@inproceedings{29235,
  author       = {{Gottschalk, Sebastian and Aziz, Muhammad Suffyan and Yigitbas, Enes and Engels, Gregor}},
  booktitle    = {{Software Business - 12th International Conference, ICSOB 2021, Drammen, Norway, December 2-3, 2021, Proceedings}},
  editor       = {{Wang, Xiaofeng and Martini, Antonio and Nguyen-Duc, Anh and Stray, Viktoria}},
  pages        = {{205–220}},
  publisher    = {{Springer}},
  title        = {{{Design Principles for a Crowd-Based Prototype Validation Platform}}},
  doi          = {{10.1007/978-3-030-91983-2_16}},
  volume       = {{434}},
  year         = {{2021}},
}

@inbook{25528,
  abstract     = {{Developing effective business models is a complex process for a company where several tasks (e.g., conduct customer interviews) need to be accomplished, and decisions (e.g., advertisement as a revenue stream) must be made. Here, domain experts can guide the choices of tasks and decisions with their knowledge. Nevertheless, this knowledge needs to match the situation of the company (e.g., financial resources) and the application domain of the product/service (e.g., mobile app) to reduce the risk of developing ineffective business models with low market penetration. This is not covered by one-size-fits-all development methods without tailoring before the enaction.
Therefore, we conduct a design science study to create a situation-specific development approach for business models. Based on situational method engineering and our previous work in storing knowledge of methods and models in distinct repositories, this paper shows the situation-specific composition and enaction of business model development methods. First, the method engineer composes the development method out of both repositories based on the situational context. Second, the business developer enacts the method and develops the business model.  We implement the approach in a tool and evaluate it with a industrial case study on mobile apps.}},
  author       = {{Gottschalk, Sebastian and Yigitbas, Enes and Nowosad, Alexander and Engels, Gregor}},
  booktitle    = {{Product-focused Software Process Improvement}},
  keywords     = {{Business Model Development, Situational Method Engineering, Lean Development, Kanban Boards, Canvas Models}},
  location     = {{Turin}},
  publisher    = {{Springer}},
  title        = {{{Situation- and  Domain-specific Composition and Enactment of Business Model Development Methods}}},
  year         = {{2021}},
}

@inproceedings{21598,
  abstract     = {{Static analysis is used to automatically detect bugs and security breaches, and aids compileroptimization. Whole-program analysis (WPA) can yield high precision, however causes long analysistimes and thus does not match common software-development workflows, making it often impracticalto use for large, real-world applications.This paper thus presents the design and implementation ofModAlyzer, a novel static-analysisapproach that aims at accelerating whole-program analysis by making the analysis modular andcompositional. It shows how to computelossless, persisted summaries for callgraph, points-to anddata-flow information, and it reports under which circumstances this function-level compositionalanalysis outperforms WPA.We implementedModAlyzeras an extension to LLVM and PhASAR, and applied it to 12 real-world C and C++ applications. At analysis time,ModAlyzermodularly and losslessly summarizesthe analysis effect of the library code those applications share, hence avoiding its repeated re-analysis.The experimental results show that the reuse of these summaries can save, on average, 72% ofanalysis time over WPA. Moreover, because it is lossless, the module-wise analysis fully retainsprecision and recall. Surprisingly, as our results show, it sometimes even yields precision superior toWPA. The initial summary generation, on average, takes about 3.67 times as long as WPA.}},
  author       = {{Schubert, Philipp and Hermann, Ben and Bodden, Eric}},
  booktitle    = {{European Conference on Object-Oriented Programming (ECOOP)}},
  title        = {{{Lossless, Persisted Summarization of Static Callgraph, Points-To and Data-Flow Analysis}}},
  year         = {{2021}},
}

@unpublished{30866,
  abstract     = {{Automated machine learning (AutoML) strives for the automatic configuration
of machine learning algorithms and their composition into an overall (software)
solution - a machine learning pipeline - tailored to the learning task
(dataset) at hand. Over the last decade, AutoML has developed into an
independent research field with hundreds of contributions. While AutoML offers
many prospects, it is also known to be quite resource-intensive, which is one
of its major points of criticism. The primary cause for a high resource
consumption is that many approaches rely on the (costly) evaluation of many
machine learning pipelines while searching for good candidates. This problem is
amplified in the context of research on AutoML methods, due to large scale
experiments conducted with many datasets and approaches, each of them being run
with several repetitions to rule out random effects. In the spirit of recent
work on Green AI, this paper is written in an attempt to raise the awareness of
AutoML researchers for the problem and to elaborate on possible remedies. To
this end, we identify four categories of actions the community may take towards
more sustainable research on AutoML, i.e. Green AutoML: design of AutoML
systems, benchmarking, transparency and research incentives.}},
  author       = {{Tornede, Tanja and Tornede, Alexander and Hanselle, Jonas Manuel and Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}},
  booktitle    = {{arXiv:2111.05850}},
  title        = {{{Towards Green Automated Machine Learning: Status Quo and Future Directions}}},
  year         = {{2021}},
}

@phdthesis{27284,
  author       = {{Wever, Marcel Dominik}},
  title        = {{{Automated Machine Learning for Multi-Label Classification}}},
  doi          = {{10.17619/UNIPB/1-1302}},
  year         = {{2021}},
}

@inbook{22052,
  abstract     = {{In this study, we describe a text processing pipeline that transforms user-generated text into structured data. To do this, we train neural and transformer-based models for aspect-based sentiment analysis. As most research deals with explicit aspects from product or service data, we extract and classify implicit and explicit aspect phrases from German-language physician review texts. Patients often rate on the basis of perceived friendliness or competence. The vocabulary is difficult, the topic sensitive, and the data user-generated. The aspect phrases come with various wordings using insertions and are not noun-based, which makes the presented case equally relevant and reality-based. To find complex, indirect aspect phrases, up-to-date deep learning approaches must be combined with supervised training data. We describe three aspect phrase datasets, one of them new, as well as a newly annotated aspect polarity dataset. Alongside this, we build an algorithm to rate the aspect phrase importance. All in all, we train eight transformers on the new raw data domain, compare 54 neural aspect extraction models and, based on this, create eight aspect polarity models for our pipeline. These models are evaluated by using Precision, Recall, and F-Score measures. Finally, we evaluate our aspect phrase importance measure algorithm.}},
  author       = {{Kersting, Joschka and Geierhos, Michaela}},
  booktitle    = {{Natural Language Processing and Information Systems}},
  editor       = {{Kapetanios, Epaminondas and Horacek, Helmut and Métais, Elisabeth and Meziane, Farid}},
  location     = {{Saarbrücken, Germany}},
  pages        = {{231----242}},
  publisher    = {{Springer}},
  title        = {{{Human Language Comprehension in Aspect Phrase Extraction with Importance Weighting}}},
  volume       = {{12801}},
  year         = {{2021}},
}

@misc{24671,
  author       = {{Ahmeckovic, Emina}},
  title        = {{{Die Sterne sind für alle da - Eine empirische Untersuchung zu Anforderungen der Gestaltung von barrierefreien Online-Bewertungssystemen}}},
  year         = {{2021}},
}

@misc{23554,
  author       = {{Pieper, Florian}},
  title        = {{{Systematische Identifikation und Analyse von Vorgehensmodellen zur Geschäftsmodellentwicklung auf digitalen Plattformen}}},
  year         = {{2021}},
}

@misc{24576,
  author       = {{Funk, Daniel}},
  title        = {{{Wer schreibt der bleibt – Eine quantitative Untersuchung der  Kundenbeziehung in Brand Communities und Smart Service Systems}}},
  year         = {{2021}},
}

@misc{25181,
  author       = {{Rümpker, Till}},
  title        = {{{Wechselwirkungen von Hemmnissen und Motivatoren mit Blick auf die Abgabe von Online-Bewertungen}}},
  year         = {{2021}},
}

@misc{24685,
  author       = {{Roslan, Jan-Philipp}},
  title        = {{{Mitten ins Herz – Eine semantische Analyse von Spendenkampagnen auf Crowdfunding Plattformen}}},
  year         = {{2021}},
}

@misc{24577,
  author       = {{Speer, Laura}},
  title        = {{{Digitale Diskriminierung – Ein systematischer Literaturüberblick}}},
  year         = {{2021}},
}

@misc{21798,
  author       = {{Richert, Laurenz Jobst}},
  title        = {{{Abhängigkeiten innerhalb und zwischen Business Model Canvas und  ArchiMate - Ein konzeptioneller Vergleich zweier Modellierungssprachen}}},
  year         = {{2021}},
}

@misc{21713,
  author       = {{Lender, Leonie}},
  title        = {{{Personality Matters – Ein systematischer Literaturüberblick zur  Anpassung von Anwendungssystemen an die Nutzerpersönlichkeit}}},
  year         = {{2021}},
}

@misc{21434,
  author       = {{Steierlein, Gergely}},
  title        = {{{Auswirkungen von Anreizsystemen zu nutzergenerierten Inhalten auf digitalen Märkten – Ein systematischer Literaturüberblick}}},
  year         = {{2021}},
}

@misc{21365,
  author       = {{Mai, Thanh Quynh}},
  title        = {{{Auswirkungen von Aggregationsmetriken von Bewertungen auf digitalen Märkten – Ein systematischer Literaturüberblick}}},
  year         = {{2021}},
}

@misc{23563,
  author       = {{Grieger, Nicole}},
  title        = {{{Achterbahn der Gefühle – Eine semantische Analyse von  Spendenkampagnen im zeitlichen Verlauf der Corona Pandemie}}},
  year         = {{2021}},
}

@misc{21523,
  author       = {{Dierkes, Lukas}},
  title        = {{{Worauf achten AirBnb Gäste? Eine semantische Analyse von Online-Bewertungen mittels Latent-Dirichlet-Allocation}}},
  year         = {{2021}},
}

@misc{21721,
  author       = {{Tiggemann, Benjamin}},
  title        = {{{Innovation in Nutzerkommentaren – Eine Analyse am Beispiel einer Rezept-Plattform}}},
  year         = {{2021}},
}

@misc{21116,
  author       = {{Rennemeier, Steffen}},
  title        = {{{Entwicklung und Pilotierung eines Experiments über Visualisierungen von Taxonomien in der Wirtschaftsinformatik}}},
  year         = {{2021}},
}