@misc{34963, author = {{Anonymous, A}}, title = {{{Cost of Privacy-preserving SMPC Protocols for NN-Based Inference}}}, year = {{2022}}, } @inproceedings{41134, author = {{Gottschalk, Sebastian and Bhat, Rakshit and Weidmann, Nils and Kirchhoff, Jonas and Engels, Gregor}}, booktitle = {{Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings}}, publisher = {{ACM}}, title = {{{Low-code experimentation on software products}}}, doi = {{10.1145/3550356.3561572}}, year = {{2022}}, } @inbook{21586, author = {{Klein, M. and Kundisch, Dennis and Stummer, C.}}, booktitle = {{Handbuch Digitalisierung}}, editor = {{Corsten, H. and Roth, S.}}, pages = {{799--814}}, publisher = {{Vahle}}, title = {{{Feeless Micropayments and Their Impact on Business Models}}}, year = {{2022}}, } @misc{35396, author = {{Regniet, Julian}}, title = {{{Vom Geschäftsmodell zum Geschäftsprozess - Eine Systematische Literaturrecherche}}}, year = {{2022}}, } @misc{30646, author = {{Amanzada, Ahmad Saki}}, title = {{{Erfolgsfaktoren von Crowdfunding-Plattformen}}}, year = {{2022}}, } @article{32307, abstract = {{The development of new business models is essential for startups to become successful, as well as for established companies to explore new business opportunities. However, developing such business models is a continuous challenging activity where different tasks need to be performed, and business decisions need to be made. Both have to fit the constantly changeable situation in which the business model is developed to reduce the risk of developing ineffective business models with low market penetration. Therefore, a method for developing situation-specific business models is needed. As a solution, we refine the concept of situational method engineering (SME) to business model development. SME, in turn, provides means to construct situation-specific development methods out of fragments from a method repository. We develop a concept for the continuous situation-specific development of business models based on design science. The approach uses the roles of a domain expert, a method engineer, and a business developer together with a repository with method fragments for developing business models and a repository with modeling artifacts for supporting the development. Both repositories are filled by utilizing the experience of domain experts. Out of these repositories, situation-specific development methods for developing business models can be continuously composed based on the changeable situation by the method engineer and enacted by the business developer. We implement it as an open-source tool and evaluate its applicability in an industrial case study of developing a business model for a local event platform. Our results show that situation awareness supports the continuous development of business models.}}, author = {{Gottschalk, Sebastian and Yigitbas, Enes and Nowosad, Alexander and Engels, Gregor}}, journal = {{International Journal on Software and Systems Modeling (SoSyM) }}, keywords = {{Business Model Development, Situational Method Engineering, Situation-specific, Business Model Canvas, Continuous Development}}, title = {{{Continuous Situation-specific Development of Business Models: Knowledge Provision, Method Composition, Method Enactment}}}, year = {{2022}}, } @inproceedings{32309, abstract = {{Due to the increasing influences of a VUCA world, design thinking workshops have been established as a standard technique to build solutions according to uncertain customer needs. Concerning the ongoing pandemic and rising development of solutions across organizations, more and more workshops were conducted online with software support. However, existing software tools insufficiently address the different workshop situations in terms of the process (i.e., fixed tasks to conduct), the place (e.g., static online whiteboards), and people (i.e., synchronous working of all stakeholders). Therefore, we propose a design science study to develop a situation-specific software support that can be configured with flexible development processes, different places, and task-related people. Based on practical experience in existing research projects, we derive the initial design requirements and map them to a set of design principles. Out of that, we design a concept with its implementation as a software tool and point out open challenges. }}, author = {{Gottschalk, Sebastian and Yigitbas, Enes and Nowosad, Alexander and Engels, Gregor}}, booktitle = {{Proceedings of the 5th International Workshop on Software-intensive Business (IWSiB'22) }}, keywords = {{design thinking, situation-specific, cross-organizational, software support}}, publisher = {{ACM}}, title = {{{Towards Situation-specific Software Support for Cross-organizational Design Thinking Processes}}}, year = {{2022}}, } @misc{45242, author = {{N., N.}}, title = {{{A Scalable and Extensible Architecture for a Crowd-Based Prototype Validation Platform}}}, year = {{2022}}, } @misc{45241, author = {{N., N.}}, title = {{{Conception and Implementation of a Situation-specific Design Thinking Tool}}}, year = {{2022}}, } @inproceedings{25174, author = {{Müller, Michelle and Seutter, Janina and Müller, Stefanie Jutta Marianne and Kundisch, Dennis}}, booktitle = {{Proceedings of the 42nd International Conference on Information Systems (ICIS)}}, title = {{{Moment or Movement – An Empirical Analysis of the Heterogeneous Impact of Media Attention on Charitable Crowdfunding Campaigns}}}, year = {{2021}}, } @inproceedings{20125, abstract = {{Datacenter applications have different resource requirements from network and developing flow scheduling heuristics for every workload is practically infeasible. In this paper, we show that deep reinforcement learning (RL) can be used to efficiently learn flow scheduling policies for different workloads without manual feature engineering. Specifically, we present LFS, which learns to optimize a high-level performance objective, e.g., maximize the number of flow admissions while meeting the deadlines. The LFS scheduler is trained through deep RL to learn a scheduling policy on continuous online flow arrivals. The evaluation results show that the trained LFS scheduler admits 1.05x more flows than the greedy flow scheduling heuristics under varying network load.}}, author = {{Hasnain, Asif and Karl, Holger}}, booktitle = {{2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC)}}, keywords = {{Flow scheduling, Deadlines, Reinforcement learning}}, location = {{Las Vegas, USA}}, publisher = {{IEEE Computer Society}}, title = {{{Learning Flow Scheduling}}}, doi = {{https://doi.org/10.1109/CCNC49032.2021.9369514}}, year = {{2021}}, } @inproceedings{20244, author = {{Gottschalk, Sebastian and Kirchhoff, Jonas and Engels, Gregor}}, booktitle = {{Business Modeling and Software Design}}, editor = {{Shishkov, Boris}}, location = {{Sofia}}, title = {{{Extending Business Model Development Tools with Consolidated Expert Knowledge }}}, doi = {{10.1007/978-3-030-79976-2_1}}, year = {{2021}}, } @misc{28998, author = {{Suermann, Dennis}}, title = {{{Schutz und Stabilisierung von Overlay-Netzwerken mithilfe des Relay-Layers}}}, year = {{2021}}, } @misc{29151, abstract = {{Automation becomes a vital part in the High-Performance computing system in situational dynamics to take the decisions on the fly. Heterogeneous compute nodes consist of computing resources such as CPU, GPU and FPGA and are the important components of the high-performance computing system that can adapt the automation to achieve the given goal. While implanting automation in the computing resources, management of the resources is one of the essential aspects that need to be taken care of. Tasks are continuously executed on the resources using its unique characteristics. Effective scheduling is essential to make the best use of the characteristics provided by each resource. Scheduling enables the execution of each task by allocating resources so that they take advantage of all the characteristics of the compute resources. Various scheduling heuristics can be used to create effective scheduling, which might require the execution time to schedule the task efficiently. Providing actual execution time is not possible in many cases; hence we can provide the estimations for the actual execution time . The purpose of this master's thesis is to design a predictive model or system that estimates the execution time required to execute tasks using historical execution time data on the heterogeneous compute nodes. In this thesis, regression techniques(SGD Regressor, Passive-Aggressive Regressor, MLP Regressor, and XCSF Regressor) are compared in terms of their prediction accuracy in order to determine which technique produces reliable predictions for the execution time. These estimations must be generated in an online learning environment in which data points arrive in any sequence, one by one, and the regression model must learn from them. After evaluating the regression algorithms, it is seen that the XCSF regressor provides the highest overall prediction accuracy for the supplied data sets. The regression technique's parameters also play a significant role in achieving an acceptable prediction accuracy. As a remark, when using online learning in regression analysis, the accuracy depends upon both the order of sequential data points that are coming to train the model and the parameter configuration for each regression technique.}}, author = {{Kashikar, Chinmay}}, publisher = {{Paderborn University}}, title = {{{A Comparison of Machine Learning Techniques for the On-line Characterization of Tasks Executed on Heterogeneous Compute Nodes}}}, year = {{2021}}, } @phdthesis{27503, author = {{Hasnain, Asif}}, title = {{{Automating Network Resource Allocation for Coflows with Deadlines}}}, doi = {{10.17619/UNIPB/1-1241 }}, year = {{2021}}, } @inproceedings{21005, abstract = {{Data-parallel applications are developed using different data programming models, e.g., MapReduce, partition/aggregate. These models represent diverse resource requirements of application in a datacenter network, which can be represented by the coflow abstraction. The conventional method of creating hand-crafted coflow heuristics for admission or scheduling for different workloads is practically infeasible. In this paper, we propose a deep reinforcement learning (DRL)-based coflow admission scheme -- LCS -- that can learn an admission policy for a higher-level performance objective, i.e., maximize successful coflow admissions, without manual feature engineering. LCS is trained on a production trace, which has online coflow arrivals. The evaluation results show that LCS is able to learn a reasonable admission policy that admits more coflows than state-of-the-art Varys heuristic while meeting their deadlines.}}, author = {{Hasnain, Asif and Karl, Holger}}, booktitle = {{IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)}}, keywords = {{Coflow scheduling, Reinforcement learning, Deadlines}}, location = {{Vancouver BC Canada}}, publisher = {{IEEE Communications Society}}, title = {{{Learning Coflow Admissions}}}, doi = {{10.1109/INFOCOMWKSHPS51825.2021.9484599}}, year = {{2021}}, } @misc{21197, author = {{Mengshi, Ma}}, title = {{{Self-stabilizing Arrow Protocol on Spanning Trees with a Low Diameter}}}, year = {{2021}}, } @article{21242, author = {{Lüttenberg, Hedda and Beverungen, Daniel and Poniatowski, Martin and Kundisch, Dennis and Wünderlich, Nancy}}, journal = {{Wirtschaftsinformatik & Management}}, number = {{2}}, pages = {{120--131}}, title = {{{Drei Strategien zur Etablierung digitaler Plattformen in der Industrie}}}, volume = {{13}}, year = {{2021}}, } @inproceedings{21543, abstract = {{Services often consist of multiple chained components such as microservices in a service mesh, or machine learning functions in a pipeline. Providing these services requires online coordination including scaling the service, placing instance of all components in the network, scheduling traffic to these instances, and routing traffic through the network. Optimized service coordination is still a hard problem due to many influencing factors such as rapidly arriving user demands and limited node and link capacity. Existing approaches to solve the problem are often built on rigid models and assumptions, tailored to specific scenarios. If the scenario changes and the assumptions no longer hold, they easily break and require manual adjustments by experts. Novel self-learning approaches using deep reinforcement learning (DRL) are promising but still have limitations as they only address simplified versions of the problem and are typically centralized and thus do not scale to practical large-scale networks. To address these issues, we propose a distributed self-learning service coordination approach using DRL. After centralized training, we deploy a distributed DRL agent at each node in the network, making fast coordination decisions locally in parallel with the other nodes. Each agent only observes its direct neighbors and does not need global knowledge. Hence, our approach scales independently from the size of the network. In our extensive evaluation using real-world network topologies and traffic traces, we show that our proposed approach outperforms a state-of-the-art conventional heuristic as well as a centralized DRL approach (60% higher throughput on average) while requiring less time per online decision (1 ms).}}, author = {{Schneider, Stefan Balthasar and Qarawlus, Haydar and Karl, Holger}}, booktitle = {{IEEE International Conference on Distributed Computing Systems (ICDCS)}}, keywords = {{network management, service management, coordination, reinforcement learning, distributed}}, location = {{Washington, DC, USA}}, publisher = {{IEEE}}, title = {{{Distributed Online Service Coordination Using Deep Reinforcement Learning}}}, year = {{2021}}, } @techreport{21569, abstract = {{Die kontinuierliche Weiterentwicklung des eigenen Geschäftsmodells ist für eine Organisation von entscheidender Bedeutung, um wettbewerbsfähig und somit nachhaltig erfolgreich zu bleiben. Während für die Entwicklung neuer Geschäftsmodelle häufig Workshops und einfache Software-Tools zur Visualisierung genutzt werden, wurden in der Forschung bereits erste Ansätze von datengetriebener Geschäftsmodellentwicklung (GME) vorgestellt. Diese Ansätze nutzen dabei Daten, Informationen oder auch Wissen aus internen und externen Unternehmensquellen, um den GME-Prozess zu unterstützen. Innerhalb dieses Beitrags zeigen wir einige Ansätze aus der aktuellen Literatur und analysieren wie ihre Datennutzung den GME-Prozess unterstützt. Weiterhin stellen wir mit dem BMDL Feature Modeler ein Tool vor, welches den GME-Prozess mit Expertenwissen unterstützt.}}, author = {{Gottschalk, Sebastian and Yigitbas, Enes}}, publisher = {{Gesellschaft für Informatik}}, title = {{{Von datenbasierter zu datengetriebener Geschäftsmodellentwicklung: Ein Überblick über Software-Tools und deren Datennutzung}}}, volume = {{1}}, year = {{2021}}, }