Software Evolution for Digital Transformation

Alfred Zimmermann

, Rainer Schmidt

, Justus Bogner

, Dierk Jugel

and Michael Möhring

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

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

Keywords: Digital Transformation, Digital Enterprise Architecture, Software Evolution.

Abstract: In current times, a lot of new business opportunities appeared using the potential of the Internet and related

digital technologies, like Internet of Things, services computing, cloud computing, big data with analytics,

mobile systems, collaboration networks, and cyber physical systems. Enterprises are presently transforming

their strategy, culture, processes, and their information systems to become more digital. The digital

transformation deeply disrupts existing enterprises and economies. Digitization fosters the development of IT

environments with many rather small and distributed structures, like Internet of Things. This has a strong

impact for architecting digital services and products. The change from a closed-world modeling perspective

to more flexible open-world and living software and system architectures defines the moving context for

adaptable and evolutionary software approaches, which are essential to enable the digital transformation. In

this paper, we are putting a spotlight to service-oriented software evolution to support the digital

transformation with micro-granular digital architectures for digital services and products.

1 INTRODUCTION

Nowadays, data, information and knowledge are

fundamental core concepts of our everyday activities

and are driving the digital transformation of today’s

global society (El-Sheikh, et al., 2016; Schmidt et al.

2015). New services and smart connected products

expand physical components by adding information

and connectivity services using the Internet (Porter et

al., 2014) (El-Sheikh, et al., 2016).

Digitization (Schmidt et al., 2016) is enabled by

four megatrends of cloud, big data, mobile, and social

technologies. This disruptive change interacts with all

information systems that are important business

enablers for the current digital transformation.

Digitized services and products amplify the basic

value and capabilities, which offer exponentially

expanding opportunities (Schmidt at al., 2016).

Digitization enables human beings and autonomous

objects to collaborate beyond their local context using

digital technologies. The exchange of information

enables better decisions of human beings, and of

intelligent objects. Furthermore, social networks,

smart devices, and intelligent cars are part of a wave

of digital economy with digital products, services,

and processes, which are driving an information-

driven vision (Zimmermann et al. 2018).

The Internet of Things (IoT) (Uckelmann et al.,

2011) connects a large number of physical devices to

each other using wireless data communication and

interaction based on the Internet as a global

communication environment. Additionally, we have

to consider challenging aspects of the overall

software and systems architecture to integrate base

technologies and systems, like cyber-physical

systems, social networks, big data with analytics,

services, and cloud computing. Typical examples for

the next wave of digitization (Zimmermann et al.

2018). are smart enterprise networks, smart cars,

smart industries, and smart portable devices.

Objects from the real world are mapped into the

virtual world (Zimmermann et al. 2018).

Furthermore, the important interaction with mobile

systems, collaboration support systems, and service-

based systems for big data as well as cloud

environments is extended. Additionally, the Internet

of Things is an important foundation of Industry 4.0

(Schmidt et al., 2015) and adaptable digital systems.

Both business and technology are impacted from

the digital transformation (Zimmermann et al., 2015)

by complex relationships between architectural

elements. This directly affects the adaptable

digitization architecture for digital services and

products and their related digital governance (El-

Zimmermann, A., Schmidt, R., Bogner, J., Jugel, D. and Möhring, M.

Software Evolution for Digital Transformation.

DOI: 10.5220/0006815702050212

In Proceedings of the 13th Inter national Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2018), pages 205-212

ISBN: 978-989-758-300-1

205

Sheikh, et al., 2016). Enterprise Architecture

Management (EAM) (Lankhorst et al., 2017) for

services computing is the approach of choice to start

from and to organize, build and utilize distributed

capabilities for the digital transformation (Weill et al.,

2015), (Westerman et al., 2014), (Brynjolfsson et al.,

2014).

Digitization (Schmidt, et al., 2016) requires the

appropriate alignment of business models and digital

technologies for new digital strategies and solutions,

as same as for their digital transformation. Current

digitized applications are integrating Internet of

Things, Web services, REST services, Microservices,

cloud computing, big data, machine learning with

new frameworks and methods, emphasizing openly

defined service-oriented software architectures

(Zimmermann, et al. 2015) with extensions for

semantic support.

A lot of software developing enterprises have

switched to integrate Microservice architectures to

handle the increase velocity (Bogner et al., 2016)

(Zimmermann et al. 2018). Therefore, applications

built this way consist of several fine-grained services

that are independently scalable and deployable. The

fast-moving process of digitization demands

flexibility to adapt to rapidly changing business

requirements and newly emerging business

opportunities.

Unfortunately, the current state of art in research

and practice of enterprise architecture lacks an

integral understanding of software evolution (El-

Sheikh et al., 2016), when integrating a huge amount

of micro-granular systems and services, like

Microservices and Internet of Things, in the context

of digital transformation and evolution of

architectures. Our goal is to extend previous quite

static approaches of enterprise architecture to fit for

flexible and adaptive digitization of new products and

services. This goal shall be achieved by introducing

suitable mechanisms for collaborative architectural

engineering and by positioning open micro-granular

architectures.

Our current short paper is part of an on-going

research and investigates the following main research

question:

What are main software evolution approaches to

support the flexible open-world transition of systems

for the digital transformation?

The following Section 2 sets the fundamentals for

the digital transformation of digitized services and

products. Section 3 focusses on architecting digital

structures, systems, and technologies, while Section 4

presents suitable service-based software evolution

approaches. Section 5 puts a snapshot on main related

work. Finally, we summarize in Section 6 our

research findings, mention some limitations, and

sketch our future research steps.

2 DIGITAL TRANSFORMATION

Digital transformation is the current dominant type of

business transformation having IT both as a

technology enabler and as a strategic driver for

digitization. Digitized services and associated

products (Westerman et al., 2014), are software-

intensive (Schmidt, et al., 2016) and therefore

malleable and usually service-oriented (El-Sheikh, et

al., 2016). Digital products are able to increase their

capabilities via accessing cloud-services and change

their current behaviour (Zimmermann et al. 2016).

How value is created by these changes is shown in

Figure 1.

Figure 1: Hybrid value creation of Digital Products.

By combining a product consisting of hardware

and software with cloud-provided services new ways

of interaction with the customer are enabled (Möhring

et al. 2018). Current research suggests that different

customers will use such devices for different use

cases enabling new ways of triggering and interaction

with business processes (Möhring et al. 2018). An

example is Amazon Alexa (Warren, 2016) that

consists of a physical device with microphone and

speaker e.g. Echo Dot, and services, called “Alexa

skills”. The set of Alexa skills is dynamic and can be

tailored to the customer’s requirements during run-

time. The lifecycle of digitized products is extended

by the acquisition and decommissioning of services.

Digitized products and services (Schmidt et al.

2015) support the co-creation of value together with

ENASE 2018 - 13th International Conference on Evaluation of Novel Approaches to Software Engineering

206

the customer and other stakeholders in different ways.

First, there is a permanent feedback to the provider of

the product. The internet connection of the digitized

product allows to collect permanently data on the

usage of the product by the customer. Second, the

data provided by a large number of digitized products

are able to provide new insights, which are not

possible with data from a single device.

Current research argues that digital products and

services are offering disruptive opportunities

(Westerman et al., 2014), (Brynjolfsson et al., 2014)

for new business solutions, having new smart

connected functionalities.

In the beginning, digitization was considered a

primarily technical term (Weill et al., 2015). Thus, a

number of technologies is often associated with

digitization (Westerman et al., 2015): cloud

computing, big data often combined with advanced

analytics (Veneberg, et al., 2016), social software,

and the Internet of Things (Atzori, et al., 2010). New

technologies are associated with digitalization such as

deep learning (Schmidthuber, 2015). They allow

computing to be applied to activities that were

considered as exclusive to human beings.

Therefore, the present emphasis on digitization

become an important area of research. Our thesis is,

that digitization embraces both a product and a value-

creation (Schmidt et al., 2016) perspective.

Classical industrial products are static

(Brynjolfsson et al., 2014). You 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. They can be upgraded

via network connections. In addition, their

functionality can be extended or adapted using

external services. Therefore, the functionality of

products is dynamic and can be adapted 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.

Digitized products are able to capture their own

state and submit this information into linked contexts.

The provider can remotely determine, whether the

product is still functional and trigger, where

appropriate, maintenance and repairs. Evaluation of

status information and analysis of the history of use

of the product can be predicted when a malfunction

of the product is probable. A maintenance or

replacement of the product is performed before

predicted data of failure. The data collected also

provide information for a repair on the spot, so that a

high first-time solution rate can be achieved. At the

same time, storage can be improved in this way of

spare parts. By this means, preventive

maintenance can be implemented. Unscheduled

stoppages can this way be significantly reduced.

This is the basis for the servitization of products.

Not a physical product, but a service is sold to the

customer. The service usage is measured and lays the

foundation for usage-based billing models.

Digitized products also enable network effects

(Weitzel et al., 2000) that grow exponentially with the

number of participating devices. An increase in the

number of digitized products increases the incentive

for providers of add-on services and complementary

skills (Brynjolfsson et al., 2014). At the same time

this increase the attractiveness for further digitized

products. In summary, an exponential growth can be

achieved.

Therefore, significant first-mover advantages

exist. Network effects emerge not only for the

functionality but also for the analytical exploitation of

data collected by the digitized products. These effects

are called network intelligence (Schmidt et al., 2016).

By bringing together data from many devices and not

only single devices, trends can be detected much

earlier and more accurately. Further improvements

can be achieved by linking data from different

sources, also external one. In this way, it is possible

to establish correlations that would not have been

possible considering data from a single device. This

effect increases with the number of devices.

The digitized products become part of an

information system, which accelerates the learning

and knowledge processes across all products (Evans,

et a., 2012). The manufacturer can win genuine

information about the use of the product. Important

information for the development of new products can

be obtained in this way. Therefore, a number of other

beneficial effects can be achieved as network

optimization, maintenance optimization, improved

restore capabilities, and additional evidence against

the consideration of individual systems.

The presented concepts enable a fundamental

change of the value-creation model. Traditional

products were created with a tayloristic view in mind,

that emphasized the separation of production and

consumer in order to enable centralized production

and thus scaling effects. Now, the co-creation (Vargo

et al., 2008) approach of service-dominant logic can

be implemented because of the continuous

connection of the products with the manufacturer.

The consumer converts dynamically to be co-

producer. Platforms are complementary to products,

which cooperate via standardized interfaces.

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207

3 DIGITAL ARCHITECTURE

The new possibilities enabled by digitized products

also require new approaches in Architecture

Management (Lankhorst et al., 2017). Today several

standards are available (TOGAF, 2011), (Archimate,

2016) containing a large set of different views and

perspectives for managing current IT. Therefore, an

effective architecture management approach for

digital enterprises should support digitization of

products and services (Schmidt, et al., 2016) and

should be both holistic and easily adaptable

(Zimmermann, et al., 2015). Architecture

management should also enable both digital

transformation of existing processes and the creation

of new business models. At the same time they have

to support technologies that are based on a large

number of systems with micro-granular architectures

(Zimmermann et al. 2015) like IoT, mobile devices,

or with Microservices. It is a huge challenge to

continuously integrate numerous dynamically

growing architectural models and metamodels from

different microstructures with micro-granular

architecture into a consistent digital architecture.

A Digital Architecture (in Section 3) provides a

conceptual blueprint to help define the structure and

operation of an organization having the goal to

determine, how a digital transformed organization

with their digitized products and services can most

effectively achieve its current and future objectives.

Several approached and methods have been proposed

in (El-Sheikh et al., 2016) to address the challenges

and trends in the evolution of Service-oriented and

Enterprise Architectures to manage the digital

transformation.

To cope with these challenges, we extend our

previous service-oriented enterprise architecture

reference model for the context of digital

transformation with Microservices and Internet of

Things considering associated architectural decision

making (Jugel, et al., 2015), which is supported by

functions of an architectural cockpit (Jugel, et al.,

2014).

Enterprise Services Architecture Reference Cube

(ESARC) provides an architectural reference model

(Zimmermann et al., 2015) enabling specific

viewpoints for evolved micro-granular enterprise

architectures (Figure 2). ESARC for digital products

and services is more specific than existing

architectural standards of architecture management

(TOGAF, 2011), (Archimate, 2016).

ESARC provides eight integral architectural

domains to provide a holistic classification model.

While it is applicable for concrete architectural

instantiations to support digital transformations, it

still abstracts from a concrete business scenario or

technologies. The Open Group Architecture

Framework (TOGAF, 2011) together with

(Archimate, 2016) provides the basic blueprint and

structure for our extended service-oriented enterprise

architecture domains.

Figure 2: Enterprise Services Architecture Reference Cube

(Zimmermann et al. 2015).

Our research extends an existing metamodel-

based model extraction and integration approach

from (Zimmermann et al., 2015) for digital enterprise

architecture viewpoints, models, standards,

frameworks and tools. The approach supports the

adaptable integration of micro-granular architectures.

Currently, we are working on the idea of continuously

integrating small architectural descriptions for

relevant objects of a digital architecture.

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). DEA-Mini-Models consists of partial

DEA-Data, partial DEA-Models, and partial EA-

Metamodel. They are associated with Microservices

and/or objects from the Internet of Things. These

structures are based on the Meta Object Facility

(MOF) standard (MOF, 2011) of the Object

Management Group (OMG).

The highest layer M3 represents abstract language

concepts used in the lower M2 layer and is called,

therefore, the meta-metamodel layer. The next layer

M2 is the metamodel integration layer and defines the

fundamental entities for M1 (e.g. models from UML

or ArchiMate (Archimate, 2016). These models are a

structured representation of the lowest layer M0 that

is formed by collected concrete data from real-world

use cases with instantiations of architectural data.

By integrating DEA-Mini-Models micro-granular

architectural cells (Figure 3) for each relevant IoT

object or Microservice, the integrated overall

architectural metamodel becomes adaptable and can

ENASE 2018 - 13th International Conference on Evaluation of Novel Approaches to Software Engineering

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mostly be automatically synthesized by respecting the

integration context from a growing number of

previous similar integrations. In the case of new

integration patterns, we have to consider additional

manual support.

Figure 3: Architectural Federation by Composition.

The challenge of our current research is to

federate these DEA-Mini-Models to an integral and

dynamically growing DEA model and information

base by promoting a mixed automatic and

collaborative decision process (Jugel, et al., 2015)

and (Jugel, et al., 2014).

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 special 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.

4 SOFTWARE EVOLUTION

The digital transformation (Porter, et al., 2014) highly

increased the competitive pressure and urges

enterprises to quickly develop new digitized products

and services. Time to market is a key differentiator in

digital transformation. The quicker a business is, the

more successful it is likely to be. But more

established businesses have delivered technology

solutions to their employees and customers on

lengthy release schedules that no longer make sense

in today’s accelerated environment.

The nature of digital assets disaggregates value

chains, creating openings for focused, fast-moving

competitors. Furthermore, the customer expects to

interact seamlessly across different channels.

Enterprises have to analyse customer behaviour in

real-time. At the same time digitization lowers the

market entry barriers for new competitors, by

dissolving long-understood boundaries between

sectors. These challenges require a better support for

software evolution.

Principally we can identify, as in (Wilde, et al.,

2016), two broad perspectives of software evolution:

First, software can be designed anticipating change

by the original software developer to make evolution

easier by predicting possible change perspectives of a

new software. The main mechanism of proactive

change is based on modularity structures of services.

Secondly, software evolution can be handled during

the maintenance phase by using special tools and

methods. The intention here is to support

understanding of software structures of the existing

code, as fast and easy as possible.

The implementation of flexible and maintainable

services strongly depends on service quality of

services (Gebhart et al., 2016). In the past, most

quality of service indicators were designed for

method-driven Web Services with SOAP. Today,

many new services are designed in a resource-

oriented way using REST or Microservices, to follow

an easier technology-independent approach. Many of

the existing quality indicators for Web Services can

be mapped to resource-oriented services. Resource-

oriented services can also be engineered using

Microservices, as mentioned.

Decision analytics (Zimmermann et al., 2016)

provides increasingly complex and decision support,

particularly for the development and evolution of

sustainable enterprise architectures (EA), and this is

duly needed. Tapping into these systems and

techniques, the engineers and managers of the

software and system architecture become part of a

viable enterprise, i.e. a resilient and continuously

evolving service-oriented architectures and systems

that enable and drive innovative business models.

Main challenges of service computing for the next

ten years guide a redefinition of service computing,

which are postulated by (Bouguettaya et al., 2017).

The service computing manifesto maps out in (as in

Figure 4) a strategy that positions emerging concepts

and technologies to support the service paradigm. The

service computing manifesto recommends focusing

on four main research directions by specifying both

challenges and a research roadmap: service design,

service composition, crowdsourcing based

reputation, and the Internet of Things.

An important prerequisite for building and

analysing sound service systems and architectures is

a formal understanding of the nature of services and

Integral Digital Enterprise Architecture

Federation by Composition of DEA-Mini-Models

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their model-supported relationships. We have to

currently consider a big change from traditional

closed-world software engineering approaches to the

open-world of service systems with autonomous parts

(Zimmermann, et al., 2015).

Figure 4: Abstractions along the Computing Value Chain.

An important prerequisite for building and

analysing sound service systems and architectures is

a formal understanding of the nature of services and

their model-supported relationships.

Cloud computing as a new service delivery model,

which gives inspiration to integrate service

computing and cloud computing. So, cloud

computing is a main influencing factor for the service

computing manifesto in (Bouguettaya et al., 2017),

while other important influencers for service

computing are mobile computing, big data, and social

computing.

In the next wave of service composition

(Bouguettaya et al., 2017), we have to integrate a fast

growing and large set of non-WSDL-described

services: REST services, Microservices, and partial

services, like IoT or Android apps. In an open-world

setting of big data existing static service selection,

composition, and recommendation are inadequate

and should be extended by large-scale Web and cloud

service composition, big data driven service

composition, and social network based service

compositions.

Trust plays an important role for a functional

service ecosystem. Crowdsourcing provides effective

means for collecting data through collaborations

within communities. Reputation mechanisms and

crowdsourcing in social networks support predicting

credibility, which is important for derive trust.

Innovative models are required for the

composition of Internet of Things (IoT) (Bouguettaya

et al., 2017) and (Patel et al. 2015). IoT poses two

fundamental challenges: communication with things,

and management of things. One additional challenge

is that things are resource-constrained, making it

practical impossible to combine IoT with heavy

standards like, SOAP and BPEL. The IoT component

model is further heterogeneous and multi-layered,

typically with devices, data, services, and

organizations. The IoT designed functionality is more

dynamic and context-aware than in traditional

settings.

The resulting fundamental IoT challenges

(Bouguettaya et al., 2017) are related to continuously

maintaining cyber personalities and context

information for IoT devices, and continuously

discovering, integrating, and reusing IoT and their

data. Graph-based approaches and machine-learning

techniques can facilitate discovery of hidden

relationships between IoT and helping detecting

correlations among IoT.

5 RELATED WORK

Research published in (Veneberg et al., 2016)

combines the more large-grained enterprise

architecture and fine-grained operational data, which

is an interesting approach. The intention of the

approach is to fill the gap between the disciplines

enterprise architecture and big data and to set a new

base for architectural decision support.

This quite new combination was called enterprise

architecture intelligence by the authors. It is aimed at

compensating existing shortcomings from each of the

two unrelated base disciplines and to provide an

integrated architectural model. Central in this

research is the new enterprise architecture

intelligence lifecycle (EAIL), which follows the

design science methodology.

Two-speed architecture (Bossert, 2016) has the

objective to separate elements that are required for

quickly changing the customer experience from other

elements that are more important for the integrity of

transactions. It differentiates systems that must be

more flexible and agile from those that must be more

reliable and deliver high stability and quality. To

enable a differentiating customer experience, a two-

speed architecture must cut across different layers of

the technology stack

The fast-speed-architecture contains the channels

that are pivotal for the customer experience. New

functionalities must be implemented at the same

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speed within the same deployment cycle. Also, the

traditional systems of record systems must move into

the fast speed architecture. It must be possible to

quickly adapt customer and product data structures to

enable new digital business models.

6 CONCLUSION

Based on our research question we have first set the

context of digital transformation for evolving

software systems to support digital architectures for

new designed digital services and products. We have

leveraged an adaptive architecture integration

approach for open-world integrations of globally

accessed systems and services with their local

architecture models, to be able to support digital

transformation mechanisms for flexible software and

systems compositions.

We contribute to the literature in different ways.

Looking to our results, we have identified the need

for a bottom-up integration of a huge amount of

dynamically growing micro-granular systems and

services, like mobile systems, Microservices and the

Internet of Things. To integrate micro-granular

architecture models from an open-world we are

extending more traditional enterprise architecture

reference models with state of art elements for agile

architectural engineering to support the digitization of

products, services, and processes. Secondly, we have

exemplarily focused on Internet of Things and

Microservices architectures, which are much

influencing the current digital enterprise architecture,

by changing the viewpoint for modelling complex

systems in an open-world. This is a fundamental

extension of our seminal work on architectural

reference models to be able to openly integrate

through a continuously bottom-up approach a huge

amount of micro-granular systems with own and

heterogeneous local architectures. We have thirdly

investigated current and next elements of a service-

oriented enterprise architecture to point to main

influence factors, challenges and research areas for

the evolution of enterprise architecture and the

evolving discipline of service computing.

Some limitations (e.g. use and adoption in

different sectors, or the IoT integration technologies)

must be considered. There is a need to integrate more

analytics-based decisions support and context-data

driven architectural decision-making. Limitations

can be currently found, while integrating Internet

of Things architecture in the field of multi-level

evaluations of our approach, as well as in

domain-specific adoptions. Furthermore, empirical

evaluations via case study research would be a good

starting point for future research.

We are currently working on extended decision

support mechanisms for an architectural cockpit for

adaptive digital enterprise architectures and related

collaborative processes. Future work will extend

mechanisms for adaptation and open integration.

REFERENCES

Porter, M. E., Heppelmann, J. E., 2014. How Smart

Connected Products are Transforming Competition.

November, 1-23, Harvard Business Review.

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.

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

Bogner, J., & 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.

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.

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

Work: Modelling, Communication and Analysis.

Springer.

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

Digital Ecosystem. MIT Sloan Management Review.

Westerman, G., Bonnet, D. McAfee, A., 2014. Leading

Digital. Harvard Business Review Press.

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

Age. W. W. Norton.

Möhring, M.; Keller, B.; Schmidt, R. 2018. Using Smart

Edge Devices to Integrate Consumers into Digitized

Processes: The Case of Amazon Dash-Button. BPM,

Workshops, LNBIP.

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.

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

Emerging Trends in the Evolution of Service-Oriented

and Enterprise Architectures. Springer.

Warren, A., 2016. Amazon Echo: The Ultimate Amazon

Echo User Guide 2016 Become An Alexa and Echo

Expert Now! USA: CreateSpace Independent

Publishing Platform.

Software Evolution for Digital Transformation

211

Tapscott, D., 1996. The digital economy: Promise and peril

in the age of networked intelligence. vol. 1. McGraw-

Hill.

Veneberg, R.K.M., Iacob, M.E., van Sinderen, M.J.,

Bodenstaff, L., 2016. Relating Business Intelligence,

and Enterprise Architecture – A Method for Combining

Operational Data With Architectural Metadata. In

International Journal of Cooperative Information

Systems, 25(2), 1-36, World Scientific Publishing

Company.

Atzori, L., Iera, A., Morabito, G., 2010. The Internet of

Things: A survey. In Journal of Computer Networks 54,

2787-2805.

Schmidthuber, J., 2015. Deep learning in neural networks:

An overview. 85–117, Neural Networks, vol. 61.

Weitzel, T. et al., 2000. Reconsidering Network Effect

Theory. ECIS 2000 Proceedings. Paper 91.

Evans, P.C., Annunziata, M., 2012. Industrial Internet:

Pushing the boundaries of minds and machines.

General Electric, p. 21, 2012.

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

continuing the evolution. Journal of the Academy of

Marketing Science, vol. 36, no. 1, pp. 1-10, 2008.

Open Group, 2011. TOGAF Version 9.1. Van Haren

Publishing.

Open Group, 2016. ArchiMate 3.0 Specification. Van

Haren Publishing.

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.

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., 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.

Object Management Group, 2011. Meta Object Facility

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

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.

Wilde, N., Gonen, B., El-Sheikh, E., Zimmermann, A.,

2016. Approaches to the Evolution of SOA. In El-

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

Emerging Trends in the Evolution of Service-Oriented

and Enterprise Architectures. 5-21, Springer.

Gebhart, M., Giessler, P., Abeck, S., 2016. Flexible and

Maintainable Service-Oriented Architectures with

Resource-Oriented Web Services. In El-Sheikh, E.,

Zimmermann, A., Jain, L. (Eds.), 2016. Emerging

Trends in the Evolution of Service-Oriented and

Enterprise Architectures. 23-39, Springer.

Zimmermann, A., Schmidt, R., Sandkuhl, K., El-Sheikh, E.,

Jugel, D., Schweda, C., Möhring, M., Wißotzki, M.,

Lantow, B., 2016. Leveraging Analytics for Digital

Transformation of Enterprise Services and

Architectures. In El-Sheikh, E., Zimmermann, A., Jain,

L. (Eds.), 2016. Emerging Trends in the Evolution of

Service-Oriented and Enterprise Architectures. 91-112,

Springer.

Bossert, O., 2016. A Two-Speed Architecture for the Digital

Enterprise. In El-Sheikh, E., Zimmermann, A., Jain, L.

(Eds.), 2016. Emerging Trends in the Evolution of

Service-Oriented and Enterprise Architectures. 139-

150, Springer.

Bouguettaya, A. et al., 2017. A Service Computing

Manifesto: The Next 10 Years. Communications of

ACM, vol. 60, No. 4, 64-72, ACM.

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.

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