Using artificial intelligence and natural language processing for data collection in message oriented middleware frameworks

1. An apparatus comprising:

at least one processing platform comprising a plurality of processing devices;

said at least one processing platform being configured:

to receive a natural language query requesting data from a message oriented middleware infrastructure comprising a plurality of message oriented middleware providers;

to analyze the natural language query to determine one or more types of the data being requested;

to identify one or more stored queries corresponding to the determined one or more types of the data;

wherein the one or more stored queries are in native command formats corresponding to respective ones of the plurality of message oriented middleware providers;

to execute the identified one or more stored queries in the native command formats to retrieve the data from the plurality of message oriented middleware providers; and

to provide a response to the natural language query based on the retrieved data to a user via a user interface;

wherein, in identifying the one or more stored queries corresponding to the determined one or more types of the data, said at least one processing platform is configured to compute a similarity score between the one or more stored queries and the natural language query.

2. The apparatus of claim 1 wherein, in analyzing the natural language query, said at least one processing platform is configured to use natural language processing techniques to determine the one or more types of the data being requested.

3. The apparatus of claim 1 wherein the one or more types of the data comprise at least one of integration statistics, application statistics, system statistics, channel status, queue status, errors and message counts.

4. The apparatus of claim 1 wherein, in identifying the one or more stored queries corresponding to the determined one or more types of the data, said at least one processing platform is further configured to use one or more machine learning techniques.

5. The apparatus of claim 1 wherein the similarity score is based on terms in the natural language query and terms representing the one or more stored queries.

6. The apparatus of claim 1 wherein said at least one processing platform is further configured to rank the one or more stored queries according to the computed similarity score associated with each of the one or more stored queries.

7. The apparatus of claim 1 wherein the similarity score is computed using at least one of a Euclidean distance metric, a Mahalanobis distance metric, a cosine distance metric and a propensity score metric.

8. The apparatus of claim 5 wherein, in identifying the one or more stored queries corresponding to the determined one or more types of the data, said at least one processing platform is further configured:

to determine the terms in the natural language query having a frequency of use above a threshold; and

to compute the similarity score between the terms determined to have the frequency of use above the threshold and the terms representing the one or more stored queries.

9. The apparatus of claim 1 wherein said at least one processing platform is further configured to provide a natural language request to the user that the user confirm an accuracy of the determined one or more types of the data being requested.

10. The apparatus of claim 9 wherein the natural language request comprises a plurality of options for selection by the user, wherein the plurality of options respectively identify different ones of the determined one or more types of the data being requested.

11. The apparatus of claim 10 wherein said at least one processing platform is further configured to rank the plurality of options according to the computed similarity score between the natural language query and given ones of the one or more stored queries corresponding to the different ones of the determined one or more types of the data being requested.

12. The apparatus of claim 1 wherein the user interface comprises a chatbot interface.

13. The apparatus of claim 1 wherein, in executing the identified one or more stored queries, said at least one processing platform is configured to insert one or more variables based on the natural language query into the one or more stored queries.

14. The apparatus of claim 1 wherein the native command formats corresponding to the respective ones of the plurality of message oriented middleware providers are different.

15. A method comprising:

receiving a natural language query requesting data from a message oriented middleware infrastructure comprising a plurality of message oriented middleware providers;

analyzing the natural language query to determine one or more types of the data being requested;

identifying one or more stored queries corresponding to the determined one or more types of the data;

wherein the one or more stored queries are in native command formats corresponding to respective ones of the plurality of message oriented middleware providers;

executing the identified one or more stored queries in the native command formats to retrieve the data from the plurality of message oriented middleware providers; and

providing a response to the natural language query based on the retrieved data to a user via a user interface;

wherein identifying the one or more stored queries corresponding to the determined one or more types of the data comprises computing a similarity score between the one or more stored queries and the natural language query; and

wherein the method is performed by at least one processing platform comprising at least one processing device comprising a processor coupled to a memory.

16. The method of claim 15 wherein the similarity score is based on terms in the natural language query and terms representing the one or more stored queries.

17. The method of claim 15 further comprising ranking the one or more stored queries according to the computed similarity score associated with each of the one or more stored queries.

18. The method of claim 16 wherein identifying the one or more stored queries corresponding to the determined one or more types of the data comprises:

determining the terms in the natural language query having a frequency of use above a threshold; and

computing the similarity score between the terms determined to have the frequency of use above the threshold and the terms representing the one or more stored queries.

19. A computer program product comprising a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing platform causes said at least one processing platform:

to receive a natural language query requesting data from a message oriented middleware infrastructure comprising a plurality of message oriented middleware providers;

to analyze the natural language query to determine one or more types of the data being requested;

to identify one or more stored queries corresponding to the determined one or more types of the data;

wherein the one or more stored queries are in native command formats corresponding to respective ones of the plurality of message oriented middleware providers;

to execute the identified one or more stored queries in the native command formats to retrieve the data from the plurality of message oriented middleware providers; and

to provide a response to the natural language query based on the retrieved data to a user via a user interface;

wherein, in identifying the one or more stored queries corresponding to the determined one or more types of the data, the program code causes said at least one processing platform to compute a similarity score between the one or more stored queries and the natural language query.

20. The computer program product according to claim 19 wherein the similarity score is based on terms in the natural language query and terms representing the one or more stored queries.