Informatik
Refine
Year of publication
Document Type
- Conference proceeding (36) (remove)
Is part of the Bibliography
- yes (36)
Institute
- Informatik (36)
Publisher
- Springer (19)
- Gesellschaft für Informatik e.V (5)
- IEEE (5)
- RWTH Aachen (3)
- Association for Information Systems (2)
- Elsevier (1)
- SciTePress (1)
New business concepts such as Enterprise 2.0 foster the use of social software in enterprises. Especially social production significantly increases the amount of data in the context of business processes. Unfortunately, these data are still an unearthed treasure in many enterprises. Due to advances in data processing such as Big Data, the exploitation of context data becomes feasible. To provide a foundation for the methodical exploitation of context data, this paper introduces a classification, based on two classes, intrinsic and extrinsic data.
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.
Presently, many companies are transforming their strategy and product base, as well as their culture, processes and information systems to become more digital or to approach for a digital leadership. In the last years new business opportunities appeared using the potential of the Internet and related digital technologies, like Internet of Things, services computing, cloud computing, edge and fog computing, social networks, big data with analytics, mobile systems, collaboration networks, and cyber physical systems. Digitization fosters the development of IT environments with many rather small and distributed structures, like the Internet of Things, Microservices, or other micro-granular elements. This has a strong impact for architecting digital services and products. The change from a closed-world modeling perspective to more flexible open-world composition and evolution of micro-granular system architectures defines the moving context for adaptable systems. We are focusing on a continuous bottom-up integration of micro-granular architectures for a huge amount of dynamically growing systems and services, as part of a new digital enterprise architecture for service dominant digital products.
Data analysis is becoming increasingly important to pursue organizational goals, especially in the context of Industry 4.0, where a wide variety of data is available. Here numerous challenges arise, especially when using unstructured data. However, this subject has not been focused by research so far. This research paper addresses this gap, which is interesting for science and practice as well. In a study three major challenges of using unstructured data has been identified: analytical know-how, data issues, variety. Additionally, measures how to improve the analysis of unstructured data in the industry 4.0 context are described. Therefore, the paper provides empirical insights about challenges and potential measures when analyzing unstructured data. The findings are presented in a framework, too. Hence, next steps of the research project and future research points become apparent.
The Internet of Things, enterprise social networks, adaptive case management, mobility systems, analytics for big data, and cloud services environments are emerging to support smart connected products and services and the digital transformation. 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. We are investigating mechanisms for flexible adaptation and evolution for the next digital enterprise architecture systems in the context of the digital transformation. Our aim is to support flexibility and agile transformation for both business and related enterprise systems through adaptation and dynamical evolution of digital enterprise architectures. The present research paper investigates digital transformations of business and IT and integrates fundamental mappings between adaptable digital enterprise architectures and service-oriented information systems. We are putting a spotlight with the example domain – Internet of Things.
Big Data und Cloud Systeme werden zunehmend von mobilen, benutzerzentrierten und agil veränderbaren Informationssystemen im Kontext von digitalen sozialen Netzwerken genutzt. Metaphern aus der Biologie für lebendige und selbstheilende Systeme und Umgebungen liefern die Basis für intelligente adaptive Informationssysteme und für zugehörige serviceorientierte digitale Unternehmensarchitekturen. Wir berichten über unsere Forschungsarbeiten über Strukturen und Mechanismen adaptiver digitaler Unternehmensarchitekturen für die Entwicklung und Evolution von serviceorientierten Ökosystemen und deren Technologien wie Big Data, Services & Cloud Computing, Web Services und Semantikunterstützung. Für unsere aktuellen Forschungsarbeiten nutzen wir praxisrelevante SmartLife Szenarien für die Entwicklung, Wartung und Evolution zukunftsgerechter serviceorientierter Informationssysteme. Diese Systeme nutzen eine stark wachsende Zahl externer und interner Services und fokussieren auf die Besonderheiten der Weiterentwicklung der Informationssysteme für integrierte Big Data und Cloud Kontexte. Unser Forschungsansatz beschäftigt sich mit der systematischen und ganzheitlichen Modellbildung adaptiver digitaler Unternehmensarchitekturen - gemäß standardisierter Referenzmodelle und auf Standards aufsetzenden Referenzarchitekturen, die für besondere Einsatzszenarien auch bei kleineren Anwendungskontexten oder an neue Kontexte einfacher adaptiert werden können. Um Semantik-gestützte Analysen zur Entscheidungsunterstützung von System- und Unternehmensarchitekten zu ermöglichen, erweitern wir unser bisheriges Referenzmodell für ITUnternehmensarchitekturen ESARC – Enterprise Services Architecture Reference Cube – um agile Mechanismen der Adaption und Konsistenzbehandlung sowie die zugehörigen Metamodelle und Ontologien für Digitale Enterprise Architekturen um neue Aspekte wie Big Data und Cloud Kontexte.