Don't Repeat Yourself: Seamless Execution and Analysis of Extensive Network Experiments

This paper presents MACI, the first bespoke framework for the management, the scalable execution, and the interactive analysis of a large number of network experiments. Driven by the desire to avoid repetitive implementation of just a few scripts for the execution and analysis of experiments, MACI emerged as a generic framework for network experiments that significantly increases efficiency and ensures reproducibility. To this end, MACI incorporates and integrates established simulators and analysis tools to foster rapid but systematic network experiments. We found MACI indispensable in all phases of the research and development process of various communication systems, such as i) an extensive DASH video streaming study, ii) the systematic development and improvement of Multipath TCP schedulers, and iii) research on a distributed topology graph pattern matching algorithm. With this work, we make MACI publicly available to the research community to advance efficient and reproducible network experiments

The Evolution of an Architectural Paradigm - Using Blockchain to Build a Cross-Organizational Enterprise Service Bus

Cross-organizational collaboration and the exchange of process data are indispensable for many processes in federally organized governments. Conventional IT solutions, such as cross-organizational workflow management systems, address these requirements through centralized process management and architectures. However, such centralization is difficult and often undesirable in federal contexts. One alternative solution that emphasizes decentralized process management and a decentralized architecture is the blockchain solution of Germany’s Federal Office for Migration and Refugees. Here, we investigate the architecture of this solution and examine how it addresses the requirements of federal contexts. We find that the solution’s architecture resembles an improvement and cross-organizational adaption of an old architectural paradigm, the enterprise service bus

Artificial Intelligence as a Call for Retail Banking: Applying Digital Options Thinking to Artificial Intelligence Adoption

Technology-driven challenges, both existing and emerging, require banks to invest in IT capabilities, especially in artificial intelligence (AI). Digital options theory presents a valuable guide rail for these investments. However, the nature of AI as a moving frontier of computing requires certain extensions to established digital option thinking. Based on interviews with 23 experts in the retail banking industry, we highlight the importance of thinking broadly when laying the foundation for AI options and being mindful of the dynamic effects of contextual factors. Drawing from digital options theory and the Technology-Organization-Environment framework as dual lens, our study adds a structured approach to consciously balance resources and AI-related capability investments with a broader consideration of the banking industry’s complex environment. In this way, our study complements recent research on the interplay between incumbents’ resources and digital opportunities

Affordance-Experimentation-Actualization Theory in Artificial Intelligence Research – A Predictive Maintenance Story

Artificial intelligence currently counts among the most prominent digital technologies and promises to generate significant business value in the future. Despite a growing body of knowledge, research could further benefit from incorporating technological features, human actors, and organizational goals into the examination of artificial intelligence-enabled systems. This integrative perspective is crucial for effective implementation. Our study intends to fill this gap by introducing affordance-experimentation-actualization theory to artificial intelligence research. In doing so, we conduct a case study on the implementation of predictive maintenance using affordance-experimentation-actualization theory as our theoretical lens. From our study, we find further evidence for the existence of the experimentation phase during which organizations make new technologies ready for effective use. We propose extending the experimentation phase with the activity of ‘conceptual exploration’ in order to make affordance-experimentation-actualization theory applicable to a broader range of technologies and the domain of AI-enabled systems in particular

Business Value of the Internet of Things – A Project Portfolio Selection Approach

The Internet of Things (IoT) counts among the most disruptive digital technologies on the market. De-spite the IoT’s emerging nature, there is an increasing body of knowledge related to technological and business topics. Nevertheless, there is a lack of prescriptive knowledge that provides organizations with guidance on the economic valuation of investments in the IoT perspective. Such knowledge, however, is crucial for pursuing the organizational goal of long-term value maximization. Against this backdrop, we develop an economic decision model that helps organizations determine an optimal IoT project port-folio from a manufacturer’s perspective and complies with the principles of project portfolio selection and value-based management. For our purposes, IoT project portfolios are compilations of projects that aim to implement IoT technology in an organization’s production process, products, or infrastructure. Our decision model schedules IoT projects for multiple planning periods and considers monetary as well as monetized project effects. On this foundation, it determines the project sequence with the highest value contribution. To evaluate our decision model, we discussed its real-world fidelity and under-standability with an industry expert renowned for its proficiency in IoT technology, implemented a soft-ware prototype, and demonstrated its applicability based on real-world data. Keywords: Internet of Things, economic valuation of IoT, value-based management, project portfolio management

Graphene ribbon growth on structured silicon carbide

Structured Silicon Carbide was proposed to be an ideal template for the production of arrays of edge specific graphene nanoribbons (GNRs), which could be used as a base material for graphene transistors. We prepared periodic arrays of nanoscaled stripe-mesas on SiC surfaces using electron beam lithography and reactive ion etching. Subsequent epitaxial graphene growth by annealing is differentiated between the basal-plane mesas and the faceting stripe walls as monitored by means of atomic force microscopy (AFM). Microscopic low energy electron diffraction (μ-LEED) revealed that the graphene ribbons on the facetted mesa side walls grow in epitaxial relation to the basal-plane graphene with an armchair orientation at the facet edges. The ¼- band system of the ribbons exhibits linear bands with a Dirac like shape corresponding to monolayer graphene as identified by angle-resolved photoemission spectroscopy (ARPES)