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Marlon Dumas
University of Tartu, Estonia
Petri Nets 2015 | Brussels | 24 June 2015
Process Mining
/
event log
discovered model
Discovery
Conformance
Deviance
Differencediagnostics
Performance
input model
Enhanced model
event log’
2
Automated Process Discovery
3
CID Task Time Stamp …
13219 Enter Loan Application 2007-11-09 T 11:20:10 -
13219 Retrieve Applicant Data 2007-11-09 T 11:22:15 -
13220 Enter Loan Application 2007-11-09 T 11:22:40 -
13219 Compute Installments 2007-11-09 T 11:22:45 -
13219 Notify Eligibility 2007-11-09 T 11:23:00 -
13219 Approve Simple Application 2007-11-09 T 11:24:30 -
13220 Compute Installements 2007-11-09 T 11:24:35 -
… … … …
Automated Process Discovery
• Relations-based– Alpha
4
Alpha Algorithm
• Direct successors:A > B, B > C, C > D, A > C, C > B, B > E, E > FC > E, E > GB > D
A B C D
A C B E F
• Causality:A B, C D, A C, B E, C E, E F, E G , B D
• Concurrency:B ║ C
• Exclusiveness: all other pairs
A B C E G
A
C
B
D
5
Alpha Relations Matrix
A B C D E F G
A # # # #
B # || # #
C || # # #
D # # # # #
E # # #
F # # # # # #
G # # # # # #
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A B C D E F G
A # # # # #
B # || # #
C || # # #
D # # # # #
E # # #
F # # # # # #
G # # # # # #
Alpha Algorithm – Patterns
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a b, a c,b ║ c
Automated Process Discovery
• Relations-based– Alpha: lossy (Badouel, Petri Nets 2012)– Alpha++– Heuristics miner (frequency information)
• Genetic• Region theory• Petri net synthesis• Integer Linear Programming (ILP)• …
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Automated Process Discovery
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Conformance Checking
?
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Alignment-Based ConformanceLog Model
A B C D EA B B C
Alignment
E
Fitness PrecisionHow much behavior of the log
is captured by the model?How accurate is the model
describing the log?Munoz-Gama et al. Petri nets 2013
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Deviance Mining
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T1 <e11[d111:v111, …, d11n:v11n] e12[d121:v121, …, d12m:v12m] … e1p[d1p1:v1p1, …, d1pm:v1pm]>…Tq <eq1[dq11:vq11, …, dq1n:vq1n] eq2[dq21:vq21, …, dq2m:vq2m] … eqp[dqp1:vqp1, …, dqpm:vqpm]>
T1 <e11[d111:v111, …, d11n:v11n] e12[d121:v121, …, d12m:v12m] … e1p[d1p1:v1p1, …, d1pm:v1pm]>…Tq <eq1[dq11:vq11, …, dq1n:vq1n] eq2[dq21:vq21, …, dq2m:vq2m] … eqp[dqp1:vqp1, …, dqpm:vqpm]>
Find a function F: Trace Boolean (or probability [0…1]) s.t.•F is an accurate approximation of the given labeling•F is explainable, e.g. set of simple predicates
Simple “timely” claims Simple “slow” claims
Deviance Mining via Model Delta Analysis
13Suriadi et al. Understanding Process Behaviours in a Large Insurance Company in Australia. CAiSE 2013
Deviance Mining via Model Delta Analysis
14
Deviance Mining via Sequence Classification
• Apply discriminative sequence mining methods to extract features characteristic of one class
• Build classification models (e.g. decision trees)• Extract difference diagnostics from classification model
C. Sun et al. Mining explicit rules for software process evaluation. ICSSP’2013.
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No Unified Foundation
≠ 16
(Prime) Event Structures
• Model of concurrency based on events (occurrences of actions) and three relations– Causality– Conflict– Concurrency
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Petri Nets Event Structures
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Nets With Cycles Prefix Unfolding
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Petri net NPetri net N
Complete prefix unfolding
Complete prefix unfolding
Causality-preserving prefix unfolding
Causality-preserving prefix unfolding
Comparison of Event Structures
22
?
ES1
ES2
Armas-Cervantes et al. Behavioral Comparison of Process Models Based on […] Event Structures. BPM’2014
Partially Synchronized Product (PSP)
PSP Difference Statements
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Comparison of Event Structures
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In ES1, tasks C and B are mutually exclusive, while in ES2, B precedes C
In ES1, tasks C and B are mutually exclusive, while in ES2, B precedes C
?
ES1
ES2
Armas-Cervantes et al. Behavioral Comparison of Process Models Based on […] Event Structures. BPM’2014
BP-Diff: BPMN model comparison
25http://diffbp-bpdiff.rhcloud.com/
Event Logs Event Structures
B || C
Concurrency Oracle
RunMerger
55 22 33
26
Event Structures for Log Delta Analysis
27van Beest et al. Log delta analysis: Interpretable differencing of business process event logs. BPM’2015
Event Structures for Log Delta Analysis
In L1, task C can be skipped after B, whereas in L2 it cannot
In L1, task C can be skipped after B, whereas in L2 it cannot
van Beest et al. Log delta analysis: Interpretable differencing of business process event logs. BPM’201528
Log Delta Analysis vs. Sequence Classification
448 cases7329 events
363 cases, 7496 events
Sequence classification 106-130 statements
IF |“NursingProgressNotes”| > 7.5 THEN L1IF |“Nursing Progress Notes”| ≤ 7.5 AND |“Nursing Assessment”| > 1.5 THEN L2…
Sequence classification 106-130 statements
IF |“NursingProgressNotes”| > 7.5 THEN L1IF |“Nursing Progress Notes”| ≤ 7.5 AND |“Nursing Assessment”| > 1.5 THEN L2…
Log delta analysis48 statements
In L1, “Nursing Primary Assessment” is repeated after “Medical Assign Start” and “Triage Request”, while in L2 it is not.…
Log delta analysis48 statements
In L1, “Nursing Primary Assessment” is repeated after “Medical Assign Start” and “Triage Request”, while in L2 it is not.… 29
van Beest et al. Log delta analysis: Interpretable differencing of business process event logs. BPM’2015
Event Structures for Conformance Checking
30
ABDEADBEACDEADCE
Event Structures for Conformance Checking
31
In the model, task C and B are in conflict, whereas in the log, B precedes C
In the model, task C and B are in conflict, whereas in the log, B precedes C
… vs. alignment-based conformance checking
32
ABDEADBEACDEADCE
ABCDEABDCEADBCE
A B C D E
A C D E
A B D C E
A B D E A D B C E
A D C E
?
Event Structures for Process Discovery?
33
ABDEACDEACDF
Fold
Process Mining Reloaded
34
The Road Ahead
• Developing more accurate concurrency oracles– Dealing with (short) loops in parallel branches
• Defining folding operators to generalize & simplify Petri nets synthesized from ES– Controlled generalization
• Extensions to events with data payloads
35
Discovering concurrency
36
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