Optimal persistence of a business process

A computer system for automatically selecting fetch settings for business process data retrieval points as a function of database query load and relational context, the computer system comprising:

• a processor;
• a computer readable memory in circuit communication with the processor;
• and
• a computer readable storage system in circuit communication with the processor and with the computer readable memory;
• wherein the processor executes program instructions stored on the computer-readable storage system via the computer readable memory and thereby:
• monitors usage of at least one data retrieval point with respect to a defined unit of work, wherein the at least one data retrieval point is enabled during a creation of a business process execution language process;
• uses a multilayer feed-forward neural network to predict, as a function of training sets composed of historical data generated by the monitored usage of the at least one data retrieval point, a future value of a data size of results from an eager fetch setting for the at least one data retrieval point;
• and
• automatically revises the eager fetch setting for the at least one data retrieval point to a lazy fetch setting in response to determining that the future value of the data size of results from an eager fetch setting is larger than a permissible memory resource threshold.

A computer program product for automatically selecting optimal fetch settings for each of a plurality of business processes as a function of database query load and relational context, the computer program product comprising:

• a computer readable storage device having computer readable program code embodied therewith, the computer readable program code comprising instructions for execution by a processor that cause the processor to:
• monitor usage of at least one data retrieval point with respect to a defined unit of work, wherein the at least one data retrieval point is enabled during a creation of a business process execution language process;
• use a multilayer feed-forward neural network to predict, as a function of training sets composed of historical data generated by the monitored usage of the at least one data retrieval point, a future value of a data size of results from an eager fetch setting for the at least one data retrieval point;
• and
• automatically revise the eager fetch setting for the at least one data retrieval point to a lazy fetch setting in response to determining that the future value of the data size of results from an eager fetch setting is larger than a permissible memory resource threshold.