=Paper=
{{Paper
|id=Vol-1420/ilog-paper2
|storemode=property
|title=Collaborative Decision Support for Adaptive Digital Enterprise Architecture
|pdfUrl=https://ceur-ws.org/Vol-1420/ilog-paper2.pdf
|volume=Vol-1420
|dblpUrl=https://dblp.org/rec/conf/bis/ZimmermannJSSM15
}}
==Collaborative Decision Support for Adaptive Digital Enterprise Architecture==
https://ceur-ws.org/Vol-1420/ilog-paper2.pdf
Collaborative Decision Support for Adaptive
Digital Enterprise Architecture
Alfred Zimmermann1, Dierk Jugel1,2, Rainer Schmidt3,
Christian Schweda1 and Michael Möhring4
1
Reutlingen University, Faculty of Informatics,
Alteburgstrasse 150, 72762 Reutlingen, Germany
{alfred.zimmermann, dierk.jugel,
christian.schweda}@reutlingen-university.de
2
University of Rostock, Faculty of Computer Science and Engineering,
Albert Einstein Str. 21, 18059 Rostock, Germany
dierk.jugel@uni-rostock.de
3
Munich University, Faculty of Computer Science and Mathematics,
Lothstrasse 64, 80335 München, Germany
rainer.schmidt@hm.edu
4
Aalen University, Faculty of Business Sciences,
Rombacherstrasse 99, 73430 Aalen, Germany
michael.moehring@htw-aalen.de
Abstract. The digitization of our society changes the way we live, work, learn,
communicate, and collaborate. This disruptive change interacts with all
information processes and systems that are important business enablers for the
context of digitization since years. Our aim is to support flexibility and agile
transformations for both business domains and related information technology
and enterprise systems through adaptation and evolution of digital enterprise
architectures. The present research paper investigates collaborative decision
mechanisms for adaptive digital enterprise architectures by extending original
architecture reference models with state of art elements for agile architectural
engineering for the digitization and collaborative architectural decision support.
Keywords: Decision Support, Collaboration, Digital Enterprise Architecture,
Architectural Engineering and Transformation
1 Introduction
Digitization is the collaboration of human beings and autonomous objects beyond
their local context using digital technologies. Digitization further increases the
importance of information, data and knowledge as fundamental concepts of our
everyday activities. By exchanging information human beings and intelligent objects
are able to make decisions in a broader context and with higher quality. Social
networks, smart portable devices, and intelligent cars, represent only a few instances
of a pervasive, information-driven vision [1] for the next wave of digital economy and
better-aligned information systems. Major trends for the digitization are investigated
by [2] itemizing the digitization of products and services, context-sensitive value
Copyright © 2015 by the authors. Copying permitted for private and academic purposes.
This volume is published and copyrighted by its editors.
24
creation, consumerization of IT, digitization of work, and the digitization of business
models. The Internet of Things, Adaptive Case Management, Decision Support
Systems, Mobility Systems, and Services for Big Data in Cloud Ecosystems are
emerging to support intelligent user-centered and social community systems. They
will shape future trends of business innovation and the next wave of information and
communication technology. Biological metaphors of living and adaptable ecosystems
provide the logical foundation for self-optimizing and resilient run-time environments
for intelligent business services and related distributed information systems with
service-oriented enterprise architectures.
The technological and business architectural impact of digitization has multiple
aspects, which directly affect adaptable digital enterprise architectures and their
supported systems. Smart companies are extending their capabilities continuously
managing their changing Business Operating Model [3] by developing and
maintaining Enterprise Architectures as the architectural part of a changing IT
Governance [4]. Enterprise Architecture Management [5] and Services Computing is
the approach of choice to organize, build, utilize, and distribute capabilities for the
digital enterprise architectures [6], [7]. They provide flexibility and agility in business
and IT systems. The development of such applications integrates Web and REST
Services, Cloud Computing and Big Data management, among other frameworks and
methods for the architectural semantic support.
Today’s information systems span a broad range of domains including: intelligent
mobility systems and services, intelligent energy support systems, smart personal
health-care systems and services, intelligent transportation and logistics services,
smart environmental systems and services, intelligent systems and software
engineering. One of the challenges is the safe integration of mobile devices into
managed enterprise architecture of both business and IT. Nowadays it is much easier
to work together over large distances, which allows often an uncomplicated
outsourcing of business tasks. Businesses need to adapt and have to rethink their
business models to develop innovative business models according to employees’
current skills and competencies.
Digitization of products and services requires the close alignment of business
models and digital technologies for creative digital strategies and solutions, as well as
for their digital transformation. Unfortunately, the current state of art and practice of
enterprise architecture lacks an integral understanding and support of collaborative
decisions in the process of architectural adaptation and enterprise transformation. Our
main motivation and the current presented work is to extend previous approaches of
quiet static enterprise architecture to fit for flexible and adaptive digitization of new
products and services and by introducing suitable mechanisms for collaborative
architectural engineering and decision support with adaptive case management for
agile changing business models, information systems and their digital enterprise
architecture. We report about our work in progress research to provide a unified
collaborative decision framework for adaptable digital enterprise architecture models
from relevant information resources of digital products and services and their digital
transformation.
Our current research paper is investigating the following questions:
RQ1: What is the blueprint of extended digital enterprise architecture for the digital
transformation with mechanisms of adaptation and adaptive case management?
25
RQ2: How can processes of architectural engineering and transformation be
supported collaboratively?
RQ3: How can collaborative decision support mechanisms be specifically designed
by introducing decision-making metamodels for digital enterprise architecture?
The following Section 2 describes our research platform for digital enterprise
architecture, which was extended by concepts from adaptive case management,
architectural adaptation mechanisms and a specific model integration method. Section
3 presents our collaborative architectural engineering and transformation approach
and links it with specific decisional and prediction mechanisms. In Section 4 we focus
on collaborative decision techniques and present a decisional metamodel for digital
enterprise architectures. Finally, we summarize in Section 5 our research findings, our
ongoing work in academic and practical environments and our future research plans.
2 Digital Enterprise Architecture
Enterprise Architecture Management (EAM) [8], [9], [10] defines today with
frameworks, standards [11], [12], tools and practical expertise a quite large set of
Hochschule Reutlingen
Enterprise Architecture
different viewsManagement
and perspectives. We argue in this paper that a new refocused digital
Reutlingen University
enterprise architecture approach should support digitization of products and services,
ESARC © - Enterprise
and should be both Software
holistic [5] Architecture Reference
and [12] and easily adaptableCube
[13] to support the
Architecture Capability Diagnostics, Monitoring,
digital transformation with new business models and andtechnologies
Optimization like social
software, big data, services & cloud computing, mobility platforms and
! ESARC defines an original holistic classification scheme for cyclic diagnostics and optimizationsystems,
of eight
types (viewpoints)
security of service-oriented
systems, enterprise
and semantics software
support. We architectures
are evolving the first versions of
! ESARCESARC–Enterprise Services
substantiates the TOGAF Architecture
standard with otherReference Cube [5],
models to provide [12]mapping
a useful (Fig. 1).foundation of a
reference architecture, defining main architecture artifacts and their relationships
Fig. 1. Enterprise Services Architecture Reference Cube © Prof. Dr. Alfred Zimmermann
25 © Alfred Zimmermann and SOA Innovation Lab 2011
In this paper we extend our service-oriented enterprise architecture reference
model for the context of managed adaptive cases and decisions [14], [15], which are
supported by case services of a collaborative case framework [14] within an adaptive
26
case management environment [16]. Additionally we have tailored our architectural
metamodel integration approach [17] to support digital enterprise architectures for
digital transformations [6] and the integration of Internet of Things [7] architectures.
ESARC – Enterprise Services Architecture Reference Cube [5], [12] is an
architectural reference model for an extended view on evolved digital enterprise
architectures. ESARC is more specific than existing architectural standards of EAM –
Enterprise Architecture Management [11] and [12] and extends these architectural
standards for digital enterprise architectures with services and cloud computing.
ESARC provides a holistic classification model with eight integral architectural
domains. These architectural domains cover specific architectural viewpoint
descriptions [18] and [19] in accordance to orthogonal dimensions of both
architectural layers and architectural aspects [12], [9], and [20]. ESARC abstracts
from a concrete business scenario or technologies, but it is applicable for concrete
architectural instantiations to support digital transformations. The Open Group
Architecture Framework [11] provides the basic blueprint and structure for our
extended service-oriented enterprise architecture domains of ESARC [5], [13] having:
Architecture Governance, Architecture Management, Business and Information
Architecture, Information Systems Architecture, Technology Architecture, Operation
Architecture, and Cloud Services Architecture. ESARC provides a coherent aid for
examination, comparison, classification, quality evaluation and optimization.
We developed an architectural evolution approach to integrate and adapt most
valuable parts of existing EA frameworks and metamodels from theory and practice
[17]. Additionally to handling architectural structures for dynamically extending core
metamodels we see a chance to integrate decentralized mini-metamodels, models and
data of architectural descriptions coming from small devices and new decentralized
architectural elements, which traditionally are not covert by enterprise architecture
environments. The focused model integration approach is based on special correlation
matrixes to identify similarities between analyzed model elements from different
provenience and integrate them according their most valuable contribution for an
integrated model. According to [21] we are building the conceptualization of EA in 4
steps – from stakeholders’ needs, to the concerns of stakeholders, then the extraction
of stakeholder relevant concepts, and last but not least the definition of relationships
for new tailored architectural metamodels.
Our research consists of a metamodel-based model extraction and integration
approach [17] for digital enterprise architecture viewpoints, models, standards,
frameworks and tools to support digital transformations [6] and [7]. Currently we are
working on the idea of continuously integrating small EA descriptions for relevant
objects of digital enterprise architecture. These EA-Mini-Descriptions consists of
partial EA data and partial EA models and related metamodels. Our goal is to be able
to support an integral architectural engineering and transformation process.
Adaptation drives the survival [22], [23], [24] of digital enterprise architectures
[13], platforms and application ecosystems. Adapting rapidly to new technology and
market contexts improves the fitness of adaptive ecosystems. Volatile technologies
and markets typically drive the evolution of ecosystems. We have additionally to
consider internal factors. The alignment of Architecture-Governance [3], [4] shapes
resiliency, scalability and composability of components and services for distributed
information systems.
27
3 Architectural Engineering and Transformation
Although concepts such as Business Process Management [25] introduced a
customer-oriented perspective, it still contains many concepts following the ideas
developed already in [26]. These are the division of larger tasks into defined, smaller
tasks and the assignment of individual responsible to accomplish these tasks.
Therefore it does not surprise, that a plenty of approaches such as [27], [14] tried to
develop support for cooperation beyond strictly structured business processes as
almost all WFMSs and most of the BPMSs, but also some groupware and case
management systems. However these approaches become not as successful as
expected.
One has to meet a number of challenges when supporting EA management
processes. The first challenge is the lack of a pre-defined workflow. Similar to
adaptive case management [28], the control-flow of EA management processes
cannot be predefined in most situation. Instead the control-flow is defined “on-the-
fly” during execution of the EA management process.
The second challenge is organizational integration [28]. Many early approaches
addressing the support of EA management processes limited the participation of
stakeholders. E.g. although classical groupware abstained from pre-defining a strict
control flow, specific access rights to documents had been assigned. Thus the group
of possible contributors had been limited. In this way an apriori-decision had been
made deciding who may contribute and who may not. Some stakeholders were not
able to contribute.
The third challenge is semantic integration [29]. Due to the involvement of a
multitude of stakeholders, semantic frictions such as homonyms and synonyms create
misunderstandings between the process participants. These semantic frictions may
delay the EA management process or even worse, may cause deficient architectures.
Social software is based on four basic principles: social production [30], weak ties
[31], collective decisions [32], and value co-creation [33]. Each of these principles
support EA management processes by addressing one or more challenges, as
addressed in Fig. 2.
Reconciling terminology
Social production Semantic integration
Collective0decisions
Ad.hoc0workflow definition
Value0co.production
Weak ties Organisational0integration
Stakeholder0integation
Fig. 2. Architectural Engineering and Transformation [34]
28
Social production [30] is the creation of artifacts without a top-down created plan
but by combining the suggestions and decisions from independent contributors. By
abstaining from Tayloristic top-down planning, new and innovative contributions
outside the original scope can be identified and added. Due to these properties, social
production matches the requirements of EA management processes. The control flow
of EA management processes can be defined in an ad-hoc manner. During execution
of the EA process, artifacts as architecture models can be created in a cooperative
way.
Collective decisions [32] provide a new way in EA management processes to make
decisions. They provide statistically better results than experts, if the decision cannot
be made using scientific means and the participants decide independently. Surowiecki
describes in [35] the approach of the so-called the wisdom of crowds. He argues that a
decision made by several persons often leads to better results, because each person
has a specific knowledge. Value-co-production [33] is also supporting the definition
and execution of EA management processes by integrating contributions from the
business side. By abolishing the separation between artifact producer and consumer, a
better adaptation to the individual requirements can be achieved. Furthermore value
co-production enhances the organizational integration.
Adaptive Case Management (ACM) [14] and [15] offers a lightweight model to
support knowledge-intensive processes, which are driven by user decision-making.
Knowledge processes of usually high-skilled stakeholders, like enterprise architects,
require process adaptations at run-time. ACM is not dictating a predefined course of
action [36] and provides the necessary information and knowledge support to be able
to solve a case.
A case [15] is typically a collection of all relevant information into one place,
which is handled by one or more knowledge workers during solving this case. The
case is the jointly used focal point for assessing the situation, initiating activities and
processes, implementing the work, and reflecting results based on a history record
about what was really done. A case brings together all the necessary resources and
also tracks everything that has happened into a record history, which can be mined to
synthesize best practices, patterns of success, and used and extended instruments.
Fundamental aspects and requirements for ACM, are mentioned in [36]:
1. The adaptation aspect of ACM consists of content, people, and reporting
capabilities to be able to change the knowledge process at run-time by end-users.
Additionally to the adaptation aspect a knowledge worker should be able to
continuously improve his case templates.
2. The organization aspect groups policies, processes, and data. In ACM data is the
dominant factor as opposed to the process-oriented view from BPM. Knowledge
work requires the integration of data [36] into the execution process.
3. The case handling aspect is about collaboration, decision support, and integration
of resources, events, and communication. Complex problems are typically solved
collaboratively by involving individual stakeholders in respect of different
necessary knowledge types and stakeholder concerns. Decision support requires
transparency within a shared understanding of analyzed scenarios of enterprise
29
architecture by named stakeholders.
Opposed to routine work, which can be supported by business process management
because of its repeatable kind, knowledge work is typically unpredictable. Knowledge
workers [37], [38] are acting under uncertainty. An unpredictable process [15] does
not repeat in routine patterns and emerges as the work is done. The practice of
preparing for many possible courses is called agility. Differentiating seven domains of
predictability [15] case management can be focused on two main types:
1. Product Case Management: Supports design-time knowledge processes with a
well-known set of actions, having much variation between individual cases. It is
not possible to set out a single fixed process. Knowledge workers are actively
involved in deciding the course of events for a case.
2. Adaptive Case Management: Knowledge workers are involved not only in the
case, and picking predefined actions, but they are constantly adapting the process
and striving for innovative approaches, and may want to share and discuss
process plans.
The Object Management Group (OMG) has published the Case Management
Model and Notation (CMMN) [39] as a first step to support modeling for case
management scenarios. In [40] was implemented a case study of a TOGAF-style
process for EAM with CMMN. The upcoming standard Decision Model and Notation
(DMN) of OMG [41] discern three usage models: for modeling human decision-
making, for modeling requirements for automated decision-making, and for
implementing automated decision-making.
DMN bridges the gap between business decision designs and their implementation
by providing a common notation for decision models. The purpose of DMN is to
facilitate a decision model framework, which is easily usable for decision diagrams
and as a base for optionally automating decisions. Decision-making support is
addressed from basically two perspectives: normal BPMN business Process Models
can be expanded by defining specific decision tasks, or decision logic can be used to
support individual decisions, e.g. business rules, decision tables, or executable
analytic models.
DMN can additionally provide a third perspective to bridge between business
process models and decision logic by introducing the Decision Requirements
Diagram. Complementary to the DMN notation, which is used to model decisional
relationships and concepts like Decision, Input Data, Business Logic, Application,
Application Risk, etc. DMN introduces an expression language to represent decision
tables, decision rules, and function invocations. Today we are exploring the suitable
usage and close link of DNM for decisional support logic within our architectural
engineering and analytics research.
30
4 Decision Support
A Decision support system (DSS) in general is a system “[…] to help improve the
effectiveness of managerial decision making in semi structured tasks” ([42] p. 255
according to [43]). Semi structured tasks like in EAM need a basement to improve
architectural decision-making trough a DSS. In the following we consider decisional
prerequisites from previous section and look how they are fulfilled using an EA
cockpit [29]. A cockpit is characterized as a room with multiple displays to be able to
consider several coherent viewpoints in parallel. Each stakeholder who takes place in
a cockpit meeting has his own information because each stakeholder can say, which
views are relevant for him and all these views are displayed simultaneously. Each
stakeholder has his own specific knowledge because stakeholders have different roles
like Application Architect, Business Process Owner or Technology expert and comes
from different areas of the enterprise. A meeting moderator can put together the
relevant stakeholder’s knowledge by discussion. By using our architectural cockpit
we make impacts of a change on other views visible.
Jugel et al. [44] enhanced the approach from [45] with collaborative aspects. The
authors developed a collaborative decision-making case by using methods of ACM
and case modeling techniques by using CMMN [39] (see Fig. 3).
Fig. 3. CMMN model of a collaborative decision-making case
The starting point of the case is an initial issue. The issue is the reason why an EA
has to be adapted. For instance, new business requirements have to be realized. A
decision-making case consists of several "Decision-making steps". For each step one
or more stakeholders are responsible to perform it. Thereby stakeholders can employ
31
analysis techniques of a predefined and case independent catalogue to obtain
additional insights. For each step the basis is the underlying EA model and results of
previous steps. Buckl et al. [46] defines three kinds of analysis techniques: (1) expert-
based, (2) rule-based, and (3) indicator-based analysis techniques.
Expert-based analysis techniques are done manually without any formalization.
Stakeholders perform such a technique by using their experience. Rule-based analysis
techniques are formalized and can be automated by performing an algorithm (e.g. an
impact analysis). Indicator-based analysis techniques are also formalized. Instead of
rule-based techniques, the results of indicator-based technique are values of KPIs and
not identified elements in case of an impact analysis.
The result of an analysis technique is made visible for stakeholders e.g. by
highlighting calculated elements of an impact analysis within different views. Thus
stakeholders are able to consider results within the views they are interested. After
considering the analysis technique's result stakeholders can choose how they want to
finish the current step. They can model an evaluation to assess the analysis
technique's result. In case they are able to take a design decision they can model a
decision to describe the change. The last option is modeling another issue, e.g. to
refine the initial issue based on new findings. After each step the stakeholders have to
decide whether they need further decision-making steps or if they have finished. In
case of performing another step, the outputs of previous steps provide the basis for the
following steps.
Furthermore the authors enhanced the decisional metamodel of [45] by adding
collaborative concepts and elements to support modeling the case. Thereby the
decisional metamodel enables a retraceable documentation of decision-making works.
The presented metamodel contains only a few elements to fuel practicability through
reduced modeling overhead [44].
5 Conclusions and Future Work
In this paper we identified the need for an integral understanding and support of
collaborative decisions in the process of architectural adaptation and enterprise
transformation. According to our research questions we have leveraged a new model
of extended digital enterprise architecture, which is well suited for adaptive models
and transformation mechanisms. We have extended the previous more static defined
basic enterprise reference architecture by new metamodel elements for supporting
cooperative decisions using mechanisms from adaptive case management.
Related to our second research question we have presented our approach for
collaborative processes in architectural engineering and transformation endeavors. We
have additionally combined architectural engineering and transformation processes
with elements from adaptive case management. We have extended typical
architectural engineering processes with elements from social production, collective
decision-making, value co-production, and week ties. Adaptive case management
offers a lightweight model for knowledge-intensive processes.
We have finally merged architectural viewpoints with user decision-making
processes within cooperative distributed environments for enterprise architecture
32
management. We have introduced suitable individual decision support models and
embedded them into cooperative analysis and engineering environments. We are
currently working on extended decision support mechanisms for an architectural
cockpit for digital enterprise architectures and related engineering processes.
Additionally we are currently considering elements from collaborative systems
combined with semantic support, as in Gruber [46].
Future work will extend both mechanisms for adaptation and flexible integration of
digital enterprise architectures as well as will extend decisional processes by
rationales and explanations. There are also a need to integrate more analytics based
decisions support [47], [48], [49] and context-data driven architectural decision-
making [50].
References
1. Aier, S. et al.: Towards a More Integrated EA Planning: Linking Transformation Planning
with Evolutionary Change. In: Proceedings of EMISA 2011, Hamburg, Germany, 23--36
(2011)
2. Leimeister, J. M. et al.: Research Program “Digital Business Transformation HSG”. In:
Working Paper Services of University of St. Gallen’s Institute of Information Management,
No. 1, St. Gallen, Switzerland (2014)
3. Ross, J. W. et al.: Enterprise Architecture as Strategy – Creating a Foundation for Business
Execution. Harvard Business School Press, Harvard Business School Press (2006)
4. Weill, P., Ross, J. W.: It Governance: How Top Performers Manage It Decision Rights for
Superior Results. Harvard Business School Press (2004)
5. Zimmermann, A. et al.: Capability Diagnostics of Enterprise Service Architectures using a
dedicated Software Architecture Reference Model. In IEEE International Conference on
Services Computing (SCC), Washington DC, USA, 2011; 592--599, (2011)
6. Zimmermann, A. et al.: Evolving Enterprise Architectures for Digital Transformations. In:
Zimmermann, A., Rossmann, A. (eds.) DEC 15, 25-26 June 2015, Böblingen, Germany,
Lecture Notes in Informatics, Volume P-244, pp. 183--194, (2015)
7. Zimmermann, A. et al.: Digital Enterprise Architecture – Transformation for the Internet of
Things. Submitted for EDOCW 2015 with SoEA4EE, 21-25 September 2015, Adelaide,
Australia, (2015)
8. Johnson, P. et al.: IT Management with Enterprise Architecture. KTH, Stockholm (2014)
9. Lankhorst, M. et al.: Enterprise Architecture at Work: Modelling, Communication and
Analysis. Springer (2013)
10.Bente, S. et al.: Collaborative Enterprise Architecture. Morgan Kaufmann (2012)
11.The Open Group: TOGAF Version 9.1. Van Haren Publishing (2011)
12.Zimmermann, A. et al.: Towards Service-oriented Enterprise Architectures for Big Data
Applications in the Cloud. EDOC 2013 with SoEA4EE, 9-13 September 2013, Vancouver,
BC, Canada, 130--135 (2013)
13.Zimmermann, A. et al.: Adaptable Enterprise Architectures for Software Evolution of
SmartLife Ecosystems. In: Proceedings of the 18th IEEE International Enterprise
Distributed Object Computing Conference Workshops (EDOCW 2014), Ulm / Germany,
316--323 (2014)
14.Swenson, K. D.: Mastering the Unpredictable: How adaptive case management will
revolutionize the way that knowledge workers get things done, Meghan-Kiffer Press (2010)
15.Swenson, K. D.: State of the Art In Case Management, White Paper Fujitsu (2013)
33
16.Collenbusch, D. et al.: Experiencing Adaptive Case Management Capabilities with
Cognoscenti. In: Zimmermann, A., Rossmann, A. (eds.) DEC 15, 25-26 June 2015,
Böblingen, Germany, Lecture Notes in Informatics, Volume P-244, pp. 233--243 (2015)
17.Zimmermann, A. et al.: Towards an Integrated Service-Oriented Reference Enterprise
Architecture. ESEC / WEA 2013 on Software Ecosystem Architectures, St. Petersburg,
Russia, 26--30 (2013)
18.ISO/IEC/IEEE: Systems and Software Engineering – Architecture Description. Technical
Standard (2011)
19.Emery, D., Hilliard, R.: Every Architecture Description needs a Framework: Expressing
Architecture Frameworks Using ISO/IEC 42010. IEEE/IFIP WICSA/ECSA 2009; 31--39,
(2009)
20.Iacob, M.-E. et al.: Delivering Business Outcome with TOGAF® and ArchiMate®. eBook
BiZZdesign (2015)
21.Buckl, S. et al.: Modeling the supply and demand of architectural information on enterprise
level. 15th IEEE International EDOC Conference 2011, Helsinki, Finland, 44--51, (2011)
22.Tiwana, A.: Platform Ecosystems: Aligning Architecture, Governance, and Strategy.
Morgan Kaufmann (2013)
23.Heistacher, T. et al.: Pervasive service architecture for a digital business ecosystem. arXiv
preprint cs/0408047 (2004)
24.Bertossi, L.: Database Repairing and Consistent Query Answering. Morgan & Claypool
Publishers (2011)
25.Weske, M.: Business Process Management: Concepts, Languages, Architectures. Springer-
Verlag Berlin Heidelberg (2007)
26.Taylor, F. W.: The principles of scientific management. N. Y., vol. 202 (1911)
27.Bruno, G.: Requirements Elicitation as a Case of Social Process: An Approach to Its
Description. In Business Process Management Workshops, 2010, 243--254 (2010)
28.Bruno, G., Dengler, F., Jennings, B., Khalaf, R., Nurcan, S., Prilla, M., Sarini, M., Schmidt,
R., Silva, R.: Key challenges for enabling agile BPM with social software. J. Softw. Maint.
Evol. Res. Pract., vol. 23, no. 4, 297--326 (2011)
29.Jugel, D., Schweda, C. M.: Interactive functions of a Cockpit for Enterprise Architecture
Planning. In: International Enterprise Distributed Object Computing Conference Workshops
and Demonstrations (EDOCW 2014), Ulm, Germany, 33--40 (2014)
30.Benkler, Y.: The Wealth of Networks: How Social Production Transforms Markets and
Freedom. Yale University Press (2006)
31.Granovetter, M.: The Strength of Weak Ties,” Am. J. Sociol., vol. 78, no. 6, 1360--1380
(1973)
32.Tapscott, D., Williams, A.: Wikinomics: How Mass Collaboration Changes Everything.
(2006)
33.Vargo, S. L., Maglio, P. P., Akaka, M. A.: On value and value co-creation: a service systems
and service logic perspective. Eur. Manag. J., vol. 26, no. 3, 145--152 (2008)
34.Schmidt, R.; Zimmermann, A.; Möhring, M.; Jugel, D.; Bär, F., Schweda, C. M.: Social-
Software-Based Support for Enterprise Architecture Management Processes. In: Business
Process Management Workshops, Springer, pp. 452-462 (2014)
35.Surowiecki, J.: The Wisdom of the Clouds. Anchor (2005)
36.Hauder, M.; Pigat, S.; Matthes, F.: Research Challenges in Adaptive Case Management: A
Literature Review. In: International Enterprise Distributed Object Conference Workshops
and Demonstrations (EDOCW 2014), Ulm, Germany, 98--107 (2014)
37.Fischer, L.: Taming the Unpredictable Real World Adaptive Case Management: Case
Studies and Practical Guidance, Future Strategies (2011)
38.Fischer, L.: Empowering Knowledge Workers, Future Strategies (2014)
39.Object Management Group: Case Management Modeling Notation 1.0 (2014)
34
40.Hauder, M.; Münch, D.; Michel, F.; Utz, A.; Matthes, F.: Examining Adaptive Case
Management to Support Processes for Enterprise Architecture Management. In:
International Enterprise Distributed Object Conference Workshops and Demonstrations
(EDOCW), Ulm, Germany, pp. 23-32 (2014)
41.Object Management Group: Decision Model and Notation 1.0 - Beta 1 (2014)
42.Keen, P.G.W.: Decision support systems: the next decade. In: Decision Support Systems,
vol. 3(3), Elsevier, pp. 253-265 (1987)
43.Keen, P.G.W.; Morton, M.S.S. (1978): Decision support systems: an organizational
perspective, Addison-Wesley (1978)
44.Jugel, D.; Kehrer, S.; Schweda, C.M.; Zimmermann, A.: A Decision-Making Case for
Collaborative Enterprise Architecture Engineering. In: Cunningham, D.; Hofstedt, P.; Meer,
K.; Schmitt, I. (Eds.): Informatik 2015, Lecture Notes in Informatics (LNI) (2015)
45.Plataniotis, G; De Kinderen, S.; Proper, H.A.: 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 (2014)
46.Gruber, T.: Collective knowledge systems: Where the Social Web meets the Semantic Web.
In: Journal Web Semantics: Science, Services and Agents on the World Wide Web, Volume
6, Issue 1, February, Elsevier, pp. 4--13 (2008)
47.Buckl, S.; Matthes, F.; Schweda, C.M.: Classifying Enterprise Architecture Analysis
Approaches. In: The 2nd IFIP WG5.8 Workshop on Enterprise Interoperability
(IWEI’2009), Valencia, Spain, 66--79 (2009)
48.Govedarski, K.; Hauptman, C.; Schweda, C.M.: Bottom-up EA Management Governance
using Recommender Systems. In: Zimmermann, A., Rossmann, A. (eds.) DEC 15, 25-26
June 2015, Böblingen, Germany, Lecture Notes in Informatics, Volume P-244, pp. 163--173
(2015)
49.Zimmermann, A., Sandkuhl, K., Schmidt, R., Jugel, D., Wissotzki, M., Möhring, M.:
Adaptive Digitale Enterprise Architekturen für Big Data und Cloud-Systeme.
INFORMATIK 2014: Big Data - Komplexität meistern, 44. Jahrestagung der Gesellschaft
für Informatik Stuttgart. pp. 417--428 (2014).
50.Möhring, M., Schmidt, R., Härting, R.-C., Bär, F., Zimmermann, A.: Classification
Framework for Context Data from Business Processes. In: Fournier, F. and Mendling, J.
(eds.) Business Process Management Workshops, Springer International Publishing, pp.
440--445 (2014).
35