Pontificia Universidad Católica de Chile School of Engineering Department of Computer Science A feedback-based framework for process enhancement of causal

Pontificia Universidad Católica de ChileSchool of EngineeringDepartment of Computer Science

A feedback-based framework for process enhancement of causal nets

Nicolás Javier Pizarro de la FuenteThesis submitted in fulfillment of the requirements for the degree of Master of Science in Engineering

Advisor: Marcos Sepúlveda Fernández, Ph.D.


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Agenda

• General context• Problems with existing methods• Proposals• Implementation• Algorithm evaluation• Conclusions and future work

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Agenda


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What is Process Mining?

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Business Intelligence

Data Science

Data Mining

Process Mining

Business Process Management


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Event logs: A process binnacle

• Process operational information– Case number– Activities– Resources– Execution times

• Who made what, when and for which case?

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Event logs: A process binnacle

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Instance Activity Timestamp Resource

001 Register vehicle 05-07-2015 07:43:22 Nicolás

001 Generate invoice 05-07-2015 08:10:15 Jorge

002 Register vehicle 05-07-2015 08:12:42 Marcos

001 Generate payment 05-07-2015 08:13:55 Jorge

002 Illumination test 05-07-2015 08:20:36 Nicolás

002 Suspension test 05-07-2015 08:25:59 Jorge

… … … …

986 Car delivery 30-07-2015 17:57:37 Marcos


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Categories in Process Mining

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Process Mining

Process Discovery

Conformance Checking

Process Enhancement

Inputs Outputs


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Categories in Process Mining

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Process Enhancement

Process Extension

Inputs Outputs

Process Repair


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Agenda


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Problems with existing methods

• Logs are usually biased– Noise– Incompleteness

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Model behaviour

Log behaviour

Noise

Incompleteness

All behaviour


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• Logs are usually biased– Noise– Incompleteness

• No “silver bullet algorithm” in Process Discovery– Different results– Different notations

Strong context dependency

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• Development of repair techniques– Based on event logs• Use of log information: Incompleteness

– Based on reference models• Requires the construction of a whole reference model

Need of post-processing stages that correct the log incompleteness problem

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Research hypothesis

“Process models generated by discovery algorithms can be enhanced using the

knowledge of a domain expert”

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Agenda


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Operators and feedback types

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“These two activities occur simultaneously”

“This activity occurs after this other activity”

“These two activities are unrelated”

Order relations

Parallelism(||)

Causality()

Indifference(#)


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Dual-activity feedback enhancement

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“Activities X and Y occur simultaneously”

X || Y


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Process Models: Causal nets

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Input (I) Activity Output (O){ }∅ a {{b, c, d}}{{a}} b {{e}}{{a}} c {{h}}

… … …{{e, c, g}} h {{i}, {j}}

{{h}} i {{k}}{{h}} j {{k}}

{{i}, {j}} k { }∅


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Proposal: Running example

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a

b e

f g

c h

d || f

d


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a

b e

d

fg

c h


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Proposal: SESEs

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a

b e

f g

c h

d || (f, g)

d

S

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a

b e

S

c h

d || S

d

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a

b e

S

c h

d

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a

b e

f

c h

d

gS


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Proposal: SESEs y RPST

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d f g b e

c h i j k

R

a h

RPST - Refined Process Structure Tree


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Recommendations

• Based on logs– Conformance checking– Frequent behavior

• Based on models– Deadlocks– Complexity

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Alignments

Log-based recommendations

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Non-fitting traces

Fitting traces


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Conformance Checking: Alignments

a, b, c, d, f, g, h, j, ka, d, f, b, g, e, h, j, ka, d, b, e, c, f, g, h, i ,k

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Model a b c d f g e h j kLog a b c d f g >> h j k

Model a d f b g e c h j kLog a d f b g e >> h j k

Model a d b e c f g h i kLog a d b e c f g h i k


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Non-fitting traces

Fitting traces


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Model a d b e c f g h i kLog a d b e c f g h i k


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Model-based recommendations

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• Complexity


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Model-based recommendations

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• Deadlocks


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Agenda


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Implementation

• Developed on ProM– FeedbackRepair Plugin– Available for version 6.4 nighly builds

• Other libraries– jBPT - Business Process Technologies for Java

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Agenda


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Evaluation Methodology

• User evaluation– Two user types• Analysts• Reaserchers

• Algorithm performance– Application of techniques on large models

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User evaluation

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Algorithm performance

• 50 simulated operations of each type in real model c-net

|A| = 113 y |D| = 150

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Operation C-net modification RPST calculation

Causality () 0,12 3,2

Parallelism (||) 0,11 3,6

Indifference (#) 0,11 3,1

SESE collapse 0,15 3,4

Total 0,11 3,5

Average execution time (s)


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Agenda


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Conclusions

• Users agree on the usefulness of these tools and the validity of the methodology– Operators allows to include all kind of feedback– Subprocess collapsing is useful in this context

• Feasibility of incorporating multiple feedback operations in a few seconds

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Future work

• New GUI• Exhaustive SESE search methodology• New operators• New visualization libraries

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Pontificia Universidad Católica de ChileSchool of EngineeringDepartment of Computer Science


Nicolás Javier Pizarro de la FuenteThesis submitted in fulfillment of the requirements for the degree of Master of Science in Engineering

Advisor: Marcos Sepúlveda Fernández, Ph.D.


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ANNEXES

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Causal nets

C = (A, ai, ao, D, I, O)– A is a set of activities– ai and ao are the initial and final activities, respectively– D is the set of dependency relations– I and O are the input and output functions

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Causal nets

– I and O are functions such that:

with

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XØØXP(A)XAS |ASAOASAI :y :


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Petri nets v/s causal nets

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Adoption levels: Gartner Hype Cycle for Emergent Technologies

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Definitions: Causality ()

• Causality ( )

(1): “y tiene que estar en el output set de x, así como x tiene que estar en el input set de y”

(2) y (3): “Ninguna otra actividad que contenga a y en su input o output set puede tener a x y viceversa”

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III

OOO

OIOO

asxasyaIasyxAa

asyasxaOasyxAa

asxyIasasyxOas

)(,, \

)(,, \

|)( |)(

yx

(1)

(2)

(3)


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Definitions: Parallelism (||)

• Parallelism ( )

(1) y (2): “Si y aparece en el input o output set de alguna actividad, también debe aparecer x y viceversa”

(3) y (4): “y no aparece ni en el input ni en el output set de x y viceversa”

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IIOO

IIOO

III

OOO

asxyIasasxyOas

asyxIasasyxOas

asxasyaIasyxAa

asyasxaOasyxAa

)( )(

)( )(

)(,, \

)(,, \

yx ||(1)

(2)

(3)

(4)


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Definitions: Indifference (#)

• Indifference ( )

(1): “Las actividades no son ni una causalidad de la otra, ni tampoco son paralelas”

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yxxyyx ||

yx#

(1)


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Algorithms

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Causality


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Parallelism

Indifference

Documents

Pontificia Universidad Católica de Chile School of Engineering Department of Computer Science A feedback-based framework for process enhancement of causal