Business Process Model Recommendation as a Transformation
Process in MDE: Conceptualization and First Experiments
Hadjer Khider
1,2 a
, Slimane Hammoudi
3
and Abdelkrim Meziane
1
1
Information Systems and Multimedia Systems Department, CERIST, Algiers, Algeria
2
Computer Science Department, Faculty of Exact Sciences, University of Bejaia, Bejaia 06000, Algeria
3
ERIS Team, Computer Science Department – ESEO, Angers, France
Keywords: Business Process Model Reuse, User Business Profile, Business Process Modeling, Recommender System,
MDA, Meta Models, Transformation, Weaving, Recommendation Business Process Model.
Abstract: Business Process (BP) model repositories have been proposed to store models of BP and make them available
to their stakeholders for future reuse. One of the challenges facing users of such repositories concerns the
retrieval of models that suit their business needs in a given situation, which is not provided by current
repositories. In order to overcome this lack, one important issue to investigate is to provide recommendation
of BP models based on the user profile as the most important way to better meet his business needs which
promote BP model reusability. In this paper, we propose a conceptual framework of BP model
recommendation based on the user social profile and implemented as a transformation process in model driven
engineering (MDE). In our experiments, the LinkedIn social network is used to extract the users’ business
interests. These user business interests are then used to recommend the appropriate BP models that could fit
to the user. Our proposed framework is based on model driven architecture (OMG MDE approach) where
techniques of models, metamodels, transformation and weaving are used to implement a generic
recommendation process.
1 INTRODUCTION
A Business Process (BP) modeling is fundamental
part of Business Process Management (BPM)
lifecycle for improving organizational efficiency and
quality of business processes (BPs) in enterprises
(Gerth, 2013). However, modeling BP from scratch is
fallible, complex, time-consuming and error prone
task (Markovic, Pereira, and Stojanovic, 2008). One
of the promising solutions to these issues is the reuse
of BP models (Elias and Johannesson, 2012).
Therefore, it is important to provide a repository to
store thousands of BP models for business reuse (Gao
and Krogstie, 2010).
Re-use techniques facilitate the use of existing BP
models in order to simplify the development of new
models or the improvement of existing BP models.
Reuse of BP models is designing BPs by using
existing process models (Gao and Krogstie, 2010).
Exploiting already designed BP models is one of the
promising solutions to reduce the time and cost
a
https://orcid.org/0000-0002-0566-9235
consumed to model new BP, minimize errors and
increase BP model quality and flexibility (Gao and
Krogstie, 2010), (Fellmann, Koschmider, and
Schoknecht, 2014), (Shahzad et al., 2009), (Mendling
et al., 2017). To reuse BP models, it is important to
provide a BP models repository to store BP models
for future reuse. Fortunately, many organizations
have establish BP model repositories to maintain and
reuse existing BP models (Elias and Johannesson,
2012),(Gao and Krogstie, 2010), (Fellmann,
Koschmider, and Schoknecht, 2014), (Shahzad et al.,
2009). For example, SAP, MIT.
Recent works (Elias and Johannesson, 2012),
(Shahzad et al., 2009), (Yan, Dijkman, and Grefen,
2017),(Yan and Grefen, 2010) show that the existing
repositories do not adequately support reuse of BP
models. So, it is difficult for business actors to find
the relevant BP models in BP model repositories and
share process knowledge. The lack of an efficient
models retrieval system (Yan, Dijkman, and Grefen,
2017), the different process repositories
classifications, the heterogeneity of repositories
Khider, H., Hammoudi, S. and Meziane, A.
Business Process Model Recommendation as a Transformation Process in MDE: Conceptualization and First Experiments.
DOI: 10.5220/0009155600650075
In Proceedings of the 8th International Conference on Model-Driven Engineering and Software Development (MODELSWARD 2020), pages 65-75
ISBN: 978-989-758-400-8; ISSN: 2184-4348
Copyright
c
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
65
structure and the potential size of existing repositories
do not allow their users to find easily models for
future reuse. On the other hand, process model
retrieval from a process repository still suffer from
much manual work (Li et al, 2014) and cannot relieve
business users from the highly complex, time
consuming, and error-prone task of building new BPs
from scratch.
Considering the above issues, there is a need for
an automated approach which can recommend BP
models to help repository stakeholders to find and
therefore reuse models to build new one.
Recommender systems have been widely used as an
effective answer to these difficulties. Recently,
Recommender systems became a hot topic and have
been widely used in both academic research and
industry applications. However, its applicability in
the field of BPM remains very modest, there are only
a few works (Li et al., 2014), (Deng et al., 2017),
(Hornung, Koschmider, and Oberweis, 2009),(Imad,
Elkindy, and Corea, 2019), (Schonenberg et al.,
2008),(Setiawan, Sadiq, and Kirkman 2011)
(Laurenzi et al., 2019) in BPM domain.
We claim that by integrating recommender
system into BP model repository, we can increase the
accessibility of BP models for repository users.
Furthermore, integrating recommender system into
current BP model repositories presents an effective
mechanism that can help users, to find BP models
easily and reuse the available knowledge of BP model
repositories to reduce time and effort, and improve
the quality of newly designed models. Motivated by
this idea, SBPR, a Social Business Process
Recommender for BP model reuse, is proposed.
According to the study presented by Shahzad, et
al. in (Shahzad, Elias, and Johannesson, 2010), in
order to reuse BP models, it is important to know the
environment in which a process can or is intended to
work. This environment consists of the business
context in which the BP can be applied, the goals of
the process, and the actors of the process. We believe
that the business environment in which stakeholders
are involved can influence the BP models that may
interest them. The LinkedIn social network is used to
extract the business profiles of users. Research has
shown that LinkedIn is the primary business-oriented
social networking site that professionals use (Salman,
2019). LinkedIn had more than 630 million members
making it the largest online professional network
(Salman, 2019). A LinkedIn profile contains a
member’s current and past work history, education,
career information and projects which reflects the
business environment in which the users are actually
involved.
In this paper, we propose a novel recommender
system, which generates predictions simply by
combining the user business profile to determinate the
business environment in which user is actually
involved with the metadata of BP models stored in
repository. The power of our recommender system
lies in its ability to make a recommendation of BP
models in real time from users’ business profiles. Our
approach is based on model driven development
(MDD) through model driven architecture (MDA)
where metamodels allow to define a generic
representation of user profiles in one side and BP
models in other side. Taking advantage as much as
possible of existing MDA technologies, we have
implemented our recommender system as a
transformation process which takes as input a
LinkedIn user profile and proposes in output a set of
potential BP models.
In this paper, we propose a conceptual framework
of BP model recommendation to help users find new
BP models from repository in an efficient and
accurate way. The proposed framework is based on
the user social profile and implemented as a
transformation process in model driven architecture
(MDA).
The remainder of this paper is organized as
follows: Section 2 presents related work. Section 3
gives an overview of our SBPR framework. Section 4
presents our Model Driven Approach where
metamodels for both user business profile and BP
metadata are presented. Section 5 presents a case
study, results and discussion. Finally, section 6
concludes and outlines our future research directions.
2 RELATED WORK
Recommendation has already been investigated in
BPM to assist stakeholders in choosing the suitable
BP for a given activity. Presently, there are two kinds
of work on process model recommendation (Deng et
al., 2017):
- Complete process model recommendation by
reusing existing complete process models and
- Process model fragments recommendation in
order to model new process models by reusing
process model fragments.
2.1 Process Model Fragments
Recommendation for Modeling
A lot of work has been devoted to Process model
fragments recommendation. For example:
MODELSWARD 2020 - 8th International Conference on Model-Driven Engineering and Software Development
66
H. Schonenberg et al. in (Schonenberg et al.,
2008) proposed a recommendation service, which,
when used in combination with flexible PAISs
1
, can
support end users during process execution by giving
recommendations on possible next steps.
Recommendations are generated based on similar
past process executions by considering the specific
optimization goals.
Hornung et al. in (Hornung, Koschmider, and
Oberweis, 2009) have proposed a recommender
system that suggests a list of correct and fitting
process fragments for an edited BP model, which can
be used to complete the process model being edited.
The benefit of such a system is to assist users during
process modeling by reusing process fragments from
a process repository.
M. Andri Setiawan in (Setiawan, Sadiq, and
Kirkman, 2011) presented an approach to assist
process designers and promote process improvement
by the development of methods for multi-criteria
based process ranking and personalized
recommendation, the approach intended to assist
users by learning from already existing successful
practices.
Ying Li in (Li et al., 2014) proposed a workflow
recommendation for improving BP modeling that
precede associate rules mining between business
activity nodes and process fragments within the
workflow repository, and provide decision support
for modeling processes. The associate rules mining
refer to pattern extraction. Extraction of those
patterns from the workflow repository is based on
graph-mining technique. which is the foundation of
workflow recommendation. Based on measuring
similarity and calculating distance between reference
model and process patterns, technique could
recommend appropriate nodes for modelers to
automate the construction of BPs.
Rangiha et al. in (Rangiha, Comuzzi, and
Karakostas, 2015) described a recommender task
system that uses social tagging to collect relevant
information from discussions between process actors
during process execution. Analysis of these tags
allows the system for recommending new tasks when
the same process must be executed again.
Deng et al. in (Deng et al., 2017) have proposed a
process recommendation system that can assist BP
analysts build new BPs from scratch. It can
recommend proper nodes (fragments) based on
patterns mined from existing process repositories.
1
Process Aware Information Systems
E. Laurenzi et al. in (Laurenzi et al., 2019)
proposed a process modeling approach that assists
domain experts in the creation and adaptation of
process models. To get an appropriate assistance, the
approach is driven by semantic patterns and learning.
Semantic patterns are domain-specific and consist of
process model fragments (or end-to-end process
models), which are learned from feedback from
process modeling experts.
2.2 Complete Process Model
Recommendation
Complete process model recommendation helps to
reuse process models by discovering or retrieving
such process models from repository that satisfy
users’ explicit requirements or conform to their
implicit intentions.
A. Koschmider et al. in (Koschmider, Hornung,
and Oberweis, 2011) proposed a system for
supporting users at modeling time by providing a
recommender component and search functionality for
process model parts stored in a repository. The
recommendation-based modeling support system is
based on users’ tagging behavior and intentions, this
system implements other functionality, such as a
search interface.
K. Kluza et al. in (Kluza et al., 2013) presented
several machine learning methods which can be used
for recommending features of BP models.
Furthermore, the study in Kluza et al. (Kluza et al.,
2013) suggests a classification schema to the
recommendations.
Bobek et al. in (Bobek et al., 2013) presented a
method that uses Bayesian Networks for
recommendation purposes in process modeling and
configurable models, such a method can help in
speeding up modeling process and producing models
that are less error prone compared to these designed
from scratch.
Ariouat et al., in (Ariouat, Andonoff, and
Hanachi, 2018) has addressed process
recommendation in crisis management field. The
Process recommendation uses data observed in the
field, i.e. risk and damage of the crisis, along with
business knowledge of actors involved in crisis
resolution in order to recommend different strategies
with observed facts depending to the context and then
build processes corresponding to chosen strategies.
Business Process Model Recommendation as a Transformation Process in MDE: Conceptualization and First Experiments
67
Figure 1: SBPR Framework Source: (Khider et al., 2018).
2.3 Synthesis and Positioning
However, the above-cited approaches use
recommendation suggestions during design time and
usually focused on how to guide the modeler during
the modeling process on possible next activities to
create. Furthermore, those approaches ensure a
certain degree of modeling support but yet with little
focus on the user interests
. The recommendations
provided is based on factors as labels of elements,
current progress of modeling process, and additional
information, such as process description. but they
never take the business interests of the user into
consideration (Kluza et al., 2013). In this paper we
propose a process recommender system to help
business users find new process models from
repository in an efficient and accurate way.
Furthermore, our approach is not limited to designer
or modeling experts but it is destined to any user who
accesses a repository of process models in order to
find a BP model to reuse. Moreover, the power of our
recommender system lies in its ability to make
recommendation based on business profile of user
and implemented as a transformation process in
model driven architecture (MDA). Our proposition is
not limited to the modeling stage but it can be spread
on all stages of BPM. To our knowledge, there are no
approaches that have proposed the integration of a
recommender system to current process model
repositories to help their users find models easily and
according to their business needs.
Moreover, till now, according to our knowledge
there is no an approach that uses the MDA model
transformation process for the recommendation of BP
models. Thus, recommending the appropriate BP
models that could fit to the user would be
implemented as a transformation of a user business
profile into BPs models.
3 OUR SBPR FRAMEWORK
In this section, we propose the Social Business
Process Recommender (SBPR) Framework in order
to overcome some of problems that affect the actual
BP model repositories. The figure 1 illustrates the
different elements characterizing this framework and
their relationships. On the left hand side of this figure,
we find the main properties of a user profile according
to LinkedIn. On the right side, the metadata of BP
model are presented. SBPR recommender aims to
recommend to the users of process models
repositories BP models for reuse. LinkedIn User
profile is the source of social data for SBPR
recommender; BP models are target items to be
recommended to user. SBPR recommender
recommend BP models based on the business
environment of user which is learned from his
LinkedIn business profile.
MODELSWARD 2020 - 8th International Conference on Model-Driven Engineering and Software Development
68
4 A MODEL DRIVEN
APPROACH
In conformity with MDD approach we have defined
two metamodels, the first one is the LinkedIn user
profile metamodel and the second one is the BP
metadata metamodel.
4.1 LinkedIn User Profile Metamodel
LinkedIn is an
online
professional
social network
which may represent real-world professional
relationships
. We have chosen LinkedIn
2
for the
reason that LinkedIn is a business-oriented social
networking site where users post their professional
and career information, which allow us to have an
image of the user’s business environment. Figure 2
presents a potential meta-model for the LinkedIn User
profile (fragment).
The meta-model defined in this section was
designed as a synthesis of concepts proposed by
several authors, and more specifically the work of
Jean-Marie Favre presented in (Jean-Marie Favre
2007). The main elements of this metamodel are: (as
shown in figure 2)
The LinkedIn User Profile: LinkedIn is used for
professional networking; it allows members to create
profiles. LinkedIn user profile is formed by a set of
profile fields
3
predefined in LinkedIn.
LinkedIn User Business Profile: from the fields
that constitute the full user profile we have chosen
those that reflect his business environment. The main
elements that make up the business profile are:
Industry, Position, Skills.
Industry: the user on LinkedIn can specify his
industry according to the reference table of industry
4
codes available on LinkedIn. the 10 top industries in
LinkedIn are: Information Technology and Services,
Marketing and Advertising, Human Resources,
Computer Software, Financial Services, Staffing and
Recruiting, Internet, Management Consulting,
Telecommunications, Retail
5
1
.
Business Experience: includes information about
the business accomplishments, skills, company,
industry, area of expertise and professional
competencies of the user.
4.2 BP Metadata Metamodel
Figure 3 presents a possible meta-model for the BP
metadata (fragment). The BP metadata metamodel
(Figure 3) captures these elements and their
relationships in detail.
Figure 2: LinkedIn User profile metamodel.
2
https://www.linkedin.com/
3
https://developer.linkedin.com/docs/fields/full-profile
4
https://developer.linkedin.com/docs/reference/industry-
codes
5
https://www.linkedin.com/pulse/linkedin-industry-rank
ings-see-which-tops-list-joshua-waldman
Business Process Model Recommendation as a Transformation Process in MDE: Conceptualization and First Experiments
69
Figure 3: BP metadata metamodel (Fragment).
This metamodel is based on set of proposed
developed metamodels in literature among which the
metamodel developed by zur Muehlen in (Zur
Muehlen, 2004), the metamodel developed by M.
Rosemann (Rosemann, Recker, and Flender, 2008)
and metamodel developed by M.Elias in (Elias,
Shahzad, and Johannesson, 2010). The main elements
of this metamodel are:
BP Description
Many organizations complement their BP models
with textual descriptions specify additional details
that describes the activities of a process, the involved
entities and their interaction. Process repositories in
practice do not only consist of BP models, but often
also contain textual BP descriptions (Leopold et al.,
2019). Taking the information from textual
descriptions into account may provide a clearer view
of the BP models that may interest the user in order
to reuse them. By taking into account the semantic of
textual descriptions of BP models in our
recommendation process allows us to identify more
relevant models from repository.
Business Context
To reuse a BP model, it is important for a user to
understand the business environment in which a BP
is aimed to work. The business context in which the
BP takes place is specified by a set of categories and
their associates values (Hofreiter and Huemer, 2006).
e.g. Industry, Manufacturing. SAP repository
classifies BP models according to their business
context. According to Born et al. (Born, Kirchner, and
Müller, 2009), the context defines the environment in
which a BP is used.
Business Goal
BP model describes activities conducted in order to
achieve business goals. Business goals express what
the organization wants to achieve from the business
perspective (Markovic, Pereira, and Stojanovic,
2008). As defined by M. Weske in (Weske, 2019) a
business goal is the target that an organization aims
to achieve by performing correctly, the related BP.
Relating BP models with goals in the repository can
help users to understand and thereby reuse BP
models.
4.3 A Methodology for Transformation
Process
In our proposed approach the transformation process
of a User business profile metamodel (source) into a
BP Metadata metamodel (target) aims to find
correspondences between the elements of two
metamodels source and target, and then generate
metadata to allow us to predict models that fit to
business environment of the user for reuse.
In conformity with the research work discussed in
(Hammoudi et al., 2010), the transformation process
is structured in two phases: mapping specification and
transformation definition.
A mapping specification is a definition of the
correspondences between two metamodels. From a
conceptual point of view, the explicit distinction
between mapping specification and transformation
definition remains in agreement with the MDA
philosophy, i.e. the separation of concerns.
Moreover, a mapping specification could be
MODELSWARD 2020 - 8th International Conference on Model-Driven Engineering and Software Development
70
associated with different transformation definitions,
where each transformation definition is based on a
giving transformation definition metamodel. Figure 4
hereafter represents a mapping specification from
LinkedIn User business profile into BP metadata. We
have used ATL transformation language (Bézivin et
al., 2003) to define transformation rules.
4.3.1 Generation of Transformation Rules
This phase aims to automatically generate
transformation rules from mappings, and format them
into a transformation models. According to Figure 4,
we have the following mappings:
Figure 4: Mapping from User business profile into BP
model metadata.
The User business profile business
environment is mapped into BP metadata
business context through the rule Be2Bc.
The User business profile business
experience is mapped into BP metadata business
textual description through the rule Be2T.
The User business profile Professional
Goal is mapped into BP metadata Business
Goal through the rule Pg2Bg.
The table 1 below show the different rules
generated.
Table1: Transformation Rules from mappings.
Transformation
rules in human
language
Transformation rules in ATL
R1 Be2Bc:
Business
environment
in LinkedIn
User business
Profile
corresponds to
business
context in BP
metadata
rule Be2Bc {
from be:
U
ser
b
usiness profile! business
environment to bCxt_
Grtd
:
BP
metadata! business context(
context<- Be.category)
- - Cxt_
Grtd
: name of local
v
ariable referencing the
i
nstance created at the
output of the rule }
R2 Be2T:
Business
experience
(industry,
company,
position) in
LinkedIn User
Profile
corresponds to
textual
description in
BP metadata
rule Be2T {
from bxp: User
business profile! business
experience to
Des
_Grtd
:
BP Metadata!
Textual Description (
description<-
bxp.industry_title+bxp.pos
i
tion
_
title +
bxp.company_name)
- - Des
_Grtd
: name of
l
ocal variable referencing
t
he instance created at the
output of the rule}
R3 Pg2Bg:
Professional
Goal from
LinkedIn User
business
Profile
corresponds to
Business Goal
in BP
metadata
rule Pg2Bg {
from Pg: User
b
usiness profile!
professional goal
to Gl_
Grtd
:
B
P
Metadata! business goal (
BGoal<-pg.Goal)
- - Gl
_Grtd
: name of
l
ocal variable referencing
t
he instance created at the
output of the rule}
5 A FIRST EXPERIMENTS
5.1 Dataset Description
To test our approach, we have established dataset of
BP models and related metadata. The dataset we use
for applying our approach for recommendation of BP
Target
Transformation
Source
R
-Context: String
Business Process Model Recommendation as a Transformation Process in MDE: Conceptualization and First Experiments
71
models consists of 600 BP models modeled in the
Business Process Model and Notation (BPMN).The
collection of BP models is collected manually from
different BPM platform like Signavio
6
,Apromore
7
and other sources. In addition to the BP models, the
dataset contains 600 textual BP model descriptions in
the text format. Table 2 summarizes the key
characteristics of our dataset collection. The BP
models cover various business domain including
manufacturing process, selling process, booking
process …etc.
Table 2: Characteristics of dataset collection.
No. of BP models 600
Format
BPMN
No. of BP model textual
descriptions
600
No. of words (average)
No. of words / model
(average)
Format
64 800
108
.txt
5.2 Case Study
For the purpose of evaluation, we conduct a case
study to evaluate the effectiveness of our
recommender system by conducting experiments
applied to our dataset of 600 BP models in BPMN
format when user invokes a query about a claim
process. We have the following user: Patty Adel with
the following LinkedIn profile (view the table 3
below). In our case study the user enters ‘claim’ as a
keyword using the classic search functionality
available in repository to specify that he needs models
on claim process.
Table 3: LinkedIn user business profile.
Positions Title: Assistant, Business Development
positions/is-current: true
Positions/company-name: The Economical Insurance
Group
Public-profile-url:/pub/patty-adelvard-eig/39/90b/631
Location: Calgary, Canada Area
First-name: Patty
Last-name: Adel
Industry: Insurance
Skills: null
Current project: no specified
6
https://www.signavio.com/
7
https://apromore.org/
User inquiry (search keyword): claim
Probably results: for the user inquiry we have the
following results as shown in Table 4 below:
P70LR, P122HR,
P70NC, P11 ...etc. denotes BP
Models.
Table 4: Case study results.
User keyword claim
Repository collection Ours (dataset)
Process models founds
(by the classic search
functionality)
P70LR, P120LR, P116LR
P122, P120, P119, P116,
P113, P70, P11
P122HR, P119HR,
P116HR, P113HR, P70HR
P70NC, P122NC,
P120NC,113NC, P86NC
Number of the BP
models found
(classic search)
20
BP models format BPMN
Recommended BP
models according our
approach Social
recommendation + user
inquiry
P120LR
P120, P113
P113HR
P120 NC
P113NC
Number Of BP models
found via our
approach+ user inquiry
6
Total BP models in
dataset
600
5.3 Evaluation
To evaluate the performance of the proposed SBPR
recommender system in terms of Top-N
recommendations, we have chosen the following
metrics (Nadee, 2016) (i.e. Precision at N, Recall at N,
F1 measure) for the evaluation of the recommender.
We have chosen to evaluate the accuracy performance
of our recommender system in terms of top-5
recommendations. We ask recommender to provide
five BP models (we have chosen top 5 recommended
BP models).
We hide some BP models relevant to user U (our
user ‘Patty Adel’ case study) as a testing set.
Testing test= {
P120, P113, P113HR, P120 NC}
MODELSWARD 2020 - 8th International Conference on Model-Driven Engineering and Software Development
72
- Precision on top-5:
 
/
- Recall on top-5:
 
/
- F1 measure:
1 2  /

Table 5: Accuracy performance metrics computing for
SBPR recommender.
Accuracy performance
metrics (for SBPR
Recommender)
values
Precision
Recall
F1 measure
4/6=66.667 %
4/4=1=100 %
0.8
Figure 5: BP models found (results of the case study).
Figure 6: The accuracy performance comparison between
SBPR recommender and classic search functionality.
Table 6: Accuracy performance metrics computing for
classic search functionality.
Accuracy performance
metrics (for classic search
functionality)
values
Precision
Recall
F1 measure
4/20=0.2=20%
4/4=1
0.33
5.4 Experimental Results and
Discussion
Results: The results indicated that after combining the
proposed SBPR recommender system (as mentioned
in the case study) based on the user’s LinkedIn
business profile (Patty Adel in the case study) with a
user’s inquiry about the process model he wants,
(claim process) the accuracy of the BP model
recommendations can be further improved.
Discussion: As we see in the Table 4 the number
of BP models recommended to user when applying
for our recommender system is decreased to six BP
models while it was at twenty BP models found via
the classic search functionality provided in
repository. The results show (as mentioned in figure
5) that the number of BP models found that fit both
user business profile and manual inquiry of user
(where the user specified the process he wants by
keyword ‘claim’) is decreased by 70 percent
compared to the number of models found via the
classic search functionality. The case study presented
is conducted on our established dataset of 600 BP
models.
6 CONCLUSIONS
In this paper we presented a conceptual framework of
BP model recommendation based on the user social
profile and implemented as a transformation process
in model driven engineering (MDE). The benefit of
such a Framework is to facilitate the process
modeling by reusing existing BP models from the BP
model repository. The recommended BP models are
based on business profile of user. The users’ business
profiles are captured from their online LinkedIn
social network.
We have proposed to integrated our SBPR
recommender to current process model repositories.
without any modifications in repositories’ current
structures. Most of actual repositories offer simple
interfaces, where users can only search by keywords
such as MIT repository or explore the available
content via taxonomical navigation such as in SAP
0
100
200
300
400
500
600
Our
approach
Classic
search
Total
models
in
dataset
6
20
600
NumberofBPmodels
numberofBPmodels
0
0.5
1
1.5
Precision Recall F1
Accurancy Performance Comparaison
Metrics
Accuracy performance
comparison
Classic SBPR
Business Process Model Recommendation as a Transformation Process in MDE: Conceptualization and First Experiments
73
repository. The performance metrics have shown that
the precision metric in our SBPR recommender
system is more relevant compared to the classic
search functionality. Furthermore, the number of
models recommended is decreased compared to the
number of models found via the classic search
functionality. In this paper also, we have proposed to
implement our SBPR recommender system as a MDE
model transformation process, consequently we have
proposed two metamodels User business profile
metamodel and BP metadata metamodel, to find
correspondences between the two metamodels we
have relied on the mapping specification. Therefore,
we proposed a metamodel for mapping specification
between the two metamodels. We have briefly
presented transformation rules with ATL
transformation language to specify the model
transformation. Once mappings are specified between
the two metamodels, metadata (that reflect business
environment of user) are generated automatically.
these metadata are then used to find the closest
matches in the BP model repository.
We discussed the application of our approach to a
case study where user search for specific BP model
and specify it as a keyword. The results show that the
number of BP models found that fit both user business
profile and manual inquiry of user is decreased by 70
percent compared to the number of models found via
the classical search functionality. The case study is
conducted on a dataset of 600 BP models. In our
future research, we will use a transformation tool to
facilitate the transformation from User profile into BP
model metadata metamodel and then, we will develop
a prototype to validate our approach.
REFERENCES
Ariouat, Hanane, Eric Andonoff, and Chihab Hanachi.
2018. “Process Recommendation Using Context in
Crisis Management: Application to Flood
Management.” In 15th International Conference on E-
Business and Telecommunications (ICETE 2018),
Scitepress, 277–88.
Bézivin, Jean et al. 2003. “First Experiments with the ATL
Model Transformation Language: Transforming XSLT
into XQuery 1.” In Nd OOPSLA Workshop on
Generative Techniques in the Context of Model Driven
Architecture , , 46.
Bobek, Szymon, Mateusz Baran, Krzysztof Kluza, and
Grzegorz J Nalepa. 2013. “Application of Bayesian
Networks to Recommendations in Business Process
Modeling.” In Business Process Modeling In: AIBP@
AI* IA., , 41–50. http://prosecco.agh.edu.pl.
Born, Matthias, Jens Kirchner, and Jörg P Müller. 2009.
“Context-Driven Business Process Modelling.” In The
1st International Workshop on Managing Data with
Mobile Devices (MDMD 2009), Milan, Italy, 6–10.
Deng, Shuiguang et al. 2017. “A Recommendation System
to Facilitate Business Process Modeling.” IEEE
Transactions on Cybernetics 47(6): 1380–94.
Elias, Mturi, and Paul Johannesson. 2012. “A Survey of
Process Model Reuse Repositories.” In , 64–76.
http://link.springer.com/10.1007/978-3-642-29166-
1_6 (September 8, 2019).
Elias, Mturi, Khurram Shahzad, and Paul Johannesson.
2010. “A Business Process Metadata Model for a
Process Model Repository.” In , 287–300.
http://link.springer.com/10.1007/978-3-642-13051-
9_24 (September 8, 2019).
Fellmann, Michael, Agnes Koschmider, and Andreas
Schoknecht. 2014. “Analysis of Business Process
Model Reuse Literature: Are Research Concepts
Empirically Validated?” In Lecture Notes in
Informatics (LNI), Proceedings - Series of the
Gesellschaft Fur Informatik (GI), Gesellschaft fur
Informatik (GI), 185–92.
Gao, Shang, and John Krogstie. 2010. “A Repository
Architecture for Business Process Characterizing
Models.” In , 162–76. http://link.springer.com/10.1007/
978-3-642-16782-9_12 (September 8, 2019).
Gerth, Christian. 2013. 7849 Business Process Models.
Change Management. Berlin, Heidelberg: Springer
Berlin Heidelberg. http://link.springer.com/10.1007/
978-3-642-38604-6.
Hammoudi, Slimane, Wajih Alouini, Denivaldo Lopes, and
Marianne Huchard. 2010. “Towards A Semi-Automatic
Transformation Process in MDA: Architecture,
Methodology and First Experiments.” Journal of
Information System Modeling and Design (IJISMD)
1(4): 48–76. www.igi-global.com.
Hofreiter, Birgit, and Christian Huemer. 2006. “From a
UMM Business Process Model to a Business
Environment Specific EbXML Process.” Journal of
Ecommerce Research 7(3): 138–53.
Hornung, Thomas, Agnes Koschmider, and Andreas
Oberweis. 2009. “A Recommender System for
Business Process Models.” In 17th Annual Workshop
on Information Technologies & Systems (WITS).,.
Imad, Abdullah, Abdullah Elkindy, and M Sc Carl Corea.
2019. “Survey of Business Process Modeling
Recommender Systems.” Koblenz.
Jean-Marie Favre. 2007. “Metamodel Linkedin.”
Wikimedia. https://commons.wikimedia.org/w/index.
php?title=File:Metamodel_Linkedin.jpg&oldid=18006
6553.
Khider, Hadjer, Slimane Hammoudi, Amel Benna, and
Abdelkrim Meziane. 2018. “Social Business Process
Model Recommender: An MDE Approach.” In 2018
Fifth International Conference on Social Networks
Analysis, Management and Security (SNAMS), IEEE,
106–13. https://ieeexplore.ieee.org/document/8554
581/ (September 9, 2019).
Kluza, Krzysztof, Mateusz Baran, Szymon Bobek, and
Grzegorz J Nalepa. 2013. “Overview of
Recommendation Techniques in Business Process
MODELSWARD 2020 - 8th International Conference on Model-Driven Engineering and Software Development
74
Modeling.” In In Proceedings of 9th Workshop on
Knowledge Engineering and Software Engineering
(KESE9) , ed. (2013). Overview of recommendation
techniques in business process modeling. In
Proceedings of 9th Workshop on Knowledge
Engineering and Software Engineering (KESE9) (pp. ).
, 46–57.
Koschmider, Agnes, Thomas Hornung, and Andreas
Oberweis. 2011. “Recommendation-Based Editor for
Business Process Modeling.” Data & Knowledge
Engineering 70(6): 483–503.
Laurenzi, Emanuele et al. 2019. “Towards An Assistive and
Pattern Learning-Driven Process Modeling Approach.”
In AAAI Spring Symposium: Combining Machine
Learning with Knowledge Engineering.,.
Leopold, Henrik et al. 2019. “Searching Textual and
Model-Based Process Descriptions Based on a Unified
Data Format.” Software and Systems Modeling 18(2):
1179–94.
Li, Ying et al. 2014. “An Efficient Recommendation
Method for Improving Business Process Modeling.”
IEEE Transactions on Industrial Informatics 10(1):
502–13.
Markovic, Ivan, Alessandro Costa Pereira, and Nenad
Stojanovic. 2008. “A Framework for Querying in
Business Process Modelling.” In Business Process
Modelling. In Multikonferenz Wirtschaftsinformatik, ,
1703–14.
Mendling, Jan, Bart Baesens, Abraham Bernstein, and
Michael Fellmann. 2017. “Challenges of Smart
Business Process Management: An Introduction to the
Special Issue.Decision Support Systems 100: 1–5.
Zur Muehlen, Michael. 2004. “Workflow-Based Process
Controlling Foundation , Design ,.” In Information
Systems, , 299.
Nadee, Wanvimol. 2016. “Modelling User Profiles For
Recommender Systems.” Queensland University of
Technology.
Rangiha, Mohammad Ehson, Marco Comuzzi, and Bill
Karakostas. 2015. “Role and Task Recommendation
and Social Tagging to Enable Social Business Process
Management.” In Lecture Notes in Business
Information Processing, Springer Verlag, 68–82.
Rosemann, Michael, Jan Recker, and Christian Flender.
2008. 3 International Journal of Business Process
Integration and Management Contextualisation of
Business Processes.
Salman, Aslam. 2019. “Linkedin by the Numbers: Stats,
Demographics, Fun Facts.” https://www.
omnicoreagency.com/linkedin-statistics/ (November
10, 2019).
Schonenberg, Helen, Barbara Weber, Boudewijn Van
Dongen, and Wil Van Der Aalst. 2008. “Supporting
Flexible Processes Through Recommendations Based
on History.” In International Conference on Business
Process Management, Springer, Berlin, Heidelberg.,
51–66.
Setiawan, Mukhammad Andri, Shazia Sadiq, and Ryan
Kirkman. 2011. 87 LNBIP Facilitating Business
Process Improvement through Personalized
Recommendation.
Shahzad, Khurram et al. 2009. “Elicitation of Requirements
for a Business Process Model Repository.” In , 44–55.
http://link.springer.com/10.1007/978-3-642-00328-
8_5 (September 8, 2019).
Shahzad, Khurram, Mturi Elias, and Paul Johannesson.
2010. “Requirements for a Business Process Model
Repository: A Stakeholders’ Perspective.” In Lecture
Notes in Business Information Processing, Springer
Verlag, 158–70.
Weske, Mathias. 2019. “Business Process Management
Architectures.” In Business Process Management:
Concepts, Languages, Architectures, Berlin,
Heidelberg: Springer Berlin Heidelberg, 351–84.
Yan, Zhiqiang, Remco Dijkman, and Paul Grefen. 2017.
“Generating Process Model Collections.” Software and
Systems Modeling 16(4): 979–95.
Yan, Zhiqiang, and Paul Grefen. 2010. LNBIP 66 - A
Framework for Business Process Model Repositories.
Business Process Model Recommendation as a Transformation Process in MDE: Conceptualization and First Experiments
75