Informatik/Technik

Dauerhafte URI für die Sektionhttps://epub.uni-luebeck.de/handle/zhb_hl/4

Listen

Auflistung Informatik/Technik nach Instituten/Kliniken "Institut für Informationssysteme"

Oder geben Sie die ersten Buchstaben ein:
Gerade angezeigt 1 - 20 von 24
  • Item
  • Item
  • Item
    Conceptual orthospaces
    (2024) Leemhuis, Mena
  • Item
    Context is the key
    (2022) Kuhr, Felix
  • Item
  • Item
  • Item
  • Item
    Enabling research data management for non IT-professionals
    (2025-02-16) Schiff, Simon
    In 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
  • Item
  • Item
  • Item
    KeyX
    (2005) Hammerschmidt, Beda Christoph
  • Item
    Learning from ups and downs
    (2023) Mohr, Marisa
  • Item
    Navigate through troubled water
    (2023) Finke, Nils
  • Item
  • Item
    On Markov decision processes with the stochastic differential Bellman Equation
    (2025) Cakir, Merve Nur
    Stochastic 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
  • Item
    PDT logic
    (2018) Martiny, Karsten
  • Item
    Rescued from a sea of queries
    (2020) Braun, Tanya
  • Item
    Semantic assets
    (2019) Melzer, Sylvia

KONTAKT

Universität zu Lübeck
Zentrale Hochschulbibliothek - Haus 60
Ratzeburger Allee 160
23562 Lübeck
Tel. +49 451 3101 2201
Fax +49 451 3101 2204


IMPRESSUM

DATENSCHUTZ

BARIEREFREIHEIT

Feedback schicken

Cookie-Einstellungen