=Paper=
{{Paper
|id=None
|storemode=property
|title=Ontology and Automatic Code Generation on Modeling and Simulation
|pdfUrl=https://ceur-ws.org/Vol-942/paper_2.pdf
|volume=Vol-942
}}
==Ontology and Automatic Code Generation on Modeling and Simulation==
https://ceur-ws.org/Vol-942/paper_2.pdf
Ontology and automatic code generation on
modeling and simulation
Youcef Gheraibia Abdelhabib Bourouis
Computing Department Computing Department
University Md Messadia University of Larbi Ben M'Hidi
Souk Ahras, 41000, Algeria Oum El Bouaghi, 4000, Algeria
youcef.gheraibia@gmail.com habib.bourouis@univ-batna.dz
Abstract— In this paper we present a new approach for using describing discrete events simulation models according to the
semantic web technologies in modeling and simulation. In recent process approach.
years ontologies have been used popularly in many fields to
represent and structure their concepts. This work is an attempt Today, technology developing and ambitions of researchers
to create a specific ontology for the process oriented discrete are increasing. One of these ambitions is the automatic code
event simulation domain. The ontology instances represent the generation from a conceptual model, which is not something
model instances. This instance described in XML format and
easy. It allows avoiding several error sources, save the time,
then transformed to another form that is used to generate the
simulation code via XSLT rules. The code is generated according and verifying the transition from conceptual model to the
to the open source library Japrosim. The objective of this work is executable model with formal methods. With the definition of
to enhance interoperability and automation of the transition from transformation rules, the passage from an XPISM instance to
the ontology to the code execution. executable simulation code (especially Java) became possible,
these rules are written using XSLT (extensible Stylesheet
Keywords-component; Ontology, Semantic Web, modeling and
Language Transformation), which is a declarative language.
simulation, code generation, interoperability.
By carefully examining the course of our work, we build the
I. INTRODUCTION domain ontology, the construction of the scheme XPISM and
The web of today is basically syntactic and the finally defining the XSLT rules to build the conceptual model
interpretation of the resources content is available only to and executable code generation, it will be obvious to see the
humans, the machine addresses only document structure. interest of this work, which aims to enhance interoperability,
Generally there is no rigid method for classifying semantic define a standard vocabulary to represent concepts of
content of Web documents. This is one of the reasons of the simulation model, and full automate of the process of
development of semantic web. The Semantic Web is an modeling and simulation.
extension of the syntactic web, we add semantics layer, its In the next section, we present the motivation and use of
objectives to make the semantic content of Web resources ontologies in modeling and simulation. Section 3 presents the
accessible by the software agents through a set of languages, related work on the use of ontologies in process-oriented
meta-data and formal knowledge representation tools. One of discrete event simulation. Section 4 is devoted to develop
the rich knowledge representation tools is the ontology, which PIDESO ontology. Section 5 deals with devolved XML
is a set of concepts based on the meaning of an information schema XPISM. In Section 6, we present the whole passage
field [9]. from domain ontology to exactable model and in section 7 a
conclusion is given which focus on the path from the domain
The use of ontologies is now become widespread because ontology to simulation executable.
many fields have used this technology like medicine,
architecture, geography and computing [6]. The simulation is II. ONTOLOGIES IN SIMULATION.
one of the computing fields that can make a successful Simulation knowledge representation approaches require
exploitation of ontologies, especially during the first stages of the handling of highly structured knowledge, including
a simulation project that is the formulation of the problem and ontologies. Ontologies are useful in the process of modeling,
develop the conceptual model. simulation and analysis cycle, particularly in the problem
analysis and in the conceptual model development [12]. One
XML (eXtensible Markup Language) is a computer of the motivations for modeling and simulation is the
language that allows structuring of information and promotes decomposition of the model of the whole system into smaller
the exchange of information on the Internet. It ensures high components and easy manipulated to distribute the
interoperability in the exchange of models [4]. This benefit development effort of the model to different working groups,
has motivated to define an XML dialect (noted XPISM) for and also in communication between deferent groups work
[10]. Ontologies play an important role in the development
process of the conceptual model. This occurs mainly in two The model structure and logic refer to the characterization of
ways; capture the needs and the formulation of the conceptual the relationship between activities in the model. An activity
model [15]. represents the dynamic behavior that occurs when objects
interact one over the other. Ontologies play a key role in
A. Identifying needs eliminating the ambiguity of interpretation of information
The simulation model is often designed to achieve a set of contained in the description of the system to correctly
modeling objectives or respond to a set of questions. The understand the logical flow of objects and the decision logic in
ambiguity of natural language is always a problem but real-world process.
ontologies can help to facilitate the different tasks as described
III. Similar works
below [11]:
Provide a mechanism to interpret and understand the A. PIMODES
description of the problem.
The Process Interaction Modeling Ontology for Discrete
Assist the designer to capture the user requirements
Event Simulation (PIMODES) [7], is a general ontology for
(the information necessary and sufficient for the
the domain of process-oriented discrete event simulation, it is
model set).
using ontologies to formalize a representation language for
B. Conceptual model formulation process-oriented discrete event simulation models. This
The process of constructing the conceptual model includes the formalization is intended to lead to a formal specification of
following activities: acquisition and analysis system concepts for the automatic interpretation of these concepts.
description, identification and classification of goals in PIMODES proposes a set of classes for models
modeling, determining the roles of system objects, boundaries representation, each model must be identified with a single
and level of abstraction and the determining the model identifier and a clear description (annotation), the structure of
structure and logic of it, [11], [10]. the model represented by a set of processes, a set of activities
and a graph of traffic control of entities [7]. PIMODES offers
1) Acquire and analyze the system description : a strong structure and a clear chain of concepts but the
Ontologies can facilitate the identification of inconsistency management of activities in the same level as the process,
and incompleteness in a description of a system. For example, causes increased complexity in synchronizing the activities of
ontologies can be used to interpret the descriptive information the control graph.
on system objects. Flowchart Nodes and Arcs
Node
Arc
2) Identify and classify targets modeling : #N1 #C1
Node Arc #C3 Node
C2
An important step in developing the conceptual model is to Node
Arc
# #N3 #N4
#N2
determine the specific goals of the simulation study based on
control flow
"the application of decision data" provided by the domain relationship
represented by
expert. This process of reasoning uses knowledge of the report control flow
relationship
and stresses in the system description (these are interpreted represented by
control flow
using domain ontologies). Activity #A1
relationship
represented by
control flow
relationship
3) Determine the roles of objects, boundaries and level of takes place at
Activity #A2
represented by
abstraction: takes place at Activity #A3
The following tasks are performed once the specific aims of takes place at
the analysis were established. Location
Activity #A4
takes place at
Establish the boundaries of the model: the first #L1
Location
activity in the development of the conceptual model #L2
is to choose the part of the system under study. Location
Establish the level of abstraction: A simple rule for #L3
determining the appropriate level of abstraction is to
"include only those elements of a system that is able Figure 1. Activities control graph of PIMODES [PER 07]
to meet the objectives and content and the level of
abstraction as up ". B. PIMODEL
Identify the roles of objects in the model: this step is The Demo (The Discrete Event Modeling Ontology) [13], is
to determine the model objects (resource, entity …), an ontology for domain of discrete event simulation. OWL
and the role of each object, for example queue 'x' is (OWL: Ontology Web Language) has been used to define
the activity therein '. more than 60 classes and several properties associated with
them. This ontology consists of four main parts: Concept
4) Determine the model structure and logic Model, DeModel, Model Component Model and Mechanism.
DeModel is also divided into four parts representing the
simulation approaches, State Oriented Model, Activity Model
Oriented, Event-Oriented Model and Process Oriented Model Attributes Feature
[13]. PIModel is the DEMO class that focuses on process-
Feature Type
oriented simulation models. Models can be represented with
OWL instances that can undergo treatment to achieve the Resource
automatic programmed model.
Variable
C. Automatic generation of simulation code
Arc
Automated code generation is a difficult task that falls
within the agile development movement. The generation of the Node
code is done automatically from a set of information (model,
meta-data ...). In the simulation, the model can be Transition
programmed directly from the encoded conceptual model Connecting Activities
using translation rules (type: IF THEN) with high-level
language or languages of the simulation. These rules are Creation of entities Activities
written in software which does not facilitate their maintenance
Change of activities
in the event of changes in the target language. At this level
there is a lack of interoperability is a low reuse [3]. Queue Activity
Duration of activity
IV. Process Interaction Discrete Event Simulation
Ontology (PIDESO) Manage resource
Process interaction discrete event simulation ontology
(PIDESO), it’s an ontology specific to represents the concepts B. Ontologization
of process-oriented discrete event simulation domain. It This step leads to a semi-formal. This partial formalization
consists of a set of classes organized in different levels in a facilitates its subsequent representation in a formal language
hierarchy very clear to help designers to build their models and fully operational. Here is a diagram used to specify each
without ambiguity and in a formal framework provided by class of the ontology. Figure 1 shows the classes in the
OWL. A model is a set of processes where each is a set of ontology PIDESO and semantic links between different
activities and controlled by a control graph [1]. PIDESO plays concepts [8].
an important role in the exchange of simulation models by Defined by
providing a standard vocabulary for communication and reuse. Project
Model description
The construction of ontology PIDESO passes through three
stages, Conceptualization, Operationalization and
ontologization [7], [12].
contain *
A. Conceptualization
This step allows reaching an informal model, semantically
ambiguous and therefore usually expressed in natural Contains
language. This step is done to identify concepts and Variable Process * Activity
relationships between these concepts from raw data, these
concepts to describe informally cognitive entities of Contains
Resource * Contains
simulation domain. The ontology in this research is divided
into two levels. The first one is to identify the major elements
Activity Type
of the system: model, processes, activities (general view) and Entity
the graphs of control. The second level is to represent the
Manage resource
elements of characterization of first-level classes such as the
types of each activity, the components of graph control, Attributes
additional information on the model ... etc. Queue
Controlled
by
First level: Exit
Model
Process Arc
Contain *
Graph Activity
Transition Activity graph
Activities
Node
Second level:
Project Description Figure 2. Ontology classes diagram (PIDESO)
C. Operationalization represented by an XML schema by limiting the principles of
Operationalization aims to have a formal structure of discrete event simulation, process-oriented. Each class of
concepts and relations between them, represented as a web XPISM is associated with a class of ontology (PIDESO). The
language OWL classes using an ontology editor Protégé-2000 OWL ontology instances are transformed into another form
[13]. In the knowledge model of Protégé-2000 ontologies for code generation according to the scheme XPISM. This
consist of a hierarchy of classes that have properties (slots), intermediate representation simplifies the transformation of
which may themselves have certain properties (facets). The the conceptual model into a model program, provides a clear
edition of these three types of objects is with a GUI, without structure of the model concepts.
need to express what was specified in an operational target
language, it is enough to fill out the forms corresponding that
we want to specify.
Figure 3. Ontology classes representation with Protégé-2000 Figure 4. XPISM Schema
V. Extensible Process Interaction Simulation Model VI. Automatic generation of Java code from the ontology
(XPISM schema) instance
An XML document is well formed if it adheres to XML
A. From PODESO instance to XPISM instance
syntax rules that are explicitly designed to make documents
easily interpreted by a computer, and an XML document is Process-oriented discrete event simulation models can be
valid if it adheres to the rules described in a document as an described as instances of OWL (Ontology instance). The idea
Associate DTD or schema. is to change the shape of the model (OWL instances) to
We have defined an XML dialect to describe process-oriented another more appropriate form (XPISM instance). This new
discrete event simulation models, named XPISM (extensible description of the model (XPISM pending) is an intermediate
process interaction simulation model). It describes the representation for the executable model. An XSLT stylesheet
simulation models in a hierarchy. The model consists of a containing the transformation rules allows instances of classes
project description (sets the name of analyst / designer / in ontology elements XPISM by an XSLT processor. XSLT
author, title of project, etc ....) And the whole model process rules are based on a locator called XPath to identify nodes in
components such as activities, resources associated with each the source document (OWL instance) and build a result
activity, variables, attributes associated with such entities and document (instance XPISM). This transformation is
the graph of activities [14]. A simulation model for discrete independent of all simulation languages or programming that
event oriented process consists of a set of entities that flow ensures and strengthens interoperability and facilitates reuse.
through the system. Entities arrive according to a probability B. From XPISM instance of java code
distribution and perform activities that are supported by
Generating java code contains a main class containing a
resources and managed queues [5]. These model elements are
main () method that initializes the variables of the simulation
model reflects its original state (number of replications, the instance PIDESO and this instance will be transformed into
simulation time ... etc.). It also launches the first arrivals in the another form more appropriate XPISM noted. Also, it is easy
simulation, then it call the start method (start ()) that initializes to obtain a conceptual model based on domain ontology. A set
the coordinator and the simulation begins. After initialization of rules defined XSLT transforms this model into an
of the first arrivals of each process, must be defined for each executable Java code based on the framework JAPROSIM.
body that represents its life cycle in the system. Each process The interest of our approach is summarized in three main
contains its own resources, variables, queues… etc points. First, the introduction of semantics in the simulation
models, automatic generation of executable model, and the
automation of the first steps of a simulation project. The result
is certainly a gain in productivity, security development,
enhanced interoperability and ease of maintenance and
updating. To this is added clarity of approach and is now more
rigorous.
We envision in the future work, to use the results to extend
the code generation part to other simulation languages, given
that the approach is independent of any simulation languages
and simulation tools. And it is possible to provide new
Figure 5. Generated simulation code ontologies for other approaches to discrete event simulation,
especially for events.
C. XPISM instance to java code
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