Learning machine behavior related to install base information and determining event sequences based thereon

1. A computer-implemented method comprising steps of: parsing a set of data storage information based at least in part on multiple parameters related to install base information, wherein the multiple parameters comprise at least one temporal parameter and one or more event-related parameters; formatting the parsed set of data storage information into a parsed set of sequential data storage information compatible with at least one neural network model, wherein formatting the parsed set of data storage information comprises obtaining one or more event identifiers from the parsed set of data storage information, assigning at least one label to each of the one or more obtained event identifiers, and generating a vector representation of at least a portion of the one or more assigned labels corresponding to the event identifiers from the formatted parsed set of data storage information; training the at least one neural network model using the parsed set of sequential data storage information and one or more additional training parameters, wherein training comprises learning one or more patterns in the parsed set of sequential data storage information and a connection between the one or more patterns and at least one of a data unavailability event and a data loss event; predicting, by applying the at least one trained neural network model to the parsed set of sequential data storage information, at least one of a future data unavailability event and a future data loss event; and outputting an alert based at least in part on the predicting of at least one of a future data unavailability event and a future data loss event; 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 , further comprising:

applying a machine learning algorithm to the parsed set of data storage information to suppress multiple iterations of a repetitive event identifier into a single event identifier.

3. The computer-implemented method of claim 1 , wherein the set of data storage information comprises a set of virtual data storage logs.

4. The computer-implemented method of claim 1 , wherein the set of data storage information comprises at least one of firmware information, message logging information, input and output logs, and product configuration logs.

5. The computer-implemented method of claim 1 , wherein the at least one temporal parameter comprises a time-stamp.

6. The computer-implemented method of claim 1 , wherein the one or more event-related parameters comprises at least one of internet protocol address, event identifier, event severity, and sequence number.

7. The computer-implemented method of claim 1 , wherein assigning at least one label to each of the one or more obtained event identifiers comprises

assigning at least one integer label to each of the one or more obtained event identifiers.

8. The computer-implemented method of claim 1 , wherein the parsed set of sequential data storage information compatible with the at least one neural network model comprises at least one map file comprising information pertaining to mapping of at least one of the one or more event identifiers to the vector representation of at least a portion of the assigned labels.

9. The computer-implemented method of claim 8 , further comprising:

encoding at least one of the one or more event identifiers from the parsed set of sequential data storage information using the at least one map file; and

storing the at least one encoded event identifier in the memory.

10. The computer-implemented method of claim 9 , wherein predicting comprises predicting a future instance of the vector representation of at least a portion of the one or more assigned labels corresponding to at least one of a data unavailability event and a data loss event.

11. The computer-implemented method of claim 10 , further comprising:

deriving one of the at least one encoded event identifier from the memory based at least in part on the predicted future instance of the vector representation of at least a portion of the one or more assigned labels; and

decoding the derived encoded event identifier.

12. The computer-implemented method of claim 1 , wherein the at least one neural network model comprises at least one recurrent neural network model.

13. The computer-implemented method of claim 1 , wherein the at least one neural network model comprises a neural network model trained via one or more deep queue networks, and wherein the one or more deep queue networks implement an artificial intelligence agent to interact with a given sequence of events and generate at least one of a reward and a penalty based at least in part on the interaction with the given sequence of events.

14. The computer-implemented method of claim 1 , wherein the one or more additional training parameters comprise at least one of number of hidden layers, learning rate, number of epochs, number of cycles, and neural network momentum.

15. 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 said at least one processing device: to parse a set of data storage information based at least in part on multiple parameters related to install base information, wherein the multiple parameters comprise at least one temporal parameter and one or more event-related parameters; to format the parsed set of data storage information into a parsed set of sequential data storage information compatible with at least one neural network model, wherein formatting the parsed set of data storage information comprises obtaining one or more event identifiers from the parsed set of data storage information, assigning at least one label to each of the one or more obtained event identifiers, and generating a vector representation of at least a portion of the one or more assigned labels corresponding to the event identifiers from the formatted parsed set of data storage information; to train the at least one neural network model using the parsed set of sequential data storage information and one or more additional training parameters, wherein training comprises learning one or more patterns in the parsed set of sequential data storage information and a connection between the one or more patterns and at least one of a data unavailability event and a data loss event; to predict, by applying the at least one trained neural network model to the parsed set of sequential data storage information, at least one of a future data unavailability event and a future data loss event; and to output an alert based at least in part on the predicting of at least one of a future data unavailability event and a future data loss event.

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

to apply a machine learning algorithm to the parsed set of data storage information to suppress multiple iterations of a repetitive event identifier into a single event identifier.

17. The non-transitory processor-readable storage medium of claim 15 , wherein assigning at least one label to each of the one or more obtained event identifiers comprises

assigning at least one integer label to each of the one or more obtained event identifiers.

18. An apparatus comprising: at least one processing device comprising a processor coupled to a memory; said at least one processing device being configured: to parse a set of data storage information based at least in part on multiple parameters related to install base information, wherein the multiple parameters comprise at least one temporal parameter and one or more event-related parameters; to format the parsed set of data storage information into a parsed set of sequential data storage information compatible with at least one neural network model, wherein formatting the parsed set of data storage information comprises obtaining one or more event identifiers from the parsed set of data storage information, assigning at least one label to each of the one or more obtained event identifiers, and generating a vector representation of at least a portion of the one or more assigned labels corresponding to the event identifiers from the formatted parsed set of data storage information; to train the at least one neural network model using the parsed set of sequential data storage information and one or more additional training parameters, wherein training comprises learning one or more patterns in the parsed set of sequential data storage information and a connection between the one or more patterns and at least one of a data unavailability event and a data loss event; to predict, by applying the at least one trained neural network model to the parsed set of sequential data storage information, at least one of a future data unavailability event and a future data loss event; and to output an alert based at least in part on the predicting of at least one of a future data unavailability event and a future data loss event.

19. The apparatus of claim 18 , wherein said at least one processing device being further configured:

to apply a machine learning algorithm to the parsed set of data storage information to suppress multiple iterations of a repetitive event identifier into a single event identifier.

20. The apparatus of claim 18 , wherein assigning at least one label to each of the one or more obtained event identifiers comprises

assigning at least one integer label to each of the one or more obtained event identifiers.