Boudewijn van Dongen

Multi-phase process mining

Building instance graphs

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

Process mining overview

1) basic performance metrics

2) process modelStart

Register order

Prepareshipment

Ship goods

(Re)send bill

Receive paymentContact

customer

Archive order

End

3) organizational model

4) social network

5) performance characteristics

If …then …

6) Security issues

Multi phase process mining

1) basic performance metrics

2) process modelStart

Register order

Prepareshipment

Ship goods

(Re)send bill

Receive paymentContact

customer

Archive order

End

3) organizational model

4) social network

5) performance characteristics

If …then …

6) Security issues

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?

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 Cockpit Aris PPM

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

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.

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,EA,C,B,D,EA,D,E

A

B

C

D E

And the following Causal relations:

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

D E

A

B

C

D E

Process instance: A,D,E

Multi-phase process mining – Converting to EPCs

Each instance-graph is then converted

into a human readable format, such as

an EPC

Startprocess A

Initiate B B

Initiate C C

Initiate D D Initiate E E Processready

Process instance: A,B,C,D,E and A,C,B,D,E

Process instance: A,D,E

Initiate D D Initiate E E Processready

Startprocess A

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

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.