Message management using machine learning techniques

1. A computer-implemented method comprising:

processing a message prior to submission of the message from a first enterprise entity to a second enterprise entity, wherein processing the message comprises identifying at least one message type of the message;

determining, based at least in part on the at least one identified message type, one or more message failures by applying one or more machine learning-based rules to at least a portion of the message;

determining one or more remedial actions by processing, using one or more machine learning techniques, the at least one identified message type, the one or more determined message failures, and multiple attributes of the message; and

performing one or more automated actions based at least in part on the one or more determined remedial actions;

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

2. The computer-implemented method of claim 1 , wherein performing one or more automated actions comprises implementing one or more message-oriented middleware-agnostic application programming interfaces.

3. The computer-implemented method of claim 1 , wherein processing, using one or more machine learning techniques, the at least one identified message type, the one or more determined message failures, and multiple attributes of the message comprises processing, using at least one neural network, the at least one identified message type, the one or more determined message failures, and multiple attributes of the message, and wherein processing using the at least one neural network comprises predicting values for one or more states associated with message management relative to one or more targets in at least one deep reinforcement learning model.

4. The computer-implemented method of claim 3 , wherein processing using the at least one neural network comprises inputting, to the at least one neural network, the multiple management comprising two or more of message type, message header, message format, message size, queue depth, channel status, cipher suite, target system, source system, service-level agreement information, expiry information, coded character set identifier, error code information, quality of service information, producer rate, and consumer rate.

5. The computer-implemented method of claim 1 , wherein performing one or more automatic actions comprises modifying the message by carrying out at least a portion of the one or more determined remedial actions.

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

training the one or more machine learning techniques using historical message data and historical remedial action data.

7. The computer-implemented method of claim 1 , wherein determining one or more message failures comprises identifying one or more data formatting errors in the message.

8. The computer-implemented method of claim 7 , wherein performing the one or more automated actions comprises remedying the one or more identified data formatting errors in the message.

9. The computer-implemented method of claim 1 , wherein determining one or more message failures comprises identifying one or more items of data missing from the message.

10. The computer-implemented method of claim 9 , wherein performing the one or more automated actions comprises augmenting the message based at least in part on the one or more identified items of data missing from the message.

11. 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 device causes the at least one processing device:

to process a message prior to submission of the message from a first enterprise entity to a second enterprise entity, wherein processing the message comprises identifying at least one message type of the message;

to determine, based at least in part on the at least one identified message type, one or more message failures by applying one or more machine learning-based rules to at least a portion of the message;

to determine one or more remedial actions by processing, using one or more machine learning techniques, the at least one identified message type, the one or more determined message failures, and multiple attributes of the message; and

to perform one or more automated actions based at least in part on the one or more determined remedial actions.

12. The non-transitory processor-readable storage medium of claim 11 , wherein performing one or more automated actions comprises implementing one or more message-oriented middleware-agnostic application programming interfaces.

13. The non-transitory processor-readable storage medium of claim 11 , wherein processing, using one or more machine learning techniques, the at least one identified message type, the one or more determined message failures, and multiple attributes of the message comprises processing, using at least one neural network, the at least one identified message type, the one or more determined message failures, and multiple attributes of the message, and wherein processing using the at least one neural network comprises predicting values for one or more states associated with message management relative to one or more targets in at least one deep reinforcement learning model.

14. The non-transitory processor-readable storage medium of claim 11 , wherein performing one or more automatic actions comprises modifying the message by carrying out at least a portion of the one or more determined remedial actions.

15. The non-transitory processor-readable storage medium of claim 11 , wherein the program code when executed by the at least one processing device further causes the at least one processing device:

to train the one or more machine learning techniques using historical message data and historical remedial action data.

16. An apparatus comprising:

at least one processing device comprising a processor coupled to a memory;

the at least one processing device being configured:

to process a message prior to submission of the message from a first enterprise entity to a second enterprise entity, wherein processing the message comprises identifying at least one message type of the message;

to determine, based at least in part on the at least one identified message type, one or more message failures by applying one or more machine learning-based rules to at least a portion of the message;

to determine one or more remedial actions by processing, using one or more machine learning techniques, the at least one identified message type, the one or more determined message failures, and multiple attributes of the message; and

to perform one or more automated actions based at least in part on the one or more determined remedial actions.

17. The apparatus of claim 16 , wherein performing one or more automated actions comprises implementing one or more message-oriented middleware-agnostic application programming interfaces.

18. The apparatus of claim 16 , wherein processing, using one or more machine learning techniques, the at least one identified message type, the one or more determined message failures, and multiple attributes of the message comprises processing, using at least one neural network, the at least one identified message type, the one or more determined message failures, and multiple attributes of the message, and wherein processing using the at least one neural network comprises predicting values for one or more states associated with message management relative to one or more targets in at least one deep reinforcement learning model.

19. The apparatus of claim 16 , wherein performing one or more automatic actions comprises modifying the message by carrying out at least a portion of the one or more determined remedial actions.

20. The apparatus of claim 16 , wherein the at least one processing device being further configured:

to train the one or more machine learning techniques using historical message data and historical remedial action data.