Process model generated using biased process mining

• US 9,208,449 B2
• Filed: 03/15/2013
• Issued: 12/08/2015
• Est. Priority Date: 03/15/2013
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Goodwin, Richard T., Mazzoleni, Pietro, Rembert, Aubrey J.
• Primary Examiner(s): Chaki, Kakali
• Assistant Examiner(s): SITIRICHE, LUIS A
• Application Number:
  US13/833,216
• Publication Number:
  US 20140279735A1
• Time in Patent Office: 998 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06Q 10/067 Enterprise or organisation modelling
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes

Abstract

Embodiments relate to a method, system, and computer program product for a process model. The method includes extracting data associated with a process execution trace of a running process and extracting any prior knowledge data relating to the running process. The method also includes calculating at least one transition confidence parameter for the prior knowledge data; and identifying any existing process models relating to the running process. A confidence trace bias is also generated for any existing process model identified. An enhanced bias value is then calculated by combining the confidence trace bias value and value of the transition confidence parameter. Using as input the extracted process execution trace data, the prior knowledge data, the identified existing model and the enhanced bias value, a learned process model is then generated.

Claims

1. A method comprising:

extracting data associated with a process execution trace of a running process and extracting any prior knowledge data relating to said running process;

calculating at least one transition confidence parameter for said prior knowledge data;

identifying any existing process models relating to said running process;

generating a confidence trace bias for any said existing process model identified;

calculating an enhanced bias value by combining said confidence trace bias value and value of said transition confidence parameter; and

generating a learned process model by using as input said extracted process execution trace data, said prior knowledge data, said identified existing model and said enhanced bias value.

2. The method of claim 1, further comprising using said learned process model to automate said running process.

3. The method of claim 2, wherein said extracting prior knowledge data has an associated transition confidence parameter.

4. The method of claim 3, further comprising identifying any matches in activities between any identified existing process models and said process execution trace.

5. The method of claim 4, further comprising generating a confidence trace bias based on an identified match existing between said existing process model and said process execution trace.

6. The method of claim 5, wherein said confidence trace bias is generated also based on said transition confidence parameter associated with said prior knowledge data.

7. The method of claim 6, further comprising applying any existing model against bias enhanced trace to enhance or produce a new model.

8. The method of claim 1, wherein said prior knowledge data includes data extracted from documentation and diagrams.

9. The method of claim 8, wherein prior knowledge data also includes user input.

10. The method of claim 9, wherein update to said learned process model is made when there is revising of said transition confidence parameters.

11. The method of claim 1, wherein said process execution trace data includes database logs, server logs, and application logs.

12. The method of claim 1 wherein said process execution trace data includes event traces associate with one or more activities.

13. The method of claim 1, wherein process execution trace data includes trace data associated with a plurality of activities.

14. The method of claim 13, wherein each of said activities are a sequence of activities.

15. The method of claim 1 wherein said learned process model is updated automatically.

16. The method of claim 15, wherein said learned process model is updated when any of said input is revised.

17. The method of claim 15, wherein said input includes matching of activities from a previous process model to one or more events.