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Business Process Modeling, Management and Mining

Business Process Mining

Prof. Cesare Pautasso
http://www.pautasso.info
cesare.pautasso@usi.ch
@pautasso

Discovery Gap

Process Mining Steps

Logging

Capture enough information that can be trusted to be fed into the mining algorithm

Log Granularity

Where to find events?

Extract the process model by mining the logs

Monitor the process execution

Mining Outcome

  1. Process Model:
    control flow with partial ordering of events
  2. Social Network:
    based on the frequency of handovers
  3. Decision rules:
    branch probability based on data/known state
  4. Performance:
    activity and process duration statistics, resource utilization

Process Mining Algorithm

Assumptions

Basic Mining Algorithm

  1. Abstract event log: identify event sequences
  2. Determine partial order of event pairs
  3. Classify causal, parallel and non-succession relationships
  4. Build the footprint matrix
  5. Reconstruct the control flow

Order Relationship

a>b

task a is directly followed by b

Causality Relationship

a→b := a>b ∧ !(b>a)

task a is directly followed by task b,
and task a never directly follows task b

Parallelism Relationship

a∥b := a>b ∧ b>a

task a both directly follows and directly precedes task b

No-Direct-Succession Relationship

a#b := !(a>b) ∧ !(b>a)

neither task a directly follows b, nor task b directly follows task a

Footprint Matrix

Complete classification of all possible event pairs according to the causality, parallelism and no-direct-succession relationships

Input for the control flow reconstruction algorithm

Challenges

How to deal with:

Wil van der Aalst

BPM Lifecycle

References

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