Predicting outcomes of a content driven process instance execution

• US 8,589,331 B2
• Filed: 10/22/2010
• Issued: 11/19/2013
• Est. Priority Date: 10/22/2010
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Duan, Songyun, Keyser, Paul T., Khalaf, Rania Yousef, Lakshmanan, Geetika T., Shamsi, Davood
• Primary Examiner(s): Chaki, Kakali
• Assistant Examiner(s): Wu, Fuming
• Application Number:
  US12/910,573
• Publication Number:
  US 20120101974A1
• Time in Patent Office: 1,124 Days
• Field of Search: 706/52
• US Class Current: 706/52
• CPC Class Codes: G06N 5/048 Fuzzy inferencing
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

A method for predictive analytics in a semi-structured process including receiving traces of the semi-structured process, at least one of the traces including a document content value, determining a process model from the traces, the process model comprising tasks at nodes of the semi-structured process and embodies all possible execution sequences in the process, determining a probabilistic graph including a probability at each of the tasks of the semi-structured process advancing from one task to another task, and combining the process model and probabilistic graph to determine a probabilistic process model including probabilities and strengths of transitions between tasks.

Claims

1. A computer readable non-transitory storage medium embodying instructions executed by a plurality of processors for predictive analytics in a semi-structured business process, the method comprising:

receiving execution traces generated by one or more computer system elements generated during the execution of a plurality of tasks of the semi-structured business process, wherein an outcome of the execution of each of the plurality of tasks is dependent upon one or more documents containing document content values, at least one of the execution traces including the document content value, wherein the document content value influenced the execution of the plurality of tasks;

determining a process model from the execution traces, the process model comprising tasks at nodes of the semi-structured business process, wherein the determined process model includes all possible execution sequences of the semi-structured business process;

determining a probabilistic graph including a probability, at each of the tasks of the semi-structured business process, for advancing to each of the other tasks of the semi-structured business process, wherein the probability is influenced by the document content value; and

combining the process model and probabilistic graph to determine a probabilistic process model including probabilities and strengths of transitions between each and every task of the semi-structured business process.

2. The computer readable storage medium of claim 1, wherein the probabilistic process model comprises nodes representing the tasks and edges between the nodes associated with respective probabilities of a child node occurring given an occurrence of a parent node.

3. The computer readable storage medium of claim 1, wherein the process model identifies at least one of hidden tasks that identify exclusive versus parallel paths, nodes where the business process splits, nodes where the business process joins, edges where the business process execution is parallel, and edges where the business process execution is exclusive.

4. The computer readable storage medium of claim 1, wherein the process model identifies loops in the semi-structured business process.

5. The computer readable storage medium of claim 1, the method further comprising determining a probability distribution at a decision node based on available document contents at that decision node, wherein the probability distribution gives a probability of the semi-structured business process advancing from a task corresponding to the decision node to one or more children tasks.

6. The computer readable storage medium of claim 5, the method further comprising:

receiving a status and content of an executing case instance of the business process;

updating the probability distribution using the status and the content; and

updating at least one probability of the probabilistic business process model using an updated probability distribution.

7. The computer readable storage medium of claim 5, wherein the content includes document content values as attributes in an instance of the business process.

8. The computer readable storage medium of claim 5, the method further comprising:

receiving a status of an executing case instance of the business process;

updating the probability distribution using the status; and

updating at least one probability of the probabilistic business process model using an updated probability distribution.

9. The computer readable storage medium of claim 1, the method further comprising constructing an extended probabilistic process model describing relations between the tasks of the business process based on the probabilistic process model, wherein the extended probabilistic process model enables predictive analytics in the business process based on content associated with the tasks.

10. A computer readable non-transitory storage medium embodying instructions executed by a plurality of processors for predictive analytics in a semi-structured business process, the method comprising:

receiving execution traces generated by one or more computer system elements generated during the execution of a plurality of tasks of the semi-structured business process, wherein an outcome of the execution of each of the plurality of tasks is dependent upon one or more documents containing document content values, at least one of the execution traces including the document content value, wherein the document content value influenced the execution of the plurality of tasks;

determining a process model from the execution traces, the process model comprising tasks at nodes of the semi-structured business process and assigns semantics to the nodes and edges connecting the nodes, wherein the determined process model includes all possible execution sequences of the semi-structured business process;

determining a probabilistic graph including a probability at each and every task of the plurality of tasks of the semi-structured business process for advancing to each of the other tasks of the semi-structured business process, wherein the probability is influenced by available document contents at respective decision nodes;

combining the process model and probabilistic graph to determine a probabilistic process model including probabilities and strengths of transitions between each and every task of the semi-structured business process;

determining a probability distribution at a decision node based on available document contents at that decision node, wherein the probability distribution gives a probability of the semi-structured business process advancing from a task corresponding to the decision node to one or more children tasks; and

updating the probabilistic process model using probabilities derived by the probability distribution and predicting one of a subsequent task in an execution the business process given a current task or any task in the business process given the probabilistic process model.

11. The computer readable storage medium of claim 10, the method further comprising:

receiving a new execution trace;

re-determining the probability distribution for new execution trace; and

updating the probabilistic process model using probabilities derived by a re-determined probability distribution.

12. The computer readable storage medium of claim 10, the method further comprising constructing an extended probabilistic process model describing relations between the tasks of the business process based on the probabilistic process model, wherein the extended probabilistic process model enables predictive analytics in the business process based on content associated with the tasks.

13. A computer readable non-transitory storage medium embodying instructions executed by a plurality of processors for updating a probabilistic process model (PPM) of a semi-structured business process, the method comprising:

receiving the PPM and at least one probability distribution corresponding to a decision node of the PPM, wherein the received PPM includes all possible execution sequences of the semi-structured business process, wherein the received PPM is generated from a process model (PM) and a probabilistic graph including a probability, at each and every task of the semi-structured business process, for advancing to each of the other tasks of the semi-structured business process, and wherein the PM is derived from execution traces generated by one or more computer system elements generated during execution of a plurality of tasks of a semi-structured business process;

receiving a status and content of an executing case instance of the business process; and

updating at least one probability of the PPM using the probability distribution and the received status and content of said executing case instance of the business process.

14. The computer readable storage medium of claim 13, wherein updating further comprises mapping the PPM to an extended probabilistic process model with additional nodes representing states achievable through parallel paths in the PPM.

15. The computer readable storage medium of claim 13, the method further comprising constructing an extended probabilistic process model describing relations between tasks of the business process based on an updated probabilistic process model, wherein the extended probabilistic process model enables predictive analytics in the business process based on content associated with the tasks.

16. The computer readable storage medium of claim 13, wherein the PPM comprises nodes representing the tasks and edges between the nodes associated with respective probabilities of a child node occurring given an occurrence of a parent node.