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Autonomous and integrated passenger and freight transport (APFIT) is a promising approach to tackle both, traffic and last-mile-related issues such as environmental emissions, social and spatial conflicts or operational inefficiencies. By conducting an agent-based simulation, we shed light on this widely unexplored research topic and provide first indications regarding influential target figures of such a system in the rural area of Sarstedt, Germany. Our results show that larger fleets entail inefficiencies due to suboptimal utilization of monetary and material resources and increase traffic volume while higher amounts of unused vehicles may exacerbate spatial conflicts. Nevertheless, to fit the given demand within our study area, a comparatively large fleet of about 25 vehicles is necessary to provide reliable service, assuming maximum passenger waiting times of six minutes to the expense of higher standby times, rebalancing effort, and higher costs for vehicle acquisition and maintenance.
On November 30th, 2022, OpenAI released the large language model ChatGPT, an extension of GPT-3. The AI chatbot provides real-time communication in response to users’ requests. The quality of ChatGPT’s natural speaking answers marks a major shift in how we will use AI-generated information in our day-to-day lives. For a software engineering student, the use cases for ChatGPT are manifold: assessment preparation, translation, and creation of specified source code, to name a few. It can even handle more complex aspects of scientific writing, such as summarizing literature and paraphrasing text. Hence, this position paper addresses the need for discussion of potential approaches for integrating ChatGPT into higher education. Therefore, we focus on articles that address the effects of ChatGPT on higher education in the areas of software engineering and scientific writing. As ChatGPT was only recently released, there have been no peer-reviewed articles on the subject. Thus, we performed a structured grey literature review using Google Scholar to identify preprints of primary studies. In total, five out of 55 preprints are used for our analysis. Furthermore, we held informal discussions and talks with other lecturers and researchers and took into account the authors’ test results from using ChatGPT. We present five challenges and three opportunities for the higher education context that emerge from the release of ChatGPT. The main contribution of this paper is a proposal for how to integrate ChatGPT into higher education in four main areas.
We present an approach towards a data acquisition system for digital twins that uses a 5G net- work for data transmission and localization. The current hardware setup, which utilizes stereo vision and LiDAR for 3D mapping, is explained together with two recorded point cloud data sets. Furthermore, a resulting digital twin comprised of voxelized point cloud data is shown. Ideas for future applications and challenges regarding the system are discussed and an outlook on further development is given.
During the Corona-Pandemic, information (e.g. from the analysis of balance sheets and payment behavior) traditionally used for corporate credit risk analysis became less valuable because it represents only past circumstances. Therefore, the use of currently published data from social media platforms, which have shown to contain valuable information regarding the financial stability of companies, should be evaluated. In this data e. g. additional information from disappointed employees or customers can be present. In order to analyze in how far this data can improve the information base for corporate credit risk assessment, Twitter data regarding the ten greatest insolvencies of German companies in 2020 and solvent counterparts is analyzed in this paper. The results from t-tests show, that sentiment before the insolvencies is significantly worse than in the comparison group which is in alignment with previously conducted research endeavors. Furthermore, companies can be classified as prospectively solvent or insolvent with up to 70% accuracy by applying the k-nearest-neighbor algorithm to monthly aggregated sentiment scores. No significant differences in the number of Tweets for both groups can be proven, which is in contrast to findings from studies which were conducted before the Corona-Pandemic. The results can be utilized by practitioners and scientists in order to improve decision support systems in the domain of corporate credit risk analysis. From a scientific point of view, the results show, that the information asymmetry between lenders and borrowers in credit relationships, which are principals and agents according to the principal-agent-theory, can be reduced based on user generated content from social media platforms. In future studies, it should be evaluated in how far the data can be integrated in established processes for credit decision making. Furthermore, additional social media platforms as well as samples of companies should be analyzed. Lastly, the authenticity of user generated contend should be taken into account in order to ensure, that credit decisions rely on truthful information only.
In this paper we describe the selection of a modern build automation tool for an industry research partner of ours, namely an insurance company. Build automation has become increasingly important over the years. Today, build automation became one of the central concepts in topics such as cloud native development based on microservices and DevOps. Since more and more products for build automation have entered the market and existing tools have changed their functional scope, there is nowadays a large number of tools on the market that differ greatly in their functional scope. Based on requirements from our partner company, a build server analysis was conducted. This paper presents our analysis requirements, a detailed look at one of the examined tools and a summarizes our comparison of all three tools from our final comparison round.
Even for the more traditional insurance industry, the Microservices Architecture (MSA) style plays an increasingly important role in provisioning insurance services. However, insurance businesses must operate legacy applications, enterprise software, and service-based applications in parallel for a more extended transition period. The ultimate goal of our ongoing research is to design a microservice reference architecture in cooperation with our industry partners from the insurance domain that provides an approach for the integration of applications from different architecture paradigms. In Germany, individual insurance services are classified as part of the critical infrastructure. Therefore, German insurance companies must comply with the Federal Office for Information Security requirements, which the Federal Supervisory Authority enforces. Additionally, insurance companies must comply with relevant laws, regulations, and standards as part of the business’s compliance requirements. Note: Since Germany is seen as relatively ’tough’ with respect to privacy and security demands, fullfilling those demands might well be suitable (if not even ’over-achieving’) for insurances in other countries as well. The question raises thus, of how insurance services can be secured in an application landscape shaped by the MSA style to comply with the architectural and security requirements depicted above. This article highlights the specific regulations, laws, and standards the insurance industry must comply with. We present initial architectural patterns to address authentication and authorization in an MSA tailored to the requirements of our insurance industry partners.
Cloud computing has become well established in private and public sector projects over the past few years, opening ever new opportunities for research and development, but also for education. One of these opportunities presents itself in the form of dynamically deployable, virtual lab environments, granting educational institutions increased flexibility with the allocation of their computing resources. These fully sandboxed labs provide students with their own, internal network and full access to all machines within, granting them the flexibility necessary to gather hands-on experience with building heterogeneous microservice architectures. The eduDScloud provides a private cloud infrastructure to which labs like the microservice lab outlined in this paper can be flexibly deployed at a moment’s notice.
In this paper the workflow of the project 'Untersuchungs-, Simulations- und Evaluationstool für Urbane Logistik` (USEfUL) is presented. Aiming to create a web-based decision support tool for urban logistics, the project needed to integrate multiple steps into a single workflow, which in turn needed to be executed multiple times. While a service-oriented system could not be created, the principles of service orientation was utilized to increase workflow efficiency and flexibility, allowing the workflow to be easily adapted to new concepts or research areas.
In the context of modern mobility, topics such as smart-cities, Car2Car-Communication, extensive vehicle sensor-data, e-mobility and charging point management systems have to be considered. These topics of modern mobility often have in common that they are characterized by complex and extensive data situations. Vehicle position data, sensor data or vehicle communication data must be preprocessed, aggregated and analyzed. In many cases, the data is interdependent. For example, the vehicle position data of electric vehicles and surrounding charging points have a dependence on one another and characterize a competition situation between the vehicles. In the case of Car2Car-Communication, the positions of the vehicles must also be viewed in relation to each other. The data are dependent on each other and will influence the ability to establish a communication. This dependency can provoke very complex and large data situations, which can no longer be treated efficiently. With this work, a model is presented in order to be able to map such typical data situations with a strong dependency of the data among each other. Microservices can help reduce complexity.
Microservices build a deeply distributed system. Although this offers significant flexibility for development teams and helps to find solutions for scalability or security questions, it also intensifies the drawbacks of a distributed system. This article offers a decision framework, which helps to increase the resiliency of microservices. A metamodel is used to represent services, resiliency patterns, and quality attributes. Furthermore, the general idea for a suggestion procedure is outlined.