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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.

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.

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.

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.

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.

The emergence of a closer relationship between cloud service providers in the cloud computing market is the inevitable consequence of the computing as utility concept. The closer cooperation creates competitive advantages for providers and users of cloud services as well. Capacities and services can be used in a collaborative and flexible way. Despite the numerous potentials of composite cloud services, trust, policy and privacy are the major challenges resulting from the distributed and flexible data handling. The paper derives requirements and solutions in the field of inter-cloud service communication with a special focus on security. The proposed architecture is evaluated with a sample collaborative business process of inter-cloud service interaction.

In this paper we present an approach for an information system which is capable of processing and analysing vast amounts of data. In addition to Big Data solutions we do not focus on ex post batch processing but on online stream processing. We use Apache Storm in combination with Complex Event Processing to provide a scalable and dynamic event-driven information system, providing logistics businesses with relevant information in real-time to increase their data and process transparency.

Logistics and its involved parties are nowadays faced with demanding challenges, in order to fulfill their customers' needs. Hence logistics service providers are constrained to cooperate with each other, which leads to the challenge of ‘understanding’ each other's service descriptions and integrating the strongly differing IT-systems. An emerging approach to solve this problem is the operation of cloud platforms. Main tasks are service retrieval and composition. However, a suitable visualization is needed to generate a high user acceptance. With the service map concept a first step is taken, that certainly needs further improvement. This paper briefly gives an introduction to general information visualization and analyzes the suitability of several approaches for improving the service map concept with regards to different scales of measurement. After their comparison a general guideline for fostering the visualization concept is derived. Objective is the increase of information content while keeping an intuitive usability.

In today's logistics market, small and medium- sized logistics companies are exposed to increasing cooperation pressure to use rationalization potentials, win new groups of cus- tomers and assert themselves in the competition with big logistics companies yielding a distinct market power. In the past, numer- ous SME-size logistics companies have joined to form regional logistics associations in order to assess possible cooperation and develop cooperation potentials. In a parallel process, logistics cloud service providers develop new types of cooperation-support- ing logistics cloud services. This paper describes a method of how regional logistics associations can apply a cloud-based coopera- tion development process. The method was developed as part of a doctoral thesis and evaluated first hand by expert interviews in several logistics association in relation to the application in- volved. The results of the evaluation suggest that a suitable ap- proach was found by which regional logistics associations can tap the potentials of logistics cloud services for their member companies.

Streszczenie: Big Data jako kompleks zagadnien informatycznych stanowi jedno z najwazniejszych wyzwan wspolczesnego świata cyfrowego. W obecnych czasach, przy ciąglym naplywie duzej ilości informacji pochodzących z roznych źrodel, a zatem o roznej charakterystyce, wymaga sie wprowadzenia nowych technik analizy danych oraz rozwiązan technologicznych. W szczegolności Big Data wymaga stosowania rownoleglego przetwarzania danych oraz odejścia od klasycznego schematu przechowywania danych. Zatem w niniejszej pracy dokonano przeglądu podstawowych zagadnien związanych z tematyką Big Data. Przedstawiono rozne definicje Big Data, problemy badawcze i technologiczne oraz wyzwania dotyczące wolumenu danych, ich zroznicowania, redukcji wymiaru, jakości danych i mozliwości wnioskowania. Wskazano takze dalszy kierunek prac w zakresie rozpoznania mozliwości Big Data w roznych obszarach zarządzania. Slowa kluczowe: Big Data, definicja Big Data, wyzwania Big Data, Hadoop, NoSql, Map Reduce, przetwarzanie rownolegle.

With an increasing demand for higher value services, comprehensive support for service customization, provisioning and management is needed. This paper details ongoing research work regarding a service management approach, which supports the handling of service artifacts during the service lifecycle. The approach introduces an information model for e-Service Management, as well as a tailored view concept to provide individual views to participating stakeholders. The outlined approach tackles nonfunctional management issues which lead to better maintainable, adaptable and extendable service systems.

Bedingt durch eine immer enger zusammenruckende Welt, durch zunehmende Verbreitung von Client/Server-Architekturen und durch das Verschmelzen von Ursprunglich eigenstandigen DV-Landschaften tritt das Problem der Verstandigung zwischen unterschiedlichen Anwendungen, zwischen Clients und Servern in einer heterogen, verteilten Systemlandschaft immer mehr in den Vordergrund. Dabei ist ein solches Umfeld ideal gestaltet, wenn Anwendungen und Dienste jeweils exakt das Hard- und Softwareumfeld vorfinden, das ihnen eine optimale Leistungsfahigkeit ermoglicht. Allerdings bedeutet dies, dass die dann gegebene Hard- und Softwarelandschaft zwangslaufig verteilt und heterogen sein muss. Um unter diesen Bedingungen eine handlungsfahige und schlagkraftige Einheit formen zu konnen, wurden die so genannten N-tier Architekturen entworfen. Beispielsweise reprasentiert in einer 3-tier Architektur die unterste Ebene die Dienste der Diensteanbieter, die obere die Anwendung, wahrend dazwischen die so genannte Middleware arbeitet. Ziel ist es, den Anwendungen die durch beliebige Diensteanbieter gebotene Funktionalitat transparent, effizient und fehlertolerant zur Verfugung zu stellen, also die durch die Heterogenitat und Verteiltheit auftretenden Kommunikationsprobleme zwischen Clients (Anwendungen) und Servern (Diensteanbietern) zu losen. Dieser Beitrag baut auf dem vorherigen von Koch & Schlichter auf.

Modelle haben sich zu Dokumentationszwecken bewährt, existieren in der Praxis aber oftmals losgelöst voneinander. Durch die wachsende Komplexität in den Unternehmen reicht jedoch eine getrennte Betrachtung nicht mehr aus. Das Zusammenspiel der Unternehmensbestandteile muss bei Entscheidungen berücksichtigt werden. Eine Enterprise Architecture (EA) eignet sich zur Herstellung einer übergreifenden Sichtweise. Wobei hauptsächlich aggregierte Inhalte enthalten sind, die manuell erstellt werden. Damit ist die EA ein weiteres Datensilo. Durch das Fehlen detaillierter Informationen in der EA sind außerdem die Möglichkeiten einer ganzheitlichen Analyse begrenzt. Die vorliegende Arbeit entwickelt daher ein Gesamtkonzept, um Detailinhalte zu vernetzen und übergreifende Analysen zu ermöglichen. Insbesondere werden auch Datenwerte (z.B. Kosten) einbezogen. Eine Indirektstufe kann die Teilmodelle lose verknüpfen. Zugleich dient ein einfaches EA-Vokabular als neutrale Begriffsschicht. Mithilfe der Technologien des Semantic Web entsteht so eine integrierte Datenbasis. Sie positioniert sich als Ebene oberhalb der Datenquellen. Anschließend kann eine übergreifende Analyse erfolgen, in der alle Inhalte kombiniert werden. Zur Konkretisierung des Ansatzes fokussiert sich die Arbeit auf die Ableitung des Bedarfs an Handlungen. Mit der Importance-Performance-Analyse wird ein Verfahren aus dem Qualitätsmanagement von Dienstleistungen entliehen und auf die EA-Analyse übertragen. Die Berechnung basiert auf flexibel zu beschreibenden Kennzahlen, bei deren Definition das EA-Vokabular verwendet wird. Als Ergebnis werden Gesamtratings für alle Untersuchungsobjekte ausgewiesen. Sie sagen etwas über einen Handlungsbedarf und die Dringlichkeit aus. Auch die Analyse basiert auf Technologien des Semantic Web. Als Nachweis der Realisierbarkeit wurde der Ansatz in einem Prototyp umgesetzt. Außerdem wird ein praxisnaher Anwendungsfall einer Digitalisierungsinitiative bei einer Versicherung skizziert.

International audience; We study the feasibility of using Belief, Desire and Intention agents for modeling the phenomena of urban growth and segregation. Uncertainty, typical of real world situations is modeled using possibility theory. We have also implemented a simple visualization tool whose aim is to track the changes in the model. Some preliminary experiments suggest that such an approach might allow a decision-maker to dynamically track the changes in the model. Besides, it is also possible to interact with the different steps of the simulation via the model. Our proposal makes it possible to simulate the interactions between cognitive agents in an economical environment while taking the spatial context into account.