=Paper=
{{Paper
|id=Vol-1731/paper-02
|storemode=property
|title=Alignment of viewpoint heterogeneous design models: “Emergency Department” Case Study
|pdfUrl=https://ceur-ws.org/Vol-1731/paper_2.pdf
|volume=Vol-1731
|dblpUrl=https://dblp.org/rec/conf/models/HamlaouiCEBNABJ16
}}
==Alignment of viewpoint heterogeneous design models: “Emergency Department” Case Study==
https://ceur-ws.org/Vol-1731/paper_2.pdf
Alignment of viewpoint heterogeneous design models:
“Emergency Department” Case Study
M. El Hamlaoui (1, 2), B. Coulette (2), S. Ebersold (2), S. Bennani (1), M. Nassar (1),
A. Anwar (5), A. Beugnard (3), JC. Bach(3), Y. Jamoussi (4), H. N. Tran(2)
(1) SIME laboratory, ENSIAS, Mohammed V University in Rabat, Morocco
(2) IRIT laboratory, UT2J, University of Toulouse, France
(3) Telecom Bretagne, Brest, France
(4) RIADI laboratory, Tunis, Tunisia
(5) SIWEB, EMI, Mohammed V University in Rabat, Morocco
Abstract. Generally, various models can be used to describe a given application
domain on different aspects and thus give rise to several views. To have a com-
plete view of the application domain, heterogeneous models need to be unified,
which is a hard task to do. To tackle this problem, we have proposed a method
to relate partial models without combining them in a single model. In our ap-
proach, partial models are organized as a network of models through a virtual
global model called M1C (Model of correspondences between models) which
conforms to a ubiquitous language based on a Meta-Model of Correspondences
(MMC). This paper presents an application of our method to an “Emergency De-
partment” case study. It has been performed as a collaborative process involv-
ing model designers and a supervisor. The focus is put on the building of the
M1C model from 3 partial models.
Keywords: heterogeneous meta-models, partial models, correspondences, se-
mantic expression, refinement mechanism.
1 Introduction
The development of a complex system is usually based on a varied set of lan-
guages, tools and environments that are generally used separately by model-
ing experts working on different dimensions of the system. In addition, devel-
opers are often located in distant geographical areas, as it is the case in dis-
tributed collaborative development. This complicates their cooperation. Thus,
a complex system can be often divided into several subsystems: each subsys-
tem belongs to a specific business domain and may be represented by one or
several partial models designed in a specific language that describes this busi-
ness area. It is mandatory to construct a global model to understand and ef-
fectively use knowledge of such a system. The creation of this global model
requires identifying the existing connections between elements of these par-
tial models. However, the global model construction remains hard, given the
different semantics and the difficulty of identifying correspondences between
these partial models. This issue is typically known as heterogeneity problem.
This problem is shared by the community of complex software systems design-
ers which gave birth to the GEMOC initiative [1]. A classification of the differ-
ent levels of heterogeneity in software engineering has been addressed in
Baudry and al. [2]. Several research works related to models matching have
been discussed and compared according to several criteria in [3] namely: Mod-
els federation [4], Matchbox [5], SAMT4MDE [6], etc.
Our approach sets a process that allows the creation of a global view of the
system through a composition based on aligning partial models. For establish-
ing correspondences between models, the process first identifies correspond-
ences (HLC - High Level Correspondences) between elements of related meta-
models that we call meta-elements, and then generates semi-automatically
correspondences between model elements (LLC - Low Level Correspond-
ences).
In El Hamlaoui and al. [7] we present our approach and the first bricks of our
tool called HMCS (Heterogeneous Matching and Consistency management
Suite). The correspondences used in this tool have only a syntactic description.
In our previous GEMOC publication [8], we presented a first attempt to exploit
ontology-based matching techniques that uses a semantic description of cor-
respondences to enhance the automation of the matching process. Corre-
spondences presented in this paper were restricted to the similarity relation-
ship. In our previous papers we presented how to obtain the correspondence
model but not how to use it. In a paper presented in 2015 [9] we explain one
of the different ways to exploit the correspondence model which is con-
sistency management when source models evolve.
In this paper we present the design of a complex system via a collaborative
process by modeling the Emergency Department (ED) of a hospital. Partial
models are produced by separate designers who work independently. First,
the produced models were analyzed and treated manually by a design expert
named “supervisor” so as to remove conflicts due to possible contradictions
(e.g. incompatible attribute values, contradictory relationships among classes,
etc.). Then the obtained models were aligned by means of correspondences
so as to produce a global model which is in fact a network of partial models.
The rest of this paper is organized as follows. Section 2 presents a recall of our
matching approach. Section 3 introduces the ED case study. Section 4 presents
the models defined by different designers, shows the correspondence model
that has been obtained and discusses the result of the ED case study. Finally,
we present in section 5 some conclusions and future works.
2 Recall: Alignment of Heterogeneous Models (AHM)
Our solution consists in aligning heterogeneous models by establishing corre-
spondences between their model elements. Correspondences are saved in a
correspondence model. The following process – see [8,9,10,13] for more de-
tails – describes the steps required to produce this model. In the first step,
the process takes as input the various meta-models and the kernel of our pro-
posed MMC. Subsequently, a check is performed to inspect and ensure that
the MMC contains all needed relationships to set up correspondences in the
scope of a given application domain. If the supervisor considers that the pro-
posed relationships are not sufficient to express some correspondences
among (meta-)model elements, the DSR (Domain Specific Relationship) part
of MMC is specialized in a second step. The third step aims to enrich the MMC
with semantic expressions defined for each relationship. For this purpose, a
Semantic Expression DSL, described in [10] has been proposed to permit the
creation of a semantic expression model that is woven with the MMC. The
result is an MMC with semantics added as annotations on the different rela-
tionships.
Fig. 1. Annotated MMC for the ED system
The fourth step of the alignment process consists in identifying correspond-
ences among meta-elements so as to produce a correspondence model called
M2C. This latter stores HLCs that contain meta-elements linked by HLRs (High
Level Relationships). HLCs are then refined to produce LLCs stored into a
model called M1C, which this time contains model elements linked by LLRs
(Low Level Relationships).
Fig. 1. above shows the MMC, annotated with the semantic expression of the
relationships, applicable for the Emergency Department case study. For exam-
ple, the Similarity relationship is described by an expression in Java. The ex-
pression explicitly states, using the sameAs function, that the related elements
are similar. For the Dependency relationship, since we did not manage to find
an expression that can describe it, we have specified it in a natural language.
3 Presentation of the ED case study
Emergency Departments (EDs) represent a critical branch of any country's
health system. Such departments are usually faced with emergency situations
(accidents, natural disasters, terrorist attacks, wars, epidemics, etc.) that need
special skills provided by a multidisciplinary approach where viewpoints are
complementary. Moreover, a need of coordination between actors must be
taken into account in the design phase of such systems, so that the different
partial models developed in this phase are synchronized. In addition, models
usually evolve due to changing laws, business rules, security constraints and
personal data protection. In this case, it is important to re-align partials models
to ensure the overall coherence of the system.
Many business domains are involved to represent the functioning of an ED. To
design this application domain, we have chosen to represent the scope of
three points of view managed separately by the following designers:
Medical report designer: responsible for building digital mockups that de-
fine an Emergency Examination Report (EER). He creates a model expressed
through a form meta-model,
Software designer: responsible for the representation of organizational
data of the information system. He creates a model expressed through an
object-oriented meta-model,
Process designer: responsible for the establishment of medical protocols to
be applied by ED staffs. He creates a model expressed through a process-
based meta-model.
Due to space limit, we invite readers to see meta-models in [11].
4 ED case study enactment
4.1 Organization
To lead this study, we asked various partners to participate in the elaboration
of partial models describing parts of this complex system. Table 1 shows an
overview of designers and their produced models whereas [11] provides a de-
tailed vision in our extension of SPEM called CMSPEM [12].
Actor, Laboratory Role Model produced
Bennani S., SIME Designer Organizational model
Beugnard A./Bach JC., Telecom Bretagne Designer Medical protocol model
Jamoussi Y., RIADI Designer EER model
Osterweil L./ Shin SY., UMASS Designer Medical protocol model
Tran HN., IRIT Designer EER model
El Hamlaoui M., SIME Supervisor M2C, M1C
Table 1. Teams involved in the partial models' production
4.2 Designed models
We present here three models designed in conformity with the meta-models
described in [11]. For EER and Medical protocol models, as we received two
proposals for each, the supervisor performed a merge operation, in order to
get a unique and representative model. This merging process is out of scope
of this paper.
Fig. 2. Extract of the EER model
An EER is a form that contains information concerning the patient such as his
medical history and diagnosis. Fig. 2 presents an excerpt of this model by using
UML object diagram’s concrete syntax.
Fig. 3 below presents an extract of the organizational model of ED, based on
UML class diagram’s concrete syntax. For instance, an emergency physician
treats a patient and makes diagnosis. The diagnosis decision can lead to a sur-
gery operation.
Fig. 3. Extract of the Organizational model
Fig. 4. Extract of the medical protocol model
The medical protocols model intends to describe the different protocols ap-
plied by the staff. In a given ED, each type of staff has a specific role. An extract
of this model using a custom concrete syntax proposed by its authors is pre-
sented in Fig. 4. The model describes the tasks done by a nurse and an emer-
gency physician. For instance, the emergency physician achieves a consulta-
tion and makes prescriptions, whereas the nurse takes care of the patient,
takes his blood sample and explains doctor’s prescriptions.
4.3 Correspondence model
Defining correspondences at the meta-model level.
Before starting the creation of the different correspondences models, the
MMC may have to be specialized to add some relationships specific to the ED
domain. Within this context, Fig. 1. (see section 2 above) shows the relation-
ships which have been added to MMC’s kernel with their semantics: Require-
ment, Deduction and Induction. The first one allows to know the fields that an
activity needs for its smooth running. The second one allows to deduce,
through a function, the value of another element. The third one is used to
represent the operations that an activity invokes for it execution. The follow-
ing step is the creation of the M2C model (Fig. 5). An example of created HLC
is those that relate the meta-element Attribute from the organizational meta-
model, to the meta-element Field from the Form meta-model through the fol-
lowing relationships: Similarity, Deduction and Aggregation.
A HLC allows anticipating the complexity of matching by first establishing cor-
respondences between meta-model elements. Thereafter, the accuracy of
certain details of abstract model can be managed at the LLC level, obtained by
refining HLCs through a propagation operation. HLCs are then refined to pro-
duce the M1C model. Primarily, a reproduction operation is initiated on the
M2C followed by a selection operation. In other words: M1C = Propagation
(M2C) = Selection o Reproduction (M2C).
Fig. 5. M2C – ED HLCs
Defining correspondences at the model level.
Reproduction.
This operation is a homomorphism (structural preservation from one algebraic
structure to another one) between correspondences in M2C and M1C. Its role
is to duplicate all correspondences defined at the meta-model level into the
model ones. In other words, there are as many potential LLCs for a given HLC
as Cartesian product of instances of meta-elements involved in the HLC. This
operation limits the generation of correspondences to elements whose type
participates in a HLC. Even if the contextual information helps avoiding the
creation of correspondences between elements of types that do not match
(e.g., an Operation and a Field) it does not guarantee that all generated corre-
spondences are semantically correct.
Selection.
This operation consists in filtering out correspondences produced by the re-
production operation in order to keep only those who are valid, with respect
to the semantic expression associated to relationships, and filter out the in-
correct ones.
Fig. 6. M1C – ED LLCs
For relationships with informal expression (in natural language), it is supervi-
sor’s role to decide whether or not to keep the correspondences depending
on the expression associated to relationships. Considering the relationships
with a formal expression, their expressions (represented as a note in Fig. 1. )
have to be executed. Execution of body’s expressions requires an interpreter
of the language in which the expression is written (a Java Virtual Machine JVM
in our case). Fig. 6 illustrates the M1C model obtained at end of selection
phase. For example, execution of the following method: “tel”. sameAs(“pho-
neNumber”) returns true. The decision consists in keeping the correspond-
ence involving both elements and deleting the others.
4.4 Discussion
As illustrated in Fig. 5, the alignment at meta-model level is composed of 7
HLCs created semi-automatically with the HMCS tool. Alignment at model
level should be obtained automatically. In our ED case study, 15 LLCs have
been produced semi-automatically (automatically for the reproduction step
and in assisted way for the selection step). The reason for this is firstly to have
a model that can serve as reference alignments to evaluate our approach (a
golden model) and secondly, because the Semantic Expression DSL has not
been yet fully implemented in HMCS to take into account all relationships’ se-
mantics.
In the presented approach we assume that semi-automatic tasks are per-
formed by the supervisor with a global understanding of the various models.
This assumption makes the process dependent on him and therefore relatively
centralized. For instance, he intervenes in checking whereas the MMC con-
tains all needed specific relationships related to the studied domain. Through-
out HMCS, he is responsible for adding appropriate semantics, defining corre-
spondences between meta-elements and removing some correspondences
generated at model level, expressed in natural language, that are not valid.
These tasks are very difficult to perform by a single person. Relationships de-
fined between partial models may be complex and the number of correspond-
ences generated in reproduction step may be huge. Supervisor may have to
ask models’ designers to clarify the scope or meaning of an element and to
help deciding whether or not to keep the correspondence, particularly when
the semantics is expressed in a natural language.
5 Conclusion and future work
Our general research addresses the matching of interrelated heterogeneous
models in the context of complex system development. Thereby, we are inter-
ested in establishing correspondences between heterogeneous models de-
scribed through different meta-models used in a given application domain. In
industrial developments, this work is done by several (ream of) designers
working collaboratively by involving partial models’ designers. We are aware
that the case studies treated in our previous works (Bug Tracking System, [13],
Conference Management System [8]), had some limits and did not allow to
validate our approach on a large scale. For that we sought the participation of
design partners, having no knowledge of the approach used in our work, in the
elaboration of models describing viewpoints on the studied system. This way
of performing the ED case study has demonstrated the relevance of our ap-
proach. Indeed at the end of the process, a model was created containing the
needed correspondences used to relate heterogeneous models. Thereafter,
we have initiated a study to transform the current alignment process into a
collaborative one. This will result in redefining tasks to perform by different
designers and in using collaborative tools and strategies instead of a process
based on the assumption that the supervisor has an overall knowledge of the
application domain. For this we will use the results that we got in the Galaxy
ANR project and especially the CMSPEM meta-model dedicated to collabora-
tive process description [12].
Acknowledgment – We thank very much all the participants to the ED case study
and in particular Lee Osterweil and Seung Yeob Shin who provided a medical protocol
model.
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