodes may be organized in a hierarchical or network structure.

For the purposes of organizing and managing nodes, a user may also perform several operations on the nodes and these operations may involve the joining of a group, the detaching from the group, creating the group for several of the nodes, ungrouping several of the nodes, deleting several of the nodes, selecting several of the nodes, selecting several of the nodes, dragging and dropping several of the nodes from one location to another on the virtual canvas.

It is important- to note that this suggested grouping is not unique and may be just one variation of multiple combinations. The purpose and objective of the clustering algorithm is to accelerate the classification process by helping the user make decisions based on a suggested output generated by an algorithm which is driven by user-defined parameters. It is not intended as a means to derive the globally optimal solution for clustering the nodes.

With reference to Figure 8, a fully expanded four-level graph structure is presented. The assumption is that the nodes have been grouped hierarchically according to some meaningful criteria to arrive at its current state. The abstraction mechanism in the present invention operates as follows. During an abstracting mode of operation, all nodes except the highest level root node are collapsed. With reference to Figure 8, an eighth Node 810 is the parent node in the highest level of the graph hierarchy and is the only visible node. When the user selects the eighth Node 810 as shown in Figure 9(a), the node will be enlarged visually for easy viewing and its children will appear in a certain meaningful sequence and via a certain visual transition effect (such as fading in). At this point in time, only eighth Node 810 children are visible and they form the set of options for the user before any further decision is made. Suppose the user decides to select ninth Node 820 which is a child of the eighth Node 810. This will result in the other children of eighth Node 810 being removed visually as shown in Figure 9(b). Subsequently, the children of ninth Node 820 will appear since the current node of interest is the ninth Node 820. Given that after some consideration, the tenth Node 830 is being selected. This will cause all other children nodes of ninth Node 820 to be removed visually as shown in Figure 9(c). Thereafter, all the children of tenth Node 830 will appear. The aforementioned process and pattern in displaying and hiding nodes continues until the last selected child node of an eleventh Node 832 as shown in Figure 9(d).

From the description on the abstraction mechanism in the present invention provided above, it is noteworthy that at every adjacent stage between selection and presentation, two very important aspects of the abstraction concept are captured and offered to the user. First, only the children of the select node are visible. Since the aforementioned assumption that the hierarchical organization is meaningful from a contextual perspective, this implies the children of the selected node exists as a pool of relevant information sources to the current subject matter of interest. When user moves down to the next level in the hierarchy via the selected node, the previous unselected children nodes are removed visually. This corresponds to the principle in abstraction where superfluous information is reduced or removed such that only relevant information is retained. In addition, the present invention provides a powerful means by which the history of the selected abstraction is preserved. This is shown in Figure 9(d) where the path of the selection is traced from the highest level to the final selected node. Conceptually, the traced route represents the flow of a concept through different levels in a hierarchy of importance or relevance.

Consider a parent node with 1 ,000 children nodes. The 1 ,000 children nodes may be prearranged in a particular order or otherwise. There are likely two ways to support the organizing of such a number of nodes - by either increasing the circumference of the selected node in order to accommodate the nodes visually around the parent node, or to display them in some adjacent layers. The present invention introduces the concept of a modulo classification approach that can remove the aforementioned problem. In a modulo-7 setup, the children nodes in the previous scenario would be organized into a maximum of six groups per level with the seventh group as just a placeholder group that indicates the next level of hierarchy. With reference to Figure 10, a modulo graph structure is presented. A twelfth Node 10100 is the parent node with seven children nodes spanning a thirteenth Node 10110, a fourteenth Node 10120, a fifteenth Node 10130, a sixteenth Node 10140, a seventeenth Node 10150, an eighteenth Node 10160 and a nineteenth Node 10170. The Node 10170 is a special Node that has the characteristic call a node group which holds the next level. Note that the nineteenth Node 10170 possesses different visual properties in comparison with the other children nodes. This purpose for such a design is to allow the user to easily distinguish between the current level nodes and those that are classified into groups that exist in the subsequent levels of the hierarchy. With reference to Figure 1 , the user may inspect the nineteenth Node 10170 and discover that there are subsequently six more children nodes, a twentieth Node 10171 , a twenty-first node 10172, a twenty-second node 10173, a twenty-third node 10174, a twenty-fourth node 10175, a twenty-fifth node 10176 and a twenty-sixth node 10177. The twenty-sixth node 10177 is also a group node.

The modulo classification technique requires a user-defined sort criteria to be provided. The sort criteria will be used to decide which nodes would be retained in the current level and those that would be classified into the next level. Based on the sort criteria, nodes will also obtain their individual ranking at a particular level in the hierarchy. Apart from the above usage, the ranking value would also be utilized in the visual ordering or arrangement of the nodes around its parent node. The present invention does not limit the user in applying different sort criteria at various levels in the graph hierarchy as well. This gives greater contextual flexibility to the way the nodes are organized in the graph system. To illustrate this concept, the children of the first parent node (the twelfth Node 10100) in Figure 11 may be selected and sorted according to Criteria A while in the subsequent level, Criteria B may be applied to the children nodes of the nineteenth Node 10170 for selection and sorting. This process may even be iterated at other levels with different combinations of the various criteria. The modulo classification technique aids mnemonics by keeping a user-preferred maximum set of nodes for optimal operation efficiency since there is a threshold to which the human mind can only work in terms of visual complexity.

The apparent benefit of a modulo grouping system is that the user only needs to remember a small number of nodes that are well-organized at every level compared to the problem nentioned previously with a "sea of unstructured nodes". Notwithstanding the differences in cognitive abilities in mnemonics for different users, the modulo grouping technique ensures hat the reduced number of nodes per level and their specific order in terms of visual irrangement can help alleviate the burden associated with usage of the graphical user nterface in the absence of such a mechanism. The difference between the clustering and the modulo classification algorithms described in the present invention is such that the clustering function operates at the pre-process stage to offer the user "suggested" grouping patterns - there is absolutely no a-priori organizational information about the nodes at this stage. The modulo classification algorithm however, is designed to solve imbalances in the graph hierarchy where there may be overwhelming number of nodes at certain levels of the hierarchy that may inhibit proper visual display or disrupts visual understanding. The modulo classification algorithm operates based on user- defined rules (criteria) and may be iterated throughout the graph or be terminated at any level according to the preference of the user.

While the invention has been particularly shown and described, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention. Therefore, the claims should be interpreted in a broad manner, consistent with the present invention

Claims

CLAIMS:

1. A method for providing a self-organizing contextual graphical user interface from a plurality of nodes comprising the steps of:

clustering of the plurality of nodes into a plurality of groups;

abstracting of the plurality of nodes and the plurality of groups;

organizing the plurality of nodes in a group; and

rendering a visualisation of the plurality of groups in a graphical format.

2. The method according to claim 1 , wherein the plurality of nodes comprise of a plurality of discrete data and information from a plurality of data sources.

3. The method according to claim 1, wherein the plurality of nodes are prearranged.

4. The method according to claim 1, wherein the plurality of nodes are not prearranged.

5. The method according to claim 1 , wherein the plurality of groups are organized in a hierarchical structure.

6. The method according to claim 1 , wherein the plurality of groups are organized in a network structure.

7. The method according to claim 1, wherein the plurality of nodes is a visual element on a graphical display system that supports some form of interaction as part of a feedback- response mechanism.

8. The method according to claim 1 , wherein the plurality of nodes being selected receives magnification in its display size and enhancement in terms of a visual change.

9. The method according to claim 1, wherein the plurality of nodes being in focus receives magnification in its display size and enhancement in terms of a second visual change.

10. The method according to any one of the preceding claims, wherein the graphical display system further comprising a virtual canvas.

1 . The method according to claim 10, wherein the virtual canvas holds all the plurality of nodes of which a second plurality of nodes may not be visible in the graphical display system.

12. The method according to any one of the preceding claims, wherein the plurality of nodes allows the user to perform a number of operations surrounding the organization and management of the plurality of nodes wherein the number of operations include:

joining the group;

detaching from the group;

creating the group for the plurality of nodes;

ungrouping the plurality of nodes;

grouping the plurality of nodes;

deleting the node;

selecting the node;

unselecting the node;

selecting the plurality of nodes;

unselecting the plurality of nodes; and

dragging and dropping the plurality of nodes from one location to another on the virtual canvas.

13. The method according to claim 10 to 12, wherein the virtual canvas supports the visualization of the plurality of nodes.

14. The method according to any one of the preceding claims, wherein a layout of the plurality of nodes on the virtual canvas is based on the underlying principle of a function that comprise of a node previous position, a cluster multiplier, an attraction force and a repulsion force.

15. The method according to any one of the preceding claims, wherein a virtual camera system provides an automated visual magnification to display the node at various sizes and panning capabilities to move a view window to a vantage position where no clipping of the plurality of nodes at the boundaries occurs.

16. The method according to claim 14, wherein the principle further comprising a computation derived from a plurality of physical forces.

17. The method according to claim 15, wherein the computation further comprising using a force-directed layout computation that can prevent an overlapping of the nodes due to the existence a plurality of repulsive forces.

18. The method according to any one of the preceding claims, wherein the clustering will organized the plurality of nodes into a plurality of visually distinct groups based on a weighted computation of a selected set of properties.

19. The method according to any one of the preceding claims, wherein the data and information is displayed via the visual element.

20. The method according to any one of the preceding claims, wherein a node property associated to a data field or a property in the data source can be assigned a numerical value.

21. The method according to claim 1 , wherein the abstracting further comprise:

displaying a root node;

-displaying a second node at a highest level of the hierarchy at an initial state, such that at a level, a selected node, and plurality of children nodes would be visible;

for a previously selected node, the plurality of children nodes would be made invisible except a children node which was selected or is currently selected; and

keeping a visual trace of the plurality of nodes previously and currently selected through a plurality of levels of the hierarchy.

22. The method according to claim 21 , wherein the organizing of a plurality of nodes uses a modulo function in terms of a plurality of children nodes for the plurality of levels of the hierarchy.

23. The method according to claim 22, wherein the modulo function allows the user to set a sorting process criteria such that the plurality of children nodes of the group can be positioned in a particular order.

24. The method according to claims 21-23, wherein the modulo function allows the user to set a different sorting criteria for each selected group and at the level of the hierarchy.

25. The method according to claims 22, wherein the modulo function allows the user to move the plurality of nodes in the plurality of groups.

26. A system for providing a self-organizing contextual graphical user interface, the system comprising:

a memory storage medium for loading the plurality of nodes;

a processor; and

a processor-readable storage medium in communication with the processor, wherein the processor-readable storage medium contains a plurality of programming instructions for providing the self-organizing contextual graphical user interface.

27. A computer program comprising program code means for performing all the steps of any one of the claims 1 to 25 when the program is run on a computer.

28. A computer program product comprising program code means stored on a computer readable medium for performing the method of any one of the claims 1 to 25 when the program product is run on a computer.