process variability through automated late selection of · pdf file ·...

Aitor [email protected]

IK4-Ikerlan, Research Centerhttp://www.ikerlan.es

Information Technologies Area

Process Variability through Automated Late Selection of Fragments

VALENCIA 06.17.2013

CAiSE’13

Private not-for-profit institution

Created in 1964, Mondragon CoorporationTransfer and dissemination to enterprises6 research lines

Members of Innobasque

Members of IK4 alliance

WE ARE IK4-IKERLAN

1 INTR

2 VARI

3 LATV

4 CONC

MOTIVATING EXAMPLEDYNAMIC ENVIRONMENTS

PROCESS FLEXIBILITYPROCESS (RUNTIME) VARIABILITY

CONCLUSIONSFUTURE WORK

| OPERATION CONTRACTS

Elevators remote predictive maintenance and monitoring process

INTR - MOTIVATION #5

«VarPoint»Door

Save data «VarPoint»Pulley

Verify elevator status

Schedule maintenance

date

Check appoitment

maintenance not included

no errors

MOTIVATING EXAMPLE

EQUITMENT | STAKEHOLDERS|ELEVATORS TYPE

...VARIABILITY

INTR - MOTIVATION #2

DYNAMIC ENVIRONMENTSRuntime variability is required

WHEN:ComplexityLarge number of process instances are requiredDynamic data Fragment selection depends on context data only available at runtimeAvailabilityProcess execution may not be interrupted (24x7 is required)ExperienceEnactment and historical data become crucial for variant configuration

INTR - DYNAMIC ENVIRONMENTS #3

1 INTR

2 VARI

3 LATV

4 CONC




FLEXIBILITYIS REQUIRED

hey guys,are not process-aware solutions too rigid?

Which are such process flexibility and variability techniques?

Murguzur, A., Makhani, R., Tao, B., Zambon, D.: Flexible Processes and Process Mining: A Brief Survey. IBM Report (2013)

Murguzur, A., Intxausti, K., Urbieta, A., Sagardui, G.: Business Process Flexibility In Service Orchestration: A Systematic Literature Survey For Dynamic Business Environments. IJCIS (2013)

(on submission)

HEINL ET AL.1999

SCHONENBERG ET AL.2008

REICHERT AND WEBER2012

Flexibility by Selection Flexibility by Design Variability

-- Flexibility by Deviation Planned Adaptation

Instance adaptation Flexibility by Change Unplanned Adaptation

Type adaptation Flexibility by Change Evolution

Late ModellingFlexibility by

UnderspecificationLooseness

Classification

PROCESS FLEXIBILITY

Main proposals:

Others:- REGEV ET AL. 2006 | Regev, G., Soffer, P., Schmidt, R.: Taxonomy of flexibility in business processes. In: BPMDS. (2006) - BALKO ET AL. 2010 | Balko, S., ter Hofstede, A., Barros, A., La Rosa, M., Adams, M.: Business process extensibility. Enterprise Modelling and Information Systems Architectures (2010)

Main Proposals:- Heinl, P., Horn, S., Jablonski, S., Neeb, J., Stein, K., Teschke, M.: A comprehensive approach to flexibility in workflow management systems. ACM SIGSOFT Software Engineering Notes (1999) - Schonenberg, M.H., Mans, R., Russell, N., Mulyar, N., van der Aalst, W.M.P.: Process flexibility: a survey of contemporary approaches. In: CAiSE. (2008) - Reichert, M., Weber, B.: Enabling Flexibility in Process-Aware Information Sys- tems: Challenges, Methods, Technologies. Springer (2012)

VARI - PROCESS FLEXIBILITY #7

PROCESS VARIABILITYProcess flexibility: a taxonomy

BP variability handles different process variants (share a common part of core process whereas, concrete parts fluctuate from variant to variant ) depending on the particular context.

Drivers: product and service variability, difference in regulations, different customer groups,...

A

B

C

D

E

Process Variant PV1

A

E

C

D

H

Process Variant PV2

A

D

C

D

Y

Process Variant PV3

A

H

C

D

Z

Process Variant PV4

B E D H

A

...

A configurable process model


PROCESS ADAPTATIONProcess flexibility: a taxonomy

BP adaptation represents the ability to deal with changes and consequently adapt process behavior and its structure.

Drivers: Exceptions (planned) | Special situations (unplanned)

A

B

C

D

E

Process Instance PI1

A

E

C

D

H

Process Instance PI2

Gskip

EXCEPTIONS (PLANNED CHANGES)

invoke new activiti

SPECIAL SITUATIONS (UNPLANNED CHANGES)


PROCESS EVOLUTIONProcess flexibility: a taxonomy

BP evolution represent the ability of process instances to change when the corresponding process schema evolves.

Drivers: Internals (e.g., design errors) | Externals (e.g., change of tech context)

Figure from: Weber, B., Sadiq, S.,Reichert, M., : Beyond rigidity - dynamic process lifecycle support. CSRD (2012)


RUNTIME VARIABILITY

PROCESS LOOSENESSProcess flexibility: a taxonomy

BP looseness represents the ability of a process to execute on the basis of a partially specified model, where the full specification of the model is made at runtime.

Drivers: Unpredictability, non-repeatability, and emergence

Late Binding or Selection:Defer the selection of a placeholder activity to runtime

Late Modeling:Defer the modeling of a placeholder activity to runtime

Late Composition:Defer the whole composition plan creation to runtime

DESIGN-TIME

RUNTIME


Smart environments (e.g., SGBs, smart cities) in dynamic conditions

DYNAMIC ENVIRONMENTS

DYNAMIC

degr

ee o

f cha

nge

pred

icta

bilit

y

degree of change of the environment

DYNAMIC

DYNAMICSTATICDES

IGN

-TIM

ERU

NTI

ME

phase of decision making

RUNTIME VARIABILITY

DESIGN-TIME VARIABILITY

flexibility for loosely-specified processes

flexibility for pre-specified processes

LATE MODELING LATE COMPOSITION

ADAPTATION

EVOLUTION

VARI - PROCESS VARIABILITY #13

1 INTR

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A preliminary architecture

LateVa

LateVaMODELER

MODELSrepository

Hist. datarepository

LateVaEXPLORER

ProcessENGINE

FragmentSELECTOR

FragmentRECOMMENDER

LateVaRUNTIME ENGINE

BASE MODELFRAGMENTSVARIABILITY MODEL

Context-monitor

Murguzur, A., Sagardui, G., Intxausti, K., Trujillo, S.: Process Variability through Automated Late Selection of Fragments. In: VarIS workshop, collocated at CAiSE. (2013)

LATV #17

A variability model (VM)

LateVa: Modeler

Process individualities | Variability and constraint sub-models

- Variability modeling: feature models, decision trees, CVL, etc.- Process variability related to domain variability (context-aware variability)- VM translation into a CSP - CSP solvers: JaCoP, Choco, Copris, OscaR, etc.

LATV - MODELER #18

A base model (BM)+fragments

LateVa: Modeler

STATIC

PARTIAL

DYNAMIC

Process commonalities, fragments and variation points

Types of Variation Points

[Design-time resolution]

[Design-time/Runtime resolution]

[Runtime resolution]

- Business process modeling language: BPMN, BPEL, EPC, UML Activity diagrams, Petri Nets- Control-flow perspective- Fragments and base model complexity- Variation points related to a variability model

Base model

Fragments

LATV - MODELER #19

LateVa: Runtime EngineFragment selector

Context-monitor

Process.id = P01XZElevator.id = 000001Elevator.type = El4056Elevator.door.type = D01XCElevator.pulley.type = P02TR

ElA_Door ElA_Pulley

F1 ü û

F2 û ü

F3 ü û

F4 ü û

F5 û ü

first stage second stageElA_Door ElA_Pulley

ü û

û ü

û û

ü û

û ü

MODELSrepository

ProcessENGINE

deploy

save

FRAGMENT selector

resolvestart instance

resolve

get modelsupdate RM

MORE THAN ONE ALTERNATIVES...HOW TO SOLVE THAT?

LATV - RUNTIME ENGINE #20ST

ATIC

D

ATA

«VarPoint» Door = F1 + F3 + F4«VarPoint» Pulley = F2 + F5

DYN

AMIC

D

ATA

Elevator.door.paramA = 0.95089Elevator.door.paramB = 0.395889Elevator.pulley.paramX = 347Elevator.pulley.paramY = 0.21254«VarPoint» Door = F1 + F3 + F4«VarPoint» Pulley = F2 + F5

LateVa: Runtime EngineFragment recommender

Context-monitor

MODELSrepository

ProcessENGINE

saveresolve

start instanceresolve

get modelsupdate RM

FRAGMENTselector

Hist. datarepository

FRAGMENT recommender

second stage’’ElA_Door ElA_Pulley

ü û

û ü

û û

û û

û û

Properties:-source-status-dimension

resolve

-functional data-non-functional data

get data

solvedsolved

get fragments

WE GOT IT!

LATV - RUNTIME ENGINE #21

deploy

Process.id = P01XZElevator.id = 000001Elevator.type = El4056Elevator.door.type = D01XCElevator.pulley.type = P02TR

STAT

IC

DAT

A

«VarPoint» Door = F1 + F3 + F4«VarPoint» Pulley = F2 + F5

DYN

AMIC

D

ATA

Elevator.door.paramA = 0.95089Elevator.door.paramB = 0.395889Elevator.pulley.paramX = 347Elevator.pulley.paramY = 0.21254«VarPoint» Door = F1 + F3 + F4«VarPoint» Pulley = F2 + F5

Variability management

Variability monitoring

LateVa: Explorer

Instance inspection

Data monitoring and analysis

- Dynamic reconfiguration of variability model(s)- Fragments repository management

- Inspect and filter running process base model instances and fragment instances

- Analyze previous process executions- Customize recommender and fragment selector properties

LATV - EXPLORER #22

THE END IS NIGH...

1 INTR

2 VARI

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4 CONC




Some thoughts for discussion

CONCLUSIONS

Managing process (runtime) variability could bring benefit to both multiple stakeholders working in dynamic environments (e.g., smart cities, SGBs, smart healthcare, smart logistics) and process designers and administrators who are dealing with large collections of process variants

1Existing attempts do not well covered runtime process variability needs2We have introduced the foundations of the LateVa approach to provide an end-to-end solution for process runtime variability management by means of:

- Separation of concerns in process (multi-level) variability management- Automatically selecting process fragments using CSP and context data- Making decisions by relying on past experiences made in similar context

3

CONC #24

FUTURE WORK?

CONC #25

SMART ENVIRONMENTS

CONC #25

Murguzur, A., Truong, H-l, Dustdar, S.: Modeling Multi-level Process Variability in Smart Environments. In: CoopIS. (2013) (on submission)

VARIABILITY?

THANKS

Aitor [email protected]

IK4-Ikerlan, Research Centerhttp://www.ikerlan.es

Information Technologies Area

FOR YOUR ATTENTION!

http://aitormurguzur.com | @amurguzur

mailto:[email protected]

mailto:[email protected]

http://aitormurguzur.com

http://aitormurguzur.com

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