User interface (UI) design compliance determination

1. A computer-implemented method comprising:

receiving as an input, a user interface (UI) comprising a plurality of elements;

rendering an image of the UI;

identifying the plurality of elements from the image according to a recursive procedure;

storing the plurality of elements in a first logic tree exhibiting a first hierarchical structure having the plurality of elements as leaf nodes, and logic terms as intermediate nodes;

using a Bayesian network, an in-memory database engine of an in-memory database comparing the first logic tree to a second logic tree that,

exhibits a second hierarchical structure,

is labeled with semantic metadata, and

is stored within a knowledge base of the in-memory database;

based upon the comparing, generating an output indicating a similarity between the first logic tree and the second logic tree;

generating a recommendation regarding a cluster of elements present in the knowledge base, the recommendation comprising,

a probability,

a user interface platform component, and

a location of a GIT address for reuse of code.

2. The method as in claim 1 wherein the recursive procedure comprises:

generating from the image, a first two-dimensional array reflecting pixel information; and

generating from the first two-dimensional array, a second two-dimensional array reflecting information density.

3. The method as in claim 2 wherein the recursive procedure further comprises cropping the image based upon the second two-dimensional array.

4. The method as in claim 1 further comprising constructing the knowledge base utilizing a machine learning technique.

5. The method as in claim 4 wherein the machine learning technique comprises case-based reasoning (CBR).

6. The method as in claim 5 wherein the comparing is first performed between the semantic metadata and a solution description, and is then only performed if necessary between the semantic metadata and a problem description.

7. A non-transitory computer readable storage medium embodying a computer program for performing a method, said method comprising:

receiving as an input, a user interface (UI) comprising a plurality of elements;

rendering an image of the UI;

identifying the plurality of elements from the image according to a recursive procedure;

storing the plurality of elements in a first logic tree exhibiting a first hierarchical structure having the plurality of elements as leaf nodes, and a plurality of logic terms as intermediate nodes;

utilizing a Bayesian network, an in-memory database engine of an in-memory database comparing the first logic tree to a second logic tree that,

exhibits a second hierarchical structure,

is labeled with semantic metadata, and

is stored within a knowledge base of the in-memory database;

based upon the comparing, generating an output indicating a similarity between the first logic tree and the second logic tree; and

generating a recommendation regarding a cluster of elements present in the knowledge base, the recommendation comprising,

a probability,

a user interface platform component, and

a location of a GIT address for reuse of code.

8. The non-transitory computer readable storage medium as in claim 7 wherein the method further comprises:

generating from the image, a first two-dimensional array reflecting pixel information; and

generating from the first two-dimensional array, a second two-dimensional array reflecting information density.

9. The non-transitory computer readable storage medium as in claim 8 wherein the recursive procedure further comprises cropping the image based upon the second two-dimensional array.

10. The non-transitory computer readable storage medium as in claim 7 wherein the method further comprises:

constructing the knowledge base utilizing a machine learning technique.

11. The non-transitory computer readable storage medium as in claim 10 wherein:

the machine learning technique comprises case-based reasoning; and

the comparing is performed between the semantic metadata and a solution description.

12. A computer system comprising:

one or more processors;

a software program, executable on said computer system, the software program configured to cause an in-memory database engine of an in-memory database to:

receive as an input, a user interface (UI) comprising a plurality of elements;

render an image of the UI;

identify the plurality of elements from the image according to a recursive procedure;

store the plurality of elements in a first logic tree exhibiting a first hierarchical structure having the plurality of elements as leaf nodes, and a plurality of logic terms as intermediate nodes;

use a Bayesian network to compare the first logic tree to a second logic tree that,

exhibits a second hierarchical structure,

is labeled with semantic metadata, and

is stored within a knowledge base of the in-memory database;

based upon the comparing, generate an output indicating a similarity between the first logic tree and the second logic tree; and

generate a recommendation regarding a cluster of elements present in the knowledge base, the recommendation comprising,

a probability,

a user interface platform component, and

a location of a GIT address for reuse of code.

13. The computer system as in claim 12 wherein the software program is further configured to cause the in-memory database engine to construct the knowledge base utilizing a machine learning technique.

14. The computer system as in claim 13 wherein:

the machine learning technique comprises case-based reasoning; and

the software program is configured to cause the in-memory database to compare the semantic metadata and a solution description.

15. The computer system as in claim 12 wherein the recursive procedure comprises:

generating from the image, a first two-dimensional array reflecting pixel information;

generating from the first two-dimensional array, a second two-dimensional array reflecting information density; and

cropping the image based upon the second two-dimensional array.