@inproceedings{17368, author = {{Vorbohle, Christian and Szopinski, Daniel and Kundisch, Dennis}}, editor = {{Shishkov, B.}}, isbn = {{978-3-030-52305-3}}, location = {{Potsdam, Germany}}, publisher = {{Springer}}, title = {{{Business Model Dependencies: Towards conceptualizing dependencies for extending modeling languages for business models}}}, volume = {{391}}, year = {{2020}}, } @inproceedings{18249, abstract = {{Augmented Reality (AR) has recently found high attention in mobile shopping apps such as in domains like furniture or decoration. Here, the developers of the apps focus on the positioning of atomic 3D objects in the physical environment. With this focus, they neglect the configuration of multi-faceted 3D object composition according to the user needs and environmental constraints. To tackle these challenges, we present a model-based approach to support AR-assisted product con-figuration based on the concept of Dynamic Software Product Lines. Our approach splits products (e.g. table) into parts (e.g. tabletop, ta-ble legs, funnier) with their 3D objects and additional information (e.g. name, price). The possible products, which can be configured out of these parts, are stored in a feature model. At runtime, this feature model can be used to configure 3D object compositions out of the product parts and adapt to user needs and environmental constraints. The benefits of this approach are demonstrated by a case study of configuring modular kitchens with the help of a prototypical mobile-based implementation.}}, author = {{Gottschalk, Sebastian and Yigitbas, Enes and Schmidt, Eugen and Engels, Gregor}}, booktitle = {{Human-Centered Software Engineering. HCSE 2020}}, editor = {{Bernhaupt, Regina and Ardito, Carmelo and Sauer, Stefan}}, keywords = {{Product Configuration, Augmented Reality, Runtime Adaptation, Dynamic Software Product Lines}}, location = {{Eindhoven}}, publisher = {{Springer}}, title = {{{Model-based Product Configuration in Augmented Reality Applications}}}, doi = {{10.1007/978-3-030-64266-2_5}}, volume = {{12481}}, year = {{2020}}, } @phdthesis{18520, author = {{Setzer, Alexander}}, publisher = {{Universität Paderborn}}, title = {{{Local Graph Transformation Primitives For Some Basic Problems In Overlay Networks}}}, doi = {{10.17619/UNIPB/1-1026}}, year = {{2020}}, } @misc{18637, author = {{Schürmann, Patrick}}, publisher = {{Universität Paderborn}}, title = {{{A Group Signature Scheme from Flexible Public Key Signatures and Structure-Preserving Signatures on Equivalence Classes}}}, year = {{2020}}, } @misc{18639, author = {{Terfort, Tobias}}, publisher = {{Universität Paderborn}}, title = {{{Enhancing Security by Usage of Universal One-Way Hash Functions}}}, year = {{2020}}, } @techreport{22161, author = {{Kundisch, Dennis}}, pages = {{1}}, title = {{{Digitale mehrseitige Plattformen – Besser verstehen, wie digitale Plattformen funktionieren}}}, volume = {{3}}, year = {{2020}}, } @article{6202, author = {{Szopinski, Daniel and Schoormann, T. and John, Thomas and Knackstedt, R. and Kundisch, Dennis}}, journal = {{Electronic Markets}}, number = {{3}}, pages = {{469--494}}, title = {{{Software tools for business model innovation: Current state and future challenges}}}, volume = {{30}}, year = {{2020}}, } @inproceedings{16726, author = {{Razzaghi Kouchaksaraei, Hadi and Shivarpatna Venkatesh, Ashwin Prasad and Churi, Amey and Illian, Marvin and Karl, Holger}}, booktitle = {{European Conference on Networks and Communications (EUCNC 2020)}}, title = {{{Dynamic Provisioning of Network Services on Heterogeneous Resources}}}, year = {{2020}}, } @inproceedings{16933, abstract = {{The continuous innovation of its business models is an important task for a company to stay competitive. During this process, the company has to validate various hypotheses about its business models by adapting to uncertain and changing customer needs effectively and efficiently. This adaptation, in turn, can be supported by the concept of Software Product Lines (SPLs). SPLs reduce the time to market by deriving products for customers with changing requirements using a common set of features, structured as a feature model. Analogously, we support the process of business model adaptation by applying the engineering process of SPLs to the structure of the Business Model Canvas (BMC). We call this concept a Business Model Decision Line (BMDL). The BMDL matches business domain knowledge in the form of a feature model with customer needs to derive hypotheses about the business model together with experiments for validation. Our approach is effective by providing a comprehensive overview of possible business model adaptations and efficient by reusing experiments for different hypotheses. We implement our approach in a tool and illustrate the usefulness with an example of developing business models for a mobile application.}}, author = {{Gottschalk, Sebastian and Rittmeier, Florian and Engels, Gregor}}, booktitle = {{Proceedings of the 22nd IEEE International Conference on Business Informatics}}, keywords = {{Business Model Decision Line, Business Model Adaptation, Hypothesis-driven Adaptation, Software Product Line, Feature Model}}, location = {{Antwerp}}, publisher = {{IEEE}}, title = {{{Hypothesis-driven Adaptation of Business Models based on Product Line Engineering}}}, doi = {{10.1109/CBI49978.2020.00022}}, year = {{2020}}, } @inproceedings{16934, abstract = {{To build successful products, the developers have to adapt their product features and business models to uncertain customer needs. This adaptation is part of the research discipline of Hypotheses Engineering (HE) where customer needs can be seen as hypotheses that need to be tested iteratively by conducting experiments together with the customer. So far, modeling support and associated traceability of this iterative process are missing. Both, in turn, are important to document the adaptation to the customer needs and identify experiments that provide most evidence to the customer needs. To target this issue, we introduce a model-based HE approach with a twofold contribution: First, we develop a modeling language that models hypotheses and experiments as interrelated hierarchies together with a mapping between them. While the hypotheses are labeled with a score level of their current evidence, the experiments are labeled with a score level of maximum evidence that can be achieved during conduction. Second, we provide an iterative process to determine experiments that offer the most evidence improvement to the modeled hypotheses. We illustrate the usefulness of the approach with an example of testing the business model of a mobile application.}}, author = {{Gottschalk, Sebastian and Yigitbas, Enes and Engels, Gregor}}, booktitle = {{Business Modeling and Software Design}}, editor = {{Shishkov, Boris}}, keywords = {{Hypothesis Engineering, Model-based, Customer Need Adaptation, Business Model, Product Features}}, location = {{Potsdam}}, pages = {{276--286}}, publisher = {{Springer International Publishing}}, title = {{{Model-based Hypothesis Engineering for Supporting Adaptation to Uncertain Customer Needs}}}, doi = {{10.1007/978-3-030-52306-0_18}}, volume = {{391}}, year = {{2020}}, } @inproceedings{17063, author = {{Hansmeier, Tim and Kaufmann, Paul and Platzner, Marco}}, booktitle = {{GECCO '20: Proceedings of the Genetic and Evolutionary Computation Conference Companion}}, isbn = {{978-1-4503-7127-8}}, location = {{Cancún, Mexico}}, pages = {{1756--1764}}, publisher = {{Association for Computing Machinery (ACM)}}, title = {{{An Adaption Mechanism for the Error Threshold of XCSF}}}, doi = {{10.1145/3377929.3398106}}, year = {{2020}}, } @inproceedings{17082, abstract = {{Data-parallel applications run on cluster of servers in a datacenter and their communication triggers correlated resource demand on multiple links that can be abstracted as coflow. They often desire predictable network performance, which can be passed to network via coflow abstraction for application-aware network scheduling. In this paper, we propose a heuristic and an optimization algorithm for predictable network performance such that they guarantee coflows completion within their deadlines. The algorithms also ensure high network utilization, i.e., it's work-conserving, and avoids starvation of coflows. We evaluate both algorithms via trace-driven simulation and show that they admit 1.1x more coflows than the Varys scheme while meeting their deadlines.}}, author = {{Hasnain, Asif and Karl, Holger}}, booktitle = {{2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID)}}, keywords = {{Coflow, Scheduling, Deadlines, Data centers}}, location = {{Melbourne, Australia}}, publisher = {{IEEE Computer Society}}, title = {{{Coflow Scheduling with Performance Guarantees for Data Center Applications}}}, doi = {{https://doi.org/10.1109/CCGrid49817.2020.00010}}, year = {{2020}}, } @misc{15770, author = {{Warner, Daniel}}, publisher = {{Universität Paderborn}}, title = {{{On the complexity of local transformations in SDN overlays}}}, year = {{2020}}, } @inproceedings{15211, author = {{Szopinski, Daniel and Schoormann, Thorsten and Kundisch, Dennis}}, booktitle = {{Tagungsband der 15. Internationalen Tagung Wirtschaftsinformatik 2020 (WI)}}, location = {{Potsdam, Germany}}, title = {{{Visualize different: Towards researching the fit between taxonomy visualizations and taxonomy tasks}}}, year = {{2020}}, } @inproceedings{16219, abstract = {{Network function virtualization (NFV) proposes to replace physical middleboxes with more flexible virtual network functions (VNFs). To dynamically adjust to everchanging traffic demands, VNFs have to be instantiated and their allocated resources have to be adjusted on demand. Deciding the amount of allocated resources is non-trivial. Existing optimization approaches often assume fixed resource requirements for each VNF instance. However, this can easily lead to either waste of resources or bad service quality if too many or too few resources are allocated. To solve this problem, we train machine learning models on real VNF data, containing measurements of performance and resource requirements. For each VNF, the trained models can then accurately predict the required resources to handle a certain traffic load. We integrate these machine learning models into an algorithm for joint VNF scaling and placement and evaluate their impact on resulting VNF placements. Our evaluation based on real-world data shows that using suitable machine learning models effectively avoids over- and underallocation of resources, leading to up to 12 times lower resource consumption and better service quality with up to 4.5 times lower total delay than using standard fixed resource allocation.}}, author = {{Schneider, Stefan Balthasar and Satheeschandran, Narayanan Puthenpurayil and Peuster, Manuel and Karl, Holger}}, booktitle = {{IEEE Conference on Network Softwarization (NetSoft)}}, location = {{Ghent, Belgium}}, publisher = {{IEEE}}, title = {{{Machine Learning for Dynamic Resource Allocation in Network Function Virtualization}}}, year = {{2020}}, } @inproceedings{16363, author = {{Hansmeier, Tim and Kaufmann, Paul and Platzner, Marco}}, booktitle = {{GECCO '20: Proceedings of the Genetic and Evolutionary Computation Conference Companion}}, isbn = {{978-1-4503-7127-8}}, location = {{Cancún, Mexico}}, pages = {{125--126}}, publisher = {{Association for Computing Machinery (ACM)}}, title = {{{Enabling XCSF to Cope with Dynamic Environments via an Adaptive Error Threshold}}}, doi = {{10.1145/3377929.3389968}}, year = {{2020}}, } @inproceedings{16400, abstract = {{Softwarization facilitates the introduction of smart manufacturing applications in the industry. Manifold devices such as machine computers, Industrial IoT devices, tablets, smartphones and smart glasses are integrated into factory networks to enable shop floor digitalization and big data analysis. To handle the increasing number of devices and the resulting traffic, a flexible and scalable factory network is necessary which can be realized using softwarization technologies like Network Function Virtualization (NFV). However, the security risks increase with the increasing number of new devices, so that cyber security must also be considered in NFV-based networks. Therefore, extending our previous work, we showcase threat detection using a cloud-native NFV-driven intrusion detection system (IDS) that is integrated in our industrial-specific network services. As a result of the threat detection, the affected network service is put into quarantine via automatic network reconfiguration. We use the 5GTANGO service platform to deploy our developed network services on Kubernetes and to initiate the network reconfiguration.}}, author = {{Müller, Marcel and Behnke, Daniel and Bök, Patrick-Benjamin and Schneider, Stefan Balthasar and Peuster, Manuel and Karl, Holger}}, booktitle = {{IEEE Conference on Network Softwarization (NetSoft) Demo Track}}, location = {{Ghent, Belgium}}, publisher = {{IEEE}}, title = {{{Cloud-Native Threat Detection and Containment for Smart Manufacturing}}}, year = {{2020}}, } @inproceedings{13868, author = {{Pukrop, Simon and Mäcker, Alexander and Meyer auf der Heide, Friedhelm}}, booktitle = {{Proceedings of the 46th International Conference on Current Trends in Theory and Practice of Computer Science (SOFSEM)}}, title = {{{Approximating Weighted Completion Time for Order Scheduling with Setup Times}}}, year = {{2020}}, } @inproceedings{13584, author = {{Szopinski, Daniel and Schoormann, Thorsten and Kundisch, Dennis}}, booktitle = {{Proceedings of the 53rd Hawaii International Conference on System Sciences (HICSS)}}, location = {{Maui, Hawaii}}, title = {{{Criteria as a prelude for guiding taxonomy evaluation}}}, year = {{2020}}, } @misc{16940, author = {{Relard, Sebastian}}, title = {{{Experimente in der Wirtschaftsinfromatik zu Kreativität - Ein systematischer Literaturüberblick und Implikationen für Geschäftsmodellmodellierungstools}}}, year = {{2020}}, }