1. Constructs Competition Miner: Process Control-flow Discovery of BP-domain Constructs
David Redlich, Thomas Molka, Wasif Gilani, Awais Rashid, Gordon Blair
BPM2014 – Process Discovery Track
external PhD student enrolled since August 2011,
academic supervisors: Gordon and Awais
employed at SAP Research Belfast
Before: working on decision support for business processes
Idea: do it at real-time
Contacted Lancaster University as they are already involved in
outline

2. Outline Introduction Constructs Competition Miner Evaluation
Overview – Divide & Conquer
Trace/Log Footprint Calculation
Constructs Evaluation
Constructs Competition
BP Construction Example
Evaluation
Methodology/Logs
Results
Current/Future Work + Demo
Conclusion

3. Introduction Process Discovery is great
Many approaches/solutions exist
Assumptions:
Logs are incomplete and/or noisy
There is always a “well enough”-fitting block-structured BP representation without duplicated activities for a given log
(BPs are strongly nested)
Goals:
Direct discovery of human-readable block-structured BPs (like Process Tree)
Robust: Deal with noisy/incomplete logs/inefficient representation
General Idea:
Utilize a Top-Down approach: Divide and Conquer
 on every stage different BP Constructs compete with each other
Utilize global rather than local relations between activity occurrences

4. Overview – Divide & Conquer
Find Best Construct:
All Activities
Log
Activity Set
Find Best Construct
Winning Construct L1
L1 Winning Construct
Footprint Calculation
Activity Subset 1
Activity Subset 2
L2 Winning Construct 2 :
Log Footprint
Find Best Construct
Construct Suitability Evaluation :
Find Best Construct
L2 Winning Construct 1 :
Construct Suitability Matrices :
Constructs Competition
BP Model
Winning Construct, Activity Subsets 1 and 2

5. Trace/Log Footprint
Log
Activity Set
Focus on global relationships between Activity Occurrences:
Appears Before First: x y
Appears Before: x y
Occurrence Once Oon, Occurrence Overall Oov, First Element Fel
Footprint Calculation Approach:
Calculation of a Trace Footprint
Calculation of a Log Footprint ∆
Footprint Calculation ∆
Log Footprint
Construct Suitability Evaluation
Construct Suitability Matrices
Constructs Competition
Winning Construct, Activity Subsets 1 and 2

6. Constructs Suitability Evaluation
Log
Activity Set
Footprint Calculation
Log Footprint
Construct Suitability Evaluation
Construct Suitability Matrices
Constructs Competition
Winning Construct, Activity Subsets 1 and 2 …

7. Constructs Competition Algorithm
Log
Activity Set
Footprint Calculation
 Utilizing the fact that we only have penalties between the subsets a c
Log Footprint d b e f
Construct Suitability Evaluation
Exemplary Execution for one Construct (Choice):
Construct Suitability Matrices
Constructs Competition
Winning Construct, Activity Subsets 1 and 2

8. Example: Footprint InterpretationB C D E F G ⃝ V⃝ +  ⃝ ⃝ - 
Sign
BP-Construct 
Sequence V
Choice +
Split ⃝
Loop ⃝
Loop-Sequ. V⃝
Loop-Choice +⃝
Loop-Split x x
A,B,C A x x G B
B,C C x
F,G x F +
A,B,C,D D + x
E,F,G E x

9. Evaluation Methodology
Process Discovery Tools:
HeuristicsMiner
Inductive Miner (SotA)
CCM
Flower Miner
(all with default settings)
Test Logs 1 L1
EX5
REP
BE1
BE2
BE3
BE4
BE5 DF
FLA
|A| 8 14 20 18 10
|T| 34
100
1104
8204
8206
8194
8153
8190
13T
|E|
204
1498
7733
189T
132T
240T
254T
152T
3354
61T 3 3 4 5 2
Methodology: 6 6
6 PNetReplayer Plugin - Van Der Aalst et al.: Replaying history on process models for conformance checking and performance analysis. WIREs Data Mining and Knowledge Discovery, 2(2), , 2012
1 Weijters et al.: Process Mining with the Heuristics Miner-algorithm. BETA Working Paper
Series, WP 166, Eindhoven University of Technology, 2006
2 Leemans et al.: Discovering Block-Structured Process Models from Event Logs Containing
Infrequent Behaviour, In: BPM Workshops 2013, LNBIP, pp , Springer, 2013
3 Logs from ProM website
4 Galushka et al.: DrugFusion - Retrieval Knowledge Management for
Prediction of Adverse Drug Events. In: BIS2014, pp , LNBIP, Springer, 2014
5 Partial Financial Log from BPI Challenge 2012

10. Evaluation Results Trace Fitness Precision Generalization Simplicity
Log HM IM
CCM L1
0.679
0.863
1.000
0.532
0.529
0.550
0.638
0.422
0.654 86 91 81
EX5
0.985
0.935
0.495
0.560
0.931
0.996
0.998
155
102 80
REP
0.905
0.955
0.999 72 46 49
BE1
0.991
0.838
0.814
0.818
192
132
122
BE2
0.924
0.981
0.737
0.594
0.621
196
156
146
BE3
0.822
0.983
0.891
0.443
0.525
178
149
139
BE4
0.876
0.707
0.406
0.608
193
173
BE5
0.942
0.590
0.668
0.711
206
181
167 DF
0.911
0.970
0.563
0.559
0.588
0.914
0.906
0.832
177
136
121
FLA
0.974
0.920
0.695
0.727
0.925
0.825 98 62 65
ALL
0.919
0.966
0.979
0.718
0.622
0.663
0.941
0.915
0.930
155.3
122.8
111.9

11. Current Work/Future Work
Static Process Discovery:
Static Constructs Competition Miner
Log  BP Model
(e1, e2, … , en-1, en)  BPn
Dynamic Process Discovery:
Dynamic Constructs Competition Algorithm
Additional Scalability Constraints
 work at run-time (event-based)
(BPn-1, en)  BPn

13. Demo

14. Conclusion
Investigation how far global relations can be utilized to discover a model
Application of run-time efficient divide-and-conquer approach (due to global relationships)
CCM with few adaptations usable on event-streams
Advantages:
Works very well on strongly nested BPs
Promising approach for improving the discovery of parallel constructs
Tuning possible: Trace Fitness vs. Precision
Robust: incomplete or noisy logs
Discovered Disadvantages:
“Wrong” early decision leads to compensation at a later stage (no backtracking)
Certain behaviour cannot be determined only through global relations, e.g. loop that is always executed a fixed amount of times > 1

15. Thank you. mr.redlich@gmail.com