Self-organizing neural network approach to the automatic layout of business process diagrams

• US 9,959,517 B2
• Filed: 12/22/2014
• Issued: 05/01/2018
• Est. Priority Date: 12/22/2014
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Mourra, John
• Primary Examiner(s): Augustine, Nicholas
• Application Number:
  US14/579,338
• Publication Number:
  US 20160180263A1
• Time in Patent Office: 1,226 Days
• CPC Class Codes: G06F 3/04817 using icons graphical or visual programming using iconic symbols G06F8/34
• CPC Class Codes: G06Q 10/067 Enterprise or organisation modelling

Abstract

A method, system, and/or computer program product generates self-organizing layouts of process diagrams. Initial weight vectors are distributed uniformly within boundaries of regions in the process diagram. A spatial input vector is randomly generated within the boundaries of each region. In each region in the process diagram, a closest graphical node is found, and a position of a winning graphical node that is the closest graphical node to the random input vector is adjusted. Positions of all non-immutable graphical objects, w_i, in a topographical neighborhood N(k) of a closest graphical node w_c that can cross a boundary of one or more regions from the multiple regions are adjusted. The spatial input vector is recursively generated, the closest graphical node is recursively located, and the positions of all non-immutable graphical objects, w_i, in the topographical neighborhood N(k) are recursively adjusted until a maximum number of iterations, k_max is reached.

Claims

1. A computer program product for generating self-organizing layouts of process diagrams, the computer program product comprising a computer readable storage medium having program code embodied therewith, wherein the computer readable storage medium is not a transitory signal per se, and wherein the program code is readable and executable by a processor to perform a method comprising:

randomly selecting initial spatial position vectors, w_i, for all graphical nodes in a process diagram, wherein a first node in the process diagram is in a first lane of the process diagram, wherein a second node in the process diagram is in a second lane of the process diagram;

distributing the initial spatial position vectors, w_i, uniformly within boundaries of multiple regions in the process diagram;

in each region in the process diagram, randomly generating a spatial input vector, x, within the boundaries of each region;

in each region in the process diagram, finding a closest graphical node w_c, to a random input vector generated in said each region;

in each region in the process diagram, adjusting a position of a winning graphical node that is the closest graphical node to the random input vector generated in said each region, wherein said adjusting the position of the winning graphical node brings the winning graphical node closer to the random input vector;

adjusting a weight vector of each immutable closest graphical node in the process diagram, wherein each immutable closest graphical node w_c has a fixed location on the process diagram;

adjusting positions of all non-immutable graphical objects in a topographical neighborhood N(k) of the closest graphical node w_c that can cross a boundary of one or more regions from the multiple regions;

randomly generating the spatial input vector x, locating the closest graphical node w_c, adjusting the position of the winning graphical node, and adjusting positions of all non-immutable graphical objects in the topographical neighborhood N(k) recursively until a maximum number of iterations, k_max is reached; and

flipping an arrangement of nodes in the second lane such that the first node and the second node are proximate to one another and are connected by a connector that does not overlap any other connectors in the second lane.

2. The computer program product claim 1, wherein the closest graphical node w_c is determined by:

minimizing a Euclidean distance between x and w_i, such that;

∥

x−

w_c∥

=min_i∥

x−

w_i∥

.

3. The computer program product claim 1, wherein w_c is not immutable, wherein w_c is from a set of non-immutable graphical nodes, w_c, in an interconnected neighborhood N(k), wherein nodes from w_c can cross the regional boundaries in the process diagram, and wherein spatial adjustment of nodes in the process diagram is calculated by:

Δ

w_i=α

(k)h(n_c,n_i)[x−

w_i], n_iϵ

N(k)

where α

(k) is an adaption rate, having a value between 0 and 1, that diminishes with every iteration k h(n_c, n_i) is a neighborhood function, having a value between 0 and 1, that diminishes as a topographical distance increases between the winning graphical node and other nodes n_i in the neighborhood N(k) and wherein N(k) defines a neighborhood boundary during an iteration k and decreases over a span of the iterations, such that n_iϵ

N(k) refers to every k iteration of output nodes n in the neighborhood N(k).

4. The computer program product claim 3, wherein the method further comprises:

determining α

(k) h(n_c, n_i);

5. The computer program product claim 1, wherein the method further comprises:

recursively randomly generating the spatial input vector x, locating the closest graphical node w_c, and adjusting the position of the closest graphical node w_c, until a weight adaption rate drops below a predefined threshold.

6. The computer program product claim 1, wherein the method further comprises setting boundaries for each region R in the process diagram by implementing an algorithm:

7. The computer program product claim 1, wherein the method further comprises determining w_c, by implementing an algorithm:

8. The computer program product claim 1, wherein the method further comprises:

characterizing N(k) by a radius, r, wherein r denotes a maximum number of connections in a path between two nodes predetermined to be in a same neighborhood, wherein r starts at a pre-selected r_max and is reduced by 1 at a pro-selected k_r interval, until r reaches a pro-selected r_min.

9. A system comprising:

a processor, a computer readable memory, and a non-transitory computer readable storage media;

first program instructions to randomly select initial spatial position vectors, w_i, for all graphical nodes in a process diagram, wherein a first node in the process diagram is in a first lane of the process diagram, wherein a second node in the process diagram is in a second lane of the process diagram;

second program instructions to distribute the initial spatial position vectors, w_i, uniformly within boundaries of multiple regions in the process diagram;

third program instructions to, in each region in the process diagram, randomly generate a spatial input vector, x, within the boundaries of each region;

fourth program instructions to, in each region in the process diagram, find a closest graphical node, w_c, to a random input vector generated in said each region;

fifth program instructions to, in each region in the process diagram, adjust a position of a winning graphical node that is the closest graphical node to the random input vector generated in said each region, wherein adjusting the position of the winning graphical node brings the winning graphical node closer to the random input vector;

sixth program instructions to adjust a weight vector of each immutable closest graphical node in the process diagram, wherein each immutable closest graphical node w_c has a fixed location on the process diagram;

seventh program instructions to adjust positions of all non-immutable graphical objects in a topographical neighborhood N(k) of the closest graphical node w_c that can cross a boundary of one or more regions from the multiple regions; and

eighth program instructions to randomly generate the spatial input vector x, locate the closest graphical node w_c, adjust the position of the winning graphical node, and adjust positions of all non-immutable graphical objects in the topographical neighborhood N(k) recursively until a maximum number of iterations, k_max is reached; and

ninth program instructions to flip an arrangement of nodes in the second lane such that the first node and the second node are proximate to one another and are connected by a connector that does not overlap any other connectors in the second lane; and

wherein

the first, second, third, fourth, fifth, sixth, seventh, eighth, and ninth program instructions are stored on the non-transitory computer readable storage media for execution by the processor via the computer readable memory.

10. The system of claim 9, further comprising:

tenth program instructions to determine the closest graphical node by minimizing a Euclidean distance between x and w_i, such that;

∥

x−

w_c∥

=min_i∥

x−

w_i∥

wherein the tenth program instructions are stored on the non-transitory computer readable storage media for execution by the processor via the computer readable memory.

11. The system of claim 9, wherein w_c is not immutable, wherein w_c is from a set of non-immutable graphical nodes, w_c, in an interconnected neighborhood N(k), wherein nodes from w_c can cross the regional boundaries in the process diagram, and wherein the system further comprises:

tenth program instructions to calculate spatial adjustment of nodes in the process diagram by executing the formula;

Δ

w_i=α

(k)h(n_c,n_i)[x−

wi], n_iϵ

N(k)

where α

(k) is an adaption rate, having a value between 0 and 1, that diminishes with every iteration k;

h(n_c, n_i) is a neighborhood function, having a value between 0 and 1, that diminishes as a topographical distance increases between the winning graphical node and other nodes n_i in the neighborhood N(k) and wherein N(k) defines a neighborhood boundary during an iteration k and decreases over a span of the iterations, such that n_iϵ

N(k) refers to every k iteration of output nodes n in the neighborhood N(k); and

eleventh program instructions to determine α

(k) h(n_c, n_i) by executing the formula;

12. The system of claim 9, further comprising:

tenth program instructions to recursively randomly generate the spatial input vector x, locate the closest graphical node w_c, and adjust the position of the closest graphical node w_c, until a weight adaption rate drops below a predefined threshold; and

wherein

the tenth program instructions are stored on the non-transitory computer readable storage media for execution by the processor via the computer readable memory.