Generating Predictions for Business Processes Whose Execution is Driven by Data

• US 20130103441A1
• Filed: 10/21/2011
• Published: 04/25/2013
• Est. Priority Date: 10/21/2011
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Doganata, Yurdaer N., Khalaf, Rania Yousef, Lakshmanan, Geetika T., Unuvar, Merve
• Application Number:
  US13/279,067
• Publication Number:
  US 20130103441A1
• Time in Patent Office: Days
• US Class Current: 705/7.12
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Abstract

A method for generating predictions includes dividing a business process model into fragments, wherein the business process model includes task nodes and at least one decision node, determining the decision node in at least one of the fragments, determining a decision tree for each decision node, determining a probability for reaching a terminal node in each fragment, and merging the probabilities obtained from the fragments to find a probability of a future task.

Claims

1. A method for generating predictions comprising:

receiving a business process model;

dividing the business process model into a plurality of fragments, wherein the business process model comprises a plurality of nodes including a plurality of task nodes and at least one decision node, wherein each decision node is associated with a plurality of outcomes among the task nodes;

determining the decision node in at least one of the fragments;

determining a decision tree for each decision node;

determining a probability for reaching a terminal node in each fragment according to a recorded execution trace of the business process model;

merging the probabilities obtained from the fragments to find a probability of a future task; and

outputting a tangible indication of the probability of the future task.

2. The method of claim 1, wherein the probability of the future task is dependent on the probabilities obtained from the fragments.

3. The method of claim 1, further comprising extracting training data from the execution trace of the business process model.

4. The method of claim 3, wherein the training data includes a unique sequence of task nodes associated with each fragment.

5. The method of claim 3, further comprising:

creating a copy of the execution trace for each fragment, wherein the execution trace lists the task nodes; and

eliminating at least one task node from each copy such that each copy includes only the tasks appearing in a respective fragment.

6. The method of claim 3, wherein determining the decision tree for each decision node further comprises training the decision tree using the execution trace to determine a likelihood of reaching each of the outcomes of the decision node.

7. The method of claim 1, further comprising determining a combined probability for at least one terminal node existing in at least two of the fragments.

8. The method of claim 1, wherein dividing a business process model into a plurality of fragments further comprises:

creating a new fragment for each AND gateway in the business process model and for each node in the business process model not already included in a previously created fragment;

labeling each task node of the business process model with a fragment identification.

9. The method of claim 1, further comprising determining a conditional probability for a task node that can be reached by more than one path in a single fragment among the plurality of fragments.

10. A computer program product for generating predictions, the computer program product comprising:

a computer readable storage medium having computer readable program code embodied therewith, the computer readable program code comprising;

computer readable program code configured to divide a business process model into a plurality of fragments, wherein the business process model comprises a plurality of nodes including a plurality of task nodes and at least one decision node, wherein each decision node is associated with a plurality of outcomes among the task nodes;

computer readable program code configured to determine the decision node in at least one of the fragments;

computer readable program code configured to determine a decision tree for each decision node;

computer readable program code configured to determine a probability for reaching a terminal node in each fragment; and

computer readable program code configured to merge the probabilities obtained from the fragments to find a probability of a future task.

11. The computer program product of claim 10, wherein the probability of the future task is dependent on the probabilities obtained from the fragments.

12. The computer program product of claim 10, further comprising computer readable program code configured to extract training data from an execution trace of the business process model.

13. The computer program product of claim 12, wherein the training data includes a unique sequence of task nodes associated with each fragment.

14. The computer program product of claim 12, further comprising:

creating a copy of the execution trace for each fragment, wherein the execution trace lists the task nodes; and

eliminating at least one task node from each copy such that each copy includes only the tasks appearing in a respective fragment.

15. The computer program product of claim 12, wherein determining the decision tree for each decision node further comprises training the decision tree using the execution trace to determine a likelihood of reaching each of the outcomes of the decision node.

16. The computer program product of claim 10, further comprising computer readable program code configured to determine a combined probability for at least one terminal node existing in at least two of the fragments.

17. The computer program product of claim 10, wherein dividing a business process model into a plurality of fragments further comprises:

creating a new fragment for each AND gateway in the business process model and for each node in the business process model not already included in a previously created fragment;

labeling each task node of the business process model with a fragment identification.

18. The computer program product of claim 10, further comprising computer readable program code configured to determine a conditional probability for a task node that can be reached by more than one path in a single fragment among the plurality of fragments.