1. Patterns extraction from process executions
19 Feb 2015
Laura Genga

2. Outline Introduction Approach Experiments Conclusion and future works
Building Instance Graphs
Patterns Extraction
Experiments
BPI2013challenge
CoseLog
Conclusion and future works

3. Introduction
Many real world domains are characterized by processes with little structure
Typical process discovery approaches have problems when dealing with such processes
«Spaghetti» models

4. Spaghetti processes analysis
Schema simplification
Trace clustering
Patterns discovery

5. Patterns discovery Existing approaches: mining on traces
Patterns abstraction
Episodes discovery
Episodes Discovery
Patterns Abstraction
P1:
P1 : <Start,b,c,d,g>
P2 : <e,f,h> …
P2:
P3:

6. Proposed Approach: Mining on Graphs
Event Log
Instance Graphs Set
Patterns Set
Case Id
Trace 1
<Start,b,c,d,g,End> 2
<Start,a,b,d,c,g,i,End> 3
<Start,a,e,f,h,i,End> 4
<Start,b,c,d,g,e,f,h, End> 1 1 2 P1 2 P2 3 4

7. Building IGs set abcdgi The parallelism is hidden in the trace
We need to know the causal relations between events
Use of process discovery approaches
abcdgi

8. Deriving causal relations from process discovery outcome
CR set can be derived by means of some process discovery approach
The mining techniques must be chosen carefully
Source
Target A B E C D F … ….
A→B
A→E

9. Instance Graphs building
For each pair of events 𝑒 𝑖 , 𝑒 𝑗 for which 𝑒 𝑖 → 𝑒 𝑗 holds, add an edge if in the trace between 𝑒 𝑖 , 𝑒 𝑗 :
(1) No successors of 𝑒 𝑖 OR (2) No predecessors of 𝑒 𝑗
Source
Target A B I C D G K
Source
Target A B I C D G K
Source
Target A B I C D G K
Source
Target A B I C D G K
Source
Target A B I C D G K c
T1: a b i c d g k a b g d
𝑎→𝑏
1 ok
𝑎→𝑖
1 no
2 ok
𝑏→𝑑 ok
1 no
2 ok
𝑏→𝑐
1 ok i k

10. Flower models problem
Representing all possible behaviors can generate a flower model
Using a flower model we obtain only sequence graphs
Look only for most frequent relations
Some traces will result “anomalous”
t1 : <Start,a,e,f,h,End>

11. Graphs with anomalies 𝑡 1 : bacdg 𝑡 2 : afehi A B E C D G K F H I
Source
Target A B E C D G K F H I
𝑡 1 : bacdg
𝑡 2 : afehi

12. Use of conformance checking techniques
Conformance checking technique provide precise information about the occurrence of an anomaly in a trace
The corresponding graph explicitly represents the anomaly occurrence
insertion
deletion

13. Updated graphs with anomalies
Source
Target A B E C D G K F H I
𝑡 1 : bacdg
𝑡 2 : afehi

14. Proposed Approach: Mining on Graphs
Event Log
Instance Graphs Set
Patterns Set
Case Id
Trace 1
<Start,b,c,d,g,End> 2
<Start,a,b,d,c,g,i,End> 3
<Start,a,e,f,h,i,End> 4
<Start,b,c,d,g,e,f,h, End> 1 1 2 P1 2 P2 3 4

15. Patterns extraction Frequent subgraph mining techniques
Extraction of subgraphs whose “support” is above a threshold
Support of a subgraph
transaction-based: number of graphs involving the subgraph
Occurrence-based: number of occurrences of the subgraph
TB supp: 2
OB supp: 3

16. SUBDUE Algorithm Supported computed by using frequency and size
Discovered patterns are arranged into a hierarchy

17. Experiments Two experiments:
Log of BPI2013 (Incident Management)
Wabo4 (CoseLog project)
CR set derived by the Inductive Miner algorithm
Patterns evaluation
Support : transaction based
Domain knowledge

18. BPI2013 Model mined by IMi

19. SUB1
Supp: 47%
DK: the event “queued + awaiting assignment” is undesired

20. SUB7
Supp: 19%
DK: high rate of incident management delegation

21. SUB12
Supp: 8%
DK: this should be the “ideal” activities order

22. Wabo4: Process Model Mined by Imi

23. SUB1
Supp: 41%
DK: Starting activities of an application management

24. SUB12
Supp: 16%
DK: Final part of an application management. Unexpected parallelism

25. SUB3
Supp: 21%
DK: can be considered a meaningful sub-process

26. Summing up
The proposed method was able to detect interesting patterns, providing an alternative way to analyze complex, spaghetti processes
The method is flexible; it can be used with any process discovery/ frequent subgraph mining technique
Limits:
The reliability of the results depends on the process discovery approach adopted
The pattern interpretation support can be improved

27. Future works Improving the pattern interpretation support
Providing for a pattern also its context
Adding a performance evaluation based on patterns
Single pattern evaluation: average costs, throughput time…
Analyzing the pattern impact on the overall process performance

28. Thank you for your attention!