1. Boudewijn van Dongen /t Multi-phase process mining Building instance graphs

2. /t Overview 1.Introduction to the area of Process Mining 2.Introduction to process performance monitoring 3.Using process mining to deploy a monitoring system 4.Conclusion

3. /t Process mining overview 1) basic performance metrics 2) process model 3) organizational model 4) social network 5) performance characteristics If …then … 6) Security issues

4. /t Multi phase process mining 1) basic performance metrics 2) process model 3) organizational model 4) social network 5) performance characteristics If …then … 6) Security issues

5. /t Process Performance Monitoring – The concepts Process/control-flow perspective: flow -, waiting -, processing - and sync- times. Questions: What is the average flow time of orders? What percentage of requests is handled within 10 days? What is the average time between scheduling an activity and starting it? Resource perspective: frequencies, time, utilization, and variability. Questions: How many times did John withdraw activity go shopping? How many times did Clare suspend some running activity? How much time did people with role Manager work on this process? What is the average utilization of people with role Manager?

6. /t Process Performance Monitoring – Commercial systems Process aware information system are capable of producing log files Performance monitoring can use these files for calculating performance metrics HP Business Process CockpitAris PPM

7. /t Process Performance Monitoring – The downsides -Deploying performance monitoring systems is expensive -Process modelling requires deep knowledge of organization and processes -People tend to think in a linear way Use the Process Mining Framework to overcome these problems. -Describe each case or process instance as a a-cyclic graph automatically -Convert these graphs into a human-readable format such as EPCs or Petri nets -Export these graphs to commercial tools such as Aris PPM

8. /t Multi-phase process mining – Source system abstraction 1.Store Log files in a generic XML-format Calculate causal dependencies between tasks: If A is followed by B in some case, and B is never followed by A, then A and B are causally related.

9. /t Multi-phase process mining – Source system abstraction Using the causal relation, we construct a general base graph for all cases: Assume we have the following cases: A,B,C,D,E A,C,B,D,E A,D,E A B C DE And the following Causal relations:

10. /t Multi-phase process mining – Creating instance graphs For each instance, we walk through the base graph and make instance graphs for each case. Process instance: A,B,C,D,E and A,C,B,D,E A B C DE A B C DE Process instance: A,D,E

11. /t Multi-phase process mining – Converting to EPCs Each instance-graph is then converted into a human readable format, such as an EPC Process instance: A,B,C,D,E and A,C,B,D,E Process instance: A,D,E

12. /t Multi-phase process mining – Exporting to commercial tools -The instance EPCs are exported into Aris PPM -Analyse the process using the advanced capabilities of PPM

13. /t Conclusions We have shown a way to automatically deploy an advanced process performance monitoring system, such as Aris PPM or the HP business cockpit. No deep knowledge of the processes as they take place is required in this process. Instead, process mining techniques are used. A great insight is provided in the deviations between the intended process, and the process as it is actually being executed.