Lecture 3 - Process mining

Question 1

What is process mining?

Answer 1

• Extracting knowledge from event 
data
• Building a process model inductively 
   • E.g., from cases A-B-C and A-D-C we 
derive (mine) a process A –(B or D) –C
• Comparing mined process model 
with process description
• Visualizing mined models for 
analysis

Question 2

Difference process mining and data mining

Answer 2

• Data mining is the computing process of discovering patterns in large
data sets involving
• Patterns e.g., in the form of linear regression functions
• Patterns relate variables in the data set
• Process mining aims at discovering the process
• Not variables, but events and event relationships
• Not only the “main stream” or “happy path” but also all secondary paths
• Also commonalities: inductive algorithms, overfitting, visualization, …

Question 3

Three main types of process mining (vd Aalst)

Answer 3

Discovery
Conformance
Enhancement

Question 4

Some recent trends in Process Mining and BPM

Answer 4

• Integrate PM results with dashboards (see SAP Celonis exercise).
• Root cause analysis: given some hick-up or elephant path in the process,
what can be the cause?
• Prediction: use the mined process as a “navigation system”

Question 5

Process mining and auditing (Jans, Vasarhelyi)

Answer 5

• Does the process conform to the audit rules?
• Built-in controls are not always present.
• Sometimes built-in controls are switched off for business purposes
• Ex-post or ex-ante insight in a process being changed
• What is added-value?
  
  • Entire population is analyzed, not a sample
  • That independent meta-data is added (external control)
  • Walkthroughs
  • Actual performance rather than assumed performance
  • Specific analyses, e.g., social relationships

Question 6

Event log

Answer 6

• Event log/audit trail in ERP system contains more data (meta-data), like
time-stamp
• Importantly, the event log is created by the IT system, not under the
control of the auditee

Question 7

Formal definition of algorithm

Answer 7

• If L is an event log, then a process discovery algorithm is a function that
maps L onto a process model such that the model is “representative” for
the behavior in the event log.
• More specifically: a function that maps L onto a marked Petri Net P such
that P is sound and all traces in L correspond to firing sequences in P.

Question 8

Conclusion process mining

Answer 8

• Process Mining has been surprisingly useful in auditing and business
process analysis.
• Still many technical and application challenges
• How to deal with very large processes? (decomposition)
• How to represent uncertainty about the result (fuzzy petri nets; responsible data science)
• How to map ERP booking events into a decent event log?
• How to combine process mining and RPA?