Application integration mapping management based upon configurable confidence level threshold

1. A computer-implemented method comprising:

receiving from at least one user a request to map from at least one source application to a target application a plurality of data fields associated with a workflow during an application integration session;

automatically mapping, via a machine learning mapping recommendation model, all data fields among the plurality of data fields for which a mapping confidence value is greater than or equal to a confidence level threshold set at a default value;

receiving from one of the at least one user an adjusted value associated with one of at least one confidence level threshold adjustment input selection;

automatically mapping, via the machine learning mapping recommendation model, all data fields among the plurality of data fields for which a mapping confidence value is greater than or equal to the confidence level threshold set at the adjusted value;

calculating average default automatic mapping precision in view of user mapping evaluations based upon the confidence level threshold set at the default value;

calculating average adjusted automatic mapping precision in view of user mapping evaluations based upon the confidence level threshold set at the adjusted value; and

storing the adjusted value as a candidate default confidence level threshold value among at least one candidate default confidence level threshold value responsive to determining that the average adjusted automatic mapping precision is greater than or equal to the average default automatic mapping precision.

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

adjusting a default value setting associated with the confidence level threshold to an updated default value selected among the at least one candidate default confidence level threshold value.

3. The computer-implemented method of claim 2 , wherein selecting the updated default value comprises analyzing, via the machine learning mapping recommendation model, each of the at least one candidate default confidence level threshold value in view of mapping history associated with the at least one user.

4. The computer-implemented method of claim 3 , wherein the mapping history includes cumulative mapping history data from any application integration session preceding the application integration session.

5. The computer-implemented method of claim 3 , wherein the mapping history includes any mapping evaluations made by one or more other users having a degree of similarity with one or more of the at least one user above a predefined user similarity threshold.

6. The computer-implemented method of claim 2 , wherein selecting the updated default value comprises analyzing, via the machine learning mapping recommendation model, each of the at least one candidate default confidence level threshold value in view of statistical precision calculated from mapping evaluation data.

7. The computer-implemented method of claim 6 , wherein analyzing each of the at least one candidate default confidence level threshold value in view of statistical precision calculated from the mapping evaluation data comprises comparing precision calculations based upon any confidence level threshold modifications made during one or more application integration sessions preceding the application integration session.

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

integrating the workflow during a subsequent application integration session based upon the confidence level threshold initially set at the updated default value.

9. The computer-implemented method of claim 1 , wherein the user mapping evaluations based upon the confidence level threshold set at the default value include each user-approved automatic data field mapping labelled as a true positive and each user-rejected automatic data field mapping labelled as a false positive.

10. The computer-implemented method of claim 9 , wherein calculating the average default automatic mapping precision comprises:

calculating a set of default precision values for the at least one user by computing a quantity of true positives divided by a sum of the quantity of true positives and a quantity of false positives as determined from the user mapping evaluations based upon the confidence level threshold set at the default value; and

averaging the set of default precision values calculated for the at least one user to obtain the average default automatic mapping precision.

11. The computer-implemented method of claim 1 , wherein the user mapping evaluations based upon the confidence level threshold set at the adjusted value include each user-approved automatic data field mapping labelled as a true positive and each user-rejected automatic data field mapping labelled as a false positive.

12. The computer-implemented method of claim 11 , wherein calculating the average adjusted automatic mapping precision comprises:

calculating a set of adjusted precision values for the at least one user by computing a quantity of true positives divided by a sum of the quantity of true positives and a quantity of false positives as determined from the user mapping evaluations based upon the confidence level threshold set at the adjusted value; and

averaging the set of adjusted precision values calculated for the at least one user to obtain the average adjusted automatic mapping precision.

13. A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computing device to cause the computing device to:

receive from at least one user a request to map from at least one source application to a target application a plurality of data fields associated with a workflow during an application integration session;

automatically map, via a machine learning mapping recommendation model, all data fields among the plurality of data fields for which a mapping confidence value is greater than or equal to a confidence level threshold set at a default value;

receive from one of the at least one user an adjusted value associated with one of at least one confidence level threshold adjustment input selection;

automatically map, via the machine learning mapping recommendation model, all data fields among the plurality of data fields for which a mapping confidence value is greater than or equal to the confidence level threshold set at the adjusted value;

calculate average default automatic mapping precision in view of user mapping evaluations based upon the confidence level threshold set at the default value;

calculate average adjusted automatic mapping precision in view of user mapping evaluations based upon the confidence level threshold set at the adjusted value; and

store the adjusted value as a candidate default confidence level threshold value among at least one candidate default confidence level threshold value responsive to determining that the average adjusted automatic mapping precision is greater than or equal to the average default automatic mapping precision.

14. The computer program product of claim 13 , wherein the program instructions further cause the computing device to:

adjust a default value setting associated with the confidence level threshold to an updated default value selected among the at least one candidate default confidence level threshold value.

15. The computer program product of claim 13 , wherein the user mapping evaluations based upon the confidence level threshold set at the default value include each user-approved automatic data field mapping labelled as a true positive and each user-rejected automatic data field mapping labelled as a false positive.

16. The computer program product of claim 13 , wherein the user mapping evaluations based upon the confidence level threshold set at the adjusted value include each user-approved automatic data field mapping labelled as a true positive and each user-rejected automatic data field mapping labelled as a false positive.

17. A system comprising:

at least one processor; and

a memory storing an application program, which, when executed on the at least one processor, performs an operation comprising:

receiving from at least one user a request to map from at least one source application to a target application a plurality of data fields associated with a workflow during an application integration session;

automatically mapping, via a machine learning mapping recommendation model, all data fields among the plurality of data fields for which a mapping confidence value is greater than or equal to a confidence level threshold set at a default value;

receiving from one of the at least one user an adjusted value associated with one of at least one confidence level threshold adjustment input selection;

automatically mapping, via the machine learning mapping recommendation model, all data fields among the plurality of data fields for which a mapping confidence value is greater than or equal to the confidence level threshold set at the adjusted value;

calculating average default automatic mapping precision in view of user mapping evaluations based upon the confidence level threshold set at the default value:

calculating average adjusted automatic mapping precision in view of user mapping evaluations based upon the confidence level threshold set at the adjusted value; and

storing the adjusted value as a candidate default confidence level threshold value among at least one candidate default confidence level threshold value responsive to determining that the average adjusted automatic mapping precision is greater than or equal to the average default automatic mapping precision.

18. The system of claim 17 , wherein the operation further comprises:

adjusting a default value setting associated with the confidence level threshold to an updated default value selected among the at least one candidate default confidence level threshold value.

19. The system of claim 17 , wherein the user mapping evaluations based upon the confidence level threshold set at the default value include each user-approved automatic data field mapping labelled as a true positive and each user-rejected automatic data field mapping labelled as a false positive.

20. The system of claim 17 , wherein the user mapping evaluations based upon the confidence level threshold set at the adjusted value include each user-approved automatic data field mapping labelled as a true positive and each user-rejected automatic data field mapping labelled as a false positive.