Formalization of Validation Extension Metamodel for Enterprise
Architecture Frameworks
Samia Oussena, Joe Essien and Peter Komisarczuk
School of Computing and Technology, University of West London, St Mary’s Road, Ealing, London, U.K.
Keywords: Web Ontology, Enterprise Architecture, Metamodel, Model, Viewpoint, Resource Description Framework
Schema, Modelling Language, Archimate, Business Strategy, Validation, Motivation.
Abstract: Formalization of Enterprise Architecture (EA) concepts as a whole is an area which has continued to
constitute a major obstacle in understanding the principles that guide its adaptations. Ubiquitous use of
terms such as models, meta-models, meta-meta-models, frameworks in the description of EA taxonomies
and the relationship between the various artefacts has not been exclusive or cohesive. Consequently variant
interpretations of schemas, conflicting methodologies, disparate implementation have ensued. Incongruent
simulation of alignment between dynamic business architectures, heterogeneous application systems and
validation techniques has been prevalent. The divergent and widespread paradigm of EA domiciliation in
practice makes it even more challenging to adopt a generic formalized constructs in which models can be
interpreted and verified (Martin et al., 2004). The unavailability of a unified EA modelling language able to
describe a wide range of Information Technology domains compounds these challenges leading to
exponentiations of EA perspectives. This paper seeks to present a formalization of concepts towards
addressing validation concerns of EA through the use of ontologies and queries based on constraints
specified in the model’s motivation taxonomy. The paper is based on experimental research and grounded
on EA taxonomies created using the ArchiMate modelling language and open source web ontology. It
delves into the use of semantics triples, Resource Description Framework Schema and relational graphs to
map EA taxonomy artefacts into classes and slots using end-to-end conventional formalization approach
applicable within heterogeneous EA domains. The paper also expounds on a proposal that postulates
implementation of the approach, enables formalized traceability of EA validation and contributes to
effective validation of EA through refined taxonomy semantics, mappings and alignment of motivation.
1 INTRODUCTION
Though several publications have referred to the
practice of EA and associated terminologies such as
framework, model, metamodel, perspective, and
viewpoint, research has shown that many concede to
its ambiguity. The fact that there is no common
understanding of the ideologies behind the concepts
is undisputed. Comparative surveys have been
carried out to identify possible dimensions of EA
based on timelines of relevant literature, author’s
background, structural dimensions, differentiation
between aspects, motivations, contributions and the
handling of definitions and terminologies.
Depositions from these studies indicate that
increasing number of IT practioners and authors use
the term EA and its associated phraseology
explicitely to expound strategies that are either
restrictive in order to demonstrate their domain
requisites, or extended to encompass architectural
understanding for all forms of EA ramifications
(Braun and Winter, 2005). These types of
inferences constitute irreconcileable extremities.
Often, there is limited significance in
relationtionship between background hypothesis and
pragmatic requirement. Considering the maturity
and the focus of contributions towards EA, most of
the approaches postulated are still evolving
especially in terms of applicability, making
formalization subject to persistent variations.
Frameworks and modelling are often surmised by
differentiation depending on the proclivity of the
practioner. With majority of presumptions being
generic, it would seem pertitent that enterprise
should evolve techniques for validating the models
that drive their business strategy in order to ensure
427
Oussena S., Essien J. and Komisarczuk P..
Formalization of Validation Extension Metamodel for Enterprise Architecture Frameworks.
DOI: 10.5220/0004950304270434
In Proceedings of the 16th International Conference on Enterprise Information Systems (ICEIS-2014), pages 427-434
ISBN: 978-989-758-029-1
Copyright
c
2014 SCITEPRESS (Science and Technology Publications, Lda.)
that its motivation and goals can be realized by the
taxonomy. However, while business views are
identified in many EA proposals, business strategy
modelling from the perspective of motivation and
business drivers are often overlooked (Lankhorst,
2013). Thus IT solutions cannot be traced back to
business strategy in a clear and unambiguous
manner. Our intended approach to formalize the
Validation of Extension Metamodel (VEM) for EA
Framework (EAF) aims to establish such process.
Formalization of VEM for EAF is the rationalization
of known validation strategy with precise semantics
enabling its model-level usage to provide strategic
awareness of EA and propose a conceptual
relationship towards EA.
Inferred from preliminary studies for this work,
formalization has not been attainable due to
ambiguity and divergence that exist within modelling
techniques. The extent of formalization differ
depending on the purpose of the design from
motivation to direct EA model, maintenance of
metamodel or even the abstract meta-metamodel. As
such instantiations do not establish meaningful
traceability as expressed by their metamodels and
frameworks. Consequently, EAF formalization is
critical in order to enable transformation of semantics
and principles from domain specific constructs to
unambiguous descriptions of concepts. The use of
ontology is a new dimension introduced to address
this phenomenon.
Following this introduction, this paper is
organised as follows. Section two presents pungent
and concise expositions of the concepts of
formalization, focusing on two main categories. The
first specifies models, metamodels, framework from
the perspective of EA and the second delves into
ontology, resource definition framework schema, and
correlations as applied to validation. Section three
focuses on description of the extended validation
elements. Section four rationalizes the methodology
adopted using a metamodel construct of ArchiMate
(TOG, 2014). Section five presents principles of
transformation to ontology. Section six delves into the
mapping methodology. Section seven presents the
resultant ontology with validation constraints and
metrics drawing inferences to query methods, graphs
and traceability. Section eight concludes the paper by
evaluating the outcomes, the principles of
formalization and areas of further research.
2 FORMALIZATION CONCEPT
EA provides the principles, methods and models
used to design and realise an enterprise’s
organizational structure, process, information
systems and infrastructure. (Braun and Winter
2005). EA proposals such as TOGAF, Zachman,
TEAF and many others, though provide principles
for architectural principles for EA and guidance for
interoperability is deficient of unified business
strategy for formalization of metamodels for
validation (Martin et al., 2004) though this
requirement is widely acknowledged (Quartel et al,
2009). The formalization concepts presented in this
paper serves as focal point through which precision
can be appropriated towards EA metamodel
validation by use of ontology and Resource
Description Framework Schema (RDFS). It
incorporates thoroughness into validation criteria
formulation allowing EA to effectively be aligned to
business strategy and motivation. Formalization of
VEM allows promotion of structured and iterative
semantics that can substantially query EA ontology
thereby producing a more dependable EA
metamodels.
EAF, widely described as an “approach which
includes models and definitions for documenting
architectural descriptions” (FEAF, Gartner, TEAF,
SEAM) makes it difficult to formerly relate its
frameworks least of all the implementation
components and artefacts that support their design.
As this paper discusses metamodels in general,
several frameworks have been examined in terms of
their structures rather than content. Inspired by
TOGAF and ArchiMate (TOG, 2014), EAF in this
context is considered as a collection of metamodels
and models which present a means for correlation
and presentation of artefacts that conceptualise and
describe an EA.
2.1 Model, Metamodel and Framework
A model, referred to as a collection of related
components within a domain aims at providing
functionality wholly or in part to achieve specific
goals is explicitly an abstraction of a metamodel. It
highlights the properties of the metamodel and
conforms to its boundaries and constraints.
Therefore, models describe the logical business
functions or capabilities, business processes, human
roles and actors, the physical organization structure,
data flows and data stores, business applications and
platform applications, hardware and
communications infrastructure of a case domain.
A metamodel consists of explicit description of
constructs and constraints of a specific domain.
Though metamodels have also been described as
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428
comprising of a formalized specification of domain-
specific notations which adhere to strict rule set for
developing EA (Gudas and Lopata, 2007),
metamodel consistently represents relevant artifacts
of enterprise architecture both from a business
perspective and from an Information Systems
perspective. Thus it can be said that while models
provide the reasoning about the systems being
designed, metamodels specify the language for
expressing these models.
In consideration of these definitions, a logical
conceptualization of a metamodel for the validation
extension of EAF is extrapolated. This is presented
in Figure 1, and described briefly in section three.
The construct is leveraged on the business layer of
EA which in much taxonomy do not emphasize
validation or the derivative values. An example of
this is the ArchiMate Business Layer of
ARCHIMATE (TOG, 2014), widely acclaimed as
the empirical standard for EA modelling.
Motivation Information Behaviour Structure
Figure 1: Validation Extension Metamodel for EAF.
The VEM presents an extension of a generic
business layer of EA with embedded artefacts for
validating its usability and important specifications
for key performance indicators, business behaviour,
perspectives and their relationships.
2.2 Contributions of VEM
Some of the contributions encapsulated within the
concepts of the VEM are as follows;
Extension of EA Modelling Language (EAML)
with validation capability thus allowing
transparency of decision patterns.
Provision of a methodology for model
transformation to ontology with capability for
validation using unified query semantics.
Enhancement of traceability capability for EA
artefacts through RDFS makes inconsistencies in
decision making more explicit.
Extension of EAF modelling methodology with
validation features means that the effect of changes
can be made more manageable.
3 VALIDATION EXTENSION
ELEMENTS DESCRIPTION
The validation extension elements are represented as
high-level information models. The design goal on
the metamodel links the business layer validation
elements to business elements aggregated to
composite behaviour and interaction. This sub
classification allows further query relation to be
distinctively applied to the business processes and
business function to ascertain the artefacts integrity
and effectuality respectively. Requirements which
specify the Goals defined in Motivation appropriate
a theme to be adopted by the evaluation iteration
process. The query structure and semantics of the
Validation elements allows criteria specified by
constraints to be tested against Business Objects,
Business Role and Business Events. The metamodel
represents high-level conceptual constructs that are
used to structure information evaluation, process
support, information and EAF responsibilities on
several model derivatives. Business viewpoints are
derived from analyzing business Roles which are
composite of Interface and Collaborations. However,
not all layers of EA are covered in this metamodel.
This is deliberate as the intention of this work is to
espouse the alignment between the business strategy
and motivation. The following therefore describe the
unique artefacts of the metamodel.
3.1 Composite Motivation
Composite Motivation (CM) of the metamodel is
composed of the intentions of the enterprise defined
in the requirements, goals and constraints. The
sources of these intentions are specified within
Assessment. CM aggregates the theme for validation
and relates with the core elements of the business
layer. At a lower level of abstraction, internal drivers
are assessed by SWOT analysis while at a higher
level composite motivation are the external drivers
namely constraints and embellishes principles,
requirements and goals.
3.2 Validation Element
The core of this work is homocentric on Validation
Business Actor
Eve nt
Interaction Process Function
Business Service
BDD Validation Elem e nt
Business Behaviour Element
Composite Motivation
GoalPrinciples Requirement
Business Role
Busine ss Int e rfa ce
Business Collaboration
analysed by
specified by
factored by
triggered by
realized by
used by
used by
specialized by aggregated by
assigned to
assigned to
Business Object
accessed by
accessed by
accessed by
available in
dependency of
authenticated by
conforms with
Assessment
decomposed to
realized by
composed of
formalised into
Stakeholder
has interests
Constraint s
refactored by
restricted by
Location
assigned to
effectuality and integrity
Value
Produc t
Meaning
Contract
Representation
assigned to
associated with
associated with
FormalizationofValidationExtensionMetamodelforEnterpriseArchitectureFrameworks
429
Element (VE). VE provides the logic, semantics and
links the ontology needed to validate the core
business layer of the enterprise. The annotations
attributed to the validation of the metamodel are
essentially transformed into ontologies in order to
allow the description and analysis of the relations
between artefacts and composite motivation. The
metamodel transformation to ontology expresses
these annotations and constructs allowing validation
semantics to be interjected in an automated iterative
framework. The semantics also provide the basis for
which the construct can be query through formalized
statements and assertions. The major characteristic
of validation element is that it consists of explicit
description of constructs and constraints of the
metamodel with ontology transformation attributes
and mappings which adhere to distinctive and
formalized business rule set. A business rule set in
this context is a statement that defines certain
aspects of the metamodel and serves as a guideline
to determine the behaviour of viewpoints of the
metamodel. In the case of this work, the business
rule sets are translations of the following validation
elements.
Availability VE (AVE) - Availability validation
determines whether the artifact required for the
actualization of business behavior is available in the
metamodel construct.
Conformity VE (CVE) – This is validation to
determine whether components meet some specified
standards that has been stipulated for achieving
desired business behavior.
Dependency VE (DVE) - This query deals with
validating relationships with other artefacts and their
ability to function as expected in normal and unusual
situations within triggered events.
Authentication VE (UVE) - This refers to the
assertion that the access properties of the component
are substantiated with adequate privileges within
roles and interfaces.
Effectual VE (EVE) - This validation assesses to
what extent the intended business functions are
achieved in relation to either the outcomes or
impacts on other components.
Integrity VE (IVE) - This refers to the assertion
that the accuracy and consistency of data stored and
manipulated over the life cycle of a process in the
metamodel is maintained.
3.3 Viewpoint
A viewpoint shapes the context of the metamodel
with the validation element as viewed from a
particular perspective. A number of standard
viewpoints for modelling motivational aspects have
been defined. Each of these viewpoints presents a
different perspective on modelling the motivation of
the EA focusing on defined abstractions of the
metamodel. In this presentation, each viewpoint is
an excerpt of business behaviour in relationship with
a specific business role and encapsulates related
requirements as extrapolated from a validation
theme. The rationale for adopting this approach is to
ensure that validation is focused on the intrinsic
values for which the model is based.
As other elements which constitute the
metamodel in Figure 1 are stereotype and are well
explicated in definitions of many generic EAF such
as TOGAF, SEAM, etc, no further explanations is
given in this presentation.
4 APPROACH JUSTIFICATION
Several interpretations have been postulated to
demonstrate that metamodels are closely related to
ontologies (Gudas and Lopata, 2007) as both
describe and analyze the relations between concepts.
The extension put forward in this work is harnessed
on this hypothesis to provide a methodology for
expressing metamodel in a form that allows
transformation to ontology description schema with
capability for validation using unified query
semantics. This extension is justified as in all cases;
meaningful semantics provide the basis for which
constructs can be interrogated by assertions (Gudas
and Lopata, 2007). The rationale for the extension
and annotation of metamodel construct with
constraints is to allow distinctive transformation of
model aspects to ontology with formalized
specification as the entity adheres to strict rule set.
In contrast to the area of ontology languages
where the Web Ontology Language (OWL) has
become a de facto standard for representing and
using ontologies, there is no agreement yet on the
nature and the right formalism for defining
mappings between ontologies. In a recent discussion
on the nature of ontology mappings, some general
aspects of mapping approaches have been identified
(Choi et al., 2006). These aspects are adopted and
extended for the mappings proposed here as they
provide explicit specification of conceptualisation
including descriptions of the assumptions regarding
both the domain structure and the terms used to
describe the domain (McShane, 2013). Hence,
ontologies are central to semantic formalization as
they allow harmonization of terms and relationship.
As there are multiple strategies for mapping
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congruent information, relational schemas and
metamodels to ontology, for establishment of
consistency, this work adopts the direct mapping
strategy as it defines a simple transformation which
provides a basis for comparison and validation
through RDF. The direct mapping takes as input a
relational database derived from metamodel
decomposition to generate direct algorithms and
graphs. This allows values relating to motivation to
be queried for the metamodel and its instances.
Central to the approach is the extraction of business
behaviour defined by the metamodel and
transformation using a ubiquitous language for the
domain driven design.
5 METAMODEL MAPPING TO
ONTOLOGY
The process of ontology mapping in this approach is
defined as follow;
Given a model, identify testable artefacts as nodes
and classes.
Identify attributes of the node in terms of
constraints.
Identify relationship that exists between the nodes
and slots. This ensures traceability.
Thus, the result of a mapping process is a set of
mapping rules. Those mapping rules connect
concepts in the transformation to concepts in
metamodel. As a complementary method this
approach provides critical insight into the contents
and semantics of the metamodel artefacts but in
general, it does not offer a means for validation of
the underlying motivation of the metamodel.
5.1 Ontology Transformation
Metaphor
A number of ontology transformation, integration
methods and tools exist. Among them, are
SEMAPHORE (Smartlogic, 2013), PROMPT (Noy,
2004, Choi et al., 2006), Protégé OWL (Horridge,
2009, McGuinness and Van Harmelen, 2004) are
few which have working prototypes. These tools
support the generating and merging of ontological
elements such as class and attribute names from
various sources. While SEMAPHORE automatically
applies metadata and classification to improve
context traceability, PROMPT provides more
automation in merging ontologies. The most recent
development in standard ontology languages is
OWL from the World Wide Web Consortium
(W3C). Protégé OWL makes it possible to describe
concepts with richer set of operators and allow
queries to be applied to the ontology. OWL for
these reason and many others is preferred for the
generation of ontology in this work.
5.2 Content Categorization
Content categorization is a link-based approach to
classification. It is used in isolation or in conjunction
with text-based classification to assign artefacts to
one or more predefined categories based on their
contents (Gyongyi et al., 2006). A number of
modelling classification and knowledge
management techniques have been applied to
content categorization such as nearest neighbour,
Support Vector Machine and Neural Networks
(McShane, 2013). More recently, some preliminary
studies have attempted to apply content
categorization techniques into merging and mapping
ontologies (Lacher and Groh 2001).
Figure 2: Conceptual framework for OWL Mapping.
Though these approaches are veteran, their analysis
stipulates that generation and mergence of
ontologies should follow a bottom up approach
guided by application-specific instances is still
widely practiced. In our approach, this theory is
enhanced. While the general implementation of the
mapping process identifies class artefacts from top
down perspective, the mapping of the properties
follow a bottom up perspective. The metamodel to
ontology elements mapping are determined by
similarity in characteristics per pair. In order to
establish definitions of similarity and to support
development of credible mapping, a framework for
the mapping is defined in Figure 2. The diagram has
associations that provide a way of establishing
dependencies and traceability of the artefact within
the schema. Definition is also attached to the content
categorization in order to establish content and
specify how the mapping of the ontology elements is
related. The intention of the artefact pedigree is to
provide an explanation of the source of its
FormalizationofValidationExtensionMetamodelforEnterpriseArchitectureFrameworks
431
derivation.
6 MAPPINGS METHODOLOGY
This research proposes an autonomous formalisation
metamodel for OWL ontology mappings. The
metamodel is a consistent extension of the
ArchiMate Business Layer for transformation to
OWL Description Logic ontologies, RDFS and
querying with SPARQL.
The UML profile can also be used to give a
visual notation for specifying ontology mappings.
The objective is to enable the specification of
mappings in a generic sense and independent of a
specific mapping language or a specific semantic
relation. Examples of visual notation for this is as
defined in the profile presented in Figures 3, 4, 5,
and 6.
Figure 3: Direct Equivalence mapping between
metamodel artefact and ontology element.
Figure 4: Equivalence mapping between business
behaviour and complex class descriptions.
Figure 5: Equivalence mapping of Properties.
Figure 6: Composition mapping between complex
motivation and ontology class.
6.1 Mapping and Creation of Classes
While there are no formalised ways of mapping
generally acknowledged as standard by practitioners,
to ensure that there is consistency in the
methodology and to avoid overlaps of artefact
mapping, a top-down class bottom-up slots approach
is proposed. The structural Hierarachy is
transformed to ontology using OWL Protégé
(Figures 7 and 8) while the naming syntax is
maintained to enforce clarity of the mapping
process. There are two sibblings identified. The first
is Composite_Motivation to represent a theme of
motivation in a business behaviour to be queried.
The second is Validation_Elements which
encapsulates the core EAF artefacts to be validated.
Figure 7: RDFS
Hierarchy.
Figure 8: OWL
implementation.
6.2 Characterization of Properties
Though the metamodel in Figure 1 shows
bidirectional properties, characteristics of properties
in EA are not strictly symmetrical. To characterise
this relationship and bind the association, inverse
functional characteristics can also be used. This
allows the meaning of the property to be enriched as
the implementation in Figure 9 shows with the
ontology bindings to domains and ranges and a
snapshot of many of the properties with unions of
classes.
The diagram in Figure 10 portrays an extensible
knowledge representation with elements of a theme
for business behaviour from a viewpoint. The
vocabulary generated with this ontology forms part
of the triplestore that will be queried.
Figure 9: Specification of Class Unions for Properties.
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Figure 10: RDFS Graph of the metamodels from
Viewpoint perspective.
Figure 11: RDFS Graph of the metamodels showing the
coverage of Composite Motivation.
7 QUERYING THE ONTOLOGY
While there are several literatures on querying
ontologies, we try to apply a simple query construct
to demonstrate whether the transformed metamodel
can be validated using the triple stores generated
with the RDFS. In OWL, the in-memory stores use
the Reasoners to perform inferences in persistent
RDFS stores, which otherwise can be difficult to
perform. An example is shown in Figure 12.
The ontology can also be queried using
SPARQL, recommended by W3C as standard query
language for the Semantic Web. It focuses on
querying RDF graphs at the triple level and RDFS,
filtering out individuals and classes with specific
characteristics or properties amongst many other
benefits. The choice for SPARQL as a validation
tool in this implementation is because it contains
capabilities for querying required and optional graph
patterns along with their conjunctions and
disjunctions and supports extensible value testing
and constraining queries by source RDF graph.
Figure 12: Querying the ontology using the Reasoner.
Figure 13: Querying the ontology using SPARQL.
Also the outcome of SPARQL queries can be results
sets or RDF graphs as in Figure 13. This modest
example illustrates that queries can in principle be
used for constraint checking in order to validate
motivation in EA models with assertions added as
annotation properties to the selected class.
8 CONCLUSIONS
This paper has presented a formalization of concepts
towards addressing validation concerns of EA
through the use of ontologies and queries based on
constraints specified in the model’s motivation
taxonomy. The postulations based on experimental
research, is grounded on the extension of an EAML
with validation capability and substantiation of its
motivation using open source web ontology. It
delved into the use of semantics triples, Resource
Description Framework Schema (RDFS) and
FormalizationofValidationExtensionMetamodelforEnterpriseArchitectureFrameworks
433
relational graphs to map EA metamodel and its
attributes directly into classes and slots using end-to-
end conventional formalization approach that can be
applied within heterogeneous EA domains. The
paper also expounds on proposals that postulate
implementation of the approach, enabling
formalized traceability of EA validation and
contributes to effective validation of EA through
refined taxonomy semantics, mappings and
alignment of motivational goals to business
behaviour and specifications. The application of the
theoretical principles presented in this paper is a
contribution towards an approach for providing
solutions to issues surrounding EA validation in
consideration of structural complexities in its
metamodels. A validation metrics for testing EA
artefacts has been conceptualized and encapsulated
into the metamodel as well as methodology for
model transformation to ontology description
schema, with capability for validation using
Reasoner and a unified query language. This
consequently adds agility to the organization’s EA
modelling processes.
As validation of EAF is an area that currently
draws very little diligence amongst practitioners due
to complexities, this paper presents a novelty
methodology through which much research can be
initiated. This include amongst many others a case
for integration of divergent EAFs through a common
vocabulary using ontology so as to allow better
congruency, traceability, validation and alignment of
business objectives to Information Technology.
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