Service-Oriented Enterprise Architecture

Emerging business models such as digital ecosystems involving collaborative innovation, participatory decision-making and flexible organizational structures are set to significantly affect inter- and intra-enterprise structure and functionality. This has seen the appearance of increasingly complex Service-oriented Architecture (SOA) scenarios featuring various partnership levels, increased resilience and adaptation to tougher compliance regulations, more seamless integration with process layers, etc. This complexity has brought about the need to broaden the SOA paradigm, so as to include business, infrastructure and multi-layering, resulting in an evolved concept known as Service-Oriented Enterprise Architecture (SOEA). This paper aims to describe the way new business and technology paradigms continue to affect the new SOEA approach and how the inherently holistic Enterprise Architecture (EA) frameworks and related practices can support the emerging complex dynamic, multi-layered and value-chained service-based scenario.

The goal of the workshop is to develop concepts and methods to assist the engineering and management of service oriented enterprise architectures and the software systems supporting them. Especially four themes of research have been pursued: 1. Alignment of the enterprise goals and strategies with the service-oriented enterprise architecture; 2. Design of the service-oriented enterprise architecture; 3. Mapping of service-oriented enterprise architecture to enterprise resources; 4. SoEA and Cloud-Computing; influence of cloud, social and big data.

High-level enterprise services are considered as wrappers of business processes and ICT capabilities in enterprise architecture. Enterprise services have distinctive features that are not typically observed in Web services, e.g. signicant portions of the functionality of these services might be executed in a human-mediated fashion. As such, a service level agreement should be described as a mixture of human-mediated functionality (e.g., service penalty) and computer-interpretable factors (e.g., reliability, payment). This paper reports our preliminary work in defining a taxonomy for co-relating SLAs with elements of enterprise architecture. It gives insight into the alignment between what the enterprise in questions contractually agrees with its partners (or customers) and what it possesses to be able to fulfill the agreement. We define the service level agreement formally. We adopt the ArchiMate - a widely used language for modeling and analyzing enterprise architectures. We then propose the corelation between items in the agreement with elements in the enterprise architecture. We showcase our work using a real-life case-study in the retailing market of domestic airfare.

SmartLife ecosystems are emerging as intelligent user-centered systems that will shape future trends in technology and communication. Biological metaphors of living adaptable ecosystems provide the logical foundation for self-optimizing and self-healing run-time environments for intelligent adaptable business services and related information systems with service-oriented enterprise architectures. The present research in progress work investigates mechanisms for adaptable enterprise architectures for the development of service-oriented ecosystems with integrated technologies like Semantic Technologies, Web Services, Cloud Computing and Big Data Management. With a large and diverse set of ecosystem services with different owners, our scenario of service-based SmartLife ecosystems can pose challenges in their development, and more importantly, for maintenance and software evolution. Our research explores the use of knowledge modeling using ontologies and flexible metamodels for adaptable enterprise architectures to support program comprehension for software engineers during maintenance and evolution tasks of service-based applications. Our previous reference enterprise architecture model ESARC--Enterprise Services Architecture Reference Cube--and the Open Group SOA Ontology was extended to support agile semantic analysis, program comprehension and software evolution for a SmartLife applications scenario. The Semantic Browser is a semantic search tool that was developed to provide knowledge-enhanced investigation capabilities for service-oriented applications and their architectures.

Digitization of societies changes the way we live, work, learn, communicate, and collaborate. In the age of digital transformation IT environments with a large number of rather small structures like Internet of Things (IoT), Microservices, or mobility systems are emerging to support flexible and agile digitized products and services. Adaptable ecosystems with service-oriented enterprise architectures are the foundation for self-optimizing, resilient run-time environments and distributed information systems. The resulting business disruptions affect almost all new information processes and systems in the context of digitization. Our aim are more flexible and agile transformations of both business and information technology domains with more flexible enterprise information systems through adaptation and evolution of digital enterprise architectures. The present research paper investigates mechanisms for decision-controlled digitization architectures for Internet of Things and Microservices by evolving enterprise architecture reference models and state of the art elements for architectural engineering for micro-granular systems.

The digital transformation of our life changes the way we work, learn, communicate, and collaborate. Enterprises are presently transforming their strategy, culture, processes, and their information systems to become digital. The digital transformation deeply disrupts existing enterprises and economies. Digitization fosters the development of IT systems with many rather small and distributed structures, like Internet of Things, Microservices and mobile services. Since years a lot of new business opportunities appear using the potential of services computing, Internet of Things, mobile systems, big data with analytics, cloud computing, collaboration networks, and decision support. Biological metaphors of living and adaptable ecosystems provide the logical foundation for self-optimizing and resilient run-time environments for intelligent business services and adaptable distributed information systems with service-oriented enterprise architectures. This has a strong impact for architecting digital services and products following both a value-oriented and a service perspective. The change from a closed-world modeling world to a more flexible open-world composition and evolution of enterprise architectures defines the moving context for adaptable and high distributed systems, which are essential to enable the digital transformation. The present research paper investigates the evolution of Enterprise Architecture considering new defined value-oriented mappings between digital strategies, digital business models and an improved digital enterprise architecture.

Organizations are facing the challenge of transforming their business models into digital firms, as companies are becoming part of the platform economy. The orchestration of the IT application landscape and strategic business–IT alignment is essential to gain transparency in surviving the digitalization. The important issues of IT and business alignment, cost reduction, organizational change, transformation, and fulfillment of legal requirements are becoming more important. The necessity of the “enterprise architect” rises importance. Enterprise architects do not, in general, consider themselves as active drivers for change. The research project answers the question of how are enterprise architects fostering business and IT alignment. The study focuses on an exploration in the evolution and changing role of the enterprise architect in the context of digital transformation. The research methodology consists of a systematic literature and applies a qualitative, explorative, and summative content analysis in the DACH region. Results indicate that fostering business and IT alignment require more than ever the role of an active intra-organizational networker and communicator in our complex digital organizational ecosystem. The profession of enterprise architects therefore has great opportunities but needs to transform the role from overseeing visualization to becoming the enabler for digitalization.

Several frameworks have been developed to assist the analysis of service management systems. However, most of these frameworks only capture limited aspects of a service-oriented enterprise. This paper first reviews several popular service management frameworks. An enterprise architecture approach that is commonly used in information systems planning has been adapted to integrate various service management frameworks and tools, as well as to support the planning and documentation of a service-oriented enterprise. The proposed service-oriented enterprise architecture (SOEA) includes the following aspects: service, process, people, information, and motivation. Each aspect can be viewed from a strategic, tactical, and operational level. The mapping of the conceptual constructs of existing service management frameworks to the proposed SOEA is discussed in detail.

Enterprise architecture (EA) is a set of documents describing various aspects of an organization from an integrated business and IT perspective. EA facilitates information systems planning and helps improve business and IT alignment. Traditionally, the concept of EA was closely coupled with the business strategy and mainstream EA methodologies recommend starting the EA effort from documenting the business strategy and then using it as the basis for defining the required structure of information systems. This conceptual paper discusses in detail four practical problems with the business strategy that question its value as the basis for EA initiatives. The presence of these problems challenges one of the most cherished beliefs or even axioms of the EA discipline: that EA should be based on the business strategy. This paper raises a number of questions regarding the information inputs necessary for the EA effort and calls for further research in respective directions.

Enterprise Architecture (EA) has undergone many changes since the IT has found its way into enterprises. At the moment the Service-Oriented Architecture (SOA) is being hyped but has also gained some importance. Implementing SOA can have many implications for an enterprise, depending on how visionary the implemented architecture is. This paper provides the description of an enterprise architecture that is fully-fledged concerning service-orientation and points out the architectural challenges that have to be mastered with future research results.

The discipline of enterprise architecture (EA) is an established approach to model and manage the interaction of business processes and IT in an organization. Thereby, the EA model as a central artifact of EA is subject to a continuous evolution caused by multiple sources of changes. The continuous evolution requires a lot of effort in controlling and managing the evolution of the EA model. This is especially true when merging the induced changes from different sources in the EA model. Additionally, the lack of tool and automation support makes this a very time-consuming and error-prone task. The evolutionary character and the automated quality assessment of artifacts is a well-known challenge in the software development domain as well. To meet these challenges, the discipline of continuous delivery (CD) has emerged to be very useful. The evolution of EA model artifacts shows similarities to the evolution of software artifacts. Therefore, we leveraged practices of CD to practices of EA maintenance. Thus, we created a conceptual framework for automated EA model maintenance. The concepts were realized in a first prototype and were evaluated in a fictitious case study against equivalence classes based on EA model metrics and a set of several requirements for automated EA model maintenance from research. Overall, the concepts prove to be a promising basis for further refinement, implementation, and evaluation in research in an industrial context.

The Business Process Management & Enterprise Architecture (BPMEA) track aims at gathering researchers and practitioners around the broad topics of business processes and enterprise architecture with a special interest in modeling. These disciplines are quickly evolving and intertwining with each other, and are often referred to with the broad term of business modeling. While several researches are addressing these aspects, there is still a strong need of exploring new paths of improvement and consolidation, especially in light of the recent trends in the business, which put a lot of emphasis on large-scale system engineering, through modeling techniques that are applied at global scale, and data analysis approaches that combine cloud-based big data analytics with enterprise and system models. Topics of relevance for the BPMEA track include enterprise and systems architecture and modeling, multilevel models tracing and alignment, models transformation, IT & business alignment (both in terms of modeling and goals) and big data analysis applied to enterprise-wide data, tackling both technical (languages, systems, patterns, tools) and social (collaboration, human-in-the-loop) issues.

Enterprise architecture supports organizational engineering in many ways. Service orientation is regarded as dominant operations model for service providers -- within and beyond IT. As a consequence, it is important to integrate service management and service orientation into enterprise architecture. This paper proposes an enterprise architecture extension that achieves such an integration. IT service management is defined according to ITIL. Based on the integration of service management into enterprise architecture, the integration of Service Oriented Architecture is discussed as a further extension. The research is based on the Business Engineering approach and the guidelines of Method Engineering.

Service oriented architectures (SOA) are becoming reality in a corporate environment. Rather than pure technology improvements SOA intends to increase manageability and sustainability of IT systems and to better align business requirements and technology implementations. Following these intentions current SOA research is focused on the management of SOA. In this paper we present a method to identify metrics for an evaluation of SOA. Therefore we adopted the goal/question/metrics method to SOA specifics. Since SOA is not limited to questions of technology we will show where SOA might be located in an enterprise architecture (EA) context. Based on the assumption that SOA follows different goals on different levels of EA abstraction, the paper shows, how these goals can be developed to metrics which can be consolidated in a measurement program.

Aim/Purpose: This study identifies the scale types and measurement units used in the measurement of enterprise architecture (EA) and analyzes the admissibility of the mathematical operations used. Background: The majority of measurement solutions proposed in the EA literature are based on researchers’ opinions and many with limited empirical validation and weak metrological properties. This means that the results generated by these solutions may not be reliable, trustworthy, or comparable, and may even lead to wrong investment decisions. While the literature proposes a number of EA measurement solutions, the designs of the mathematical operations used to measure EA have not yet been independently analyzed. It is imperative that the EA community works towards developing robust, reliable, and widely accepted measurement solutions. Only then can senior management make informed decisions about the allocation of resources for EA initiatives and ensure that their investment yields optimal results. Methodology: In previous research, we identified, through a systematic literature review, the EA measurement solutions proposed in the literature and classified them by EA entity types. In a subsequent study, we evaluated their metrology coverage from both a theoretical and empirical perspective. The metrology coverage was designed using a combination of the evaluation theory, best practices from the software measurement literature including the measurement context model, and representational theory of measurement to evaluate whether EA measurement solutions satisfy the metrology criteria. The research study reported here presents a more in-depth analysis of the mathematical operations within the proposed EA measurement solutions, and for each EA entity type, each mathematical operation used to measure EA was examined in terms of the scale types and measurement units of the inputs, their transformations through mathematical operations, the impact in terms of scale types, and measurement units of the proposed outputs. Contribution: This study adds to the body of knowledge on EA measurement by offering a metrology-based approach to analyze and design better EA measurement solutions that satisfy the validity of scale type transformations in mathematical operations and the use of explicit measurement units to allow measurement consistency for their usage in decision-making models. Findings: The findings from this study reveal that some important metrology and quantification issues have been overlooked in the design of EA measurement solutions proposed in the literature: a number of proposed EA mathematical operations produce numbers with unknown units and scale types, often the result of an aggregation of undetermined assumptions rather than explicit quantitative knowledge. The significance of such aggregation is uncertain, leading to numbers that have suffered information loss and lack clear meaning. It is also unclear if it is appropriate to add or multiply these numbers together. Such EA numbers are deemed to have low metrological quality and could potentially lead to incorrect decisions with serious and costly consequences. Recommendations for Practitioners: The results of the study provide valuable insights for professionals in the field of EA. Identifying the metrology limitations and weaknesses of existing EA measurement solutions may indicate, for instance, that practitioners should wait before using them until their design has been strengthened. In addition, practitioners can make informed choices and select solutions with a more robust metrology design. This, in turn, will benefit enterprise architects, software engineers, and other EA professionals in decision making, by enabling them to take into consideration factors more adequately such as cost, quality, risk, and value when assessing EA features. The study’s findings thus contribute to the development of more reliable and effective EA measurement solutions. Recommendation for Researchers: Researchers can use with greater confidence the EA measurement solutions with admissible mathematical operations and measurement units to develop new decision-making models. Other researchers can carry on research to address the weaknesses identified in this study and propose improved ones. Impact on Society: Developers, architects, and managers may be making inappropriate decisions based on seriously flawed EA measurement solutions proposed in the literature and providing undue confidence and a waste of resources when based on bad measurement design. Better quantitative tools will ultimately lead to better decision making in the EA domain, as in domains with a long history of rigor in the design of the measurement tools. Such advancements will benefit enterprise architects, software engineers, and other practitioners, by providing them with more meaningful measurements for informed decision making. Future Research: While the analysis described in this study has been explicitly applied to evaluating EA measurement solutions, researchers and practitioners in other domains can also examine measurement solutions proposed in their respective domains and design new ones.