Latent computing property preference discovery and computing environment migration plan recommendation

1. A system, comprising:

a memory that stores computer executable components; and

a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise:

an analysis component that employs a model to discover a latent computing property preference of an entity operating in a first computing environment, wherein the model comprises a latent Dirichlet allocation model; and

a recommendation component that recommends a computing environment migration plan to a second computing environment based on the latent computing property preference of the entity.

2. The system of claim 1 , wherein the model further comprises at least one of an artificial intelligence model or a topic model.

3. The system of claim 1 , wherein the analysis component discovers the latent computing property preference of the entity based on data corresponding to the entity comprising at least one of entity interview data, entity source data, entity application configuration data, entity middleware data, entity network data, entity storage device data, entity location data, or entity documentation data.

4. The system of claim 1 , wherein the analysis component employs the model to map one or more hierarchical topics indicative of one or more latent computing property preferences of the entity to one or more classes indicative of one or more computing properties of the second computing environment using one or more bipartite graphs.

5. The system of claim 1 , wherein the recommendation component recommends the computing environment migration plan based on at least one of: a similarity measure between one or more latent computing property preferences of the entity and one or more computing properties of the second computing environment; or a maximum support measure comprising a ratio of a number of the one or more latent computing property preferences of the entity that correspond to a computing property of the second computing environment and a total number of the one or more latent computing property preferences of the entity.

6. The system of claim 1 , wherein the analysis component employs the model to: discover one or more latent computing property preferences of the entity based on feedback data from the entity corresponding to the latent computing property preference of the entity; or modify the latent computing property preference of the entity based on the feedback data.

7. The system of claim 1 , wherein the recommendation component recommends the computing environment migration plan based on feedback data from the entity corresponding to the latent computing property preference of the entity, thereby facilitating at least one of improved implementation time, accuracy, effectiveness, or customization of the computing environment migration plan.

8. The system of claim 1 , wherein the computer executable components further comprise at least one of:

a data collection component that collects at least one of data corresponding to the entity or one or more computing properties of the second computing environment; or an interface component that presents to the entity the latent computing property preference of the entity and receives feedback data from the entity corresponding to the latent computing property preference of the entity.

9. A computer-implemented method, comprising:

employing, by a system operatively coupled to a processor, a model to discover a latent computing property preference of an entity operating in a first computing environment, wherein the model comprises a latent Dirichlet allocation model; and

recommending, by the system, a computing environment migration plan to a second computing environment based on the latent computing property preference of the entity.

10. The computer-implemented method of claim 9 , wherein the model further comprises: at least one of an artificial intelligence model or a topic model.

11. The computer-implemented method of claim 9 , wherein the employing comprises:

employing, by the system, the model to discover the latent computing property preference of the entity based on data corresponding to the entity comprising at least one of entity interview data, entity source data, entity application configuration data, entity middleware data, entity network data, entity storage device data, entity location data, or entity documentation data.

12. The computer-implemented method of claim 9 , further comprising:

employing, by the system, the model to map one or more hierarchical topics indicative of one or more latent computing property preferences of the entity to one or more classes indicative of one or more computing properties of the second computing environment using one or more bipartite graphs.

13. The computer-implemented method of claim 9 , wherein the recommending comprises:

recommending, by the system, the computing environment migration plan based on at least one of: a similarity measure between one or more latent computing property preferences of the entity and one or more computing properties of the second computing environment; or a maximum support measure comprising a ratio of a number of the one or more latent computing property preferences of the entity that correspond to a computing property of the second computing environment and a total number of the one or more latent computing property preferences of the entity.

14. The computer-implemented method of claim 9 , further comprising: employing, by the system, the model to discover one or more latent computing property preferences of the entity based on feedback data from the entity corresponding to the latent computing property preference of the entity or to modify the latent computing property preference of the entity based on the feedback data.

15. The computer-implemented method of claim 9 , further comprising:

recommending, by the system, the computing environment migration plan based on feedback data from the entity corresponding to the latent computing property preference of the entity, thereby facilitating at least one of improved implementation time, accuracy, effectiveness, or customization of the computing environment migration plan.

16. A computer program product facilitating computing environment migration plan recommendation based on one or more latent entity computing property preferences, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:

employ, by the processor, a model to discover a latent computing property preference of an entity operating in a first computing environment, wherein the model comprises a latent Dirichlet allocation model; and

recommend, by the processor, a computing environment migration plan to a second computing environment based on the latent computing property preference of the entity.

17. The computer program product of claim 16 , wherein the program instructions are further executable by the processor to cause the processor to:

employ, by the processor, at least one of an artificial intelligence model, a topic model, or the latent Dirichlet allocation model to discover the latent computing property preference of the entity based on data corresponding to the entity comprising at least one of entity interview data, entity source data, entity application configuration data, entity middleware data, entity network data, entity storage device data, entity location data, or entity documentation data.

18. The computer program product of claim 16 , wherein the program instructions are further executable by the processor to cause the processor to:

employ, by the processor, the model to map one or more hierarchical topics indicative of one or more latent computing property preferences of the entity to one or more classes indicative of one or more computing properties of the second computing environment using one or more bipartite graphs.

19. The computer program product of claim 16 , wherein the program instructions are further executable by the processor to cause the processor to:

recommend, by the processor, the computing environment migration plan based on at least one of: a similarity measure between one or more latent computing property preferences of the entity and one or more computing properties of the second computing environment; or a maximum support measure comprising a ratio of a number of the one or more latent computing property preferences of the entity that correspond to a computing property of the second computing environment and a total number of the one or more latent computing property preferences of the entity.

20. The computer program product of claim 16 , wherein the program instructions are further executable by the processor to cause the processor to:

employ, by the processor, the model to discover one or more latent computing property preferences of the entity based on feedback data from the entity corresponding to the latent computing property preference of the entity or to modify the latent computing property preference of the entity based on the feedback data.

21. The computer program product of claim 16 , wherein the program instructions are further executable by the processor to cause the processor to:

recommend, by the processor, the computing environment migration plan based on feedback data from the entity corresponding to the latent computing property preference of the entity.

22. A system, comprising:

a memory that stores computer executable components; and

a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise:

a learner component that learns one or more computing property preference patterns of an entity based on feedback data from the entity corresponding to one or more computing property preferences of the entity; and

an analysis component that employs a latent Dirichlet allocation model to discover a latent computing property preference of the entity based on the one or more computing property preference patterns of the entity.

23. The system of claim 22 , wherein the computer executable components further comprise:

a recommendation component that recommends a computing environment migration plan from a first computing environment to a second computing environment based on the latent computing property preference of the entity.

24. A computer-implemented method, comprising:

learning, by a system operatively coupled to a processor, one or more computing property preference patterns of an entity based on feedback data from the entity corresponding to one or more computing property preferences of the entity; and

employing, by the system, a latent Dirichlet allocation model to discover a latent computing property preference of the entity based on the one or more computing property preference patterns of the entity.

25. The computer-implemented method of claim 24 , further comprising:

recommending, by the system, a computing environment migration plan from a first computing environment to a second computing environment based on the latent computing property preference of the entity.