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’

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Automated Process Discovery

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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

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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

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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

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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

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?

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

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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

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ABDEADBEACDEADCE

Event Structures for Conformance Checking

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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

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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?

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ABDEACDEACDF

Fold

Process Mining Reloaded

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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

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Discovering concurrency

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