Alfred Zimmermann

Towards Service-Oriented Enterprise Architectures for Big Data Applications in the Cloud

Applications with Service-oriented Enterprise Architectures in the Cloud are emerging and will shape future trends in technology and communication. The development of such applications integrates Enterprise Architecture and Management with Architectures for Services & Cloud Computing, Web Services, Semantics and Knowledge-based Systems, Big Data Management, among other Architecture Frameworks and Software Engineering Methods. In the present work in progress research, we explore Service-oriented Enterprise Architectures and application systems in the context of Big Data applications in cloud settings. Using a Big Data scenario, we investigate the integration of Services and Cloud Computing architectures with new capabilities of Enterprise Architectures and Management. The underlying architecture reference model can be used to support semantic analysis and program comprehension of service-oriented Big Data Applications. Enterprise Services Computing is the current trend for powerful large-scale information systems, which increasingly converge with Cloud Computing environments. In this paper we combine architectures for services with cloud computing. We propose a new integration model for service-oriented Enterprise Architectures on basis of ESARC - Enterprise Services Architecture Reference Cube, which is our previous developed service-oriented enterprise architecture classification framework, with MFESA - Method Framework for Engineering System Architectures - for the design of service-oriented enterprise architectures, and the systematic development, diagnostics and optimization of architecture artifacts of service-oriented cloud-based enterprise systems for Big Data applications.

Digital Enterprise Architecture - Transformation for the Internet of Things

Excellence in IT is both a driver and a key enabler of the digital transformation. The digital transformation changes the way we live, work, learn, communicate, and collaborate. The Internet of Things (IoT) fundamentally influences todays digital strategies with disruptive business operating models and fast changing markets. New business information systems are integrating emerging Internet of Things infrastructures and components. With the huge diversity of Internet of Things technologies and products organizations have to leverage and extend previous Enterprise Architecture efforts to enable business value by integrating Internet of Things architectures. Both architecture engineering and management of current information systems and business models are complex and currently integrating beside the Internet of Things synergistic subjects, like Enterprise Architecture in context with services & cloud computing, semantic-based decision support through ontologies and knowledge-based systems, big data management, as well as mobility and collaboration networks. To provide adequate decision support for complex business/IT environments, we have to make transparent the impact of business and IT changes over the integral landscape of affected architectural capabilities, like directly and transitively impacted IoT-objects, business categories, processes, applications, services, platforms and infrastructures. The paper describes a new metamodel-based approach for integrating Internet of Things architectural objects, which are semi-automatically federated into a holistic Digital Enterprise Architecture environment.

Towards an integrated service-oriented reference enterprise architecture

New business information systems are integrating emerging cloud infrastructures with service-oriented platforms and intelligent user-centered mobile systems. Both architecture engineering and management of service-oriented enterprise architectures is complex and has to integrate synergistic disciplines like EAM - Enterprise Architecture and Management for Services & Cloud Computing, Semantic-based Decision Support through Ontologies and Knowledge-based Systems, Big Data Management, as well as Mobility and Collaboration Systems. It is necessary to identify affected decisions by runtime changes of a service-oriented runtime environment and architecture. We have to make transparent the impact of these changes over the integral landscape of affected EAM-capabilities, like directly and transitively impacted business categories, processes, applications, services, platforms and infrastructures. The paper describes a new Metamodel-based integration approach for Service-oriented Reference Enterprise Architectures.

Towards a Framework for Enterprise Architecture Analytics

Current approaches for enterprise architecture lack analytical instruments for cyclic evaluations of business and system architectures in real business enterprise system environments. This impedes the broad use of enterprise architecture methodologies. Furthermore, the permanent evolution of systems desynchronizes quickly model representation and reality. Therefore we are introducing an approach for complementing the existing top-down approach for the creation of enterprise architecture with a bottom approach. Enterprise Architecture Analytics uses the architectural information contained in many infrastructures to provide architectural information. By applying Big Data technologies it is possible to exploit this information and to create architectural information. That means, Enterprise Architectures may be discovered, analyzed and optimized using analytics. The increased availability of architectural data also improves the possibilities to verify the compliance of Enterprise Architectures. Architectural decisions are linked to clustered architecture artifacts and categories according to a holistic EAM Reference Architecture with specific architecture metamodels. A special suited EAM Maturity Framework provides the base for systematic and analytics supported assessments of architecture capabilities.

Modeling Decisions for Collaborative Enterprise Architecture Engineering

New or adapted digital business models have huge impacts on Enterprise Architectures (EA) and require them to become more agile, flexible, and adaptable. All this changes are happening frequently and are currently not well documented. An EA consists of a lot of elements with manifold relationships between them. Thus changing the business model may have multiple impacts on other architectural elements. The EA engineering process deals with the development, change and optimization of architectural elements and their dependencies. Thus an EA provides a holistic view for both business and IT from the perspective of many stakeholders, which are involved in EA decision-making processes. Different stakeholders have specific concerns and are collaborating today in often-unclear decision-making processes. In our research we are investigating information from collaborative decision-making processes to support stakeholders in taking current decisions. In addition we provide all information necessary to understand how and why decisions were taken. We are collecting the decision-related information automatically to minimize manual time intensive work as much as possible. The core contribution of our research extends a decisional metamodel, which links basic decisions with architectural elements and extends them with an associated decisional case context. Our aim is to support a new integral method for multi-perspective and collaborative decision-making processes. We illustrate this by a practice-relevant decision-making scenario for Enterprise Architecture Engineering.

Adaptable Enterprise Architectures for Software Evolution of SmartLife Ecosystems

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 – Perspectives for Conceptualization

Digitization is more than using digital technologies to transfer data and perform computations and tasks. Digitization embraces disruptive effects of digital technologies on economy and society. To capture these effects, two perspectives are introduced, the product and the value-creation perspective. In the product perspective, digitization enables the transition from material, static products to interactive and configurable services. In the value-creation perspective, digitization facilitates the transition from centralized, isolated models of value creation, to bidirectional, co-creation oriented approaches of value creation.

Benefits of Enterprise Architecture Management – Insights from European Experts

Excellence in IT is a key enabler for the digital transformation of enterprises. To realize the vision of Digital Enterprises it is necessary to cope with changing business requirements and to align business and IT. In order to evaluate the contribution of Enterprise Architecture Management to these goals, our paper explores the impact of various factors to the perceived benefit of EAM in enterprises. Based on literature, we build an empirical research model. It is tested with empirical data of European EAM experts using a structural equation modelling approach. It is shown that changing business requirements, IT Business Alignment, the complexity of Information Technology infrastructure as well as enterprise architecture knowledge of Information Technology employees are crucial impact factors to the perceived benefit of EAM in enterprises.

Towards Integrating Microservices with Adaptable Enterprise Architecture

IT environments that consist of a very large number of rather small structures like microservices, Internet of Things (IoT) components, or mobility systems are emerging to support flexible and agile products and services in the age of digital transformation. Biological metaphors of living and adaptable ecosystems with service-oriented enterprise architectures provide the foundation for self-optimizing, resilient run- time environments and distributed information systems. We are extending Enterprise Architecture (EA) methodologies and models that cover a high degree of heterogeneity and distribution to support the digital transformation and related information systems with micro-granular architectures. Our aim is to support flexibility and agile transformation for both IT and business capabilities within adaptable digital enterprise architectures. The present research paper investigates mechanisms for integrating Microservice Architectures (MSA) by extending original enterprise architecture reference models with elements for more flexible architectural metamodels and EA-mini-descriptions.

Leveraging Textual Information for Improving Decision-Making in the Business Process Lifecycle

Business process implementations fail, because requirements are elicited incompletely. At the same time, a huge amount of unstructured data is not used for decision-making during the business process lifecycle. Data from questionnaires and interviews is collected but not exploited because the effort doing so is too high. Therefore, this paper shows how to leverage textual information for improving decision making in the business process lifecycle. To do so, text mining is used for analyzing questionnaires and interviews.