1. Boudewijn van Dongen June 22, 2004 /t Process Mining, the basics

2. /t Storing Log files Log files can be imported from several commercial systems such as: - Staffware - Flower Log files are converted into a common XML Format

3. /t Basic Log based ordering relations If there is a process instance (trace, eventlist) in which two functions A and B follow each other directly, we say: A > B ABCADDE : A > B, B > C, C > A, A > D, D > D, D > E

4. /t Extended ordering relations Using the basic relations, we introduce: Causal relations: A  Bif and only if A > B and ¬B > A Parallel relations A || Bif and only if A > B and B > A Note, if two tasks A and B are in a loop of length two (I.e. there is a trace such that A B A is in that trace) then A  B and B  A and not A || B Finally, refine ordering relations using a Finite State Machine and Time information

5. /t Process Mining, the α-algorithm Using the  and || relation, we construct places for the resulting Petri net.

6. /t Adding timing information to the Petri net To gain more insight in the actual process, timing information can be added to places. This is done by replaying the original log in the Petri net.

7. /t Exporting the Petri net Petri nets build with the α-algorithm can be exported in several formats: - Jpeg - PostScript - TPN for linking with Woflan

8. /t Conclusions / Future work EMiT proves that the concept of process mining can work Links with more commercial systems should be made A selection of different algorithms should be implemented EMiT should be able to handle large log files The XML format should be extended

9. /t Current Status Currently, a new Java-based framework is under development. It contains a more refined Finite State Machine and links with the following commercial tools are available or will be available soon: - Aris PPM - Flower - Viscovery The Framework is open source and everyone is invited to incorporate their ideas in the framework!