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Zusammenfassung Hintergrund und Fragestellung Steigende Anforderungen an Dokumentation und sektorenübergreifende Kommunikation führen zu vermehrtem organisatorischem Aufwand im Notarzt- und Rettungsdienst. Die Verwendung digitaler Informationssysteme im präklinischen Einsatz kann sowohl dazu beitragen, Einsatzkräfte bei diesen Aufgaben zu unterstützen als auch neue Behandlungsmöglichkeiten für Patienten eröffnen. Die Arbeit versucht, ein möglichst umfassendes Bild der derzeitigen Verbreitung und Nutzung von Informations- und Kommunikationssystemen zur Einsatzunterstützung in Deutschland zu erstellen. Material und Methoden Die Querschnittsstudie wurde als deutschlandweite, explorative Online-Befragung unter Notärzten und Rettungsdienstfachpersonal von Juli bis August 2020 durchgeführt. Die anschließende Datenanalyse erfolgte durch deskriptive Statistikmethoden. Betrachtet wurden u. a. Lösungen für die digitale Dokumentation und Krankenhausvoranmeldung, für den Versorgungsnachweis und Telenotarztdienst sowie der Digitalfunk. Ergebnisse Es wurden 821 Antwortbogen von 481 Rettungswachen aus insgesamt 382 Städten in der Auswertung berücksichtigt. Die Verfügbarkeit von 16 untersuchten Systemen variiert deutschlandweit je nach Bundesland und Anwendungsbereich. Befragte Einsatzkräfte zeigen sich gegenüber neuen Technologien und Möglichkeiten grundsätzlich offen, wenngleich die aktuelle Umsetzung als nicht zufriedenstellend bewertet wird. Herausforderungen zeigen sich v. a. hinsichtlich Verlässlichkeit, Hardware, Benutzerfreundlichkeit und Interoperabilität. Diskussion Eine weitreichende Digitalisierung der präklinischen Notfallmedizin wurde in Deutschland bislang nicht erreicht. Die ganzheitliche Betrachtung und vernetzte Implementierung aller am Einsatz beteiligten Systeme und Prozesse kann dazu beitragen, digitale Lösungen für die Präklinik zu verbessern und weiter zu verbreiten.

Supply chain collaboration has been widely discussed in the literature. With this maturity comes a plethora of heterogeneous research that is difficult to manage and navigate. This paper, therefore...

The analysis of business processes has become increasingly important in recent years, not least due to the emergence of analysis tools that enable data-centric views of processes and thus provide increasingly operational support for process flows. In this work, a semi-supervised classification model is presented that takes into account different developments in deep learning (e.g., deep generative models), time series analysis (e.g., long short-term memory) and sequence processing (e.g., attention mechanism) and combines them in one approach. The results of the experimental implementation of the classification model show that it is able to filter activity-related and time-related anomalies from the event data and outperform existing approaches in its classification accuracy (F1 score). The classification model achieves an F1 score of up to 93%.

Data analytics is an important component for the benefit and growth of the Internet of Things (IoT). The utilization of data generated by a variety of heterogeneous smart devices offers the possibility of gaining meaningful insights into various aspects of the daily lives of end consumers, the environment and weather, but also into value-added processes of business and industry. The potential benefits derived from analyzing IoT data can be further enhanced by advancing developments in streaming and machine learning technologies. A critical factor in the application of these technologies are the underlying analytics architectures. These must overcome a variety of different challenges that are influenced by technical, but also legal or personal constraints and differ in importance and impact depending on the IoT application domain in which such an architecture is to be deployed. Solutions presented by previous research address only a handful of these challenges. An important capability to address the variety of challenges that arise from this situation is the ability to support the hybrid deployment of analytics pipelines at different network layers. Consequently, in this work, we propose an architectural solution that enables hybrid analytics pipeline deployments, addresses the challenges described in previous scientific literature and can be deployed in various IoT application domains. Finally, we experimentally evaluate the proposed solution.

A modern economy, based on information and knowledge, forces organizations to use IT tools that support management processes. The authors presented the concept of using cognitive agent programs to support management. These programs are able to track economic phenomena and processes taking place in the organization and its environment, conduct an in-depth analysis of information, draw conclusions and take specific actions. The features of cognitive agents allow organizations to gain a competitive advantage by making the right decisions faster at the operational, tactical and strategic level and by limiting the impact of such human characteristics as emotions or fatigue on task execution. The first part of the article outlines a characterisation of cognitive agent programs. The management areas in which cognitive agents can be used are then analysed and presented. The final part of the article provides conclusions and further research work.

One of the most important issues nowadays is reducing environmental pollution by decreasing car exhaust fumes. Increasing the customization of services forces companies to improve the selling process. One of the innovations used worldwide is the touchscreen in selfservice technology. The charging points for electric cars are often located in petrol stations so that the attendant carries out the payment process. However, there are more and more charging stations in other places, e.g. shopping centres, service stations, on-street parking, where there is no attendant. In such cases charging a car requires a mobile phone app for payment, and the app must be prepaid or have a connection to the customer’s bank account. There is no possibility to pay in cash or to receive an invoice.The aim of this paper is to present the Smart Payment Terminal (SPT) as an innovation in the energy-selling process for electric and hybrid cars in places with no attendant, and show its features and advantages. The paper has a R&D character. The main contribution is developing an innovative Smart Payment Terminal that allows payment with cash, credit cards, and fleet cards, and may print a receipt or an invoice directly after the purchase

The production of food in an ecologically and economically sustainable manner is of significant importance today. Agricultural producers are increasingly being accompanied by elements of Agriculture 4.0 such as automation and decision-making support. This work shows an example of how the digitization of viticulture can be significantly supported by Deep Learning. The work presents an approach that can overcome the loss of human expertise in grape identification by using image-recognition-techniques and residual network architectures. Our developed model for grape identification at a vineyard reaches an accuracy of 99% of correctly recognized grape varieties.

This paper presents a novel method for the analysis and implementation of Artificial Intelligence (AI) inspired collaborative supply chain management (SCM) processes. The evolving nature of supply networks and the underlying information systems drives the application of AI in this context. Within information systems research, design-science approaches have become more relevant in recent years while systems thinking is proven to be a useful method for the analysis of complex issues. The paper thus aims to present a novel approach for the analysis and optimization of collaborative supply networks. For this purpose, the topicality of and potential gains from the integration of AI for supply chain collaboration as well as the shared characteristics of systems thinking and design-science research are discussed. The paper contributes to the academic debate on approaches towards future SCM and collaboration as the methodological and research theoretical focus consolidates systems thinking and design-science research and is transferable to other application areas with dynamic processes. Furthermore, this paper presents a valuable contribution to supply chain processes in organizations of all sectors by providing a macro level perspective on the topic of collaborative SCM. A systemic viewpoint enables better understanding and incorporation of all aspects of sustainability and thus facilitates the development and optimization of future digital, collaborative and resilient SCM and the underlying information systems through the application of AI.

The last decade has seen rapid developments in the area of artificial intelligence (AI). While research focuses on technical challenges and enablers of AI, the number of publications examining application approaches at the buyer-supplier interface is increasing. To accelerate the related discussion and to add clarity and richness to this fragmented research field, a systematic overview of the existing comprehensive body of literature is essential. We contribute to the academic debate by applying a combined systematic literature review with a text mining and machine learning-based literature review. Thus, we categorize and cluster different research streams and analyze the application of AI at the buyer-supplier interface. Subsequently, we identify gaps resulting from the comparison of the technology and the application domain and derive the main points of discussion from the literature. As a result, we present ten central questions outlining future requirements and research opportunities in the field of AI application at the buyer-supplier interface.

In the past years, the importance of processing data streams increased with the emergence of new technologies and application domains. The Internet of Things provides many examples in which processing and analyzing data streams are critical success factors. With the growing amount of data, the usage of machine learning (ML) algorithms has become an essential part of data analysis. However, the high volume and velocity of data presents new challenges, which need to be addressed, e.g. frequent model changes, concept drift or insufficient time to train models. From our point of view, these challenges cannot be tackled alone by using an algorithm-centric approach, i.e. to focus solely on finding appropriate algorithms, and neglecting the structure of the overall processing system.

The Internet of Things (IoT) presents an extensive area for research, based on its growing importance in a multitude of different domains of everyday life, business and industry. In this context, different aspects of data analytics, e.g. algorithms or system architectures, as well as their scientific investigation play a pivotal role in the advancement of the IoT. Therefore, past research has presented a multitude of architectural approaches to enable data processing and analytics in various IoT domains, addressing different architectural challenges. In this paper, we identify and present an overview of these challenges as well as existing architectural proposals. Furthermore, we categorize found architectural proposals along various dimensions in order to highlight the evolution of research in this field and pinpoint architectural shortcomings. The results of this paper show that several challenges have been addressed by a large number of IoT system architectures for data analytics while others are either not relevant for certain domains or need further investigation. Finally, we offer points of reference for future research based on the findings of this paper.

The analysis of event data in the context of process mining is becoming increasingly important. In particular, the processing of streaming data in the sense of an real-time analysis is gaining in relevance. More and more fields of application are emerging in which an operational support becomes necessary, i.e. in surgery or manufacturing. For proper analysis a cleanup of the streaming data in a pre-processing step is necessary to ensure accurate process mining activities. This paper presents a blueprint of a lambda architecture in which an autoencoder is embedded that is supposed to allow unsupervised anomaly detection in event streams, like incorrect traces, events and attributes, and thus will help to improve results in online process mining.

In the past years, the importance of processing data streams increased with the emergence of new technologies and application domains. The Internet of Things provides many examples, in which processing and analyzing data streams are critical success factors. An important use case is to identify anomalies, i.e. something that is different or unexpected. We often have to cope with anomaly detection use cases in sequences within data streams, for instance in network intrusion, in predictive analytics or in forecasting.

Digitization in logistics bears enormous potential for increased efficiency. Especially, the logistics of the last mile that causes between 13% and 75% of the overall logistics cost of parcel shipment could strongly benefit from digitization and an increased transparency. This transparency and a smart process control can be achieved with the help of a smart service platform. Such a platform connects with sensor, ID, and authentication technology in order to operate approaches, such as crowd logistics and sharing concepts with the goal of enabling an efficient, sustainable and user friendly last mile process. The contribution of this paper is a first conceptualization of the smart service platform for last mile logistics with special regards of the underlying business process management. A design science research approach is applied.

The success of online shopping combined with the convenience of home delivery leads to massive congestion in cities. CEP (Courier-Express-Parcels) service provider have increasing cost and service pressure, especially in the last mile parcel delivery. Therefore, we propose a process-based white-label last mile delivery platform as a smart city approach to counter congestion. It allows the consolidation of parcels on the last mile, considers customer preferences, and gives local carriers access to the parcel market. This platform is conceptualized based on insights from interviews and workshops with experts. Experiences from a pilot study are discussed.

The operating room area is still one of the most expensive sections in the hospital due to the high resource requirements and the diverse uncertainties. However there are few solutions that support monitoring and decision-making in operating room management. But with new data sources and analytical methods of big data research more improvements could be achieved. In this work we utilize surgical phase events recognized in surgical device data to learn prediction models and trigger online predictions for remaining intervention times in operating rooms. To identify the best algorithm for prediction model computation with the existing data, we evaluate a set of regression algorithms. Based on this methods we propose an architecture approach for the integrated processing of real-time data and historic learning data. The evaluation and comparison with related work shows that our prototype is competitive regarding prediction accuracy.

Anomaly-based Intrusion Detection Systems (IDS) can be instrumental in detecting attacks on IT systems. For evaluation and training of IDS, data sets containing samples of common security-scenarios are essential. Existing data sets are not sufficient for training modern IDS. This work introduces a new methodology for recording data that is useful in the context of intrusion detection. The approach presented is comprised of a system architecture as well as a novel framework for simulating security-related scenarios.

The irreversible process of demographic change, especially in Germany, leads to numerous challenges. According to this, research has to face the task to integrate the constantly ageing population into the urban and public space in such a way that there are as few barriers as possible. With the support of digitalization, so-called smart urban objects are being designed in order to do make integration, so that people and the available technology can be used most efficiently. A special ontology has been developed to meet this demand.

Energy efficiency in mobile health applications is a relevant problem for long-term monitoring and user acceptance. Various parameters influence the runtime of the system to some degree. One of the parameters is the sampling rate of the individual distributed sensors. Increasing the sampling rate can lead to an increase in energy consumption within the system. By contrast, a reduction can lead to a loss of the data quality, which reduces the informative value of the results of algorithms that use this data. Using optimization methods from reinforcement learning and deep learning to adaptive adjust the sampling rates during runtime, energy efficiency could be improved in only 40 training runs without losing data quality during sampling.

Although the usage of smart home devices such as smart speakers, light bulbs and thermostats has increased rapidly in the past years, their added value, compared to conventional devices, is mostly limited to simple control and automation logic. In order to provide adaptive smart home environments, it is necessary to gain deeper insights into the data generated by these devices and use it in sophisticated data processing pipelines. Providing such analytics to a multitude of consumers requires specialised architectures, which are able to overcome various challenges identified by scientific literature. Currently available smart home analytics architectures are not designed to tackle all of these issues, specifically fault-tolerance, network-usage, latency and external regulations. In this paper, we propose an architectural solution to address these challenges based on the concept of Fog computing. Furthermore, we provide insight into the motivation for this research as well as an overview of the current state of the art in this field.

The growing popularity of Internet-connected smart devices in consumers’ homes has led to a steady increase in the number of smart homes worldwide. In order to provide meaningful added value for consumers and businesses alike, smart devices need to be accompanied by services and applications which excel conventional devices’ usages. However, integrated solutions to support the development and operation of smart home services for, and across, different devices and application scenarios are still missing in research as well as in industry. In this paper, we present the motivation, requirements and an initial concept for a fully integrated smart home service platform (SHSP), which may serve as a basis for further discussion.

A wide range of regulations is established to protect citizens health from the noxious consequences of aerosols, e.g. particulate matter (PM10). To ensure a public information and the compliance to given regulations, a resilient environmental sensor network is necessary. This paper presents a machine learning approach which utilizes low-cost platforms to build a resilient sensor network. In particular, malfunctions are compensated by learning virtual models of various particulate matter sensors. Such virtualized sensors are already utilized in the field of proprioceptive robotics [1] and are comparable to a digital twins definition. Several experiments show the proposed method yields PM10 estimates and forecasts similar to high-performance sensors.

In operational business situations it is necessary to be aware of and to understand what happens around you and what probably will happen in the near future to make optimal decisions. For example, Online Surgery Scheduling is the planning and control task of Operating Room Management and includes decisions that are difficult to deal with due to high cognitive and communicational efforts to gather the needed information. In addition, several uncertainties like complications, cancellations and emergencies as well as the need to monitor and control the interventions during execution distinguish the operational decision tasks in surgery scheduling from the tactical and strategical planning decisions. However, the emerging trend of connecting devices and intelligent methods in analytics, facilitate innovative approaches for decision support in this area. With the utilization of these concepts, we propose a data-driven approach for a Decisions Support System including components for monitoring, prediction and optimization in Online Surgery Scheduling.

For decisions on operational business level it is necessary to be aware of and to understand what happens around you and what probably will happen in the near future. E.g. in Operating Room Management, and especially in Online surgery scheduling, a lot of decisions are difficult to handle, since there are high cognitive and communicational efforts to gather all necessary information to oversee the current situation and to derive decisions based on this information. However, the emerging trend of connecting devices and new methods in data analytics, allow new approaches for decision support in these areas. By using this concepts we suggested and implemented a data-driven architecture including several components for data analysis, information generation and visualization. The presented prototype means a proof-of-concept of the idea of a real-time and situation-aware decision support system for operating room managers.

Collaboration in business environments is an ongoing trend that is enabled by and based on cloud computing. It supports flexible and ad-hoc reconfiguration and integration of different services, which are provided and used via the internet, and implemented within business processes. This is an important competitive advantage for the participating stakeholders. However, trust, policy compliance, and data privacy are emerging issues that result from the distributed data handling in cloud-based business processes. In an earlier paper, we have discussed the requirements that a Business Process Management System (BPMS) should meet in order to enable privacy preserving business process as a service. This paper presents the necessary steps for implementing such a BPMS for an architecture to enable management of privacy-preserving collaborative business process.

The foundation of the Internet of Things (IoT) consists of ubiquitous smart devices, equipped with sensors, actuators and tags, that are connected to the Internet and able to communicate with one another. It is seen as a great opportunity for organizations to improve, innovate, and reinvent their business processes. However, existing Business Process Management (BPM) tools and systems are not fully capable of integrating and utilizing smart devices as business process resources and, thus, are unable to unlock the high joint potential of BPM and the IoT. In this paper, we propose a concept for a service-oriented BPM system architecture which aims at the modeling, implementation, and execution of IoT-aware business processes. Furthermore, we introduce a first software prototype of the architecture as a proof of concept.

Driven by falling prices, continuous miniaturization and increasing functionality, low power wireless networks tap into new application areas. But an efficient utilization of embedded systems is hindered by lacks in interoperability and flexibility, because they are proprietary. In this paper, an approach for an abstraction layer in the area of embedded systems with respect to low power wireless networks is presented. This layer is used to harmonize interfaces between network-node and application functionality like sensors. The overall goal is to design a dedicated modular low power wireless network with a flexible structure and topology.

The importance of processing stream data increases with new technologies and new use cases. Applying machine learning to stream data and process them in real time leads to challenges in different ways. Model changes, concept drift or insufficient time to train models are a few examples. We illustrate big data characteristics and machine learning techniques derived from literature and conclude with available approaches and drawbacks.

Collaboration in business environments is an ongoing trend that is enabled by and based on cloud computing. It supports flexible and ad-hoc reconfiguration and integration of different services, which are provided and used via the internet, and implemented within business processes. This is an important competitive advantage for the participating stakeholders. However, trust, policy compliance, and data privacy are emerging issues that result from the distributed data handling in cloud-based business processes. Up to now, several architectures and technical systems that enable the cloud-based collaboration within business processes have been developed, but the selection of an appropriate business process management system (BPMS) is missing. An implemented BPMS has to meet certain requirements that result from the cloud-based characteristics and from the other implemented systems. This paper derives requirements for BPMSs in cloud-based environments, currently available BPMSs are evaluated against the derived requirements and the selected one is implemented subsequently.

In the digital age, where research is data-driven, understanding all involved fields of research becomes more and more important. Understanding various data sources within interdisciplinary research and beyond domain boundaries is a significant core competency. All participants should have a same-level understanding of significant information, which can be created from various data sources. Based on this fact, the paper at hand demonstrates a modeling approach for the generation of a unified data model in terms of smart urban objects. These smart objects are represented by interconnected data structures which is a prime example in context of Internet of Things. Further, an implementation of the graph database Neo4J and a correlated visualization of intuitive structuring of data sources beyond domain boundaries will be demonstrated.

How can we benefit from innovative open-source technologies such as mobile sensors and open data platforms in the field of environmental monitoring? Due to the fact that we are facing challenging problems in terms of climate change, urbanization and a growing world population, new strategies for a more comprehensive environmental monitoring have to be developed. Here, we need to create user-specific and easy to use infrastructures as assistance tools for a comprehensive and mobile data acquisition, data processing and data provision. With regard to the variety of existing data sources, data acquisition tools and scales, this paper is focused on strategies and methods for an service oriented monitoring approach based on an integrated data model. To this end, a formal connection was defined by combining spatial, temporal and contextual information of sensor readings into one measure called the individual specific exposure (ISE), which will be later extended to a predictive individual exposure by using historical data.