Paper presentation at the 29th International Conference on Information Systems Engineering (CAiSE'17), 16 June 2017. Paper available at: https://eprints.qut.edu.au/102251/1/StageMining_2.pdf

1. Mining Business Process Stages from
Event Logs
Hoang Nguyen, Marlon Dumas, Arthur H.M. ter Hofstede,
Marcello La Rosa, and Fabrizio Maria Maggi
CAISE’17

2. Process Mining
2
/
event logs
discovered model
Discovery
Conformance
DeviancePerformance
input model
if A
then B

3. Current Challenges
Spaghetti model Complex rules with limited
predictive accuracy
3
Limited knowledge of process
performance

4. Event log decomposition
Decomposition
Horizontal Vertical
Region-based (genet tool)
Aggregation (Fuzzy Miner)
Divide and Conquer (ProM 6)
Mining sub-processes (BPMN Miner)
Activity Clustering for Perf. Analysis
Conforti (2014)
van Dongen (2010)
Trace Clustering
Trace Similarity
Sequence Clustering
Contextual Clustering
Mining Hierarchies
Trace Alignment
Clustering-based Predictive
Monitoring
Bose (2012)
4
Internal Quality
External Quality

5. Multistage processes
Passenger boarding process Kanban board used in many office processes
5

6. Stage-based decomposition of directly-follows graphs
BPIC2012 BPIC2013 BPIC2015-1
Event logs with ground truth of stages
6

7. Structure of a process stage
7

8. Stage identification based on graph cuts
How to find the right
graph cuts? What
measure to guide the
decomposition to
approximate the real
stage decomposition?
8

9. Finding communities from social networks
Newman, M. E., & Girvan, M. (2004). Finding and evaluating community structure in networks.
Physical review E, 69(2), 026113
Modularity =
Fraction of edges
within one
community in the
real network
Fraction of edges
connecting to nodes
in a community in a
random network
9

10. Approach – modularity-based graph cuts
Mod. 0.4 0.8 0.85 0.81 0.5 0.78
10

11. Experiments
Event logs
Accuracy Index (result-A vs. ground truth-B)
Baselines
Divide and Conquer framework
Simple Precedence Diagram
Discover Matrix Create Graph Create Clusters Modify Clusters
𝐹𝑜𝑤𝑙𝑘𝑒𝑠 − 𝑀𝑎𝑙𝑙𝑜𝑤𝑠 =
𝑁1𝐴1𝐵
(𝑁1𝐴1𝐵 + 𝑁1𝐴0𝐵)(𝑁1𝐴1𝐵 + 𝑁0𝐴1𝐵)
N1A1B: no. of pairs in the same
clusters in both A and B. N1A0B:
no. of pairs in the same clusters
in A but diff. clusters in B. N0A1B:
no. of pairs in diff. clusters in A
but the same clusters in B.
11

12. Experimental Result
Our technique (SPM) Divide & Conquer (DC) Simple Precedence Diagram (SPD)
Logs SPM DC SPD
BPI12 1.0 0.30 0.49
BPI13 0.78 0.36 0.73
BPI15-1 0.90 0.40 0.54
BPI15-2 0.92 0.40 0.52
BPI15-3 0.86 0.42 0.50
BPI15-4 0.72 0.45 0.57
BPI15-5 0.83 0.46 0.49
Fowlkes − Mallows Index
Visualization of clustering for BPI15-2
12

13. Summary
• Contribution
– An automated technique to discover stages from event
logs which can approximate the real division of business
process stages
• Future work
– Optimize parameters for improving the accuracy of
stage mining
– Develop techniques for stage-based process discovery
– Develop techniques for stage-based process prediction
13

14. How to measure process performance from stages?
(CAiSE’16)
14Cumulative Flow Diagram

15. How to discover process models from stages?
15
Discover Compose
event log
Decompose

16. How to predict process performance from stages?
Predictor 1
Predictor 2
Predictor 3
Meta -
Predictor
16
Predictor 4
Learn Compose
event log
Decompose

17. Thank you for your attention!
Q&A
17

18. References
• Tsung, F., Li, Y., & Jin, M. (2008). Statistical process control for multistage manufacturing and service
operations: a review and some extensions. International Journal of Services Operations and
Informatics, 3(2), 191-204.
• Millard, P. H., Christodoulou, G., Jagger, C., Harrison, G. W., & McClean, S. I. (2001). Modelling
hospital and social care bed occupancy and use by elderly people in an English health district. Health
Care Management Science, 4(1), 57-62.
• Cooper, R. G. (1990). Stage-gate systems: a new tool for managing new products. Business
horizons, 33(3), 44-54.
• Bose, R. J. C., & van der Aalst, W. M. (2012). Process diagnostics using trace alignment:
opportunities, issues, and challenges. Information Systems, 37(2), 117-141.
• Conforti, R., Dumas, M., García-Bañuelos, L., & La Rosa, M. (2014). Beyond tasks and gateways:
Discovering BPMN models with subprocesses, boundary events and activity markers. In Business
Process Management (pp. 101-117): Springer.
• van Dongen, B. F., & Adriansyah, A. (2010). Process mining: fuzzy clustering and performance
visualization. In Business Process Management Workshops (pp. 158-169): Springer.
• Verbeek, H., van der Aalst, W., & Munoz-Gama, J. (2017). Divide and Conquer: A Tool Framework
for Supporting Decomposed Discovery in Process Mining. The Computer Journal, 1-26
• Newman, M. E., & Girvan, M. (2004). Finding and evaluating community structure in networks.
Physical review E, 69(2), 026113
18