@article{DoerrKrejca2021,
  author    = {Doerr, Benjamin and Krejca, Martin Stefan},
  title     = {A simplified run time analysis of the univariate marginal distribution algorithm on LeadingOnes},
  series = {Theoretical computer science},
  volume    = {851},
  journal   = {Theoretical computer science},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0304-3975},
  doi       = {10.1016/j.tcs.2020.11.028},
  pages     = {121 -- 128},
  year      = {2021},
  abstract  = {With elementary means, we prove a stronger run time guarantee for the univariate marginal distribution algorithm (UMDA) optimizing the LEADINGONES benchmark function in the desirable regime with low genetic drift. If the population size is at least quasilinear, then, with high probability, the UMDA samples the optimum in a number of iterations that is linear in the problem size divided by the logarithm of the UMDA's selection rate. This improves over the previous guarantee, obtained by Dang and Lehre (2015) via the deep level-based population method, both in terms of the run time and by demonstrating further run time gains from small selection rates. Under similar assumptions, we prove a lower bound that matches our upper bound up to constant factors.},
  language  = {en}
}
@article{Respondek2014,
  author    = {Respondek, Tobias},
  title     = {A workflow for computing potential areas for wind turbines},
  series = {Process design for natural scientists: an agile model-driven approach},
  journal   = {Process design for natural scientists: an agile model-driven approach},
  number    = {500},
  publisher = {Springer},
  address   = {Berlin},
  isbn      = {978-3-662-45005-5},
  pages     = {200 -- 215},
  year      = {2014},
  abstract  = {This paper describes the implementation of a workflow model for service-oriented computing of potential areas for wind turbines in jABC. By implementing a re-executable model the manual effort of a multi-criteria site analysis can be reduced. The aim is to determine the shift of typical geoprocessing tools of geographic information systems (GIS) from the desktop to the web. The analysis is based on a vector data set and mainly uses web services of the "Center for Spatial Information Science and Systems" (CSISS). This paper discusses effort, benefits and problems associated with the use of the web services.},
  language  = {en}
}
@article{KrauseGrosseDetersBaumannetal.2022,
  author    = {Krause, Hannes-Vincent and Große Deters, Fenne and Baumann, Annika and Krasnova, Hanna},
  title     = {Active social media use and its impact on well-being},
  series = {Journal of computer-mediated communication : a journal of the International Communication Association},
  volume    = {28},
  journal   = {Journal of computer-mediated communication : a journal of the International Communication Association},
  number    = {1},
  publisher = {Oxford Univ. Press},
  address   = {Oxford},
  issn      = {1083-6101},
  doi       = {10.1093/jcmc/zmac037},
  pages     = {12},
  year      = {2022},
  abstract  = {Active use of social networking sites (SNSs) has long been assumed to benefit users' well-being. However, this established hypothesis is increasingly being challenged, with scholars criticizing its lack of empirical support and the imprecise conceptualization of active use. Nevertheless, with considerable heterogeneity among existing studies on the hypothesis and causal evidence still limited, a final verdict on its robustness is still pending. To contribute to this ongoing debate, we conducted a week-long randomized control trial with N = 381 adult Instagram users recruited via Prolific. Specifically, we tested how active SNS use, operationalized as picture postings on Instagram, affects different dimensions of well-being. The results depicted a positive effect on users' positive affect but null findings for other well-being outcomes. The findings broadly align with the recent criticism against the active use hypothesis and support the call for a more nuanced view on the impact of SNSs. <br /> Lay Summary Active use of social networking sites (SNSs) has long been assumed to benefit users' well-being. However, this established assumption is increasingly being challenged, with scholars criticizing its lack of empirical support and the imprecise conceptualization of active use. Nevertheless, with great diversity among conducted studies on the hypothesis and a lack of causal evidence, a final verdict on its viability is still pending. To contribute to this ongoing debate, we conducted a week-long experimental investigation with 381 adult Instagram users. Specifically, we tested how posting pictures on Instagram affects different aspects of well-being. The results of this study depicted a positive effect of posting Instagram pictures on users' experienced positive emotions but no effects on other aspects of well-being. The findings broadly align with the recent criticism against the active use hypothesis and support the call for a more nuanced view on the impact of SNSs on users.},
  language  = {en}
}
@misc{HesseMatthiesSinzigetal.2019,
  author    = {Hesse, Guenter and Matthies, Christoph and Sinzig, Werner and Uflacker, Matthias},
  title     = {Adding Value by Combining Business and Sensor Data},
  series = {Database Systems for Advanced Applications},
  volume    = {11448},
  journal   = {Database Systems for Advanced Applications},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-18590-9},
  issn      = {0302-9743},
  doi       = {10.1007/978-3-030-18590-9_80},
  pages     = {528 -- 532},
  year      = {2019},
  abstract  = {Industry 4.0 and the Internet of Things are recent developments that have lead to the creation of new kinds of manufacturing data. Linking this new kind of sensor data to traditional business information is crucial for enterprises to take advantage of the data's full potential. In this paper, we present a demo which allows experiencing this data integration, both vertically between technical and business contexts and horizontally along the value chain. The tool simulates a manufacturing company, continuously producing both business and sensor data, and supports issuing ad-hoc queries that answer specific questions related to the business. In order to adapt to different environments, users can configure sensor characteristics to their needs.},
  language  = {en}
}
@phdthesis{Gruetze2018,
  author    = {Gr{\"u}tze, Toni},
  title     = {Adding value to text with user-generated content},
  school      = {Universit{\"a}t Potsdam},
  pages     = {ii, 114},
  year      = {2018},
  abstract  = {In recent years, the ever-growing amount of documents on the Web as well as in closed systems for private or business contexts led to a considerable increase of valuable textual information about topics, events, and entities. It is a truism that the majority of information (i.e., business-relevant data) is only available in unstructured textual form. The text mining research field comprises various practice areas that have the common goal of harvesting high-quality information from textual data. These information help addressing users' information needs. In this thesis, we utilize the knowledge represented in user-generated content (UGC) originating from various social media services to improve text mining results. These social media platforms provide a plethora of information with varying focuses. In many cases, an essential feature of such platforms is to share relevant content with a peer group. Thus, the data exchanged in these communities tend to be focused on the interests of the user base. The popularity of social media services is growing continuously and the inherent knowledge is available to be utilized. We show that this knowledge can be used for three different tasks. Initially, we demonstrate that when searching persons with ambiguous names, the information from Wikipedia can be bootstrapped to group web search results according to the individuals occurring in the documents. We introduce two models and different means to handle persons missing in the UGC source. We show that the proposed approaches outperform traditional algorithms for search result clustering. Secondly, we discuss how the categorization of texts according to continuously changing community-generated folksonomies helps users to identify new information related to their interests. We specifically target temporal changes in the UGC and show how they influence the quality of different tag recommendation approaches. Finally, we introduce an algorithm to attempt the entity linking problem, a necessity for harvesting entity knowledge from large text collections. The goal is the linkage of mentions within the documents with their real-world entities. A major focus lies on the efficient derivation of coherent links. For each of the contributions, we provide a wide range of experiments on various text corpora as well as different sources of UGC. The evaluation shows the added value that the usage of these sources provides and confirms the appropriateness of leveraging user-generated content to serve different information needs.},
  language  = {en}
}
@incollection{RojahnAmbrosBiruetal.2023,
  author    = {Rojahn, Marcel and Ambros, Maximilian and Biru, Tibebu and Krallmann, Hermann and Gronau, Norbert and Grum, Marcus},
  title     = {Adequate basis for the data-driven and machine-learning-based identification},
  series = {Artificial intelligence and soft computing},
  booktitle = {Artificial intelligence and soft computing},
  editor    = {Rutkowski, Leszek and Scherer, Rafał and Korytkowski, Marcin and Pedrycz, Witold and Tadeusiewicz, Ryszard and Zurada, Jacek M.},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-031-42504-2},
  doi       = {10.1007/978-3-031-42505-9_48},
  pages     = {570 -- 588},
  year      = {2023},
  abstract  = {Process mining (PM) has established itself in recent years as a main method for visualizing and analyzing processes. However, the identification of knowledge has not been addressed adequately because PM aims solely at data-driven discovering, monitoring, and improving real-world processes from event logs available in various information systems. The following paper, therefore, outlines a novel systematic analysis view on tools for data-driven and machine learning (ML)-based identification of knowledge-intensive target processes. To support the effectiveness of the identification process, the main contributions of this study are (1) to design a procedure for a systematic review and analysis for the selection of relevant dimensions, (2) to identify different categories of dimensions as evaluation metrics to select source systems, algorithms, and tools for PM and ML as well as include them in a multi-dimensional grid box model, (3) to select and assess the most relevant dimensions of the model, (4) to identify and assess source systems, algorithms, and tools in order to find evidence for the selected dimensions, and (5) to assess the relevance and applicability of the conceptualization and design procedure for tool selection in data-driven and ML-based process mining research.},
  language  = {en}
}
@article{BrewkaEllmauthalerKernIsberneretal.2018,
  author    = {Brewka, Gerhard and Ellmauthaler, Stefan and Kern-Isberner, Gabriele and Obermeier, Philipp and Ostrowski, Max and Romero, Javier and Schaub, Torsten H. and Schieweck, Steffen},
  title     = {Advanced solving technology for dynamic and reactive applications},
  series = {K{\"u}nstliche Intelligenz},
  volume    = {32},
  journal   = {K{\"u}nstliche Intelligenz},
  number    = {2-3},
  publisher = {Springer},
  address   = {Heidelberg},
  issn      = {0933-1875},
  doi       = {10.1007/s13218-018-0538-8},
  pages     = {199 -- 200},
  year      = {2018},
  language  = {en}
}
@inproceedings{AbramovaGladkayaKrasnova2021,
  author    = {Abramova, Olga and Gladkaya, Margarita and Krasnova, Hanna},
  title     = {An unusual encounter with oneself},
  series = {ICIS 2021: IS and the future of work},
  booktitle = {ICIS 2021: IS and the future of work},
  publisher = {AIS Electronic Library (AISeL)},
  address   = {[Erscheinungsort nicht ermittelbar]},
  year      = {2021},
  abstract  = {Helping overcome distance, the use of videoconferencing tools has surged during the pandemic. To shed light on the consequences of videoconferencing at work, this study takes a granular look at the implications of the self-view feature for meeting outcomes. Building on self-awareness research and self-regulation theory, we argue that by heightening the state of self-awareness, self-view engagement depletes participants' mental resources and thereby can undermine online meeting outcomes. Evaluation of our theoretical model on a sample of 179 employees reveals a nuanced picture. Self-view engagement while speaking and while listening is positively associated with self-awareness, which, in turn, is negatively associated with satisfaction with meeting process, perceived productivity, and meeting enjoyment. The criticality of the communication role is put forward: looking at self while listening to other attendees has a negative direct and indirect effect on meeting outcomes; however, looking at self while speaking produces equivocal effects.},
  language  = {en}
}
@phdthesis{Heinze2015,
  author    = {Heinze, Theodor},
  title     = {Analyse von Patientendaten und Entscheidungsunterst{\"u}tzung in der Telemedizin},
  school      = {Universit{\"a}t Potsdam},
  pages     = {173},
  year      = {2015},
  language  = {de}
}
@article{SchindlerMoldenhawerStangeetal.2021,
  author    = {Schindler, Daniel and Moldenhawer, Ted and Stange, Maike and Lepro, Valentino and Beta, Carsten and Holschneider, Matthias and Huisinga, Wilhelm},
  title     = {Analysis of protrusion dynamics in amoeboid cell motility by means of regularized contour flows},
  series = {PLoS Computational Biology : a new community journal},
  volume    = {17},
  journal   = {PLoS Computational Biology : a new community journal},
  number    = {8},
  publisher = {PLoS},
  address   = {San Fransisco},
  issn      = {1553-734X},
  doi       = {10.1371/journal.pcbi.1009268},
  pages     = {33},
  year      = {2021},
  abstract  = {Amoeboid cell motility is essential for a wide range of biological processes including wound healing, embryonic morphogenesis, and cancer metastasis. It relies on complex dynamical patterns of cell shape changes that pose long-standing challenges to mathematical modeling and raise a need for automated and reproducible approaches to extract quantitative morphological features from image sequences. Here, we introduce a theoretical framework and a computational method for obtaining smooth representations of the spatiotemporal contour dynamics from stacks of segmented microscopy images. Based on a Gaussian process regression we propose a one-parameter family of regularized contour flows that allows us to continuously track reference points (virtual markers) between successive cell contours. We use this approach to define a coordinate system on the moving cell boundary and to represent different local geometric quantities in this frame of reference. In particular, we introduce the local marker dispersion as a measure to identify localized membrane expansions and provide a fully automated way to extract the properties of such expansions, including their area and growth time. The methods are available as an open-source software package called AmoePy, a Python-based toolbox for analyzing amoeboid cell motility (based on time-lapse microscopy data), including a graphical user interface and detailed documentation. Due to the mathematical rigor of our framework, we envision it to be of use for the development of novel cell motility models. We mainly use experimental data of the social amoeba Dictyostelium discoideum to illustrate and validate our approach. <br /> Author summary Amoeboid motion is a crawling-like cell migration that plays an important key role in multiple biological processes such as wound healing and cancer metastasis. This type of cell motility results from expanding and simultaneously contracting parts of the cell membrane. From fluorescence images, we obtain a sequence of points, representing the cell membrane, for each time step. By using regression analysis on these sequences, we derive smooth representations, so-called contours, of the membrane. Since the number of measurements is discrete and often limited, the question is raised of how to link consecutive contours with each other. In this work, we present a novel mathematical framework in which these links are described by regularized flows allowing a certain degree of concentration or stretching of neighboring reference points on the same contour. This stretching rate, the so-called local dispersion, is used to identify expansions and contractions of the cell membrane providing a fully automated way of extracting properties of these cell shape changes. We applied our methods to time-lapse microscopy data of the social amoeba Dictyostelium discoideum.},
  language  = {en}
}