ONTOLOGICAL FRAMEWORK FOR THE ENTERPRISE
FROM A PROCESS PERSPECTIVE
Operational, Tactical and Strategic Integration for Improved Decision-making
Edrisi Muñoz
1
, Elisabet Capón
1
, Jose M. Laínez
2
, Antonio Espuña
1
and Luis Puigjaner
1
1
Department of Chemical Engineering, Universitat Politècnica de Catalunya, Av. Diagonal, 647, E08028 Barcelona, Spain
2
School of Chemical Engineering, Purdue University, West Lafayette, IN, U.S.A.
Keywords:
Enterprise model, Supply chain, Decision support systems, Ontology framework, Knowledge management.
Abstract:
Enterprises are highly complex systems in which one or more organizations share a definite mission, goals and
objectives to offer a product or service. Thus, enterprises comprise several functions which interact with each
other, such as production, marketing, sales, human resources or logistics. As a result, decision-making in the
enterprise becomes a highly challenging task, and such decision process is usually separated in several levels.
Nevertheless, such levels are closely related, since they share data and information. Therefore, effective
integration among the different hierarchical levels, by means of tools improving information sharing and
communication, may play a crucial role for the enhanced enterprise operation, and consequently for fulfilling
the enterprise’s goals. In order to achieve integration among the different decision levels, it is necessary to
establish a common modeling framework. In this work, an ontological framework is built as the mechanism
for information and knowledge models sharing for multiple applications. The potential of the general semantic
framework developed (model maintenance, usability and re-usability) is demonstrated in the enterprise supply
chain network design-planning problem case study presented. Further work is underway to unveil the full
potential to implement a large-scale semantic web approach to support business processes decisions.
1 INTRODUCTION
The European chemical sector, despite having an in-
tern mature market, keeps a strong dynamism over the
global market and its trade flow in 2009 was positive
in about 30 billion euros (Council, 2010). However,
the current landscape of businesses is ruled by the
globalization of trade. Such a trend has opened new
markets, business opportunities and also the adoption
of worldwide information and communication tools
which brought forth a diverse number of available al-
ternatives for customers to fulfill their demands. As a
result, enterprises not only face a fiercer competition
for a contracted market due to the recent economic
recession which leads to dwindling margins, but also
deal with a higher degree of uncertainty associated
with external factors such as demand, product prices
or raw materials supply. In addition, companies must
comply with increasingly stricter constraints related
to safety and environmental regulations.
In such scenario, enterprises must strive to remain
competitive by improving their operations to deliver a
higher customer satisfaction while still generating di-
vidends to shareholders. In order to offer a better ser-
vice to customers, quick time-to-market and opera-
tional flexibility have become crucial business drivers
in many industries to respond rapidly to the contin-
uously changing market conditions. Certainly, this
pressure for higher flexibility has made enterprises
evolve to more complex systems. Nowadays, enter-
prises consist of multiple business and process units
with different scales working together; the organiza-
tion of the different scales and levels within such com-
plex systems is crucial to understand, analyze, syn-
chronize and improve their operations. We believe
that one important step to accomplish such tasks is
to represent the enterprise in an adequate ontological
model, which captures the features relevant for man-
agers to support decision making processes.
In order to deal with the problem complexity, it is
necessary to decouple the system across a hierarchy
of appropriately chosen levels without disregarding
the interrelationship that exists among them. For this
purpose, we consider as basis the supply chain (SC)
concept which can be defined as the group of inter-
linked resources and activities required to create and
538
Muñoz E., Capón E., Laínez J., Espuña A. and Puigjaner L..
ONTOLOGICAL FRAMEWORK FOR THE ENTERPRISE FROM A PROCESS PERSPECTIVE - Operational, Tactical and Strategic Integration for
Improved Decision-making.
DOI: 10.5220/0003720705380546
In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (SSEO-2011), pages 538-546
ISBN: 978-989-8425-80-5
Copyright
c
2011 SCITEPRESS (Science and Technology Publications, Lda.)
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Figure 1: Decision levels in enterprise structure.
deliver products and services to customers. Decisions
are taken at different stages within the supply chain
and at different levels in the management hierarchy.
These decision levels differ in business scope,
time horizon and resolution, data certainty and ac-
curacy, process detail and optimization mechanism
(Lasschuit and Thijssen, 2004). Traditionally, enter-
prise management has been divided in three decision
levels: strategic, tactical and operational (Figure 1).
Long-term strategic level defines the business scope
by determining the structure of the supply chain in a
time period of years. Medium-term tactical planning
is concerned with decisions such as the assignment
of production targets to facilities and the distribution
from facilities to markets. The operational level is re-
lated to short-term planning or scheduling which de-
termines on a daily or weekly basis the assignment of
tasks to units and the sequencing of tasks in each unit.
Precisely, a day-to-day question in process plants
consists of optimally fulfilling customer’s demands
by managing production orders and accommodating
them to the available resources. Control of produc-
tion processes is an additional function concerning
the operational level that involves the real time ma-
nipulation of production variables to deal with pro-
cess disturbances and maintain product qualities and
production rates near the target values. The aforemen-
tioned functional decision levels have different space
and time scales, but they are intimately related to each
other since the decisions made at one level directly af-
fect others. According to (Shobrys and White, 2002),
companies pursuing integration among the different
decision levels report substantial economic benefits.
Similarly, it is of utmost importance to coordi-
nate and integrate information and decisions among
the various functions that comprise the whole supply
chain. Recently, enterprise-wide optimization (EWO)
has emerged as a new area which aims at optimizing
the operations of supply, production and distribution
to reduce costs and inventories. Specifically, EWO
places emphasis on production facilities focusing on
their planning, scheduling and control taking into ac-
count the knowledge in the domain of chemical engi-
neering. In this area, only some modest attempts at
integrating a small subset of enterprise-wide decision
models exist, since the complex organizational struc-
tures underlying business processes challenge our un-
derstanding of cross-functional coordination and its
business impact (Varma et al., 2007). Models and
tools that allow a comprehensive application of the
EWO are a research field that has not been deeply
studied yet.
A general classification distinguishes between
qualitative and quantitative models. The former rep-
resent the physical and logic relationships among the
elements of the system to describe the reality (i.e.,
conceptual or semantic models); whereas the latter al-
low supporting decisions based on the system’s actual
data (i.e., mathematical or statistical models). It is
also relevant to mention that information systems can
be categorized into transactional or analytical ones.
Transactional systems are concerned with the acqui-
sition, processing and communication of data over the
enterprise (e.g., ERP systems); while, analytical sys-
tems introduce some reasoning to propose solutions
for business problems (e.g., simulation and optimiza-
tion).
Despite the great advances in centralized trans-
actional systems, the huge amount of data stored in
such systems is usually not utilized to feed analyti-
cal systems that can provide smarter solutions which
ultimately could represent a competitive advantage in
the current business environment. Therefore, effort
must be devoted (i) to develop improved models and
(ii) to the readily integration of information systems
so as to provide decision support tools within a coher-
ent framework which takes into account the available
information on actual plant operations and market
conditions. Holistic analytical systems which are in-
stanced automatically from transactional systems data
by means of an ontological framework are required to
open new ways of making satisfactory overall deci-
sions.
This paper proposes a semantic model approach,
namely a heavyweight ontology, for representing an
integrated enterprise environment. A case study is
presented to demonstrate how the ontology can be
used as the link between transactional and analytical
systems.
2 PREVIOUS WORKS
Several ontological approaches have been presented
in the literature regarding the enterprise domain as an
ONTOLOGICAL FRAMEWORK FOR THE ENTERPRISE FROM A PROCESS PERSPECTIVE - Operational, Tactical
and Strategic Integration for Improved Decision-making
539
important medium for attaining information systems
interoperability. (Grubic and Fan, 2010) present a
complete review of current state-of-the-art in this area
and identify the outstanding research gaps. Basically,
the existing ontologies only address the strategic level
granularity and disregard the tactical and operational
levels. In addition, the methodological approaches
adopted are too far from the vast theoretical base re-
lated to the supply chain management, and only a very
limited view on the scope of supply chain is tackled.
No formal account of information flow supported ac-
tivities such as replenishment, transport or reverse lo-
gistics is reported. This work aims at reducing some
of the aforementioned research gaps.
Moreover, an explicit account of material trace-
ability and service is missing, a static view on supply
chain ontology prevails, and all of the work related
to supply chain ontology is centered on the organiza-
tion and structure of human knowledge of that reality
rather than with the reality itself. For this reason, it is
necessary to develop more realistic and robust supply
chain systems.
On the one hand, based on a previous work
(Munoz et al., 2011) which uses a semantic model for
an effectiveproduction plant modeling of the schedul-
ing and control levels, an improved ontology is de-
veloped to include the enterprise strategic level. As
a result, the levels integration is achieved by means
of a common model for re-usability, usability and a
shared information structure based on the ANSI/ISA
standards and supply chain management. Thus, the
level of granularity of the model comprises the strate-
gic and operational levels.
On the other hand, the supply chain enterprise
modeling structure considers the whole supply chain
ranging from suppliers, producers, distributors and re-
tailers, and includes the transport tasks.
3 ONTOLOGY FRAMEWORK
The proposed ontology supports different activities
by streamlining information and data integration, by
means of an integrated model which captures the ac-
tivities developed along the different levels of the en-
terprise structure in an enough general manner. As
a result an integrated decision making framework is
provided. This section describes the domain, in this
case the enterprise, of the ontology developed. Thus
the methodology applied for its development is out-
lined. Finally, the work done for the use of this onto-
logical model as a connection between transactional
and analytical models is presented.
3.1 Domain Definition
The domain of the ontology comprises the enterprise
entity, as defined at the introduction section. The
enterprise activities related to the operational, tac-
tical and strategic functions have been semantically
modeled using robust process-operational and supply
chain principles.
This work describes and completes the model
related to the tactical and operational functions,
whose semantical model was already developed by
(Munoz2010). The strategic functions have been in-
troduced, adding to the aforementioned model, the
supply chain management functionality. This supply
chain management considers most of the functions
that are found through the whole enterprise structure.
Information from different hierarchical levels is
needed to improve overall process performance. This
requires important changes for integrating the deci-
sion making system. However, the desired change
cannot be made unless the information system is ro-
bust. In general, at the strategic level, the supply chain
design and planning are optimized with information
contained at the different hierarchical enterprise lev-
els. For this reason the use of an ontology, which pro-
vides the shared and common domain structures that
are required for the semantic integration of informa-
tion sources, may result in an competitive advantage.
Although it is still difficult to find consensus among
ontology developers and users, there is some agree-
ment about protocols, languages and frameworks. In-
deed, ontologies are hierarchical domain structures
that provide a domain theory, have a syntactically and
semantically rich language, and a shared and consen-
sual terminology (Klein et al., 2002).
Batch Process Ontology (BaPrOn) is a proce-
dural oriented ontology that supports the manage-
ment of operational concepts (physical models, pro-
cedures, functions and processes) in accordance with
ANSI/ISA-88 batch process standards, categorizing
them and examining the relationships between them.
BaPrOn was presented in a previous work (Munoz
et al., 2010). In BaPrOn a conceptualization through
the ANSI/ISA-88 representation provides the advan-
tage of establishing a more general conceptualization
in the batch process domain. Such generalization is
the result of years of joint work by recognized batch
manufacturing experts who met to define a percep-
tive view of batch plants organization and its corre-
sponding hierarchy of control functions. As a con-
sequence, following the ANSI/ISA-88, virtually all
activities concerning batch processes can be properly
represented from control to scheduling tasks, as re-
ported by (Munoz et al., 2011). In addition, this allo-
KEOD 2011 - International Conference on Knowledge Engineering and Ontology Development
540
ws the association among the elements mentioned be-
fore, and the further identification of any information
resource if it is required. As a result, representation
of a chemical flexible process has been developed and
distributed inside an ontology.
The ANSI/ISA-88 defines a physical model
(equipment) and a procedural model (tasks). Both the
procedural and physical model are related each other
by means of the recipes: a recipe consists of the set of
information that uniquely defines the production re-
quirementsfor a specific product. The standard differ-
entiates between four types of recipes: general, site,
master and control. At planning and control level, the
information arrives detailed disposed in master and
control recipes. However when the strategic level is
to be modeled, the general and site recipes gather the
information related to this decision level.
The general recipe is handled at the company level
as the building block for lower-level recipes. It con-
tains the information about the required raw materi-
als, their quantities and processing stages for making
the product. Such recipe is further specified for the
manufacturing sites by the site recipes, which con-
tain the conditions and constraints related to the pro-
duction site for determining its scheduling. Master
recipes are derived from site recipes and are targeted
at the process cell including the following information
categories: header, formula, equipment requirements
and procedure. Control recipes are batches that are
created from master recipes. Specifically, they con-
tain the product-specific process information that is
required to manufacture a particular batch of product.
Regarding the strategic level, the supply chain
management decisions are related to the facility lo-
cation, production capacity and resources allocation,
distribution flows and inventory policies. Therefore,
the flow of materials, information and economic re-
sources along the wide enterprise structure are mod-
eled, as well as the restrictions regarding mass bal-
ances, capacity and technological constraints, such
as product recipes, product sequencing, unstable and
perishable materials, economic limitations, suppliers’
capacity and market demand among others.
3.2 Methodology
Various methodologies exist to guide the theoreti-
cal approach to the design of ontologies, and numer-
ous building tools are available. However, there is a
lack of consensus on a uniform approach to design-
ing and maintaining these ontologies. The method-
ology adopted in this paper is based on two on-
tology development methodologies "Methontology"
(López et al., 1999) and "On-To-Knowledge" (Sure
and Studer, 2002). On the one hand, by the use of
"Methontology", a support for the entire life-cycle
of ontology development is provided. On the other
hand, the analysis of usage scenarios of the "On-to-
Knowledge" methodology, allows to present knowl-
edge efficiently and effectively.
The phases that involve the characteristics of the
aforementioned methodologies are grouped inside the
PDCA cycle (Figure 2). Using the PDCA (Plan,
Do(study), Check and Act) cycle allows to coordi-
nate the continuous improvement efforts of the two
methodologies. As a result of the cycle, a good plan-
ning and effective actions come out. Moreover, the
base of quality management creates an easy manner
for improving the developed methodology about on-
tologies.
Plan Phase. This stage tries to make an arrangement
by first capturing the requirements and specifi-
cations, and next adequately documenting them.
The description of general information (e.g., date,
creators, and versions) is detailed. Besides the on-
tology motivations, the uses and applicability and
potential users are also described. The possible
knowledge sources are defined as well. Owing to
its expressive, declarative, portable, domain inde-
pendent and semantically definition, the language
used in the ontological approach is OWL (ontol-
ogy web language). One of the main benefits of
OWL is the support for automated reasoning, and
to this effect, it has a formal semantics based on
Description Logics (DL). The decidability, which
refers to the existence of an effective method for
determining membership in a set of formulas (the-
orems), of the logic ensures that sound and com-
plete DL reasoners can be built to check the con-
sistency of an OWL ontology. Furthermore, rea-
soners can be used to derive inferences from the
asserted information, e.g., infer whether a partic-
ular concept in an ontology is a subconcept of an-
other.
Do Phase. In this stage the principal components
of the conceptualization model are established.
Glossary of terms, concepts and properties, hier-
archies, the taxonomy, class and instant attributes
among others are described. Then a formaliza-
tion of all the content should be made in order
to agree with the knowledge sources. An iden-
tification of other ontologies that probably could
be reused is performed in order to determine if
they could be added to the model. In this stage the
translation of the model to an ontology language
must be done. The OWL ontology editors used
for the development of this model were Protégé
(Horridge et al., 2007) as main editor and Swoop
ONTOLOGICAL FRAMEWORK FOR THE ENTERPRISE FROM A PROCESS PERSPECTIVE - Operational, Tactical
and Strategic Integration for Improved Decision-making
541
Figure 2: Ontology Methodology Cycle.
(Kalyanpur et al., 2006) as complementary editor,
being freeware and also robust softwares.
Check Phase. In this stage, some key activities are
accomplished. The language and the conceptual-
ity are checked in order to standardize them with
the support of expertise and experts. The reason-
ing of the ontological model is done, which is
one of the most important tasks. Then, a short
informatics application can be developed in or-
der to test the ontology in the main application
environment. The support for debugging defects
in OWL ontologies has been fairly weak. Com-
mon defects include inconsistent ontologies and
unsatisfiable concepts. An unsatisfiable concept
is one that cannot possibly have any instances or
it represents the empty set. However, these er-
rors can be detected automatically using a DL
reasoner, which simply reports the errors, with-
out explaining why the error occurs or how it can
be resolved correctly. In this work the RacerPro
reasoner from Protégé and Pallet reasoner from
Swoop were used as reasoners for testing the On-
tology. They detected some problems of incon-
sistence which were related to unsatisfiable class
description and individuals that were asserted to
belong to those classes of the model
Act Phase. Having found defects in the ontology,
their resolution can be non-trivial, requiring an
exploration of remedies with a cost/benefit anal-
ysis. In this case, one would like to generate re-
pair solutions that impact the ontology minimally.
Particular care and effort must be taken to ensure
that ontology repair is carried out efficiently. Fi-
nally, by a robust implementation in the field, all
the formalization of relevant changes and the ag-
gregation of arguments are done. The necessary
documentation of the implementation is fulfilled
for the maintenance ontology task.
The effectiveness of the PDCA cycle arises from
leadership efforts toward the simultaneous creation of
a cooperative and learning guideline to facilitate the
implementation of any process-management and the
continuous improvement of processes.
3.3 Models Usability
In order to exploit the full potential of the ontologi-
cal model, for connecting transactional and analytical
systems, java has been used as a high-level program-
ming language. Using the platform NetBeans IDE 7.0
all the code was built. Java presents a good versatil-
ity, efficiency and security. Java code can run on most
computers because of its interpreters and runtime en-
vironments, known as Java Virtual Machines (VMs),
exist for most operating systems.
The application of the ontological model takes
place inside the business layer. In this particular
work the business layer is integrated by the enterprise
strategics tasks which are represented along with the
tactical and operational levels. Once strategic deci-
sions are taken by the appropriate analytical system
the actual supply chain is also represented. In addition
at the operational level the master recipe keeps the
planning data (later translated as information). The
proposed ontology is intended to promote transver-
sal process-oriented management, to enable crossover
among the different functionality silos in which busi-
nesses have typically been structured. In order to ob-
tain (and manage) a comprehensive view of the over-
all enterprise. These structures can recognize the ex-
KEOD 2011 - International Conference on Knowledge Engineering and Ontology Development
542
isting trade-offs and impacts of the available alterna-
tives at the different information aggregation levels,
and discard non-significant effects, through retuning
the decision-making/optimization model according to
the current enterprise status.
The ontological model consists of 182 classes, 64
restrictions, and 152 object properties. These compo-
nents make the ontology reasoning and its use pos-
sible. The reasoning time for the consistency of the
model and classes is 1.141 and 0.235 sCPU respec-
tively, in an Intel-Core2 @ 2.83GHz, in a successful
compilation.
Considering knowledge diversity, the technical ef-
fort required to deal with the process system and its
representation along the knowledge found at differ-
ent decision levels ensured that all parts within the
ontology were easily accessible. This is a particular
and explicit way of representing the knowledge by the
content format and the content type attributes found in
the ontology structure classes. These improvements
were brought about by making this information vis-
ible and readily available to diverse entities (human
and computers) at different enterprise decision levels.
The analytical systems for taking decisions about
the strategic level are based on mathematical opti-
mization, specifically the centralized approach to sup-
ply chain design and planning presented by (Lainez
et al., 2009) is considered in this case. In addition,
transactional systems related to data management are
represented by databases (MySQL databases) linked
to the different parts of the ontological model. It re-
sults in an improved way to manage these databases
since they are better structured and they can be ade-
quately mined by the potential users.
4 CASE STUDY
The case study is based on a supply chain network
design-planning problem presented by (Lainez et al.,
2009). It consists of three suppliers, four potential
locations for the processing sites and the distribution
centers in a planning horizon of ve annual periods
(Figure 3). The production process fulfills the de-
mand of six markets that entails two final products
and one intermediate product.
The strategic analytical optimization model pre-
sented by (Lainez et al., 2009) entails decisions re-
lated to the facilities to be opened, the increase of ca-
pacity in each time period, the linkages among facili-
ties, the assignment of manufacturing and distribution
tasks to the networks nodes, and the amount of final
products to be sold, among others. Such approach can
be friendly captured by the ontological environment.
Qualitatively speaking, the problem representation in
the proposed ontological framework results in 573 in-
stances. The reasoning time for the problem instances
is 0.922 sCPU in a successful compilation.
It is important to mention that each possible site
is fully represented in the ontology. Each produc-
tion plant (site) may contain a set of four equipment
technologies as presented by (Kondili et al., 1993), a
benchmark problem for the scheduling of batch pro-
cess industries. The production process consist of five
production tasks and nine states, namely three raw
materials, two final products and four intermediates
(Figure 4). Specifically, each site is described by 111
instances, which may be adequately used to take op-
erational decisions.
The analytical optimization model must be pro-
vided with the necessary information, which is de-
rived from the ontological model and the related data
contained in the database. Additionally, the ontologi-
cal model optimizes the way in which the databases
are distributed along the enterprise structure. As a
result, databases are well located and their data are
easily available and can be transformed into valuable
information.
In order to generate the required inputs for the
optimization model which has been implemented in
GAMS, the Java application is used. Such code gen-
erates the .txt files which are called by the optimiza-
tion problem (Lainez et al., 2009). For this case study,
the specific information is presented in Table 1.
The "task" model element is part of the ontolog-
ical model as shown in Figure 5. It is necessary to
Table 1: Information provided by the ontology to the ana-
lytical model.
Model Elements
states final products
locations raw materials
facilities distribution tasks
markets production tasks
activities supplier sites
technologies production sites
equipment distribution centers
Model Parameters
capacity transports process inputs
cost raw material process outputs
facility investment cost SC demand
facility location relationship supplier capacity
market location relationship transport costs
market price transport resources
max capacity technology max facility capacity
min capacity technology min facility capacity
ONTOLOGICAL FRAMEWORK FOR THE ENTERPRISE FROM A PROCESS PERSPECTIVE - Operational, Tactical
and Strategic Integration for Improved Decision-making
543
Figure 3: Supply chain structure of the case study.
Figure 4: State task network representation of the production process considered in the case study.
export such instances to a format readable by the ana-
lytical system, namely a .txt file (Table 2). Therefore,
it is necessary to write the adequate Java code (Figure
6) in order to create the necessary input files.
The results of the optimization model are identical
to those reported in the original paper. Furthermore,
the previous results can be dated back to the ontolog-
ical model for further exploitation by the other de-
KEOD 2011 - International Conference on Knowledge Engineering and Ontology Development
544
Figure 5: Example of instances required for defining the model element "tasks".
Figure 6: Example of the Java code for giving the model element "tasks" to the analytical model.
cision levels, such as the operational system of each
site. This can be achieved by automatically updating
the databases with the resulting optimization data.
Table 2: Elements of the model element "tasks".
tasks.set
RecipeElementP11
RecipeElementP12
RecipeElementP13
RecipeElementP21
RecipeElementP22
5 CONCLUSIONS
This ontology enhances the way for achieving a suc-
cessful enterprise decision making supporting tool
which adapts and recognizes the different elements
found through the hierarchy models that are associ-
ated to the whole supply chain.
Moreover, a general semantic framework is pro-
posed, which is able to model any enterprise particu-
lar case, proving its re-usability. Furthermore, it has
been proved the ontology usability by its application
to an optimization framework. As a whole, the main
ONTOLOGICAL FRAMEWORK FOR THE ENTERPRISE FROM A PROCESS PERSPECTIVE - Operational, Tactical
and Strategic Integration for Improved Decision-making
545
contributions of this environment and the model be-
hind are re-usability, usability, higher efficiency in
communication and coordination procedures.
This work represents a step forward to support
the integration, not just communication, of different
software tools applicable to the management and ex-
ploitation of plant database information, resulting into
an enhancement of the entire process management
structure.
In addition, it has been proved the adequacy of an
ontology as a means for sharing information about a
general model for different problem representations.
As a result, it solves the problem of integration, stan-
dardization and compatibility of heterogeneous mod-
eling systems.
Further work is underway to unveil the full poten-
tial to implement a large-scale semantic web approach
to support business processes decisions.
ACKNOWLEDGEMENTS
Dirección General de Educación Superior Tecnológ-
ica (DGEST), Academy Excellence Program, ref-
erence 072007004 - E.A. from México and finan-
cial support received through the research Project
EHMAN (DPI2009-09386) funded by the Euro-
pean Union (European Regional Development Fund
ERDF) and the Spanish "Ministerio de Ciencia e In-
novación" is fully appreciated.
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