Autonomic data assurance applied to complex data-intensive software processes by means of pattern recognition

1. A method for predicting and automatically verifying data assurance of a plurality of data values located in software processes in an e-commerce environment by a pattern recognition module for performing auditing processes, the method comprising:

running the software processes on a data processing device;

running the software processes a finite number of times for creating baseline information based on collected data values;

verifying the collected data values of the baseline information of the software processes;

evaluating the baseline information by the pattern recognition module by comparing the collected data values of the baseline information with predetermined data values of predetermined information stored in a baseline database;

establishing relationships between the collected data values and the predetermined data values to produce output data;

creating a data assurance report including the output data;

determining whether the output data is valid;

storing the output data in the storage device;

flagging data values outside permitted bounds;

permitting a user to perform manual data assurance on the data values outside the permitted bounds; and

updating the baseline database to include the collected data values of the baseline information without the data values outside the permitted bounds;

wherein the pattern recognition module compares and selects one or more patterns of the collected data values for performing the auditing processes;

wherein in the event that a degree of certainty of a pattern is greater than a predetermined threshold value, the degree of certainty of the pattern is used in the auditing processes and in the event the degree of certainty of the pattern is less than the predetermined threshold value, the pattern is not recognized and an anomaly entry is created; and

wherein the established relationships predicted are limited to relationships that affect the permitted bounds.

2. The method of claim 1 , further comprising determining whether the flagged data values are outside the permitted bounds by comparing the flagged values with predetermined data values stored in the baseline database.

3. A system for predicting and automatically verifying data assurance of a plurality of data values located in software processes in an e-commerce environment by a pattern recognition module for performing auditing processes, the system comprising:

a network; and

a host system in communication with the network, the host system including software to implement a method comprising:

running the software processes a finite number of times on a data processing device;

priming the software processes for creating baseline information based on collected data values;

verifying the collected data values of the baseline information of the software processes;

evaluating the baseline information by the pattern recognition module by comparing the collected data values of the baseline information with predetermined data values of predetermined information stored in a baseline database;

establishing relationships between the collected data values and the predetermined data values to produce output data;

creating a data assurance report including the output data;

determining whether the output data is valid;

storing the output data in the storage device;

flagging data values outside permitted bounds;

permitting a user to perform manual data assurance on the data values outside the permitted bounds; and

updating the baseline database to include the collected data values of the baseline information without the data values outside the permitted bounds;

wherein the pattern recognition module compares and selects one or more patterns of the collected data values for performing the auditing processes;

wherein in the event that a degree of certainty of a pattern is greater than a predetermined threshold value, the degree of certainty of the pattern is used in the auditing processes and in the event the degree of certainty of the pattern is less than the predetermined threshold value, the pattern is not recognized and an anomaly entry is created; and

wherein the established relationships predicted are limited to relationships that affect the permitted bounds.

4. The system of claim 3 , further comprising determining whether the flagged data values are outside the permitted bounds by comparing the flagged values with predetermined data values stored in the baseline database.

5. A computer program product for predicting and automatically verifying data assurance of a plurality of data values located in software processes in an e-commerce environment by a pattern recognition module for performing auditing processes, the computer program product comprising:

a storage medium readable by a processing circuit and storing instructions for execution by the processing circuit for performing a method comprising:

running the software processes a finite number of times on a data processing device;

priming the software processes for creating baseline information based on collected data values;

verifying the collected data values of the baseline information of the software processes;

evaluating the baseline information by the pattern recognition module by comparing the collected data values of the baseline information with predetermined data values of predetermined information stored in a baseline database;

establishing relationships between the collected data values and the predetermined data values to produce output data;

creating a data assurance report including the output data;

determining whether the output data is valid;

storing the output data in the storage device;

flagging data values outside permitted bounds;

permitting a user to perform manual data assurance on the data values outside the permitted bounds; and

updating the baseline database to include the collected data values of the baseline information without the data values outside the permitted bounds

wherein the pattern recognition module compares and selects one or more patterns of the collected data values for performing the auditing processes;

wherein in the event that a degree of certainty of a pattern is greater than a predetermined threshold value, the degree of certainty of the pattern is used in the auditing processes and in the event the degree of certainty of the pattern is less than the predetermined threshold value, the pattern is not recognized and an anomaly entry is created; and

wherein the established relationships predicted are limited to relationships that affect the permitted bounds.