Context-driven analytics selection, routing, and management

1. A computer-implemented method comprising:

receiving, by one or more processors, a user analytic request;

applying, by the one or more processors, natural language processing to the user analytic request;

associating, by the one or more processors, a first set of vectors and a second set of vectors from the user analytic request to one or more analytics models and data sources stored in an analytics model registry by utilizing a plurality of domain-specific ontologies and a plurality of knowledge bases;

performing, by the one or more processors, a semantic search for one or more concepts of the first set of vectors and the second set of vectors to determine whether the one or more concepts are found in the one or more analytics models and data sources stored in the analytics model registry, wherein the step of performing further comprises:

narrowing, by the one or more processors, the one or more analytics models and data sources stored in the analytics model registry to the one or more analytics models and data sources that contain the one or more concepts;

tracking, by the one or more processors, a number of hops taken to discover the one or more concepts in the one or more analytics models and data sources stored in the analytics model registry;

calculating, by the one or more processors, a confidence metric for each of the one or more analytics models and data sources stored in the analytics model registry based on the number of hops taken to discover the one or more concepts in the one or more analytics models and data sources stored in the analytics model registry; and

ranking, by the one or more processors, the one or more analytics models and data sources stored in the analytics model registry based on the confidence metric calculated;

selecting, by the one or more processors, an analytics model from the one or more analytics models and data sources stored in the analytics model registry to process the user analytic request; and

executing, by the one or more processors, the analytics model for a time selected by a user, at a range selected by the user, and at a latitude position and a longitude position selected by the user.

2. The computer-implemented method of claim 1 , further comprising:

requesting, by the one or more processors, feedback from the user regarding the result of the analytics model;

receiving, by the one or more processors, the feedback from the user as a satisfaction rating; and

applying, by the one or more processors, the feedback from the user to an algorithm to improve an association between the user analytic request and the one or more analytics models and data sources stored in the analytics model registry.

3. The computer-implemented method of claim 1 , wherein applying natural language processing to the user analytic request further comprises:

capturing, by the one or more processors, one or more implicit user specific features from the user analytic request;

constructing, by the one or more processors, a user vector from the one or more implicit user specific features from the user analytic request;

identifying, by the one or more processors, one or more key inputs in the user analytic request, wherein the one or more key inputs include a context of the user analytic request and one or more requirements of the user analytic request;

constructing, by the one or more processors, the first set of vectors based on the one or more key inputs identified in the user analytic request;

constructing, by the one or more processors, the second set of vectors based on a knowledge base about the user; and

appending, by the one or more processors, the second set of vectors to the first set of vectors.

4. The computer-implemented method of claim 3 , wherein the first set of vectors based on the one or more key inputs identified in the user analytic request includes a context vector, a requirements vector, and an augmentation vector.

5. The computer-implemented method of claim 3 , wherein the second set of vectors based on the knowledge base about the user includes a regionality vector, an analytics vector, a seasonality vector, and a precision requirements vector.

6. The computer-implemented method of claim 3 , wherein the knowledge base includes knowledge of a farming practice, a crop rotation pattern, an irrigation pattern, and a fertilization pattern of the user.

7. The computer-implemented method of claim 5 , wherein associating the first set of vectors and the second set of vectors to the one or more analytics models and data sources stored in the analytics model registry further comprises:

selecting, by the one or more processors, a domain-specific ontology based on the analytics vector; and

capturing, by the one or more processors, associations that vary from a first domain of the plurality of domain-specific ontologies to a second domain of the plurality of domain-specific ontologies.

8. The computer-implemented method of claim 1 , wherein selecting the analytics model from the one or more analytics models and data sources stored in the analytics model registry ranked to process the user analytic request further comprises:

determining, by the one or more processors, the confidence metric exceeds a pre-set threshold.

9. The computer-implemented method of claim 2 , wherein the algorithm includes a first weighting for the feedback from the user, a second weighting for a domain-specific ontology, and a third weighting for a location.

10. A computer program product comprising:

one or more computer readable storage media and program instructions stored on the one or more computer readable storage media, the program instructions comprising:

program instructions to receive a user analytic request;

program instructions to apply natural language processing to the user analytic request;

program instructions to associate a first set of vectors and a second set of vectors from the user analytic request to one or more analytics models and data sources stored in an analytics model registry by utilizing a plurality of domain-specific ontologies and a plurality of knowledge bases;

program instructions to perform a semantic search for one or more concepts of the first set of vectors and the second set of vectors to determine whether the one or more concepts are found in the one or more analytics models and data sources stored in the analytics model registry, wherein the step of performing further comprises:

program instructions to narrow the one or more analytics models and data sources stored in the analytics model registry to the one or more analytics models and data sources that contain the one or more concepts;

program instructions to track a number of hops taken to discover the one or more concepts in the one or more analytics models and data sources stored in the analytics model registry;

program instructions to calculate a confidence metric for each of the one or more analytics models and data sources stored in the analytics model registry based on the number of hops taken to discover the one or more concepts in the one or more analytics models and data sources stored in the analytics model registry; and

program instructions to rank the one or more analytics models and data sources stored in the analytics model registry based on the confidence metric calculated;

program instructions to select an analytics model from the one or more analytics models and data sources stored in the analytics model registry to process the user analytic request; and

program instructions to execute the analytics model for a time selected by a user, at a range selected by the user, and at a latitude position and a longitude position selected by the user.

11. The computer program product of claim 10 , further comprising:

program instructions to request feedback from the user regarding the result of the analytics model;

program instructions to receive the feedback from the user as a satisfaction rating; and

program instructions to apply the feedback from the user to an algorithm to improve an association between the user analytic request and the one or more analytics models and data sources stored in the analytics model registry.

12. The computer program product of claim 10 , wherein applying natural language processing to the user analytic request further comprises:

program instructions to capture one or more implicit user specific features from the user analytic request;

program instructions to construct a user vector from the one or more implicit user specific features from the user analytic request;

program instructions to identify one or more key inputs in the user analytic request, wherein the one or more key inputs include a context of the user analytic request and one or more requirements of the user analytic request;

program instructions to construct the first set of vectors based on the one or more key inputs identified in the user analytic request;

program instructions to construct the second set of vectors based on a knowledge base about the user; and

program instructions to append the second set of vectors to the first set of vectors.

13. The computer program product of claim 10 , wherein selecting the analytics model from the one or more analytics models and data sources stored in the analytics model registry ranked to process the user analytic request further comprises:

program instructions to determine the confidence metric exceeds a pre-set threshold.

14. A computer system comprising:

one or more computer processors;

one or more computer readable storage media;

program instructions collectively stored on the one or more computer readable storage media for execution by at least one of the one or more computer processors, the stored program instructions comprising:

program instructions to receive a user analytic request;

program instructions to apply natural language processing to the user analytic request;

program instructions to associate a first set of vectors and a second set of vectors from the user analytic request to one or more analytics models and data sources stored in an analytics model registry by utilizing a plurality of domain-specific ontologies and a plurality of knowledge bases;

program instructions to perform a semantic search for one or more concepts of the first set of vectors and the second set of vectors to determine whether the one or more concepts are found in the one or more analytics models and data sources stored in the analytics model registry, wherein the step of performing further comprises:

program instructions to narrow the one or more analytics models and data sources stored in the analytics model registry to the one or more analytics models and data sources that contain the one or more concepts;

program instructions to track a number of hops taken to discover the one or more concepts in the one or more analytics models and data sources stored in the analytics model registry;

program instructions to calculate a confidence metric for each of the one or more analytics models and data sources stored in the analytics model registry based on the number of hops taken to discover the one or more concepts in the one or more analytics models and data sources stored in the analytics model registry; and

program instructions to rank the one or more analytics models and data sources stored in the analytics model registry based on the confidence metric calculated;

program instructions to select an analytics model from the one or more analytics models and data sources stored in the analytics model registry to process the user analytic request; and

program instructions to execute the analytics model for a time selected by a user, at a range selected by the user, and at a latitude position and a longitude position selected by the user.

15. The computer system of claim 14 , further comprising:

program instructions to request feedback from the user regarding the result of the analytics model;

program instructions to receive the feedback from the user as a satisfaction rating; and

program instructions to apply the feedback from the user to an algorithm to improve an association between the user analytic request and the one or more analytics models and data sources stored in the analytics model registry.

16. The computer system of claim 14 , wherein applying natural language processing to the user analytic request further comprises:

program instructions to capture one or more implicit user specific features from the user analytic request;

program instructions to construct a user vector from the one or more implicit user specific features from the user analytic request;

program instructions to identify one or more key inputs in the user analytic request, wherein the one or more key inputs include a context of the user analytic request and one or more requirements of the user analytic request;

program instructions to construct the first set of vectors based on the one or more key inputs identified in the user analytic request;

program instructions to construct the second set of vectors based on a knowledge base about the user; and

program instructions to append the second set of vectors to the first set of vectors.

17. The computer system of claim 14 , wherein selecting the analytics model from the one or more analytics models and data sources stored in the analytics model registry ranked to process the user analytic request further comprises:

program instructions to determine the confidence metric exceeds a pre-set threshold.