Aspects of the invention provide for automatically selecting optimal fetch settings for business processes as a function of database query load and relational context by monitoring usage of a data retrieval point with respect to a defined unit of work. A multilayer feed-forward neural network is used to predict, as a function of training sets composed of historical data generated by the monitored usage of the data retrieval point, a future value of a data size of results from an eager fetch setting for the data retrieval point. The eager fetch is automatically revised to a lazy fetch setting in response to determining that the future data size value of the eager fetch setting results is larger than a permissible memory resource threshold.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for automatically selecting fetch settings for business processes as a function of database query load and relational context, the method comprising: monitoring 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; using 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 revising 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. 2. The method of claim 1, further comprising the step of: integrating computer-readable program code into a computer system comprising a processor, a computer readable memory in circuit communication with the processor, and a computer readable storage medium in circuit communication with the processor; and wherein the processor executes program code instructions stored on the computer-readable storage medium via the computer readable memory and thereby performs the steps of monitoring the usage of the at least one data retrieval point with respect to the defined unit of work, using the multilayer feed-forward neural network to predict the future value of the data size of results from the eager fetch setting for the at least one data retrieval point, automatically revising the eager fetch setting for the at least one data retrieval point to the 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 the permissible memory resource threshold. 3. The method of claim 1, wherein the using the multilayer feed-forward neural network to predict the future value of the data size of results from the eager fetch setting for the at least one data retrieval point comprises: collecting periodic resource demand statistics for the at least one data retrieval point from the monitored usage; compiling the collected periodic resource demand statistics over a specified historic timeframe; translating the compiled periodic resource demand statistics into a logged traffic load measurement; deriving object-relational mapping demand requirements associated with the logged traffic load measurement into an initial training data set comprising day and time statistics; training the multilayer feed-forward neural network using the initial training data set; using the trained multilayer feed-forward neural network to predict a frequency pattern for the at least one data retrieval point using the compiled periodic resource demand statistics over a previous specified timeframe; and assigning the predicted frequency pattern to the at least one data retrieval point as the future value of the data size of results from the eager fetch setting for the at least one data retrieval point. 4. The method of claim 3, further comprising: updating the initial training data set to contain statistics for a current time period; and periodically retraining the multilayer feed-forward neural network through a back-propagation learning algorithm and a nonlinear sigmoid activation function as a function of the updated initial training set. 5. The method of claim 4, wherein the defined unit of work is an application session defined from an open method to a close method, the method further comprising: capturing a thread stack trace at a point when the open method is invoked; and using the thread stack trace to uniquely identify and match a business process to tuned optimal settings calculated for it in the defined retrieval patterns. 6. The method of claim 4, wherein the at least one data retrieval point is one of a plurality of data retrieval points that are enabled during the creation of the business process execution language process, the method further comprising: in response to determining that future values of data sizes of results from an eager fetch setting for the plurality of data retrieval points is larger than a permissible memory resource threshold, automatically selecting one of a batch fetch setting and a join fetch setting as fetch settings for each of the plurality of data retrieval points. 7. The method of claim 6, further comprising: selecting the join fetching in response to determining that an entity relationship of the plurality of data retrieval points to a database is a collection relationship. 8. The method of claim 6, further comprising: determining for each of the plurality of data retrieval points whether data loaded is dependent upon a query result from another query process, wherein determining that the data loaded is dependent upon a query result from another query process is in response to determining that a task corresponding to the respective data retrieval point always requires at least one additional query to acquire all information needed from a database; selecting an eager fetch setting for each of the plurality of data retrieval points that are determined to be dependent upon a query result from another query process; and selecting a lazy fetch setting for each of the plurality of data retrieval points that are determined to be not dependent upon a query result from another query process. 9. 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. 10. The computer system of claim 9, wherein the processor executes program instructions stored on the computer-readable storage system via the computer readable memory and thereby uses the multilayer feed-forward neural network to predict the future value of the data size of results from the eager fetch setting for the at least one data retrieval point by: collecting periodic resource demand statistics for the at least one data retrieval point from the monitored usage; compiling the collected periodic resource demand statistics over a specified historic timeframe; translating the compiled periodic resource demand statistics into a logged traffic load measurement; deriving object-relational mapping demand requirements associated with the logged traffic load measurement into an initial training data set comprising day and time statistics; training the multilayer feed-forward neural network using the initial training data set; using the trained multilayer feed-forward neural network to predict a frequency pattern for the at least one data retrieval point using the compiled periodic resource demand statistics over a previous specified timeframe; and assigning the predicted frequency pattern to the at least one data retrieval point as the future value of the data size of results from the eager fetch setting for the at least one data retrieval point. 11. The system of claim 10, wherein the processor executes program instructions stored on the computer-readable storage system via the computer readable memory and thereby further: updates the initial training data set to contain statistics for a current time period; and periodically retrains the multilayer feed-forward neural network through a back-propagation learning algorithm and a nonlinear sigmoid activation function as a function of the updated initial training set. 12. The system of claim 11, wherein the defined unit of work is an application session defined from an open method to a close method, and wherein the processor executes program instructions stored on the computer-readable storage system via the computer readable memory and thereby further: captures a thread stack trace at a point when the open method is invoked; and uses the thread stack trace to uniquely identify and match a business process to tuned optimal settings calculated for it in the defined retrieval patterns. 13. The system of claim 11, wherein the at least one data retrieval point is one of a plurality of data retrieval point that are enabled during the creation of the business process execution language process; and wherein the processor executes program instructions stored on the computer-readable storage system via the computer readable memory and thereby further, in response to determining that future values of data sizes of results from an eager fetch setting for the plurality of data retrieval points is larger than a permissible memory resource threshold, automatically selects one of a batch fetch setting and a join fetch setting as fetch settings for each of the plurality of data retrieval points. 14. The system of claim 13, wherein the processor executes program instructions stored on the computer-readable storage system via the computer readable memory and thereby further: selects the join fetching in response to determining that an entity relationship of the plurality of data retrieval points to a database is a collection relationship. 15. The system of claim 13, wherein the processor executes program instructions stored on the computer-readable storage system via the computer readable memory and thereby further: determines for each of the plurality of data retrieval points whether data loaded is dependent upon a query result from another query process, wherein determining that the data loaded is dependent upon a query result from another query process is in response to determining that a task corresponding to the respective data retrieval point always requires at least one additional query to acquire all information needed from a database; selects an eager fetch setting for each of the plurality of data retrieval points that are determined to be dependent upon a query result from another query process; and selects a lazy fetch setting for each of the plurality of data retrieval points that are determined to be not dependent upon a query result from another query process. 16. 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. 17. The computer program product of claim 16, wherein the computer readable program code instructions for execution by the processor further cause the processor to use the multilayer feed-forward neural network to predict the future value of the data size of results from the eager fetch setting for the at least one data retrieval point by: collecting periodic resource demand statistics for the at least one data retrieval point from the monitored usage; compiling the collected periodic resource demand statistics over a specified historic timeframe; translating the compiled periodic resource demand statistics into a logged traffic load measurement; deriving object-relational mapping demand requirements associated with the logged traffic load measurement into an initial training data set comprising day and time statistics; training the multilayer feed-forward neural network using the initial training data set; using the trained multilayer feed-forward neural network to predict a frequency pattern for the at least one data retrieval point using the compiled periodic resource demand statistics over a previous specified timeframe; and assigning the predicted frequency pattern to the at least one data retrieval point as the future value of the data size of results from the eager fetch setting for the at least one data retrieval point. 18. The computer program product of claim 17, wherein the computer readable program code instructions for execution by the processor further cause the processor to: update the initial training data set to contain statistics for a current time period; and periodically retrain the multilayer feed-forward neural network through a back-propagation learning algorithm and a nonlinear sigmoid activation function as a function of the updated initial training set. 19. The computer program product of claim 18, wherein the defined unit of work is an application session defined from an open method to a close method, and wherein the computer readable program code instructions for execution by the processor further cause the processor to: capture a thread stack trace at a point when the open method is invoked; and use the thread stack trace to uniquely identify and match a business process to tuned optimal settings calculated for it in the defined retrieval patterns. 20. The computer program product of claim 18, wherein the at least one data retrieval point is one of a plurality of data retrieval point that are enabled during the creation of the business process execution language process; and wherein the computer readable program code instructions for execution by the processor further cause the processor to, in response to determining that future values of data sizes of results from an eager fetch setting for the plurality of data retrieval points is larger than a permissible memory resource threshold, automatically select one of a batch fetch setting and a join fetch setting as fetch settings for each of the plurality of data retrieval points.