An Automation Support for Creating Configurable Process Models
1. Digital Enterprise Research Institute www.deri.ie
An Automation Support for Creating
Configurable Process Models
WassimDerguech and Sami Bhiri
WISE’11, Sydney, Australia, October 2011
Copyright 2011 Digital Enterprise Research Institute. All rights reserved.
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2. Business Process Variants
Digital Enterprise Research Institute www.deri.ie
International context: different process models are
proposed for describing the same procedure.
The main differences are due to local regulations and
laws; modeller's preferences; resource restrictions…
The main challenges in such context is:
how to manage these process variants
in an efficient way.
One possible solution: using
configurable process models.
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3. Configurable process model
Digital Enterprise Research Institute www.deri.ie
… is a merger of multiple process variants that achieve the same goal in a
given domain, which can be tailored for a particular setting, leading to
ancustomized process model.
Decide
for a
Decide Online Online travel
for a Hotel Flight
travel booking booking variation point
Online Variant Phone Variant
Decide Phone Online
for a Hotel Flight Online Phone
travel booking booking Hotel Hotel
booking booking
Decide Online Phone variation point
for a Hotel Flight
travel booking booking Phone Variant Online Variant
Phone Online
Flight Flight
booking booking
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4. C-EPCs: a notation for configurable
process models
Digital Enterprise Research Institute www.deri.ie
Shipment
is to be
processed • Configurable connectors are the
1,2
Shipment
variation points.
processing
1,2
X
X
2
Order
Delivery Order
generated and
is to be generated and
delivery opened
created delivery opened
2 2,3
X
X
1
2,3
Delivery Delivery
2,3
V
V
2
2,3
Deliveries
X
X need to be
planned
1,2,3
Freight Deliveries Delivery is Delivery
packed need to be relevant for unblocked Delivery is Delivery
planned shipment
relevant for unblocked
1,2,3 shipment
1 1 2,3
V
V
X
1,2,3
Transportation Transportation
1,2,3
Shipment is
Shipment is complete
complete [Adapted from M. La Rosa 2010]
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5. What we aim to do?
Digital Enterprise Research Institute www.deri.ie
Problem: Manual process model merging is tedious, time-
consuming and error-prone.
In this context we aim to provide a merging algorithm that
allows for automatically creating configurable process
models.
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6. Requirements
Digital Enterprise Research Institute www.deri.ie
1. The merged model should allow for the behavior of all the
original models.
2. Each element of the merged process model should be easily
traced back to its original model.
3. Business analysts should be able to derive one of the input
models from the merged process model.
[Adopted from M. La Rosa 2010]
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7. Overview of the merge algorithm
Digital Enterprise Research Institute www.deri.ie
Order
generated and
delivery opened
Shipment
1- Pre-process and merge
is to be
M2
processed
Shipment
Delivery
M1
processing
Delivery Order
is to be generated and
business process models
created delivery opened
Shipment
X is to be
processed
Shipment
Delivery
processing
Delivery is
M3
V Delivery
relevant for unblocked
shipment
Deliveries
need to be
X
planned
Delivery is Transportation
Delivery
relevant for
M2
unblocked
shipment
Shipment is
X complete Deliveries
need to be
planned
Transportation
Shipment is
complete
M1 Freight
packed
V
Delivery is
relevant for
shipment
Transportation
Shipment is
complete
M3 2- Post-process the
Shipment
is to be
processed
configurable business process
model
1,2
Shipment
processing
1,2
X
2
Order
Delivery
generated and
is to be
delivery opened
created
2 2,3
X 2 X
2 3
1 X
2,3
Delivery
2,3
X
3 2
V
X 2
CM
2
Deliveries
1,2,3 need to be
planned
Freight Deliveries Delivery is Delivery
packed need to be relevant for unblocked
planned shipment
1,2,3 2,3
1 1
X X
1
V 3
3
2
X
2
1 X
3
2
X
1,2,3
Transportation
Shipment is
complete
1,2,3
CM 3- Reduce the configurable
Shipment
is to be
processed
1,2
Shipment
processing
1,2
business process model
X
2
Order
Delivery
generated and
is to be
delivery opened
created
2 2,3
X 2 X
2 3
1 X
2,3
Delivery
2,3
X
3 2
V
X 2
2
Deliveries
1,2,3 need to be
RC
planned
Freight Deliveries Delivery is Delivery
packed need to be relevant for unblocked
planned shipment
1,2,3 2,3
1 1
X X
1
V 3
3
2
M
X
2
1 X
3
RCM
2
X
1,2,3
Transportation
planning and
processing
1,2
Shipment is
complete
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22. Back to the requirements
Digital Enterprise Research Institute www.deri.ie
1. The merged model should allow for the behavior of all the
original models.
By using identical identifiers/labels + no remove/add of work nodes +
no remove of input arcs + remove only trivial connectors
2. Each element of the merged process model should be easily
traced back to its original model.
By using annotations
3. Business analysts should be able to derive one of the input
models from the merged process model.
By introducing variation points for configuration
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23. Evaluation
Digital Enterprise Research Institute www.deri.ie
4 real world business processes from Dutch municipalities
[Gottshalk CAiSE09]:
Acknowledging an unborn child
Registering a newborn
Marriage
Issuing a death certificate
Each process has 5 variants 5 x 4 = 20 models
Available in Protos1 modelling notation translated manually into EPC
Protos is part of Pallas Athena's BPM toolset BPM|one
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24. Evaluation
Digital Enterprise Research Institute www.deri.ie
Output size Output size
Execution
Input size before after
time (ms)
reduction reduction
P1 190 (29+56+52+29+24) 131 (31%) 71 (62%) 157
P2 347 (63+84+73+57+70) 276 (20%) 180 (48%) 235
P3 507 (76+127+127+114+63) 298 (41%) 214 (57%) 407
P4 355 (56+111+91+67+30) 266 (25%) 160 (54%) 282
Compression rate Around 50%
Execution Time Great value in contrast to 130 man hour for
merging 25% of an enterprise process models [M. La Rosa 2010].
Complexity O(|S|*|N|2) where |S| is the number of the input models
and |N| is the total number of nodes of the largest model.
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25. What in the future?
Digital Enterprise Research Institute www.deri.ie
1. Evaluation
Rich testbed: customer clearance processes
Formal verification, scalability...
2. Extend the algorithm to:
Support approximate matching between labels
Partial ordering between tasks
Modularity
3. Configuration
Semantic annotations to allow for automatically determine configuration
options
User friendly configuration
Tracking configuration dependencies
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Hinweis der Redaktion
Actually, creating a configurable process model by merging manually a set of process models is a tedious, time consuming and error-prone task.From this comes a pressing need for providing an automation support for creating a configurable process model by merging a set of input process models.In this context, we aim to provide a merging algorithm that allows for automatically creating configurable process models.
The algorithm that that we propose should respect the following requirements:1 (read)This requirement means that if we have 3 input models, the resulting model should allow for all their behaviours. 2 (read)This requirement should help business analysts to find out where each element in the configurable model comes from, which will help for some configuration decisions. 3 (read)This requirement means that we are not merging the input models just for having a global model for all the possible behaviours but also for being confured in order to extract one of the input models.
Recall, we have identified three requirements for our algorithm.The first requirement is respected by considering identical identifiers or labels of the work nodes (events and functions).We do not remove or add any work node or arc. We remove only trivial connectors.The second requirement is fulfilled via annotations of the arcs and configurable connectors during the reduction step.The third requirement is respected via the introduction of explicit variation points: configurable connectors.
To evaluate our work, we have used 4 real world business processes from Dutch municipalities from the work of Gottshalk.These processes are : (read from slide)Each process have 5 variants which resulted into a total number of 20 process models originally available in a Protos annotation. We have manually translated and them into EPC. (They are available to anyone interested in them on simple request).
For each process, we have created a configurable process model as the merger of its input models.First we measured what we gained in size in terms of the number of the nodes at each situation. We observed that initially for the first process we had a total size of 190 node. Which is reduced into 131 node after creating the configurable model without reduction and we reached 71 node after the reduction. We can conclude that we reached a compression rate of around 50% after the reduction step. This is really important factor especially when companies are dealing with a huge number of process variants.We can also notice that the merging operation was performed in less than a second which is a great value in contrast to 130 man hour for merging 25% of an enterprise process models as reported by La Rosa.Complexity, (read from slide).