Multiple Perspectives of Digital Enterprise Architecture
Alfred Zimmermann
1
, Rainer Schmidt
2
and Kurt Sandkuhl
3
1
Herman Hollerith Center Boeblingen, Faculty of Informatics, Reutlingen University, Reutlingen, Germany
2
Faculty of Informatics and Mathematics, Munich University, Munich, Germany
3
Faculty of Informatics and Electrical Engineering, University of Rostock, Germany
Keywords: Digital Products, Digital Enterprise Architecture, Architectural Composition, Decision Management.
Abstract: Enterprises are transforming their strategy, culture, processes, and their information systems to enlarge their
Digitalization efforts or to approach for digital leadership. The Digital Transformation profoundly disrupts
existing enterprises and economies. In current times, a lot of new business opportunities appeared using the
potential of the Internet and related digital technologies: The Internet of Things, Services Computing, Cloud
Computing, Artificial Intelligence, Big Data with Analytics, Mobile Systems, Collaboration Networks, and
Cyber-Physical Systems. Digitization fosters the development of IT environments with many rather small and
distributed structures, like the Internet of Things, Microservices, or other micro-granular elements.
Architecting micro-granular structures have a substantial impact on architecting digital services and products.
The change from a closed-world modeling perspective to more flexible Open World of living software and
system architectures defines the context for flexible and evolutionary software approaches, which are essential
to enable the Digital Transformation. In this paper, we are revealing multiple perspectives of digital enterprise
architecture and decisions to effectively support value and service-oriented software systems for intelligent
digital services and products.
1 INTRODUCTION
Data, information, and knowledge are fundamental
core concepts of our everyday activities. They are
driving the Digital Transformation of today’s global
society (McAfee, et al., 2017). Influenced by the
Digital Transformation, many companies are
currently changing their strategy (Bones, et al., 2019),
culture, processes and information systems to expand
their digital scope of action. New services and
intelligent networked digital products extend physical
components by adding information, application and
connectivity services over the Internet.
Digitization (Schmidt et al., 2016) defines the
process of Digital Transformation enabled by
important technological megatrends: Internet of
Things, Cloud, Edge and Fog Computing, Services
Computing, Artificial Intelligence, Big Data,
Analytics, Deep Learning, Mobile Systems, and
Social Networks. Digitized services and products
amplify underlying values and capabilities, which
offer exponentially expanding opportunities.
Digitization enables human beings and autonomous
objects to collaborate beyond their local context by
using digital technologies. The exchange of
information allows better decisions of humans, as
well as promote automatic decisions by intelligent
systems.
The integration of many micro-granular systems
and services has a substantial impact on architecting
digital services and products. Unfortunately, the
current state of research and practice of enterprise
architecture in the integration of a multitude of
microgranular systems and services in the context of
the Digital Transformation and evolution of
architectures lacks an essential understanding of the
diverse modeling perspectives of digital enterprise
architecture.
Our goal is to extend previous quite static
approaches to enterprise architecture (Lankhorst,
2017) to fit for flexible and adaptive Digitization of
new products and services. When architecting digital
products and services, having their origin in open
micro-granular architectures, we introduce suitable
mechanisms for co-creative architectural engineering
by combining a value perspective with a service
perspective.
Our current research paper is part of on-going
research on fundamental digital architecture methods
and models. We are investigating the following
primary research question:
Zimmermann, A., Schmidt, R. and Sandkuhl, K.
Multiple Perspectives of Digital Enterprise Architecture.
DOI: 10.5220/0007769105470554
In Proceedings of the 14th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2019), pages 547-554
ISBN: 978-989-758-375-9
Copyright
c
2019 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
547
How can an enterprise architecture and decision
management for digital products support Open World
integration across a significant number of
microgranular digital systems and services through a
holistic value and service perspective?
We will proceed as follows. First, we will set the
architectural context for our Digital Transformation
approach giving a pervasive view of a value-oriented
relationship-mapping from the digital strategy to
digital architecture. This digital enterprise
architecture defines a core model for service-oriented
digital products with a service-dominant logic. Then
we present an original digital architecture reference
model as an architectural framework, which defines
ten integral architectural dimensions of a holistic
classification model. Based on the target of digital
architecture we are focusing on architecting micro-
granular systems and services with the Internet of
Things and Microservices, and present an
architectural composition model for a bottom-up
integration of micro-granular digital products and
services into a digital enterprise architecture. Then we
provide insides to our methods and mechanisms for
architectural decision management for multi-
perspective digital architectures. Finally, we
conclude our research findings and mention our
future work.
2 DIGITAL PRODUCTS
The Digital Transformation is the current dominant
type of business transformation having IT both as a
technology enabler and as a strategic driver. Digitized
services and associated products (Brynjolfsson et al.,
2014) are software-intensive (Schmidt et al., 2016)
and therefore malleable and usually service-oriented
(El-Sheikh, et al., 2016). Digital products can
increase their capabilities by accessing Cloud-
Services and change their current behavior
(Zimmermann, et al., 2018).
Digitization fosters the development of IT
systems with many, globally available, and diverse,
rather small and distributed structures (Zimmermann,
et al., 2018), like the Internet of Things (Uckelmann
et al., 2011), (Walker, 2014), (Fremantle, 2015) or
Microservices (Newman, 2015). A lot of software
developing enterprises have switched to integrate
Microservice architectures to handle the increased
velocity (Balakrushnan et al., 2016). Therefore,
applications built this way consist of several fine-
grained services that are independently scalable and
deployable.
In the beginning, Digitalization was considered a
primarily technical term (Weill et al., 2015). Thus,
many technologies are preconditions of Digitalization
(McAfee, et al., 2017): Cloud Computing, Big Data
often combined with advanced Analytics, Social
Software, and the Internet of Things (Patel, et al.,
2015). New technologies like Artificial Intelligence
(Poole, et al., 2018) with Deep Learning
(Goodfellow, et al., 2016) supports our Digitalization
efforts. They allow intelligently automated activities
that are traditionally exclusive to human beings.
Digitized products and services (Schmidt et al.,
2016) support the co-creation of value together with
the customer and other stakeholders in different ways.
First, there is permanent feedback to the provider of
the product. The internet connection of the digitized
product allows collecting data permanently on the
usage of the product by the customer. Second, the
data provided by a large number of digital products
can offer new insights, which are not possible with
data from a single device. Current research argues
that digital products and services are offering
disruptive opportunities (McQuivey, 2013) for new
business solutions, having new smart connected
functionalities.
The business and technological impact of
Digitization (Schmidt et al., 2016) has multiple
aspects, which directly affect digital architectures of
service-dominant digital products. Unfortunately,
current modeling approach for designing proper
digital service and product models suffers from using
uncorrelated and diverse modeling approaches and
structures, with issues in integral value-orientation of
necessary composed services and systems.
High-quality digital models should follow a
definite value and service perspective. However,
today, we currently have no sound value relationship
from digital strategies to the resulting digital business
modeling, and subsequently to a value-oriented
enterprise architecture, which today often has seldom
properly aligned service and product model
representations. The present contribution shows a
newly introduced integral value-oriented model
composition approach by linking digital strategies
with digital business models for digital services and
close aligned products through an extended multi-
perspective digital enterprise architecture model.
Value is commonly associated with the worth of a
digital service or product (Osterwalder et al., 2010),
(Vargo et al., 2017) and aggregates potentially
required attributes for a successful customer
experience, such as meaning, desirability and
usefulness. The concept of value is essential in
designing adequate digital services with their
ENASE 2019 - 14th International Conference on Evaluation of Novel Approaches to Software Engineering
548
associated digital products, and to align their digital
business models with value-oriented enterprise
architectures. From a financial perspective, the value
of the integrated resources and the price defines the
main parts of the monetary worth.
A current conceptualization of value as a service-
based view is offered by (Vargo et al., 2017) and
(Meertens et al., 2012) considering a conceptual
framework of service-dominant (S-D) logic (Vargo et
al., 2008), (Vargo et al., 2016) and its service-
ecosystem perspective. The distinction between the
concepts of value-in-use and value-in-exchange dates
back to the antiquity and continue to influence our
today's value view. Since the work of Adam Smith
and the development of economic science the value-
in-exchange as a measure for a price a person is
willing to pay for a service or a product moved to the
forefront. Smith recognized the value-in-use as the
real value and value-in-exchange as the nominal
value. The digital marketing discipline nowadays
shifted to a simple use of the value perspective (Vargo
et al., 2017) considering customer experience and
customer satisfaction as critical value-related
concepts.
Characteristics of value modeling for a service
ecosystem were elaborated by (Vargo et al., 2017).
Value has important characteristics: value is
phenomenological, co-created, multidimensional,
and emergent. Value is phenomenological means that
value is perceived experimentally and differently by
various stakeholders in the changing context within a
service ecosystem. Value is co-created through the
integration and exchange of resources between
multiple stakeholders and related organizations.
Value is also multidimensional, which means that
value is aggregated up of individual, social,
technological and cultural components. Value results
as the new value from specific manifestations of
relationships between resources and resource
combinations. Therefore, the resulting real value
cannot be determined ex-ante. Value propositions are
value promises for a typical, but not precisely known
customer at design time and should be realized later
when using these digital services and associated
products.
Our current paper sketches our view of an
integrated value perspective combined with a service
perspective, as in Figure 1. Today, we are
experiencing a starting set of first digital strategy
frameworks, like in (Bones et al., 2019), in loosely
association with traditional strategy frameworks.
Our starting point is a model of the digital
strategy, which provides direction and sets the base
and a value-oriented framing for the digital business
definition models, with the business model canvas
(Osterwalder et al., 2010), and the value proposition
canvas (Osterwalder et al., 2014). Having the base
models for a value-oriented digital business, we map
these base service and product models to a digital
business operating model. An operating model (Ross,
et al., 2006) strategically defines the necessary level
of business process integration and standardization
for delivering services and products to customers.
From the value perspective of the business model
canvas (Osterwalder et al., 2010) results in suitable
mappings to enterprise architecture value models
(Meertens et al., 2012) with ArchiMate (Open Group,
2016). Finally, we are setting the frame for the precise
definition of digital services and associated products
by modeling digital services and product
compositions, following semantically related
composite patterns (Gamma et al., 1995).
Figure 1: Integrating Value and Service Perspectives.
Our thesis is that Digitization embraces both a
product and a value-creation perspective. Classical
industrial products are static. We can only change
them to a limited extent, if at all. On the contrary,
digitized products are dynamic. They contain both
hardware, software and (Cloud-)services. Digital
products are upgradeable via network connections.
Also, their functionality can be extended or adapted
using external services. Therefore, the feature of
digital products is dynamic and adjustable to
changing requirements and hitherto unknown
customer needs. In particular, it is possible to create
digitized products and services step-by-step or
provide temporarily unlockable functionalities. So,
customers whose requirements are changing can add
and modify service functionality without hardware
modification.
3 DIGITAL ARCHITECTURE
Digitalization promotes massively distributed
systems, which are IT systems with many rather small
and distributed structures, like the Internet of Things
Multiple Perspectives of Digital Enterprise Architecture
549
or Microservices. Additionally, we have to support
Digitalization by a dense and diverse amount of
different service types, like Microservices, REST
services, and put them in a close relationship with
distributed systems and the Internet of Things. The
change from a Closed-World modeling perspective to
more flexible Open World composition and evolution
of system architectures (Zimmermann et al., 2018)
defines the changing context for adaptable systems,
which are essential to enable the Digital
Transformation. Digitalization has a substantial
impact on architecting digital services and products.
The implication of architecting micro-granular
systems and services considering an Open World
approach fundamentally changes modeling contexts,
which are classical and well defined by quite static
closed-world and all-times consistent and less
sophisticated models.
Digital Transformation, Digitization (Schmidt et
al., 2016) and digital disruption (McQuivey,
2013) create many events that may impact enterprises
and organizations. Resilient enterprise architecture
management plays an essential role in fostering
strategies and capabilities for resiliency by providing
methods and tools for designing enterprises
architectures which are flexible for change. It may
address enterprises but also selected parts of
enterprise architecture such as services and processes.
Resilient Services are services that provide additional
meta-services in addition to their core functionality to
cope with disruptive events. E.g., airlines reschedule
passengers of delayed flights. Resilient Processes
provide event handlers to deal with external events
and are thus capable of leading back the control flow
on the desired track even in the case of adverse
events. Their decision points use data from a
multitude of internal and external sources allowing
them to detect and react to changes in the
environment.
Resiliency is the capability of enterprises and
their information systems (Betts et al., 2013) to cope
with fast and real-time changing events. Resiliency is
the ability of an IT system to provide, maintain and
improve disturbed services even when changes occur.
Resiliency is a challenging capability which
combines a multitude of different perspectives on
different abstraction levels such as organizational
resiliency, information system resiliency, cyber
resiliency, network and technology resiliency, as well
as organizational resiliency.
Resiliency (Romanovsky et al., 2017) refers to an
entity's ability to deliver the intended outcome despite
adverse cyber events continuously. This ability
includes response and recovery and developing
resilient-by-design systems. Resiliency requires
constructive and organizational approaches with a
strong focus on a managed environment for enterprise
architectures of information systems and services.
Enterprise Architecture (EA) (Lankhorst, 2017) is
since years a well-motivated discipline of enterprise
and IT governance. Since more than one decade
Enterprise Architecture is a discipline with a
scientific background and useful decision supporting
functions and models for forward-thinking
enterprises and organizations. Enterprise
Architecture aims to model, align and understand
significant interactions between business and IT to set
a prerequisite for a well-adjusted and strategically
oriented decision-making framework for both digital
business and digital technologies.
Enterprise Architecture Management
(Lankhorst, 2017), as today defined by several
standards like (Open Group, 2018) and (Open Group,
2016) uses a quite large set of different views and
perspectives for managing current IT. An effective
architecture management approach for digital
enterprises should additionally support the
Digitization of products and services and be both
holistic and easily adaptable (Zimmermann et al.,
2018). Furthermore, a digital architecture sets the
base for Digital Transformation driving new digital
business models and technologies with a large
number of micro-structured Digitization systems
having their local micro-granular architectures like
IoT (Patel et al., 2015), mobile devices, or with
Microservices (Newman, 2015).
A Digital Enterprise Architecture (DEA) extends
the research base in (Zimmermann et al., 2018) and
provides today in our current research ten integral
architectural domains for a holistic classification
model (Figure 2).
Figure 2: Digital Enterprise Architecture Reference Cube.
DEA covers also micro-granular architectures
for different digital services and products. DEA
abstracts from a concrete business scenario or
technologies, because it is applicable for concrete
architectural instantiations to support Digital
ENASE 2019 - 14th International Conference on Evaluation of Novel Approaches to Software Engineering
550
Transformation (Brynjolfsson et al., 2014), (Schmidt
et al., 2016) independent of different domains.
DEA supports by a holistic view metamodel-
based extraction and bottom-up integration methods
and techniques by integrating micro-granular
viewpoints, models, standards, frameworks and tools
into a consistent digital enterprise architecture model.
DEA frames these multiple elements of a digital
architecture into basic configurations of a digital
architecture by providing an ordered base of
architectural artifacts for associated multi-perspective
decision processes.
Architecture governance, as in (Weill et al.,
2004), defines the base for well-aligned management
practices through specifying management activities:
plan, define, enable, measure, and control. Digital
governance (McAfee et al., 2017) should additionally
set the frame for digital strategies, digital innovation
management, and Design Thinking methodologies.
The second aim of governance is to set rules for
value-oriented architectural compliance based on
internal and external standards, as well as regulations
and laws. Architecture governance for Digital
Transformation changes some of the fundamental
laws of traditional governance models to be able to
manage and openly integrate plenty of diverse micro-
granular structures, like the Internet of Things or
Microservices.
4 MODELING ARCHITECTURAL
COMPOSITIONS
Digitalization promotes massively distributed
systems, which many rather small and distributed
structures, like the Internet of Things, mobile
systems, cyber-physical systems. Additionally, we
are enabling Digitalization by a dense and diverse
amount of different service types, as Microservices,
REST services and put them in a close relationship
with distributed systems, like the Internet of Things.
Furthermore, the Internet of Things is an essential
foundation of Industry 4.0 (Schmidt et al., 2015) and
flexible digital enterprise architectures. The change
from a closed-world modeling perspective to more
flexible Open World composition and evolution of
system architectures defines the changing context for
adaptable systems, which are essential to enable the
Digital Transformation. The implication of
architecting micro-granular systems and services
considering an Open World approach fundamentally
changes modeling contexts, which are classical and
well defined by quite static closed-world and all-
times consistent and less sophisticated models.
Adaptability for architecting open micro-
granular systems like the Internet of Things or
Microservices is mostly concerned with
heterogeneity, distribution, and volatility. It is a
considerable challenge to continuously integrate
numerous dynamically growing open architectural
models and metamodels from different sources into a
consistent digital architecture. To address this
problem, we are currently formalizing small-
decentralized mini-metamodels, models, and data of
architectural microstructures, like Microservices and
IoT into DEA-Mini-Models (Digital Enterprise
Architecture Mini Model).
In general, such DEA-Mini-Models (Bogner et
al., 2016) consists of partial DEA-Data, partial DEA-
Models, and partial EA-Metamodel. Microservices
are associated with DEA-Mini-Models and objects
from the Internet of Things (Zimmermann et al.,
2015). The structures of EA-Mini-Descriptions
(Figure 3) are extensions of the Meta Object Facility
standard (OMG, 2011), Object Management Group.
Figure 3: EA-Mini-Description.
We have extended the base model layer M1 to be
able to host metadata additionally. Additionally, we
have associated the original metamodel from layer
M2 with our architectural ontology with integration
rules. In this way, we provide a close associated
semantic-oriented representation of the metamodel to
be able to support automatic inferences for detecting
model similarities, like model matches and model
mappings during runtime.
Regarding the structure of EA-Mini-Descriptions,
the highest layer M3 (Bogner et al., 2016) represents
an abstract language concept used in the lower M2
layer. M3 is the meta-meta-model layer. The
following layer M2 is the metamodel integration
layer. The layer defines the language entities for M1,
e.g., models from UML or ArchiMate (Open Group,
2016). These models are a structured representation
of the lowest layer M0 (OMG, 2011).
Volatile technologies, requirements, and markets
typically drive the evolution of business and IT
services. Adaptation is a crucial success factor for the
17
Integral Digital Enterprise Architecture
DEA-Mini-Models for each (even small) Architectural Artifact
M
0
Run-Time Data
M
1
M
2
M
3
Architectural Model
Meta-Data
Integration Rules
Architectural Ontology
Architectural Meta-Model
ArchiMate OWL
Run-Time-Data
Meta-Data
Model
Meta-Model
Ontology
Rules
Multiple Perspectives of Digital Enterprise Architecture
551
survival of digital enterprise architectures
(Zimmermann et al., 2015), platforms, and
application environments. The evidence from (Weill
et al., 2015) introduces the idea of digital ecosystems.
Ecosystems links with main strategic drivers for
system development and system evolution. Reacting
rapidly to new technology and market contexts
improve the fitness of such adaptive ecosystems.
During the integration of DEA-Mini-Models as
micro-granular architectural cells (Figure 4) for each
relevant object, e.g., Internet of Things object or
Microservice, the step-wise composed time-stamp
dependent architectural metamodel becomes
adaptable (Bogner et al., 2016) and (Zimmermann et
al., 2015).
Figure 4: Architecture Composition.
Being a bit closer to the architecture and design of
systems, (Trojer et al., 2015) coined the Living
Models paradigm that is concerned with the model
based creation and management of dynamically
evolving systems. Adaptive Object Modelling and its
patterns and usage provide useful techniques to react
to changing user requirements, even during the
runtime of a system. Moreover, we have to consider
model conflict resolution approaches to support
electronic documentation of digital architectures and
to summarize integration foundations for federated
architectural model management.
In the case of new integration patterns, we have to
consider additional manual support. Currently, the
challenge of our research is to federate these DEA-
Mini-Models to an integral and dynamically growing
DEA model and information base by promoting a
mixed automatic as well as a collaborative decision
process.
We are currently extending model federation and
transformation approaches (Trojer et al., 2015) by
introducing semantic-supported architectural
representations, from partial and federated ontologies
and associate mapping rules with unique inference
mechanisms.
Fast changing technologies and markets usually
drive the evolution of ecosystems. Therefore, we have
extracted the idea of digital ecosystems from
(Tiwana, 2013) and linked this with main strategic
drivers for system development and their evolution.
Adaptation drives the survival of digital architectures,
platforms, and application ecosystems.
5 DECISION MANAGEMENT
Our current research links decision objects and
processes to multi-perspective architectural models
and data. We are extending the more fundamentally
approach of decision dashboards for Enterprise
Architecture (Lankhorst, 2017), (Zimmermann et al.,
2018) and integrate this idea with an original
Architecture Management Cockpit (Figure 5) (Jugel
et al., 2014), (Jugel et al., 2015) for the context of
decision-oriented digital architecture management for
a vast amount of micro-granular architectural models
from the Open World.
Figure 5: Architecture Management Cockpit (Jugel et al.,
2014).
The Architecture Management Cockpit enables
analytics as well as optimizations using different
multi-perspective interrelated viewpoints on the
system under consideration (Jugel, 2018). Multiple
perspectives of architectural models and data result
from a magnitude of architectural objects, linking
dimension categories of digital enterprise
architecture. Additionally, we have to consider
analytics and decision viewpoints for the structural
core information of enterprise architecture.
The ISO Standard 42010 (Emery et al., 2009)
defines, how the architecture of a system relies on
architecture descriptions. Jugel (Jugel et al., 2015)
has developed a unique annotation mechanism adding
additional needed knowledge via an architectural
model to an architecture description. The
fundamental work of (Jugel et al., 2014) reveals a
viewpoint concept by dividing it into an Atomic
Viewpoint and a Viewpoint Composition.
Therefore, coherent viewpoints can be applied
simultaneously in an architecture cockpit to support
stakeholders in decision-making (Jugel et al., 2015).
Figure 5 gives an overview of the decision
metamodel, as our extension of (Plataniotis et al.,
2014), showing the conceptual model of main
decisional objects and their relationships.
17
Integral Digital Enterprise Architecture
Federation by Composition of DEA-Mini-Models
DEA
Mini
Model
DEA
Mini
Model
DEA
Mini
Model
DEA
Mini
Model
DEA
Mini
Model
DEA
Mini
Model
DEA
Mini
Model
DEA
Mini
Model
DEA
Mini
Model
Integral DEA Model
DEA
Mini
Model
DEA
Mini
Model
DEA
Mini
Model
DEA
Mini
Model
DEA
Mini
Model
DEA
Mini
Model
DEA
Mini
Model
DEA
Mini
Model
DEA
Mini
Model
DEA Composition
(t)
DEA Composition
(t+1)
Zimmermann, A. et al.: Adaptive Enterprise Architecture for Digital Transformation. ESOCC / IDEA 2015
DEA
Mini
Model
DEA
Mini
Model
ENASE 2019 - 14th International Conference on Evaluation of Novel Approaches to Software Engineering
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According the architecture management cockpit
(Jugel et al., 2014), each possible stakeholder can
utilize a viewpoint that shows the relevant
information. These viewpoints are connected in a
dynamically way to each other so that the impact of a
change performed in one view can be visualized in
different views as well. Following (Jugel et al., 2015)
and (Jugel, 2008), we have integrated the concept of
Decision Process, as a logical sequence of activities
to solve one or more identified architectural decision,
see also (Plataniotis et al., 2014), problems (Figure 6).
Figure 6: Architecture Decision Metamodel.
The concept Activity represents individual
activities of process activities. Since these process
activities performed with human participation, at least
one Stakeholder connects to an Activity, who executes
the respective action. The Stakeholder concept results
from ISO Standard 42010 (Emery et al., 2009).
Architecture Viewpoints are used to visually
represent parts of the enterprise architecture in a
stakeholder-oriented way, while Techniques contain
detailed recommendations of actions or algorithms
for automated execution of specific tasks.
6 CONCLUSION
First, we have set the context for Digital
Transformation for our research question. We
integrate first two important base perspectives, the
value perspective, and the service perspective, for a
holistic architectural design of digital products,
following fundamental premises of the service-
dominant logic.
The main results of our current paper affect a new
defined digital enterprise reference architecture by
setting a flexible framework with ten structural
domains providing additional integral perspectives
for Digitalization. To be able to support the dynamics
of Digitalization with resilient systems and service
compositions we have leveraged an adaptive digital
enterprise architecture for Open World integrations of
globally accessed micro-granular systems and
services, like the Internet of Things and
Microservices, with their local architectural models.
We have also included methods and mechanisms
for decision management of digital enterprise
architecture with related intelligent systems and
digital services. Furthermore, we have demonstrated
and mention the feasibility of our research and the
enterprise architecture cockpit through projects and
validations with partners from science and practice.
Some limitations still exist in our work. There is a
need to extend analytics-based decisions support with
mechanisms from AI explanation mechanisms and
context-data driven architectural decision-making.
Limitations are, while integrating Internet of Things
architecture in the field of multi-level evaluations of
our approach, as well as in domain-specific
adoptions.
Future research addresses mechanisms for
flexible and adaptable integration of digital enterprise
architectures. Similarly, it may be of interest to
extend human-controlled interaction and
visualizations by integrating automated decision
making by AI-based systems like ontologies with
semantic integration rules, and structural data and
model analytics with Deep Learning mechanisms as
well as mathematical comparisons (similarity,
Euclidean distance) and Data Science methods.
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