Multiple Perspectives of Digital Enterprise Architecture

Alfred Zimmermann

, Rainer Schmidt

and Kurt Sandkuhl

Herman Hollerith Center Boeblingen, Faculty of Informatics, Reutlingen University, Reutlingen, Germany

Faculty of Informatics and Mathematics, Munich University, Munich, Germany

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

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

Integral Digital Enterprise Architecture

DEA-Mini-Models for each (even small) Architectural Artifact

Run-Time Data

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.

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

552

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.

REFERENCES

McAfee, A., Brynjolfsson, E., 2017. Machine, Platform,

Crowd: Harnessing Our Digital Future. W. W. Norton

New York.

Bones, C., Hammersley, J., Shaw, N., 2019. Optimizing

Digital Strategy. Kogan Page.

Schmidt, R., Zimmermann, A., Möhring, M., Nurcan, S.,

Keller, B., Bär, F., 2016. Digitization–Perspectives for

Conceptualization. In Advances in Service-Oriented

and Cloud Computing, 263-275, Springer International

Publishing.

Lankhorst, M. et al., 2017. Enterprise Architecture at

Work: Modelling, Communication and Analysis.

Springer.

Zimmermann, A., Schmidt, R., Sandkuhl, K., Jugel, D.,

Bogner, J., and Möhring, M., 2018. Decision-

Controlled Digitization Architecture for Internet of

Things and Microservices. In Czarnowski, I., Howlett,

R., Jain, L.C. (Eds.) Intelligent Decision Technologies

2017, Part II, Springer International Publishing, 2018.

Uckelmann, D., Harrison, M., Michahelles, F., 2011.

Architecting the Internet of Things. Springer.

Walker, M. J., 2014. Leveraging Enterprise Architecture to

Enable Business Value With IoT Innovations Today.

Gartner Research.

Multiple Perspectives of Digital Enterprise Architecture

553

Fremantle, P., 2015. A Reference Architecture for the

Internet of Things. WSO2 White Paper 0.9.0.

https://wso2.com

Newman, S., 2015. Building Microservices: Designing

Fine-Grained Systems. O’Reilly.

Balakrushnan, S., Mamnoon, O., Bell, J., Currier, B.,

Harrington, E., Helstrom, B., Maloney, P., Martins, M..

2016. Microservices Architecture. White Paper, The

Open Group.

Brynjolfsson, E., McAfee, A., 2014. The Second Machine

Age. W. W. Norton.

El-Sheikh, E., Zimmermann, A., Jain, L. (Eds.), 2016.

Emerging Trends in the Evolution of Service-Oriented

and Enterprise Architectures. Springer.

Weill, P., Woerner, S., 2015. Thriving in an Increasingly

Digital Ecosystem. MIT Sloan Management Review.

Patel, P., Cassou, D., 2015. Enabling high-level application

development for the Internet of Things. In Journal of

Systems and Software, Vol. 103, 62-84, Elsevier.

Poole, D. L., Mackworth, A. K. 2018. Artificial

Intelligence. Foundations of Computational Agents.

Cambridge University Press.

Goodfellow, I., Bengio, Y., Courville, A. 2016. Deep

Learning. MIT Press.

McQuivey, J. 2013. Digital Disruption. Unleashing the

Next Wave of Innovation. Forrester Research.

Osterwalder, A., Pigneur, Y., 2010. Business Model

Generation. John Wiley.

Vargo, S. L., Akaka, M. A., Vaughan, C. M., 2017.

Conceptualizing Value: A Service-ecosystem View.

Journal of Creating Value 3(2), 1-8.

Meertens, L. O., Iacob, M. E., Nieuwenhuis, L. J. M., van

Sinderen, M. J., Jonkers, H., Quertel, D., 2012.

Mapping the Business Model canvas to ArchiMate.

SAC 2012, ACM, 1694-1701.

Vargo, S. L., Lusch, R. F., 2008. Service-dominant logic:

continuing the evolution. Journal of the Academy of

Marketing Science, 36 (1), 1-10.

Vargo, S. L., Lusch, R. F., 2016. Institutions and axioms:

an extension and update of service-dominant logic.

Journal of the Academy of Marketing Science, 44 (4),

5-23.

Osterwalder, A., Pigneur, Y., Bernarda, G., Smith, A.,

Papadokos, T., 2014. Value Proposition Design. John

Wiley.

Ross, J. W., Weill, P., Robertson, D., 2006. Enterprise

Architecture as Strategy – Creating a Foundation for

Business Execution. Harvard Business School Press,

Harvard Business School Press.

Open Group, 2016. ArchiMate 3.0 Specification. Van

Haren Publishing.

Gamma, E., Helm, R., Johnson, R., Vlissides, J., 1995.

Design Patterns. Addison Wesley.

Betts, D., Bourgault, J., Dominguez. J., 2013. Building

Elastic and Resilient Cloud Applications. Microsoft

Patterns and Practices.

Romanovsky, A., Troubitsyna, E. A., 2017. Software

Engineering for Resilient Systems. LNCS 10479,

Springer.

Open Group, 2018. TOGAF Version 9.2. The Open Group.

Weill, P., Ross, J. W., 2004. IT Governance: How Top

Performers Manage It Decision Rights for Superior

Results. Harvard Business School Press.

Schmidt, R., Möhring, M., Härting, R.-C., Reichstein, C.,

Neumaier, P., Jozinovic, P., 2015. Industry 4.0 -

Potentials for Creating Smart Products: Empirical

Research Results. 18th Conference on Business

Information Systems, Poznan 2015, Lecture Notes in

Business Information Processing, Springer.

Zimmermann, A., Schmidt, R., Sandkuhl, K., Wißotzki, M.,

Jugel, D., Möhring, M., 2015. Digital Enterprise

Architecture – Transformation for the Internet of

Things. In J. Kolb, B. Weber, S. Hall, W. Mayer, A. K.

Ghose, G. Grossmann (Eds.) EDOC 2015 with

SoEA4EE, 21-25 September 2015, Adelaide, Australia,

IEEE Proceedings, 130-138.

Object Management Group, 2011. Meta Object Facility

(MOF). Core Specification, Version 2.5, OMG.

Bogner, J., Zimmermann, A., 2016. Towards Integrating

Microservices with Adaptable Enterprise Architecture.

In: Dijkman, R., Pires, L.F., Rinderle-Ma, S.: IEEE –

EDOC Conference Workshops EDOCW 2016 Vienna,

158–163, IEEE.

Weill, P., Woerner, S., 2015. Thriving in an Increasingly

Digital Ecosystem. MIT Sloan Management Review.

Trojer, T. et., 2015. Living Modeling of IT Architectures:

Challenges and Solutions. Software, Services, and

Systems 2015, 458-474.

Tiwana, A., 2013. Platform Ecosystems: Aligning

Architecture, Governance, and Strategy. Morgan

Kaufmann.

Jugel, D., Schweda, C. M., 2014. Interactive functions of a

Cockpit for Enterprise Architecture Planning. In:

International Enterprise Distributed Object Computing

Conference Workshops and Demonstrations

(EDOCW), Ulm, Germany, 2014, 33-40, IEEE.

Jugel, D., Schweda, C.M., Zimmermann, A., 2015.

Modeling Decisions for Collaborative Enterprise

Architecture Engineering. In: 10th Workshop Trends in

Enterprise Architecture Research (TEAR), held on

CAISE 2015, Stockholm, Sweden, 351-362, Springer.

Jugel, D., 2018. Modeling Interactive Enterprise

Architecture Visualizations: An Extended Architecture

Description. In CSIMQ Journal, no. 16, 17–35.

Emery, D., Hilliard, R., 2009. Every Architecture

Description needs a Framework: Expressing

Architecture Frameworks Using ISO/IEC 42010.

IEEE/IFIP WICSA/ECSA, 31-39.

Plataniotis, G., De Kinderen, S., Proper, H. A., 2014. EA

Anamnesis: An Approach for Decision Making Analysis

in Enterprise Architecture. In: International Journal of

Information Systems Modeling and Design. Vol. 4 (1),

75-95.

ENASE 2019 - 14th International Conference on Evaluation of Novel Approaches to Software Engineering

554