=Paper=
{{Paper
|id=Vol-1301/ontocomodise2014_5
|storemode=property
|title=Ontological Framework of the Information Systems Aimed to Facilitate Business Transformations
|pdfUrl=https://ceur-ws.org/Vol-1301/ontocomodise2014_5.pdf
|volume=Vol-1301
|dblpUrl=https://dblp.org/rec/conf/fois/PoletaevaAB14
}}
==Ontological Framework of the Information Systems Aimed to Facilitate Business Transformations==
https://ceur-ws.org/Vol-1301/ontocomodise2014_5.pdf
Ontological Framework Aimed to Facilitate Business
Transformations
Tatiana Poletaeva1, Habib Abdulrab2, Eduard Babkin1
1 National Research University Higher School of Economics, Nizhny Novgorod, Russia
ta.poletaeva@gmail.com, eababkin@hse.ru
2 INSA de Rouen, LITIS Lab., Rouen, France
abdulrab@insa-rouen.fr
Abstract. Information systems aimed for the analysis and management of en-
terprises perceive the reality through their embedded data patterns. These data
patterns must be sufficient to store the information about transformations of
business processes, products and organizational structure over time. Moreover,
customization, extension and integration of data models have not to impede or-
ganizational changes. However, there is a lack of approaches to data modelling
that fit with the changing nature of business objects and that allow the seamless
extension and sharing of knowledge between business-nodes of networking or-
ganizations. This paper introduces a new ontological framework for consistent
data modelling based on the notions of the Enterprise Ontology and the Object
Paradigm. After explanation of the core elements of the proposed formal ontol-
ogy, we exemplify the use of the ontology for creation of the information sys-
tem aimed for the diagnosis of traceability problems in supply networks.
Keywords: Data Modelling, Ontology, Organizational Semiotics, Object Para-
digm, Business Objects Semantics, Business Process Model, Enterprise Ontol-
ogy.
1 Introduction
Nowadays the quality of the data models implemented in Information Systems and
their interfaces is insufficient. The research reported in [1] concerns the problems of
transferring the knowledge about enterprises into information systems. Inter-
subjective reality of enterprises [2] is built in communication and production
processes [3]. Consequently, a conceptual schema [4] with active semantics is re-
quired for successful knowledge representation inside the information systems.
On the other hand, new technologies, new markets, globalisation, mergers and ac-
quisitions require enterprises to transform and engineer themselves to deal with these
challenges and new realities. Furthermore, enterprise information systems need to be
continuously aligned with the corresponding business processes. Consequently, in-
formation systems of enterprises have to meet the requirements of integrability, inte-
roperability and extensibility. This in turn leads to the necessity of the ontological
1
core1 of knowledge bases and the implementation of related reusable data patterns. It
means that separate information about actors, their roles and interactions, production
activities and lifecycles of products must be united to the integrated whole to bring
the operation of the information systems of enterprises. In addition, the event concept
has to be embedded into the conceptual schema of enterprises, because of the event-
driven architecture of the information systems aimed for enterprise management.
Creation of the conceptual schema [4] of enterprises usually follows the conceptual
modelling languages, frameworks and standards like ARIS, BPMN, ORM, etc. How-
ever, the majority of these standards relates to the process-oriented view of enterprises
and do not reflect their inter-subjective aspect. Therefore, the loss of the linguistic and
interpersonal part of the social world within the conceptual schema, related domain
ontology and the referenced data create a drawback for effective perception and simu-
lation of human interactions by information and communication technologies.
In this paper, we present a new modelling method and the corresponding ontology-
based data meta-model that has an ability to keep comprehensive system representa-
tion of enterprises. Together they form an ontological framework, which supports
transformation of organizations. In addition, we demonstrate application of the me-
thod to supply networks. Following the standard approach [6], we express the pro-
posed ontological core in OWL and use the Apache Jena2 framework to work with the
knowledge base built upon the ontological core.
In our research, we actively exploit an approach to the enterprise modelling that is
based on the notion of Enterprise Ontology [7]. The enterprise ontology aims to de-
sign the enterprise in its essential form [7] integrating all views of the enterprise into a
comprehensive whole. We also use the particular methodology DEMO (Design and
Engineering Methodology for Organizations) [7] detailed in section 2 as the complete
theory and the methodology of enterprise ontology. Our goal is to translate the essen-
tial, comprehensive, concise, coherent and consistent [7] core of DEMO-based con-
ceptual schema of enterprises into reusable conceptual artefacts of ontology-based
data meta-models by the means of semantic modelling paradigms.
Also in our research, we use the modelling constructs of the Object Paradigm [8] to
represent the Enterprise Ontology as the ontological core of information systems. This
paradigm provides holistic modelling constructs that are aligned with non-attributed
DEMO approach to enterprise modelling and offers detailed ontological modelling
guidelines through the BORO (Business Object Reference Ontology) methodolo-
gy [9]. The Object Paradigm creates patterns sufficient for modelling of changing
entities like processes and products. Moreover, in order to provide clear referencing to
things in the world, the Object Paradigm considers everything as objects where the
object identity is a key factor for distinguishing objects from each other [9]. There-
fore, the object paradigm eliminates subjectivity of the modelling process and pro-
vides the basis for creation of reusable data patterns in dynamically changing enter-
1
In this paper, we will not elaborate on the importance of ontologies for the integration of
heterogeneous data. Those, who are interested in this topic, are referred to [5], [6]
2
http://jena.apache.org
2
prises. This paradigm is a reliable approach to avoid semantic divergence of the onto-
logical core [10].
The outline of the paper is as follows. First, the theoretical background of our work
is summarized in section 2. Proposed ontological framework of the information sys-
tems, which support business transformations, is outlined in section 3. Then, the pro-
posed framework is compared with the existing generalized upper-level ontology for
networking organizations in section 4. Finally, section 5 provides conclusions and
directions for further research.
2 Theoretical background
Following the aforementioned requirements to the information systems aimed for
enterprise management, we merge a well-founded theory about the operation of en-
terprises with the theory of information modelling. Hereafter, both theoretical pillars
are explained.
2.1 DEMO theory and methodology of Enterprise Ontology
In our research, we apply the DEMO methodology [7] to conceptualize enterprises
taking into account their communication perspective and volatility. Based on the
strong theoretical basis, the DEMO methodology describes the function and construc-
tion of social organizations by their ontological models that are essential and com-
plete at the conceptual level, logical and free from contradictions, compact and suc-
cinct, independent of its realization and implementation issues [7]. Nowadays the
Enterprise Engineering Institute3 advances and disseminates this methodology.
The interpretive and intersubjective perspective of the methodology comes from
considering an enterprise as a discrete dynamic system, of which the elements are
social individuals or actors, each of them able to communicate with others by per-
forming coordination acts and to contribute to bringing about the goods and/or servic-
es by performing production acts [7]. By performing coordination acts actors express
their intensions and comply with commitments towards each other regarding the per-
formance of production acts [7]. For example, they request, promise, state, and accept
the result of some production act. By performing these two kinds of acts, actors trans-
fer the world into the new states characterized by resulted coordination and produc-
tion facts. Thus, the changes that are brought about as the result of the actions of the
subjects are discrete. This means that they are considered to take place instantaneous-
ly, and that there is a finite number of changes within a certain period [11].
The methodology emphasizes uniform communication patterns between autonomic
actors involved in a business deal. These patterns, also called transactions, always
involve two actor roles (the initiator and the executor) and consist of the certain coor-
dination acts and the production act of particular type. The actor, who starts the trans-
action and eventually accepts the results, is called the initiator, the other one, who
3
Enterprise Engineering Institute, http://www.demo.nl
3
actually performs the objective action, is called the supplier or executor. A transaction
consists of three phases: the order phase, the execution phase, and the result phase.
During each of the phases of a transaction, new transactions may be initiated, the
result of which is needed to proceed with the original one. In this way transactions are
chained intersubjective world. By means of the DEMO models, it is possible to
achieve a solid understanding of the types of transactions taking place in an organiza-
tion, the participants involved in these transactions, the information that is needed and
created during the transactions, and the relationship between the different transaction
types [11].
DEMO transaction concept distinguishes between inter-subjective and objective
world. According to DEMO, when engaged into communication, social individuals
are trying to influence each other's behavior, in other words, an act of saying is an act
of doing [11]. Thus, the coordination acts and their results (coordination facts) relate
to the inter-subjective world. By performing production acts actors of enterprise
change the states of products or services related to the objective world. The distinc-
tion of these two worlds at the conceptual level gives an opportunity for coherent
modelling of business processes and product lifecycles of enterprises.
Finally, the methodology builds a comprehensive view on the interaction and man-
agement processes of an enterprise in four Aspect Models [7]. These models can be
considered as the core for unified description of specified business processes in or-
ganization by the use of universal concepts. Through these models, it is easy to identi-
fy the roles of business agents, their potential communications and possible changes
in production world. Moreover, the Aspect Models are free from Entity Paradigm’s
defects of seeing things [8], such as foundation on an inconsistent set of concepts as
well as poor semantics for the description of relationships, identity and changes of
objects over time. Applying the methods of the BORO methodology to the Aspect
Models it is easier to get the true set of the business objects of the enterprise and their
signs [8].
2.2 Object Paradigm for Data Modelling
Business transformations require flexible and re-usable information systems that can
respond to changes smoothly. In other words, a data model should cater for new
changes and needs without a substantial change occurring to its constructs. That is
why the authors focused on creation of the ontological data models of enterprises. The
Object Paradigm (OP) proposed by Partridge [8] has advantages in the area of ontolo-
gy engineering over other approaches.
Firstly, the models based on the OP are free from semantic divergence [10] be-
cause of the extension-based approach to modelling. Data modelers should be pre-
vented from subjective conceptualisation of business context. Extraction of dimen-
sional business objects from the conceptual model of an enterprise (e.g. the Aspect
Models in our approach) alleviates modelling errors and biases. In this research, the
set of dimensional business objects related to the DEMO theory of organizational
ontology constitutes the ontological core for data model of any organization. Accord-
ing to aforementioned points of the DEMO theory, the objects of the core are actor
4
roles, actors, coordination and production facts (per se events); processes - the com-
positions of dimensional actors, products and resources; transactions - the states of
processes, etc.
Secondly, as other perdurantist approaches, the OP assumes that all objects have a
temporal dimension in addition to spatial ones. The OP modelling principles de-
veloped based on perdurantism unambiguously explains objects’ identity through
changes (states and events), their relationships and classification. The consideration
of spatio-temporal extensions of any object, the dynamic classification and identi-
fication allows to model business processes and lifecycles of products of enterprises
(section 3).
Finally, the BORO methodology provides the set of essential information patterns
of relationships between objects for modelling the connection between business
processes and production facts, to wit: whole-part, before-after, pre-condition. As it
was explained by Partridge [8], these patterns are essential and comprehensive for
description of the sequence of physical activities like carving and selling of a statue.
The ideas of the OP formed the basis of several international modelling standards:
ISO 15926, MODAF, DoDAF, MODEM. The last three standards are based on the
IDEAS (International Defence Enterprise Architecture Specification) foundation4.
The IDEAS project was aimed to develop an ontology-based data exchange format
for military Enterprise Architectures. Despite the fact that the data meta-model of the
standard was built by the means of BORO methodology, the IDEAS data meta-model
is not suitable to store complete information about operation of an enterprise. Howev-
er, in this research the authors extend the IDEAS data format by the concepts of En-
terprise Ontology with the use of BORO modelling principles. Moreover, the authors
use the IDEAS notation to represent graphically created data meta-model.
3 Proposed Ontological Framework
3.1 Philosophical and Mathematical Stands of the Framework
As for many other ontologies matching with the OP, we built our ontological frame-
work from the objects and their relationships. The top level of objects comprises
common concepts of ‘class’ or ‘type’ (a set of objects), ‘class of classes’ or ‘power-
type’ (a set of classes), ‘tuple’ (a binary, two-ended relationship), ‘individual’ (an
object that has spatio-temporal extent) (fig. 3.1.1). In the data meta-model, we use the
concept ‘type’ instead of ‘class’ because of two reasons. Firstly, we want to highlight
the subjective nature of created sets of objects according to the notion of ontological
parallelogram invented by J. Dietz [7]. Then, we try to re-use the upper-level ontolo-
gy of the IDEAS standard where it is appropriate.
Following the BORO methodology, we built the hierarchy of relationships via rei-
fication of relationships from the objects they refer. Specific types of relationships
between objects are the classification relationships (class-member) and the specifica-
4
The International Defence Enterprise Architecture Specification standard:
http://www.ideasgroup.org
5
tion relationships (class-subclass). For all relationships between objects the modelling
logic of the BORO methodology was applied, e.g. the notions of inheritance, ban on
circularity, deducing descendant, virtual descendant, etc.
According to BORO, we consider business objects of enterprises as four-
dimensional objects, which are timeless. Perdurants, i.e. the entities, which for only a
part exists if we look at them at any given period of time, provide the opportunity to
model lifecycles of products and business processes instead of well-known descrip-
tion of the static states of an enterprise. In the scope of this research we do not con-
sider abstract objects related to the operation of enterprises such as risks, goals,
norms, etc.
Fig. 3.1.1. The top level of proposed ontological framework
In compliance with both foundation theories, at any moment we can consider an en-
terprise in a particular state, which is simply defined as a set of objects. According to
the DEMO theory, there are two kinds of objects: stata (singular: statum) and facta
(singular: factum) [7]. A statum is something that is the case, has always been the
case, and will always be the case. In other words, it is an inherent property of a thing
or an inherent relationship between things [7]. Timeless classes, tuples and individu-
als of the proposed framework fit to the definition of stata of the DEMO theory with-
out any contradiction with the OP. Contrary to a statum, a factum is the result of the
effect of an act [7]. The notion of factum matches the notion of event. Thus, afore-
mentioned production and coordination facts are the events of changing the states of
business object(s). Certain types of stata and facta of some enterprise can be observed
through the Aspect Models of this enterprise.
The foregoing comparison of two theories represents only the tip of the analysis
that should be completed in future. However, the high coherence between conceptua-
lization and modelling theories results in a strong connection between the conceptua-
lization of an enterprise and its ontological data meta-model. In other words, in our
approach there is a theoretical basis of the correspondence between our (human) men-
tal enterprise model and the model representation of information systems.
6
In order to keep comprehensive knowledge about changing systems, the ontologi-
cal framework of enterprise data models must comprise their static, kinematic, and
dynamic perspectives [12]. Whereas under statics of a system we understand its states
and transition space (the set of allowed transitions). By kinematics is understood the
time dimension in the transition space. By dynamics is understood the mechanism that
causes transitions (state changes) to take place. Following the BORO methodology,
we differentiate the temporal parts – the states –of products, human and non-human
resources. In addition, we extended BORO information patterns (before-after,
whole-part, pre-condition) for the modelling of transition space. Temporal constituent
of transitions appears in the data itself, as well as in the form of events in the ontolo-
gy. The mechanism of transitions embedded into the proposed ontological framework
duplicate the notions of organizational ontology [7].
3.2 The Main Elements of the Ontological Framework
In order to keep complete organizational knowledge in the ontology-based data mod-
el, the concepts of organizational ontology must be embedded into the ontological
framework of the model. Hereafter, we explain the main parts of the created ontologi-
cal framework.
In the definition of actors (agents), actor roles and business processes as well as
their states we follow the IDEAS standard. The interested reader is referred to
http://www.ideasgroup.org. The only new object added to this part of the meta-model
is the sub-class ‘Transaction’ of the class ‘ProcessState’. Members of the ‘Transac-
tion’ class are all possible transactions of enterprises. Then, in order to link business
processes and their production acts with product states, classes ‘Product’ and ‘Pro-
ductState’ were invented. New elements are highlighted in bold in fig. 3.2.1.
“IDEAS: IndividualType”
Individual
“IDEAS: IndividualType”
IndividualResourcePart
“IDEAS: IndividualType”
“IDEAS: IndividualType” ProcessPart
IndividualResourceState “IDEAS: IndividualType”
ProductPart “IDEAS: IndividualType”
“IDEAS: IndividualType” ProcessState
IndividualResource “IDEAS: IndividualType”
ProductState “IDEAS: IndividualType”
Process
“IDEAS: IndividualType”
Product Legend:
... - an element with a bold
frame is the invented element
Fig. 3.2.1. Product is a process part
7
Following the conclusions in section 3.1, all events of enterprises were divided on two
classes: ‘CoordinationFact’ and ‘ProductionFact’. According to the DEMO theory [7],
the certain types of coordination facts can occur in communications of actors. In the
proposed framework all these types are presented through classes, e.g. ‘Request’,
‘Promise’, ‘State’, ‘Accept’, etc. Members of these classes are coordination facts of
certain types.
In the proposed framework many sub-classes of tuples before-after and whole-part
were created, e.g. ‘processStateBeforeAfter’, ‘eventWholePart’, ‘transactionStartE-
vent’, etc. Following the BORO methodology, at least one new class of tuples was
created for each invented class of individuals. However, we will not dwell at length
on these classes, because they are sub-classes of the standard IDEAS tuples.
Significant addition to the IDEAS elements was made based on the pre-condition
information pattern proposed by Partridge [8]: the ‘perConditionEvent’ tuple class
and its sub-classes characterises relationships between the individual state and the
state change event. This class of tuples is essential for modelling of business
processes and product lifecycles.
“IDEAS: IndividualType” “IDEAS: IndividualType” “IDEAS: IndividualType”
Process Event ProductState
whole preCondition
“IDEAS: TupleType”
“IDEAS: TupleType” productPreCondition
processWholeTransaction event
“IDEAS: TupleType” “IDEAS: TupleType”
whole (1..1) part “IDEAS: IndividualType”
transactionWholeProduction event participationExtentPreCon
part ProductionFact
Fact ditionProductionFact
“IDEAS: TupleType” “IDEAS: TupleType”
“IDEAS: IndividualType” whole (1..*) part “IDEAS: IndividualType”
transactionWholeCoordination participationExtentPreCon
Transaction CoordinationFact
Fact ditionCoordinationFact
startedItem (1..1) preCondition
“IDEAS: TupleType” “IDEAS: IndividualType”
event
transactionStartEvent Request “IDEAS: IndividualType”
ParticipationExtent
endedItem (1..1) “IDEAS: TupleType” “IDEAS: IndividualType”
event participation
transactionEndEvent Quit
“IDEAS: TupleType”
endedItem (1..1) agentParticipation
“IDEAS: TupleType” event “IDEAS: IndividualType”
transactionEndEvent Stop
participant
endedItem (1..1) “IDEAS: TupleType” event “IDEAS: IndividualType” “IDEAS: IndividualType”
transactionEndEvent Accept Agent
Fig. 3.2.2. Generic information pattern of business transactions
Based on the invented classes and tuples, the transaction information pattern (fig.
3.2.2) was proposed. This pattern reflects interactions between the initiator and the
executor of business transactions, changes of a product resulted the transaction, the
relation between certain transaction and a whole business process.
Because of the lack of space, this section does not contain the full description of
the created framework. The framework as well as its domain-specific extension (sec-
tion 4) were expressed in OWL and downloaded to the triple store. Then the set of
8
tools was created on Apache Jena platform in order to retrieve information from the
knowledge base instantiated the framework.
4 Some Benefits of The Ontological Framework
In this section, we listed some benefits of using the proposed framework for data
modelling in enterprises and information systems tending to transformations.
4.1 Comparative Analysis of The Proposed Data Meta-Model
Despite on the universally recognized knowledge representation language OWL, no-
wadays there are no common agreements about the methods of semantic data model-
ling and the evaluation procedure of created models. Nevertheless, our goal is build-
ing the flexible perception of changing reality by information systems through data.
Fig. 4.1.1. Integrated semantic data meta-model of the EU 7th Framework program
The more knowledge is extracted by the information system from data, the more
power we will associate with the related data meta-model. Hereafter, we compare the
proposed ontological framework with the integrated semantic data meta-model of
three projects of the EU 7th Framework program (FP7): EURIDICE5, iCargo6, e-
5
http://www.euridice-project.eu
6
http://i-cargo.eu
9
Freight7. These projects put joint efforts on development of the new generation of
information systems in logistics, including the multilayered semantic meta-model
[13].
The domain-independent core of the FP7 integrated meta-model contains the fol-
lowing classes: ‘activity’, ‘event’, ‘role’, special entities like ‘actor’, ‘static resource’,
‘moveable resource’ (fig. 4.1). ‘Activities’ are connected with ‘roles’ via ‘hasProvid-
er’ and ‘hasConsumer’ relations, whereas roles are assigned to ‘actors’ by ‘hasRole’
relation. Beginning and ending of activities are designated by ‘startEvent’ and ‘endE-
vent’ accordingly. ‘Activities’ are connected with related resources via ‘hasStaticRe-
source’ and ‘hasMoveableResource’ relationships. If the activity is aimed on moving
products, it can be connected with products as the subclasses of ‘MoveableResource’.
However, there is no explicit relation between ‘activities’ and ‘products’ in the meta-
model.
We instantiated both ontology-based data meta-models by simulated models of
supply processes according to their descriptions by the SCOR standard8. Then the
analytic potential of the models was assessed by the series of SPARQL requests to the
OWL knowledge bases. Knowledge extraction was performed through standard-SQL
queries, while retaining the expressiveness of the logical representation. The requests
related to the known problems of the identity of activities and products over time and
the time ordering of actions, events, and states. According with the obtained results,
the proposed framework considerably improves the analytical potential of the model.
4.2 One Example of Using the Generic Information Pattern of Business
Transactions
In this section, we leave out the proof of the completeness of the generic information
pattern of business transactions shortly presented in section 3.2. Instead, we simulated
one example of using this information pattern for reasoning.
Imagine the part of supply network comprised by two companies – producer A and
surveillance company B. Producer A makes the products of type P and delivers them
to customers. Company A verifies their products before delivery or outsources this
operation to company B. Company B has many branches located in different time
zones. Both companies use the generic information pattern to store data in their own
data storages. According to the information pattern of business transactions, we as-
sume that the instances of the following types are available in both storages for each
unit of product: ProductId, ProductState, ProductionFact, CoordinationFact, Transac-
tionId, ProcessId, ParticipationExtent.
One day, two companies decided to merge their data storages. Then a data modeler
found out that there were two verification transactions for product P#abc tracked in
merged data. According to related instances of ProductionFact type, one transaction
resulted in the product state ‘Verified_ok’, another transaction resulted in the product
state ‘Verified_failed’. Moreover, the instances of ProcessId and ParticipationExtent
7
http://www.efreightproject.eu
8
SCOR Frameworks, http://supply-chain.org/resources/scor
10
related to these transactions had the same values. It was necessary to define the last
status of product P#abc delivered to a customer.
We can assume that 1) transactions were performed in different time zones. Thus,
the time of transaction execution should be excluded from consideration. 2) Identifi-
cators of processes in autonomous companies could coincide. However, it is possible
to recreate the sequence of the states of product P#abc using the relations between
elements of the information pattern of business transactions. The following properties
of the information pattern are used for reasoning:
1. each event resulted production or coordination action is tightly connected with the
particular product state. This product state is the precondition of the event. The
subclasses of tuples rigorously define the precondition
for each type of events.
2. Certain types of events occur in the transactions of a certain type.
3. Each production event causes the state change of the product.
In assumption that the possible sequence of transactions (a business process) is de-
fined by the data model, it is possible to find out the sequence of product states by the
iterative requests: 1) select all transactions related to product P#abc, where the state
‘Verified_ok’ or ‘Verified_failed’ is a precondition of any event; 2) consider produc-
tion events and resulted states of the product in selected transactions. The forgoing
example was executed with simulated data.
5 Conclusions
This research was aimed on developing the ontological framework of information
systems of enterprises tended to transformations. The authors assume that the pro-
posed solution will facilitate the transfer of knowledge about operation of enterprises
to information systems. In addition, the framework will be instantiated by flexible, re-
usable and integrable data models.
The proposed framework can be instantiated by the reference data for particular
organizations in different domains. In this paper, we demonstrated the benefits of
using the framework for modelling the standard business processes in supply network.
At the next step of the research, the meta-model will be extended by abstract busi-
ness concepts and their relations with 4D-business processes. Strategies, goals, norms,
rules, etc. will be taken into consideration. In addition, the set of tools will facilitate
the work with the framework and knowledge extraction from knowledge bases built
upon the framework.
Acknowledgments. This research is partially supported by LATNA Laboratory of the
National Research University–Higher School of Economics, RF government grant,
ag. 11.G34.31.0057, by the National Research University–Higher School of Econom-
ics’ Academic Fund Program in 2014 (research grant No 14-05-0023) and by the “Le
Passage Portuaire” project of the Grand Research Network of Transportation, Logis-
tics and Information Technologies in Upper Normandy (Grand Réseau de Recherche
11
Transport et Logistique et Technologie de l'Information de la région de Haute-
Normandie).
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