Design and Evaluation of a Smart-Glasses-based Service Support System

The character of IT transformed from an attached commodity to the center of new products and services. Especially in technical customer services, new technologies such as smart glasses offer great opportunities to overcome current challenges. Due to the complexity of service systems engineering, guidance on how to design smart glasses-based service support systems is necessary. To overcome this complexity and fill the research gap of design knowledge, we (1) analyze the domain in a multi-method approach eliciting meta-requirements, (2) propose design principles, and (3) instantiate them in a prototype. We follow a design science research approach combing the buildphase with four evaluation cycles obtaining focus groups twice, demonstration with prototype and, based on that, a survey with 105 experts from the agricultural sector. We address real-world problems of information provisioning at the point of service and, thereby, contribute to the methodological knowledge base of IS Design and Service Systems Engineering

Requirements Catalog for Business Process Modeling Recommender Systems

The manual construction of business process models is a time-consuming and error-prone task. To improve the quality of business process models, several modeling support techniques have been suggested spanning from strict auto-completion of a business process model with pre-defined model elements to suggesting closely matching recommendations. While recommendation systems are widely used and auto-completion functions are a standard feature of programming tools, such techniques have not been exploited for business process modeling although implementation strategies have already been suggested. Therefore, this paper collects requirements from different perspectives (literature and empirical studies) of how to effectively and efficiently assist process modelers in their modeling task. The condensation of requirements represents a comprehensive catalog, which constitutes a solid foundation to implement effective and efficient Process Modeling Recommender Systems (PMRSs). We expect that our contribution will fertilize the field of modeling support techniques to make them a common feature of BPM tools

Process Modeling Recommender Systems - A Generic Data Model and Its Application to a Smart Glasses-based Modeling Environment

The manual construction of business process models is a time-consuming, error-prone task and presents an obstacle to business agility. To facilitate the construction of such models, several modeling support techniques have been suggested. However, while recommendation systems are widely used, e.g., in e-commerce, these techniques are rarely implemented in process modeling tools. The creation of such systems is a complex task since a large number of requirements and parameters have to be taken into account. In order to improve the situation, the authors have developed a data model that can serve as a backbone for the development of process modeling recommender systems (PMRS). This article outlines the systematic development of this model in a stepwise approach using established requirements and validates it against a data model that has been reverse-engineered from a real-world system. In a last step, the paper illustrates an exemplary instantiation of the data model in a Smart Glasses-based modeling environment and discusses business process agility issues. The authors expect their contribution to provide a useful starting point for designing the data perspective of process modeling recommendation features that support business agility in process-intensive environments

Mobile Service Support based on Smart Glasses

Emerging technologies, such as smart glasses, offer new possibilities to support service processes. Specifically, in situations where a person providing a service, such as a technician, needs both hands to complete a complex set of tasks, hands-free speech-controlled information systems can offer support with additional information. We investigated this research field in a three-year consortium with partners from the agricultural technology sector. During the course of our research, we 1) analyzed the domain in a multi-method approach to develop (meta-)requirements, 2) proposed design principles, 3) instantiated them in a prototype, and 4) evaluated the prototype. We followed a design science research approach in which we combined the build phase with four evaluation cycles that comprised focus groups, a prototype demonstration, and, based on that demonstration, a survey with 105 domain experts. We address real-world problems in providing information at the point of service and contribute to the methodological knowledge base of IS design and service systems engineering by developing and implementing design requirements and principles for smart glasses-based service support systems

How Machines are Serviced - Design of a Virtual Reality-based Training System for Technical Customer Services

Training service provider is a crucial factor for high-quality service delivery. Due to the rise of new devices, reviving Virtual Reality (VR) offer great opportunities to overcome current training challenges. As various new interaction and visualization systems push into market, guidance on how to design VR-based training systems is necessary. The presented use case is based on technicians in technical customer services (TCS) who tackle increasing complexity of machines. We fill the research gap of design knowledge by (1) analyzing the domain in a multi-method approach to elicit meta-requirements, (2) proposing design principles, and (3) instantiating them in a prototype. The interaction of the user with the training system was identified as key aspect to foster learning. We follow a design science research approach (DSR) combing the build-phase with agile evaluation cycles obtaining focus groups and demonstration with a prototype

DESIGNING MHEALTH APPLICATIONS FOR DEVELOPING COUNTRIES

The effective use of mobile IS offers great opportunities for improving health systems in developing countries and enhancing their quality of life. A case in point and, hence, an interesting research subject is Papua New Guinea for being a country with one of the highest maternal mortality rates in the world. Despite the opportunities, many mHealth solutions remain prototypical due to their design and lack of empirical evidence and just little literature discussing success factors exists. To overcome this problem, we derived Design Requirements for the implementation of an mHealth app. We followed a Design Science Research (DSR) approach (a) embedding a triangulation of a literature study, a user survey and on-site observations, (b) working in a cross-cultural and interdisciplinary team and (c) evaluating the Design Requirements ex-ante by taking the example of an mHealth app to support midwives in Papua New Guinea. Practitioners, IS researcher, even design- or behaviourism-oriented, as well as transdis-ciplinary researchers can use the Design Requirement Framework for, on the one hand, design and implement applications in developing countries and, on the other hand, to take single already justified Design Requirements as starting point for a detailed investigation

Crop Classification Under Varying Cloud Cover With Neural Ordinary Differential Equations

Optical satellite sensors cannot see the earth’s surface through clouds. Despite the periodic revisit cycle, image sequences acquired by earth observation satellites are, therefore, irregularly sampled in time. State-of-the-art methods for crop classification (and other time-series analysis tasks) rely on techniques that implicitly assume regular temporal spacing between observations, such as recurrent neural networks (RNNs). We propose to use neural ordinary differential equations (NODEs) in combination with RNNs to classify crop types in irregularly spaced image sequences. The resulting ODE-RNN models consist of two steps: an update step, where a recurrent unit assimilates new input data into the model’s hidden state, and a prediction step, in which NODE propagates the hidden state until the next observation arrives. The prediction step is based on a continuous representation of the latent dynamics, which has several advantages. At the conceptual level, it is a more natural way to describe the mechanisms that govern the phenological cycle. From a practical point of view, it makes it possible to sample the system state at arbitrary points in time such that one can integrate observations whenever they are available and extrapolate beyond the last observation. Our experiments show that ODE-RNN, indeed, improves classification accuracy over common baselines, such as LSTM, GRU, temporal convolutional network, and transformer. The gains are most prominent in the challenging scenario where only few observations are available (i.e., frequent cloud cover). Moreover, we show that the ability to extrapolate translates to better classification performance early in the season, which is important for forecasting

Urban Change Forecasting from Satellite Images

Forecasting where and when new buildings will emerge is a rather unexplored topic, but one that is very useful in many disciplines such as urban planning, agriculture, resource management, and even autonomous flying. In the present work, we present a method that accomplishes this task with a deep neural network and a custom pretraining procedure. In Stage 1, a U-Net backbone is pretrained within a Siamese network architecture that aims to solve a (building) change detection task. In Stage 2, the backbone is repurposed to forecast the emergence of new buildings based solely on one image acquired before its construction. Furthermore, we also present a model that forecasts the time range within which the change will occur. We validate our approach using the SpaceNet7 dataset, which covers an area of 960 km$$^2$$ at 24 points in time across 2 years. In our experiments, we found that our proposed pretraining method consistently outperforms the traditional pretraining using the ImageNet dataset. We also show that it is to some degree possible to predict in advance when building changes will occur