Informatik/Technik
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Auflistung Informatik/Technik nach Instituten/Kliniken "Institut für Informationssysteme"
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Item Adaptive service migration and transaction processing in wireless sensor networks(2011) Reinke, ChristophItem Item Conceptual orthospaces(2024) Leemhuis, MenaItem Context is the key(2022) Kuhr, FelixItem Data partitioning and query optimization in the semantic internet of things(2023) Warnke, Alexander BenjaminItem Design and implementation of a database programming language for XML-based applications(2006) Schuhart, HenrikeItem Efficient XML data management and query evaluation in wireless sensor networks(2011) Höller, NilsItem Enabling research data management for non IT-professionals(2025-02-16) Schiff, SimonIn almost all academic fields, results are derived from found evidence such as objects to be digitized, case studies, observations, experiments, or research data. Ideally, results are linked to its evidence to ease data governance and reproducibility of results, and publicly stored in a research data repository to be themselves linked as evidence for new results. This linking has created a huge mesh of data over the years. Searching for information, deciding whether found information is relevant, and then using relevant information for producing results costs a lot of time in such a mesh of data. Due to the fact that a high investment of time is associated with high costs, funding agencies such as the German Research Foundation (Deutsche Forschungsgemeinschaft; DFG) or the Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung; BMBF) demand a data management plan (DMP). A DMP is designed to reduce the costs of projects submitted to a funding agency and to avoid future costs when data repositories are to be reused. Nevertheless, a DMP is often not fully implemented because it is too costly, which in the long run leads to a mesh of data. In this thesis, we identify problems and present solutions usable by non-IT-experts to spend less time on solving problems that arise at implementing a DMP at each project’s repository and coping with a huge mesh of data across many repositories. According to our observations, humanities scholars produce research data that are meant to be printed later or uploaded at a repository. Potential problems that arise at a repository, independent of other repositories, to be solved are manifold. Data to be printed is encoded with a markup language for illustration purposes and not machine interpretable formatted. We not only show that such formatted data can be structured with a parser to be interpreted by machines, but also what possibilities open up from the structured data. Structured data is automatically combined, linked, transformed into other formats, and visualized on the web. Visualized data can be cited and annotated to help others assess relevance. Once, problems are solved at each repository, we show how we cope with data linked across repositories. This is achieved by designing a human-aware information retrieval (IR) agent, that can search in a mesh of data for relevant information. We discuss in what way the interaction of a user with such an IR agent can be optimized with human-aware collaborative planning strategies.Item An engine for ontology-based stream processing theory and implementation(2018) Neuenstadt, ChristianItem Hierarchies of action control(2026) Heinrich, Nils WendelHumans act in environments that are uncertain, dynamically changing, and only partially controllable. Successful behavior in such settings requires more than reactive feedback correction or rule-based strategies. Agents must continuously regulate how precisely action goals are specified, how strongly predictions guide action, and how they adapt when control deteriorates. Despite extensive work on motor control, decision-making, and learning, existing accounts lack a unified explanation of how behavioral strategies, subjective control beliefs, and computational learning mechanisms jointly support adaptive action control in these dynamic environments that are characterized by uncertainty and action–effect contingencies that change over time. This dissertation develops and empirically grounds a multi-level framework of action control that integrates hierarchical accounts of intention, graded degrees of control, and belief-based regulation of agency. Action control emerges from the dynamic interaction between behavioral markers of ongoing regulation, cognitive forward models supporting both reactive and proactive action selection, and computational mechanisms driving parameter adjustments of these internal forward models. Across four studies that share the same continuous task environment, action control is investigated at behavioral, cognitive, and computational levels. Studies 1 and 2 show that gaze behavior implements a hierarchical organization of action goals over different temporal horizons. Close fixations support immediate control and state-dependent regulation, whereas distant fixations anchor attention to future task-relevant locations, supporting proactive planning. These complementary fixation types adapt flexibly to changes in environmental dynamics and action–effect contingencies, revealing how perceptual control implements both reactive and anticipatory action. Study 3 treats the Sense of Control, the subjective feeling of being in control, as a latent belief that is updated via Bayesian integration. Participants provided control ratings after each trial, serving as observable indicators of their evolving beliefs. Analysis showed that participants derived their ratings by integrating prior expectations with observed performance outcomes. Control ratings decreased when environmental outcomes violated participants’ expectations, and individuals differed in how strongly they weighted performance evidence, indicating variability in belief updating. These results suggest that the Sense of Control reflects the predictive accuracy of internal forward models. Study 4 translated these insights into a cognitive architecture combining forward predictions with error-driven learning. The Sense of Control from Study 3, modeled using the Bayesian framework, was reinterpreted as uncertainty associated with an internal forward model of the task’s environmental dynamics. This uncertainty modulated whether forward predictions were used during action selection. Simulations showed that when high uncertainty led the architecture to suspend forward prediction, learning was severely impaired. Adaptation was slow, performance remained below that of human participants, and internal representations failed to improve because no informative prediction errors were generated. When the architecture relied on predictions despite high uncertainty, corrective error signals enabled efficient parameter updating, resulting in learning trajectories and performance comparable to human participants. This demonstrates that engaging predictive mechanisms early, even under uncertainty, is essential for effective adaptation to dynamic environments. Together, these studies establish a process-based framework in which adaptive action control is neither purely reactive nor rigidly predictive. Instead, it emerges from coordinated mechanisms operating across multiple levels and timescales, with uncertainty guiding when internal models are updated incrementally and when they must be fundamentally restructured. By linking gaze behavior, subjective control beliefs, and predictive learning, this dissertation provides a foundation for investigating structural learning, proactive control, and failures of agency in both human and artificial systems.Item Indirect causes, dependencies and causality in Bayesian networks(2017) Motzek, AlexanderItem KeyX(2005) Hammerschmidt, Beda ChristophItem Learning from ups and downs(2023) Mohr, MarisaItem Navigate through troubled water(2023) Finke, NilsItem New methods for efficient query answering in Gaussian probabilistic graphical models(2023) Hartwig, MattisItem On Markov decision processes with the stochastic differential Bellman Equation(2025) Cakir, Merve NurStochastic differential equations play an important role in capturing the dynamics of complex systems, where uncertainty prevails in the form of noise. In complex systems noise is abundant, but its exact behaviour is unknown. However, noise can be simulated with stochastic processes. Stochastic calculi, such as the Itˆo formula, provide tools for navigating these systems. In this work, the adaptation of the Bellman equation, a cornerstone of dynamic programming, to the realm of stochastic differential equations is explored, facilitating the modeling of decision problems subject to noise. Value iteration and Q-learning, two well-known solution methods in machine learning, are extended to stochastic algorithms in order to approximate the solution for Markov decision processes with uncertainties modeled by the stochastic differential Bellman equation. These stochastic algorithms enable a realistic approach to modeling and solving decision problems in stochastic environments efficiently. The stochastic value iteration is applied when the environment is fully known, while the stochastic Q-learning extends its utility even in cases where transition probabilities remain unknown. Through theoretical analyses and case studies, these algorithms demonstrate their efficacy and applicability, delivering meaningful results. Additionally, the stochastic Q-learning achieves superior rewards compared to the deterministic algorithm, indicating its ability to optimize decision processes in stochastic environments more effectively by exploring more states. Finally, the stochastic differential Bellman equation is formulated as a system of ordinary equations, providing an alternative solution. For this, the concept of the random dynamical system is explored, of which a stochastic differential equation is an example.Item PDT logic(2018) Martiny, KarstenItem Rescued from a sea of queries(2020) Braun, Tanya