@inproceedings{29839, abstract = {{The development of business models is a challenging task that can be supported with software tools. Here, existing approaches and tools do not focus on the company’s situation in which the development takes place (e.g., financial resources, product type). To tackle this challenge, we used design science research to develop a situation-specific business model development approach that contains three stages: First, existing knowledge in terms of tasks to do (e.g., analyze competitive advantage), and decisions to be made (e.g., social media marketing) are stored in repositories. Second, the knowledge is used to compose a development method based on the company’s situation. Third, the development method is enacted to develop a business model. This demonstration paper presents a tool-support called Situational Business Model Developer that supports all stages of our approach. We release the tool under open-source and evaluate it with a case study on developing business models for mobile apps.}}, author = {{Gottschalk, Sebastian and Yigitbas, Enes and Nowosad, Alexander and Engels, Gregor}}, booktitle = {{Proceedings of the 17th International Conference on Wirtschaftsinformatik}}, keywords = {{Business Model Development, Situational Method Engineering, Tool Support}}, location = {{Nuremberg}}, publisher = {{AIS}}, title = {{{Situational Business Model Developer: A Tool-support for Situation-specific Business Model Development}}}, year = {{2022}}, } @phdthesis{29672, author = {{Schneider, Stefan Balthasar}}, title = {{{Network and Service Coordination: Conventional and Machine Learning Approaches"}}}, doi = {{10.17619/UNIPB/1-1276 }}, year = {{2022}}, } @inproceedings{29945, author = {{Witschen, Linus Matthias and Wiersema, Tobias and Reuter, Lucas David and Platzner, Marco}}, booktitle = {{2022 59th ACM/IEEE Design Automation Conference (DAC)}}, location = {{San Francisco, USA}}, title = {{{Search Space Characterization for Approximate Logic Synthesis }}}, year = {{2022}}, } @inproceedings{29865, author = {{Witschen, Linus Matthias and Wiersema, Tobias and Artmann, Matthias and Platzner, Marco}}, booktitle = {{Design, Automation and Test in Europe (DATE)}}, location = {{Online}}, title = {{{MUSCAT: MUS-based Circuit Approximation Technique}}}, year = {{2022}}, } @misc{30152, author = {{Roopa, Rajanna}}, title = {{{Evaluation of Algorithms for the Node Capacitated Clique}}}, year = {{2022}}, } @misc{30198, author = {{Korzeczek, Sebastian}}, title = {{{Aufarbeitung und lmplementierung von DAG-Rider}}}, year = {{2022}}, } @misc{30199, author = {{Nachtigall, Marcel}}, title = {{{Hybrid Routing in Three Dimensions}}}, year = {{2022}}, } @inproceedings{30236, abstract = {{Recent reinforcement learning approaches for continuous control in wireless mobile networks have shown impressive results. But due to the lack of open and compatible simulators, authors typically create their own simulation environments for training and evaluation. This is cumbersome and time-consuming for authors and limits reproducibility and comparability, ultimately impeding progress in the field. To this end, we propose mobile-env, a simple and open platform for training, evaluating, and comparing reinforcement learning and conventional approaches for continuous control in mobile wireless networks. mobile-env is lightweight and implements the common OpenAI Gym interface and additional wrappers, which allows connecting virtually any single-agent or multi-agent reinforcement learning framework to the environment. While mobile-env provides sensible default values and can be used out of the box, it also has many configuration options and is easy to extend. We therefore believe mobile-env to be a valuable platform for driving meaningful progress in autonomous coordination of wireless mobile networks.}}, author = {{Schneider, Stefan Balthasar and Werner, Stefan and Khalili, Ramin and Hecker, Artur and Karl, Holger}}, booktitle = {{IEEE/IFIP Network Operations and Management Symposium (NOMS)}}, keywords = {{wireless mobile networks, network management, continuous control, cognitive networks, autonomous coordination, reinforcement learning, gym environment, simulation, open source}}, location = {{Budapest}}, publisher = {{IEEE}}, title = {{{mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks}}}, year = {{2022}}, } @phdthesis{30201, author = {{Fanasch, Patrizia}}, title = {{{Governance and Reputation in the Market for Experience Goods}}}, doi = {{10.17619/UNIPB/1-1292 }}, year = {{2022}}, } @article{30341, author = {{Hoyer, Britta and van Straaten, Dirk}}, issn = {{2214-8043}}, journal = {{Journal of Behavioral and Experimental Economics}}, keywords = {{General Social Sciences, Economics and Econometrics, Applied Psychology}}, pages = {{101869}}, publisher = {{Elsevier BV}}, title = {{{Anonymity and Self-Expression in Online Rating Systems - An Experimental Analysis}}}, doi = {{10.1016/j.socec.2022.101869}}, volume = {{98}}, year = {{2022}}, } @unpublished{30868, abstract = {{Algorithm configuration (AC) is concerned with the automated search of the most suitable parameter configuration of a parametrized algorithm. There is currently a wide variety of AC problem variants and methods proposed in the literature. Existing reviews do not take into account all derivatives of the AC problem, nor do they offer a complete classification scheme. To this end, we introduce taxonomies to describe the AC problem and features of configuration methods, respectively. We review existing AC literature within the lens of our taxonomies, outline relevant design choices of configuration approaches, contrast methods and problem variants against each other, and describe the state of AC in industry. Finally, our review provides researchers and practitioners with a look at future research directions in the field of AC.}}, author = {{Schede, Elias and Brandt, Jasmin and Tornede, Alexander and Wever, Marcel Dominik and Bengs, Viktor and Hüllermeier, Eyke and Tierney, Kevin}}, booktitle = {{arXiv:2202.01651}}, title = {{{A Survey of Methods for Automated Algorithm Configuration}}}, year = {{2022}}, } @inproceedings{30971, author = {{Hansmeier, Tim and Platzner, Marco}}, booktitle = {{Applications of Evolutionary Computation, EvoApplications 2022, Proceedings}}, isbn = {{9783031024610}}, issn = {{0302-9743}}, location = {{Madrid}}, pages = {{386--401}}, publisher = {{Springer International Publishing}}, title = {{{Integrating Safety Guarantees into the Learning Classifier System XCS}}}, doi = {{10.1007/978-3-031-02462-7_25}}, volume = {{13224}}, year = {{2022}}, } @misc{31947, author = {{Hillebrandt, Henning}}, title = {{{Verteiltes Berechnen kompakter Routingtabellen in Unit Disk Graphen}}}, year = {{2022}}, } @article{17869, author = {{Poniatowski, Martin and Lüttenberg, Hedda and Beverungen, Daniel and Kundisch, Dennis}}, journal = {{Information Systems and e-Business Management, Special Issue on Platform Business Models and Platform Strategies}}, pages = {{257 -- 283}}, title = {{{Three Layers of Abstraction—A Conceptual Framework for Theorizing digital Multi-Sided Platforms}}}, volume = {{2}}, year = {{2022}}, } @inproceedings{32311, abstract = {{Testing is one of the most frequent means of quality assurance for software. Property-based testing aims at generating test suites for checking code against user-defined properties. Test input generation is, however, most often independent of the property to be checked, and is instead based on random or user-defined data generation.In this paper, we present property-driven unit testing of functions with numerical inputs and outputs. Alike property-based testing, it allows users to define the properties to be tested for. Contrary to property-based testing, it also uses the property for a targeted generation of test inputs. Our approach is a form of learning-based testing where we first of all learn a model of a given black-box function using standard machine learning algorithms, and in a second step use model and property for test input generation. This allows us to test both predefined functions as well as machine learned regression models. Our experimental evaluation shows that our property-driven approach is more effective than standard property-based testing techniques.}}, author = {{Sharma, Arnab and Melnikov, Vitaly and Hüllermeier, Eyke and Wehrheim, Heike}}, booktitle = {{Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE)}}, pages = {{113--123}}, publisher = {{IEEE}}, title = {{{Property-Driven Testing of Black-Box Functions}}}, year = {{2022}}, } @inproceedings{29842, abstract = {{To build successful software products, developers continuously have to discover what features the users really need. This discovery can be achieved with continuous experimentation, testing different software variants with distinct user groups, and deploying the superior variant for all users. However, existing approaches do not focus on explicit modeling of variants and experiments, which offers advantages such as traceability of decisions and combinability of experiments. Therefore, our vision is the provision of model-driven continuous experimentation, which provides the developer with a framework for structuring the experimentation process. For that, we introduce the overall concept, apply it to the experimentation on component-based software architectures and point out future research questions. In particular, we show the applicability by combining feature models for modeling the software variants, users, and experiments (i.e., model-driven) with MAPE-K for the adaptation (i.e., continuous experimentation) and implementing the concept based on the component-based Angular framework.}}, author = {{Gottschalk, Sebastian and Yigitbas, Enes and Engels, Gregor}}, booktitle = {{Proceedings of the 18th International Conference on Software Architecture Companion }}, keywords = {{continuous experimentation, model-driven, component-based software architectures, self-adaptation}}, location = {{Hawaii}}, publisher = {{IEEE}}, title = {{{Model-driven Continuous Experimentation on Component-based Software Architectures }}}, doi = {{10.1109/ICSA-C54293.2022.00011}}, year = {{2022}}, } @misc{32399, author = {{Vahle, Ella}}, title = {{{Modelling and Proving Security for a Secure MPC Protocol for Stable Matching}}}, year = {{2022}}, } @inproceedings{31847, abstract = {{The famous $k$-Server Problem covers plenty of resource allocation scenarios, and several variations have been studied extensively for decades. However, to the best of our knowledge, no research has considered the problem if the servers are not identical and requests can express which specific servers should serve them. Therefore, we present a new model generalizing the $k$-Server Problem by *preferences* of the requests and proceed to study it in a uniform metric space for deterministic online algorithms (the special case of paging). In our model, requests can either demand to be answered by any server (*general requests*) or by a specific one (*specific requests*). If only general requests appear, the instance is one of the original $k$-Server Problem, and a lower bound for the competitive ratio of $k$ applies. If only specific requests appear, a solution with a competitive ratio of $1$ becomes trivial since there is no freedom regarding the servers' movements. Perhaps counter-intuitively, we show that if both kinds of requests appear, the lower bound raises to $2k-1$. We study deterministic online algorithms in uniform metrics and present two algorithms. The first one has an adaptive competitive ratio dependent on the frequency of specific requests. It achieves a worst-case competitive ratio of $3k-2$ while it is optimal when only general or only specific requests appear (competitive ratio of $k$ and $1$, respectively). The second has a fixed close-to-optimal worst-case competitive ratio of $2k+14$. For the first algorithm, we show a lower bound of $3k-2$, while the second algorithm has a lower bound of $2k-1$ when only general requests appear. The two algorithms differ in only one behavioral rule for each server that significantly influences the competitive ratio. Each server acting according to the rule allows approaching the worst-case lower bound, while it implies an increased lower bound for $k$-Server instances. In other words, there is a trade-off between performing well against instances of the $k$-Server Problem and instances containing specific requests. We also show that no deterministic online algorithm can be optimal for both kinds of instances simultaneously.}}, author = {{Castenow, Jannik and Feldkord, Björn and Knollmann, Till and Malatyali, Manuel and Meyer auf der Heide, Friedhelm}}, booktitle = {{Proceedings of the 34th ACM Symposium on Parallelism in Algorithms and Architectures}}, isbn = {{9781450391467}}, keywords = {{K-Server Problem, Heterogeneity, Online Caching}}, pages = {{345--356}}, publisher = {{Association for Computing Machinery}}, title = {{{The k-Server with Preferences Problem}}}, doi = {{10.1145/3490148.3538595}}, year = {{2022}}, } @inproceedings{33085, author = {{Epstein, Leah and Lassota, Alexandra and Levin, Asaf and Maack, Marten and Rohwedder, Lars}}, booktitle = {{39th International Symposium on Theoretical Aspects of Computer Science, STACS 2022, March 15-18, 2022, Marseille, France (Virtual Conference)}}, editor = {{Berenbrink, Petra and Monmege, Benjamin}}, pages = {{28:1–28:15}}, publisher = {{Schloss Dagstuhl - Leibniz-Zentrum für Informatik}}, title = {{{Cardinality Constrained Scheduling in Online Models}}}, doi = {{10.4230/LIPIcs.STACS.2022.28}}, volume = {{219}}, year = {{2022}}, } @inproceedings{34103, abstract = {{It is well known that different algorithms perform differently well on an instance of an algorithmic problem, motivating algorithm selection (AS): Given an instance of an algorithmic problem, which is the most suitable algorithm to solve it? As such, the AS problem has received considerable attention resulting in various approaches - many of which either solve a regression or ranking problem under the hood. Although both of these formulations yield very natural ways to tackle AS, they have considerable weaknesses. On the one hand, correctly predicting the performance of an algorithm on an instance is a sufficient, but not a necessary condition to produce a correct ranking over algorithms and in particular ranking the best algorithm first. On the other hand, classical ranking approaches often do not account for concrete performance values available in the training data, but only leverage rankings composed from such data. We propose HARRIS- Hybrid rAnking and RegRessIon foreSts - a new algorithm selector leveraging special forests, combining the strengths of both approaches while alleviating their weaknesses. HARRIS' decisions are based on a forest model, whose trees are created based on splits optimized on a hybrid ranking and regression loss function. As our preliminary experimental study on ASLib shows, HARRIS improves over standard algorithm selection approaches on some scenarios showing that combining ranking and regression in trees is indeed promising for AS.}}, author = {{Fehring, Lukass and Hanselle, Jonas Manuel and Tornede, Alexander}}, booktitle = {{Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022}}, location = {{Baltimore}}, title = {{{HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection}}}, year = {{2022}}, }