gn
ell
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zerla
1. Introduction
The paper deals with Next Generation Enterprise Information
concept of the agile enterprise emerged in the early 1990s [3]. An
agile enterprise rapidly adapts to changing business challenges
and opportunities and it continuously improves to optimize costs,
management to
rol key business
4], which means
y. An enterprise is
varying degrees,
ess processes and
apted. As changes
om developments
usiness and IT is
Computers in Industry 79 (2016) 77–86
Received 27 June 2015
Accepted 16 July 2015
Available online 25 August 2015
Keywords:
Business-IT alignment
Enterprise engineering
Enterprise architecture
Enterprise ontology
Metamodelling
The paper deals with Next Generation Enterprise Information Systems in the context of Enterprise
Engineering. The continuous alignment of business and IT in a rapidly changing environment is a grand
challenge for today’s enterprises. The ability to react timeously to continuous and unexpected change is
called agility and is an essential quality of the modern enterprise. Being agile has consequences for the
engineering of enterprises and enterprise information systems. In this paper a new paradigm for next
generation enterprise information systems is proposed, which shifts the development approach of
model-driven engineering to continuous alignment of business and IT for the agile enterprise. It is based
on a metamodelling approach, which supports both human-interpretable graphical enterprise
architecture and machine-interpretable enterprise ontologies. Furthermore, next generation enterprise
information systems are described, which embed modelling tools and algorithms for model analysis.
 2015 Elsevier B.V. All rights reserved.
Contents lists available at ScienceDirect
Computers i
journa l homepage: www.e lsbecome a necessity for existence. Dove [2] calls this characteristic
agility and defines it as ‘‘the ability of an organization to thrive in a
continuously changing, unpredictable business environment.’’ The
needed.
The pace of change is continuously accelerating and managing
the change is increasingly beyond the control of companies. The
rate of technological progress increased throughout history. For
example, in the car industry newmodels are developed within fewSystems in the context of Enterprise Engineering (EE). Giachetti [1]
defines Enterprise Engineering as ‘‘the body of knowledge
principles and practices to design an enterprise’’ where an
enterprise is a ‘‘complex socio-technical system that comprises
interdependent resources of people, information, and technology
that must interact with each other and their environment in
support of a common mission’’.
The ability of keeping up with continuous and unexpected
change is an essential quality of modern enterprises and will
quality and speed of delivery. It enables top
quickly implement new strategies and cont
parameters to gain a competitive advantage [
that enterprise engineering is an ongoing activit
not designed just once, but an enterprise is, to
redesigned many times [1]. Implemented busin
information systems have to be continuously ad
may be triggered from the business as well as fr
in the technology, a continuous alignment of bA new paradigm for the continuous ali
Combining enterprise architecture mod
Knut Hinkelmann a,b,*, Aurona Gerber c,b, Dimitris
Alta van der Merwe b, Robert Woitsch e
a School of Business, FHNW University of Applied Sciences and Arts Northwestern Swit
bDepartment of Informatics, University of Pretoria, Pretoria, South Africa
cCSIR Meraka, Pretoria, South Africa
dKnowledge Engineering Research Group, University of Vienna, Vienna, Austria
eBOC Asset Management GmbH, Vienna, Austria
A R T I C L E I N F O
Article history:
A B S T R A C T* Corresponding author at: FHNW University of Applied Sciences and Arts
Northwestern, School of Business, Olten, Switzerland. Tel.: +41 62 957 23 01.
E-mail addresses: knut.hinkelmann@fhnw.ch (K. Hinkelmann),
AGerber@csir.co.za (A. Gerber), dk@dke.univie.ac.at (D. Karagiannis),
barbara.thoenssen@fhnw.ch (B. Thoenssen), alta.vdm@up.ac.za (A. van derMerwe),
robert.woitsch@boc-eu.com (R. Woitsch).
http://dx.doi.org/10.1016/j.compind.2015.07.009
0166-3615/ 2015 Elsevier B.V. All rights reserved.ment of business and IT:
ing and enterprise ontology
ragiannis d, Barbara Thoenssen a,
nd, 4600 Olten, Switzerland
n Industry
evier .com/ locate /compindmonths instead of years. In the banking industry, the time to
market for newfinancial products is a fewweeks instead ofmonths
[5]. Each new product or service requires new or adapted processes
and information systems to produce the products and to deliver
the services. Reduced time to market increases the demand for
changes of business processes and information systems. Consid-
ering the multiyear nature of many enterprise engineering
initiatives, the architecture at the start of a development might
not be appropriate anymore when the new business processes and
information systems are rolled out.
The grand challenge for today’s enterprises, which is deal with
in this research, is the continuous alignment of business and IT in a
rapidly changing environment. According to Gartner [6] enterprises
are facing a new era of enterprise IT, the ‘digitalization’ era, ‘‘a
period characterized by deep innovation beyond process optimi-
zation, exploitation of a broader universe of digital technology and
information, more-integrated business and IT innovation, and a
need for much faster and more agile capability’’.
In order to deal with this grand challenge an approach is
proposed, which uses model-based engineering as visualized in
Fig. 1.1. The approach builds on the principles of model-driven
enterprise engineering [7] and is supplemented with two
innovative and challenging developments:
- Shift the paradigm of model-driven engineering from develop-
g
knowledge engineering for business process management pre-
sented in [9]. These are the main characteristic of the approach:
co
b
b
it
2.1. Enterprise agility
th
it can be managed. He claims that because of the complexity of
K. Hinkelmann et al. / Computers in Industry 79 (2016) 77–8678Fig. 1.1. The modelling approach for continuous business-IT alignment.[(Fig._1.1)TD$FIG]on models that retrieve and interpret information for decision
making. The focusing on machine interpretable knowledge is
called knowledge engineering (KE) [9] and is distinguished from
knowledge management (KM), which is focusing on human
interpretable knowledge. The challenge is to keep both
representations consistent.
To meet these challenges a metamodel approach for next
eneration information systems is proposed, which builds on thepment to continuous adaptation. In contrast to software develop-
ment it is unusual for enterprise engineering to follow a
greenfield approach and start from scratch. Instead, typically a
‘running’ enterprise is adapted. The challenge is to react on
change in the business (e.g. due to an altered business strategy)
and IT (e.g. due to innovative technology) alike and to
continuously keep business and IT aligned. Models are used
for designing and adapting enterprises and enterprise informa-
tion systems before they are changed in reality.
- Supportmachine interpretable and human interpretablemodels:
McCauley [8] defines an agile organization as ‘‘one that can sense
opportunity or threat, prioritize its potential responses and act
efficiently and effectively’’. In order to support in sensing,
prioritizing and acting, the models should not only be passive
storage of knowledge intended for human use but model
rocessing in this context also demands automated operations1 Reichert and Weber call it ‘‘flexibility’’ but here the term ‘‘agility’’ is used in
order to be consistent in naming.pop
e most dominant problem identified in scientific as well as in
ular science on enterprisemanagement, is complexity and howis iCummins [4] divides agility into four dimensions: dynamism,
adaptability, flexibility and awareness.
 Dynamism is defined as the ability to change the process definition
of an enterprise. The need to change a process definition may
result from process improvements to process innovation or
process reengineering.
 Adaptability is the ability of an enterprise to react to exceptional
circumstances or unexpected events during the performance of a
process instance, which may or may not be foreseen.
 Flexibility is the ability to deal with a fair degree of uncertainty.
 Awareness is the ability to detect opportunities and risks.
Reichert and Weber [12] also distinguish between four types of
agility1 needs in Process Aware Information Systems (PAIS) namely
variability, looseness, evolution and adaptation. Evolution repre-
sents the ability of the process implementations to change. Since
business processes can evolve over time, it is not sufficient to
implement them once and then to never touch the PAIS again.
Evolution is equivalent to the dynamism in [4] and in this paper the
focus is on evolution/dynamism as well as awareness.
2.2. Complexity and change
In order to identify the need for changes, an organization has to
continuously monitor itself and be prepared to react quickly to
threads and opportunities. However, the challenge to react quickly
ncreased by the complexity of today’s IT. According to Dietz [13]usiness–IT alignment in the context of an agile enterprise. Lastly
ackground on enterprise modelling is provided and showing how
supports the alignment of business and IT.typIn this section background information in relevant topics for
ntinuous alignment of business and IT is provided. First different
es of agility are discussed followed by an explanation of2. Graphical notations are provided, which can easily be under-
stood by humans.
 Semantic lifting makes the semantics of metamodels explicit
[10,11] such that the analysis, adaptation and evaluation of
models can be done by a machine. The semantics of the
metamodel is specified by an ontology.
The next section provides some background information aswell
as more detail on metamodelling. Solutions, which are already
available to realize the proposed approach for the next generation
enterprise information systems, are discussed. Challenges that still
need to be solved in order to fully realize this approach are
highlighted. In Section 3 elements of modelling methods are
explained. Then in Section 4 the modelling method for continuous
business-IT alignment is presented. Finally in Section 5 the
contribution is summarized and an outlook on future work is
given.
Background
and the Business Motivation Model (BMM) [25]. The purpose of
these graphical modelling languages is to support communication
between human stakeholders. They are not intended for machine
interpretation—although there does exist execution engines for
BPMN.
The ArchiMate Standard [22] introduces an integrated language
for describing enterprise architectures. ArchiMate fits into the
TOGAF framework as it provides concepts for creating amodel that
correlates to its three architectures (layers). According to [26]
ArchiMate can be used to describe all aspects of the EA in a
coherent way, while tailoring the content for a specific audience.
ArchiMate provides a graphical representation of its language
2
w
re
si
K. Hinkelmann et al. / Computers in Industry 79 (2016) 77–86 79enterprises a conceptual model is needed that ‘‘only shows the
essence of the operation of an enterprise’’ and therefore ‘‘themodel
abstracts from all realization and implementation’’ [13]. Hence,
Chen et al. [14] consider enterprise architecture as the foundation
of enterprise systems engineering with the goal to support
stakeholders of an enterprise to manage system engineering and
changes. Zachman regards enterprise architecture as the determi-
nant of survival in the Information Age in order to deal with
increased complexity and change of enterprises [15].
2.3. Enterprise architecture (EA)
There are various definitions of enterprise architecture (EA). A
definition that is in line with the ISO/IEC/IEEE 42010 standard [16]
defines an enterprise architecture as ‘‘fundamental concepts or
properties of an enterprise in its environment embodied in its
elements, relationships, and in the principles of its design and
evolution.’’ An enterprise architecture is typically described using
models. An architecture description is a work product used to
express architecture. The description of the enterprise architecture
is a helpful and necessary tool to understand complexity and
manage change [17].
Due to the complexity of an enterprise architecture description,
many frameworks were developed to assist in this task. A
framework is a logical structure for classifying and organizing
complex information. An architecture framework is defined by the
ISO/IEC/IEEE 42010 standard as ‘‘conventions, principles and
practices for the description of architectures established within a
specific domain of application and/or community of stakeholders’’
[16].
There is a huge variety of EA frameworks (EAF). Matthes [18]
points out that to date more than 50 frameworks are available. In
his compendium Matthes gives a detailed description of 34 EA
frameworks, based on clearly structured and well defined criteria.
Here two EA frameworks are briefly mentioned, which are widely
used. The purpose is to show that although the content is
comparable the structure of the frameworks can differ.
The Zachman framework is of particular interest because
according to [18] it is widespread with an approximate market
share amounts between 22% and 25% and builds the basis formany
other frameworks. The Zachman Framework is a two dimensional
matrix [19]. Rows depict different perspectives of the role a
stakeholder may take (named planner, owner, designer, builder
and subcontractor), and columns represent the various aspects
that should be considered. They are ‘‘different abstractions from or
different ways to describe the real world’’ ([20] p. 592). The aspects
(rows) are named based on the fundamentals of communication.
The interrogativesWhat (data), How (function),When (time),Who
(people), Where (network), and Why (motivation) build the basis
for the concise description of complex ideas [19].
TOGAF is another well-known EA framework [21]. The overall
enterprise architecture as composed of a set of closely inter-related
architectures: Business Architecture, Information Systems Archi-
tecture (comprising Data Architecture and Application Architec-
ture), and Technology (IT) Architecture [22].
2.4. Enterprise architecture descriptions
Zachman gives no advice on how the enterprise architecture
description should look: intersections of perspectives and aspects
can be represented in models of various model types, like a data
model or a process model. Those model types can in turn be
represented in various languages. OMG has developed several
specialized modelling languages for enterprise architecture
modelling, for example Business Process Model and Notation
(BPMN) [23], CaseManagementModel and Notation (CMMN) [24],engineering is applied. In the area of Enterprise Engineering
the content comprises the enterprise architecture, which can be
the as-is architecture or a planned to-be architecture. The model
is then the enterprise architecture description.
 Depending on the intended use only a subset of the knowledge
space’s content might be of interest. Views and viewpoints are a
means to specify which part of an architecture description is of
relevance for one or more stakeholders to address specific
concerns.
 The representation of knowledge is either focused on machine
interpretation or on human interpretation. In this context
enterprise architecture is typically represented by the means of
graphical models which typically are cognitively more adequate
for human interpretation and enterprise ontologies, on the other
hand, are formal representations which can be interpreted by
machines.
In the following sections the human-interpretable, graphical
modelling is referred to as enterprise architecture and to
[(Fig._2.1)TD$FIG]
Fig. 2.1. The four dimension of a knowledge space [9]. T
 The form represents the syntax and semantic.
he content is seen as the domain in which knowledgeodelling language. The term ‘‘knowledge space’’ is used to name
hat is represented in a model. The actual knowledge space
presented in models is specified according to the four dimen-
ons form, content, interpretation, and use (see Fig. 2.1).mterprise architecture modelling purposes [28].
.5. The enterprise engineering knowledge space
Models are representing part of reality or a vision in an agreedcon
enelements based on UML class diagram but customized and limited
to a small set ofmodelling constructs in the interest of simplicity of
learning and use. The standard claims that architecture descrip-
tions ‘‘are formal descriptions of an information system, organized
in a way that supports reasoning about the structural and
behavioural properties of the system and its evolution.’’ [22].
However, the ArchiMate language has one shortcoming: it is
intended for human interpretation and not suitable for automatic
reasoning for two reasons. It is too coarse grained as it only
tains basic concepts and relationships that serve general
machine-interpretable, formal representations as enterprise ontol-
ogy. A modelling method for Model-Driven Enterprise Engineering
is proposed that allows fordescribing the knowledge space inboth a
human-interpretable form (enterprise architecture models) and
machine-interpretable form (enterprise ontology).
2.6. Enterprise ontology (EO)
Because of the complexity of enterprise architecture, machine
intelligibility of enterprise architecture descriptions is considered
essential for agile enterprises. A machine-understandable and
interpretable architecture description would allow to answer
questions like ‘‘which processes are affected by the replacement of
an application?’’, ‘‘which roles are involved in the process?’’, ‘‘why
did we decide to customize this specific application?’’
As shown by [29,30] an enterprise ontology (EO) could meet
this request. Describing enterprise architecture as an ontology
started in the 1990s with TOVE [31], The Edinburgh Enterprise
Ontology [32] and the organizational memory [33]. In contrary to
EA enterprise ontologies are concrete representations of (general-
ized) enterprise architectures developed to be re-used in
enterprises [34], adopted and enhanced to an enterprise’s specific
needs. Den Haan [35] has used an enterprise ontology to realize a
Model-Driven Enterprise Engineering. ArchiMEO is an example of
an enterprise ontology based on the ArchiMate standard. It
contains the concepts of ArchiMate 2.0 and extends them by more
generic concepts to express more specific elements, for example
activities or types of business actors.
The advantage of having an ontological representation of an
enterprise architecture that is machine understandable and hence
allows for automation was proved in two research projects.
One building an early warning system for risks in the supply chain
operational databases to provide an integrated view and manage-
ment of enterprise entities spread over various data stores,
represented in different ways and levels of granularity [37].
There are a variety of representation formalisms for ontologies
which allow for machine interpretations. Recent approaches like
RDFS and OWL were developed in the context of the semantic web
[38]. There is no ‘right’ language to formally describe an enterprise
ontology. The ‘‘choice of the language to use in a system or analysis
will ultimately depend on what types of facts and conclusions are
most important for the application’’ [39]. It is not the purpose of
this paper to propose the appropriate ontology representation
formalism. This is left to future research.
3. A model-based approach for enterprise engineering
In the previous chapter the background of human-interpretable
and machine-interpretable enterprise modelling was provided. In
this section the basis for integrating these two modelling
approaches is presented. According to [41] a modelling method
consists of a modelling technique, which is further divided into a
modelling language and a modelling procedure, as well as
modelling mechanisms and algorithms [41]. The components of
a modelling method and their relations are visualized in Fig. 3.1.
Each of the main components (modelling language, modelling
procedure and mechanism and algorithms) is accented with a
different colour. In this section each of the three main constituents
in the context of enterprise engineering are explained.
3.1. Modelling language
A modelling language is defined by syntax, semantics, and
notation that provide the necessary modelling primitives in order
f mo
K. Hinkelmann et al. / Computers in Industry 79 (2016) 77–8680[36]; the other linking enterprise architecture description with
[(Fig._3.1)TD$FIG]
Fig. 3.1. Components oto build the model. The concepts that describe the modelling
delling methods [41].
language are defined in the metamodel language, which itself has
to be represented in a modelling language. Fig. 3.2 shows a layered
model stack, which was proposed by Strahringer [42] and adapted
by Karagiannis (e.g. [9,41]). The stack could be extended
indefinitely but typically four layers are sufficient.
A prominent metamodelling framework is the Meta Object
Facility (MOF), an OMG standard for Model-driven Engineering
[43]. MOF is based on the UML infrastructure and thus it is a
candidate for object-oriented enterprise modelling. MOF meta-
models for modelling languages like BPMN, CMMN or BMM are
typically modelled as UML class diagrams. ArchiMate is also a
modelling language createdwith UML asmetamodelling language.
MOF has also been used to define metamodels for Ontology
ADOxx1 is a meta-metamodelling framework for defining
graphical modelling languages. It has been researched at the
University of Vienna (see for example [45–47]) and implemented
in the commercial tool ADONIS1. The ADOxx1 meta-metamodel
provides the basic metamodelling classes that are necessary to
define graphical modelling languages such as class, attribute, and
relation. It also introduces several concepts for the enterprise
architecturemodelling, such asmodel types, views, and predefined
classes for directed graphs for business processes and undirected
graphs for organizational structure.
The ADOxx1 meta-metamodelling integrates concrete syntax
and abstract syntax. The definition of the classes, attributes and
relations defines the semantics and the abstract syntax of the
modelling language (see left part of Fig. 3.3). The concrete syntax
corresponds to the graphical notation for the modelling elements
(see right part of Fig. 3.3). Each class has an attributeGraphRep. The
value of this attribute is a script which defines the notation. Due to
the expressiveness of the GraphRep script language, ADOxx1 is a
good fit to define metamodels for human-interpretable enterprise
architecture modelling.
Hence for integrating enterprise architecture and enterprise
ontology, the challenge is to integrate metamodels derived from
frameworks like MOF – which are used for machine interpretable
modelling languages –withmetamodels derived from frameworks
like ADOxx1—which are used for human interpretable graphical
languages. Some approaches are shown in Section 4.
3.2. Modelling procedures
The modelling procedures depend on the use of the knowledge
[(Fig._3.2)TD$FIG]
Fig. 3.2. Layered model stack for Enterprise Architecture (adapted from [41]).
K. Hinkelmann et al. / Computers in Industry 79 (2016) 77–86 81languages like OWL and RDFS [44]. MOF expresses abstract syntax
and semantics but does not support the definition of the graphical
representation, i.e. the notation or concrete syntax. Thus, MOF is
applicable to define metamodels for a machine-interpretable
modelling language, i.e. an enterprise ontology.
[(Fig._3.3)TD$FIG]Fig. 3.3. Abstract and concrete srepresented in the models (see Section 2.5). They support different
tasks of enterprise engineering for example business process
management, business-IT alignment, risk management, decision
management, business analytics and supply chain management.
ADOxx1 is themetamodel framework of choice for the openmodels
yntax of a task in ADOxx1.
initiative (OMi, www.openmodels.at), a community of practice for
the creation of modelling methods [48]. In a recent booklet of the
OMi, 25 modellingmethods for a variety of application domains are
described [49]. In recent projects a modelling languages and
procedures have been developed for strategic alignment of business
and IT [50] and for integrating enterprise risk management with
business motivation, business processes and business rules [51].
Since modelling is a human task, it typically starts with
graphical models, which are cognitively more adequate than
formal methods for most stakeholders. The graphical models are
used as a means for communication between the stakeholders
involved in enterprise design.
4.1. Modelling procedure for continuous business-it alignment
The approach consists of four phases and is a variant of the Plan-
Do-Check-Act cycle (Fig. 4.1):
(1) Establish/adjust goals: strategic and operative goals both for
business and IT and their relations.
(2) (Re-)Engineer the enterprise: modelling resp. adapting the
business, application and technology architectures as well as
their relationships.
(3) Implement the enterprise architecture and run the enterprise.
(4) Monitor the running of the enterprise and recognize adaptation
co
m
to
bus
K. Hinkelmann et al. / Computers in Industry 79 (2016) 77–8682Achallenge is toextendthesemodellingprocedureswithphasesof
machine-interpretation,whichhasbeen realized for somespecialized
procedures only. For business process management there are
procedures that realize the execution of graphically generated
processmodels. The idea ofmodel-driven engineering is to transform
models on higher abstraction levels into lower level models until
the model can be made executable. In [37] a procedure to generate
meta data from enterprise architecture model is described.
Another challenge is to keep the connection between graphical
and machine-interpretable models. If for example an information
system, which implements a process, is changed this change
should be mirrored back to the graphical model in order to keep
both models consistent.
3.3. Mechanisms and algorithms
Machine-interpretation of the models is implemented in
modelling mechanisms and algorithms, which realize the model
processing operations. To automate these operations, the model-
ling language should have a well-defined semantics and syntax.
The concrete syntax is exploited for supporting the modeler in
modelling design, e.g. by visualizing particular aspects of the
model. Mechanisms and algorithms process the abstract syntax.
The challenges to engineer the agile enterprise demand for
mechanisms and algorithms that can analyse the models in order
to detect potential risks and to seize opportunities. The continuous
alignment of business and IT can be supported by mechanisms, to
identify information systems, which are affected by a change of the
business. The other way round one might need mechanisms to
identify business processes, which are affected by modifications in
the IT.
4. A modelling method for continuous business-it alignment
In this section the model-based approach for enterprise
engineering is presented. As already argued in the beginning,
engineering theagileenterprise is anongoingendeavorofdesignand
redesign, which requires a continuous alignment of Business and IT.
In the rest of this section the elements of the modelling method
consisting of the modelling procedure, the modelling language and
the mechanisms and algorithms are described (see Fig. 3.1).
[(Fig._4.1)TD$FIG]
Fig. 4.1. Continuous iness–IT Alignment.between metamodels and ontologies for defining modelling
languages. Metamodels and ontologies are different but comple-
mentary concepts. Ontologies basically provide the semantics of
themodelling language constructs [10,53] as well as the semantics
of model instances. Metamodels provide the syntax of a modelling
language; they define all available modelling constructs as well as
valid ways to combine them. Some semantics is also included in
language constructs. Therefore, there are two approaches to define
the human-interpretable and the machine-interpretable model-
ling languages, including through Semantic lifting and Semantic
Metamodels:
 Semantic lifting: The metamodels for the human-interpretable
graphical enterprise architecture and the machine-interpretable
enterprise ontology are strictly separated. Metamodels and
ontologies are merged by transformation, which is called
semantic lifting.
 Semantic metamodels: The semantics of all modelling concepts is
expressed by an ontology. The ontology is extended by a
metamodel, which defines the notation and syntax of the
graphical modelling language. This has the advantage that the
semantics is expressed only once.
In the following sections a description of these two approaches
is given.ticsIn this section the modelling approach is described, which
mbines human-interpretable graphical enterprise architecture
odels andmachine-interpretable formalmodels. The challenge is
keep both representations consistent.
A modelling language consists of notation, syntax and seman-
[41] (see Fig. 3.1). Ho¨fferer [52] discusses the relationshipneeds.
If in the monitoring phase a need for adaptation is recognized
the cycle starts again. Enterprise models are particularly used for
the identification of adaptation needs (Phase 4) and implementing
changes (Phase 2).
4.2. Metamodelling and enterprise ontologies
4.2.1. Semantic lifting: Separating metamodels and ontologies
Fig. 4.2 shows the conceptual architecture for semantic lifting.
Different models of the enterprise architecture are created
corresponding to different metamodels, which define primarily
syntactical but also some semantic aspects of model elements. The
ontologies define the machine-interpretable semantics of the
language concepts. Semantic lifting is closes related to semantic
annotation, which combines the human-readable and machine-
readable information (see [54] for a recent overview).
The ontologies are independent from the concepts for the
graphical languages. The basis for interoperability is provided via
linking model elements of the metamodels with ontology
concepts. Since ontologies are, of course, also models, they need
to use a language that is also defined by metamodels. Ku¨hn has
identified four kinds of merging patterns, which can be applied for
integrating enterprise architecture and Enterprise Ontologies, as
means to integrate different types of metamodels [55,56]:
- Reference pattern: defines links that relate exactly one element
in the EA metamodel to exactly one element in the ontology
metamodel.
- Extension pattern: specifies how the EA model can be extended
by concepts of the EO. New concepts can be integrated.
- Transformation pattern: rules are responsible for creating parts
of one or more EA framework metamodels in an ontology. This
mechanism enables for example the generation of an ontology
from a business process.
- Merge pattern: The merge pattern can be regarded as a
specialization of the transformation pattern, where a merge rule
generates a part of the ontology from two or more EA framework
models.
models explicit, is called lifting [10]. It has been implemented in
ADOxx1 [11].
In the project plugIT this approach was applied to enable a
computer-supported business-IT alignment using semantic tech-
nologies [57]. Business people and IT providers externalise their
knowledge via the use of graphical models. These models are then
translated into instances of enterprise and domain ontologies to
enable automated support of business and IT alignment.
The disadvantage of completely separating metamodels and
ontologies is that they can have incompatible semantics. To
overcome the problem, the LearnPAd project (http://www.
learnpad.eu) initially agreed on a shared understanding of
important terms before defining the metamodels and the
ontologies. There is no way, however, to strictly enforce that the
semantics of metamodels and ontologies are consistent.
4.2.2. Semantic metamodelling
In order to avoid the consistency problem betweenmetamodels
and ontologies a semantic metamodelling approach is proposed.
The ontology defines the complete semantics of all the concepts.
The ontology is extended by a specification of the graphical
notation, which corresponds to the concrete syntax of the
modelling language. The difference to the transformation approach
is that the semantics is expressed only once for both human-
interpretable enterprise architecture and machine-interpretable
enterprise ontology. The semantic modelling can be regarded as a
variant of the MOF metamodelling framework [43] where UML is
replaced by an ontology language as a metamodelling language.
The graphical notation for each concept is defined separate
from the semantic description (see Fig. 4.3). A mapping is defined
between concept definition and graphical definition [58]. This is a
able
K. Hinkelmann et al. / Computers in Industry 79 (2016) 77–86 83Fig. 4.2. Metamodels for human-interpretThe transformation between enterprise architecture and
enterprise ontology, which makes the semantics of the graphical
[(Fig._4.2)TD$FIG]difference to the approach of Fig. 1.1, where the graphical notation
is part to the class definition. This semantic metamodelling
approach has been prototypically implemented in the ATHENE
and machine-interpretable models [52].
[(Fig._4.3)TD$FIG] K. Hinkelmann et al. / Computers in Industry 79 (2016) 77–8684system [59]. The concepts of the modelling language are defined
using the Resource Description Framework RDFS 3.0.
4.3. Mechanisms for identification of adaptation needs
Enterprise engineering should be a conscious, purposeful
endeavor, and managers should regularly review their business
processes and information systems from multiple perspectives to
ascertain whether they are meeting enterprise needs [1]. This is
typically a human task. Business analysts exploit the human-
interpretable, graphical models in order to detect potential risks
and to seize opportunities.
Because of the complexity of the enterprise and the enterprise
models it cannot be assumed that business analysts are able to
detect all required changes and are able to assess the consequences
of all potential influencers. This is where machine-interpretable
models and enterprise ontologies could contribute.
In a recent study it has been shown that adaptation needs of an
enterprise architecture can be identified by observing content of
information systems [60]. Several events have been recognized,
Fig. 4.3. Semantic metamodelling.which can be checked automatically in order to trigger adaptations
of the enterprise architecture. This requires the enterprise
architecture to be represented in amachine-interpretable waywith
a formal semantics, as it is defined by an enterprise ontology. The
actual change of the enterprise architecture and of information
systems still requires human judgment. Thus, a combination of
machine-interpretable models to identify adaptation needs and
human-interpretablegraphicalmodels to supportabusinessanalyst
in making appropriate decisions on how to implement the changes
offers new opportunities for continuous business-IT alignment.
A similar approach was used to improve contract management
in the DokLife project [61]. Monitoring obligations and liabilities is
time consuming and error prone. Whereas Contract Management
Systems (CMS) deal well with time-triggered obligations like
periodical payments, they fail to trigger obligations based on
events, as this knowledge is out of the systems’ scope. In the
DokLife project an approach to fill the gap was introduced.
Information about the obligations managed in a CMS is related to
background knowledge modelled in an enterprise ontology, e.g.
processes to be triggered, responsible roles and required resources.
This allows to trigger processes based on pre-defined events.
In the APPRIS project is was shown how an enterprise ontology
can be applied to analyse early warning indicators for supply riskmanagement [36]. An inference engine regularly assesses data
fromvarious internal and external information sources in order to
identify potential procurement risks. Risks depend on enterprise
knowledge which is represented in the enterprise ontology.
Results of risk identification and assessment are displayed in an
easy-to-understand way for a human user to decide for
appropriate actions. This means that the knowledge needs to
be understood by both machines (for risk assessment) and
humans (for deciding about actions). The combination of
graphical and formal representations with a common semantics
is an appropriate approach.
5. Conclusion
This paper proposed a new paradigm for next generation
enterprise information systems, which shifts the development
approach of model-driven engineering to continuous adaptation of
the agile enterprise. Enterprise information systems are closely
integrated with (1) model analysis tools which allow assessing
influencers, to identify risks and to seize opportunities and (2)
modelling tools for changing the enterprise. The proposed
metamodelling approach for the implementation of these infor-
mation systems supports both human-interpretable graphical
models with machine-interpretable enterprise ontologies. It was
shown that the integration is possible; it has been applied in
several projects. It is still some time until commercial tools are
available and business architects and IT architects are using this
modelling paradigm.
It is a future long-term challenge to involve business people not
only in the adaptation of enterprise architecture but also into the
implementation and adaptation of enterprise information systems.
Evolving application flexibility via embedded modelling tools has
been identified in a recent study as one of the 10 most important
technology trends in business application architecture [62]. The
authors predict that future business applications will incorporate
business-orientedgraphicalmodelling tools that enable rapid, code-
free modifications to business applications, including process
orchestration, business rules, notification, organizational structures,
embedded business intelligence, and even the assembly of new
functionality from existing functional elements. To automate the
modification and adaption of applications – or at least to support the
human user in adapting the current models – a formal semantics of
the models is essential. The modelling approach presented in this
paper provides a solid basis for this future challenge.
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Prof. Knut Hinkelmann is dean of the Master of
Science in Business Information Systems at the
University of Applied Sciences and Arts Northwestern
Switzerland FHNW and research associate at the
University of Pretoria. At the University of Camerino
he is permanent member of the PhD Committee. In
1988 he obtained a diploma in Computer Science from
the University of Kaiserslautern and in 1995a PhD in
Natural Sciences from the Computer Science Depart-
ment of the same university. From 1990 until 1998 he
was researcher and head of the Knowledge Manage-
ment research group at the German Research Center for
Artificial Intelligence (DFKI). From 1998 until 2000 he
worked as product manager for Insiders Information
Management GmbH. He joined the FHNW in August 2000 as a professor for
Information Systems. From 2002 to 2008 he was dean of the Bachelor of Science in
Business Information Technology. His research topics include modelling of
knowledge-intensive processes, knowledge management and knowledge technol-
ogies. He has been supervisor and external examiner of many PhD theses and guest
lecturer at the University of Vienna and University of Camerino. Furthermore he
was CEO of the KIBG GmbH from 1996 until 1998; and from 2006 until 2012 hewas
Scientific Advisor of STEAG & Partner AG. He is member of IEEE and GI.
Prof Aurona Gerber completed an electronic engi-
neering degree at the University of Pretoria in 1987.
Aurona have been employed in different milieus’,
including a research institution, academic institutions
and industry. During her employment in these
different environments, she was involved in the
development, research and academic side of ICT
applications and information systems. She completed
her PhD in 2007 in Semantic Web technologies, and is
currently employed at the CSIR as principal researcher.
In addition to research and development, Aurona is
involved in supervision of Master and PhD students as
well as teaching of post-graduate courses. Aurona
Gerber serves on different programme committees for
conferences, review panels for journals and is currently the chair of SMCS South
Africa. Her research interest is within the domain of Ontology Engineering and
Data Science, with a special interest in the use of models and meta-models to do
advanced modelling.Modelling Business and IT Strategy and its Relations to Enterprise
Architecture, in: Second IEEE Conference on Enterprise Systems, 2014.
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Prof Dimitris Karagiannis read at the Technical
University of Berlin, where he graduated with a PhD in
Computer Science. Hewas a visiting scientist at research
institutions in the US and Japan. From 1987 to 1992 he
headed the Business Information Systems group at the
Research Institute for Applied Management (FAW) in
Ulmasa scientificdirector. Since1993 he is full professor
at the Faculty of Computer Science at the University of
Vienna and head of the Research Group Knowledge
Engineering. 2011 he was awarded an honorary profes-
sorship by the Babes-Bolayi University Cluj-Napoca in
Romania. As head of the Knowledge Engineering group
his main research areas are Business Process Manage-
ment, Meta-Modelling, and Knowledge Management.
Prof. Karagiannis has published several books and scientific papers in journals and
conferences on Knowledge Databases, Expert Systems, Business Process Manage-
ment, Workflow-Systems and Knowledge Management. In 1995 he established the
Business Process Management Systems Approach (BPMS), which has been
successfully implemented in several industrial and service companies. He is the
founder and head of the supervisory board of the BOC Group (http://www.bocgroup.
com). 2008 he was a founding-member of the Open Models Initiative and has
created2012 theOpenModelsLaboratory (http://www.omilab.org). Inaddition tohis
long-standing engagement in national and EU-funded research project, Prof.
Karaginnis is acting since 2005 as a reviewer for the European Commission. He is
a member of IEEE and ACM and serves on the steering committee of the Austrian
Computer Society.
Prof Barbara Tho¨nssen is a full professor and senior
researcher with the Business Information Systems
Department at the University of Applied Sciences and
Arts Northwestern Switzerland (FHNW). She did her
PhD at theUniversity of Camerino in theDipartmento di
Matematica e Informatica, where her thesis focused on
automatic, format-independent generation ofmetadata
for documents based on semantically enriched context
information. She started her professional work in the
field of natural language processing, developing elec-
tronic dictionaries to be used for spelling checking and
and international research projects. Her current research focuses on bringing
semantic technologies into practice.
Prof Alta van der Merwe is currently the Head of
Department and associate professor in the Informatics
Department within the School of Information Technol-
ogy. Informatics is a diverse and young field where
much of the research done is still using grounded
theory to establish frameworks and methods to
understand the field and to investigate the complexity
of the use of technology in different domains. Infor-
matics is also the field where technology and people
‘meets’. In this unique field Prof Alta van der Merwe
focuses on the design of socio-technical solutions with
research activities in Enterprise Architecture, Data
Science and different theories supporting the successful
use of technology in the organization. Her interest also
includes the design of systems using innovative and new approaches such as Crowd
Sourcing and Content Awareness. Prof van der Merwe is the founder and past chair
of the South African IEEE SMCS Chapter, specialist editor of the SAIEE journal
(Software Engineering track) and co-founder and past chair of the Enterprise
Architecture Research Forum (EARF). On International level she was involved in the
proposal and acceptance of the IEEE Enterprise Engineering and Enterprise
Architecture Technical Committee, where she still acts as co-chair.
Dr. RobertWoitsch holds a PhD in business informatics
and is currently responsible as managing director for
innovation management via European and National
research projects at the consulting company BOC
(www.boc-group.com) in Vienna. He deals with con-
cept modelling and knowledge management projects
since 2000 starting with the EU-funded project
PROMOTE and has been working in more than twenty
EU-projects in the domain of technology enhanced
learning, knowledge management, business and IT
alignment as well as business process management.
Beside his participation in those EU projects, he
coordinated the business and IT alignment project
plugIT and is now coordinating the cloud project
K. Hinkelmann et al. / Computers in Industry 79 (2016) 77–8686electronic archiving, documentmanagement andwork-
flow management for a number of large Swiss banks.
She was responsible for E-Government and electronic archiving solutions for the
Zurich City Council. In 2004 she joined FHNW where she lectures on Business
Information Systems and is responsible for their Certificate of Advanced Studies for
Information and Records Management. She is currently engaged in several nationalCloudSocket. Dr. Woitsch is involved in commercial KM projects for skill
management and knowledge balances especially in the security domain and was
a member of the Austrian Standardization Institute. He published about 40 papers
and is involved as a reviewer for EU funded projects and acts as a member of
program committees in KM-conferences. He recentlymanages the Open Innovation
Community adoxx.org.automatic indexing. She was leading projects in