@phdthesis{Schwartges2015,
  author    = {Schwartges, Nadine},
  title     = {Dynamic Label Placement in Practice},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-115003},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2015},
  abstract  = {The general map-labeling problem is as follows: given a set of geometric objects to be labeled, or features, in the plane, and for each feature a set of label positions, maximize the number of placed labels such that there is at most one label per feature and no two labels overlap. There are three types of features in a map: point, line, and area features. Unfortunately, one cannot expect to find efficient algorithms that solve the labeling problem optimally. Interactive maps are digital maps that only show a small part of the entire map whereas the user can manipulate the shown part, the view, by continuously panning, zooming, rotating, and tilting (that is, changing the perspective between a top and a bird view). An example for the application of interactive maps is in navigational devices. Interactive maps are challenging in that the labeling must be updated whenever labels leave the view and, while zooming, the label size must be constant on the screen (which either makes space for further labels or makes labels overlap when zooming in or out, respectively). These updates must be computed in real time, that is, the computation must be so fast that the user does not notice that we spend time on the computation. Additionally, labels must not jump or flicker, that is, labels must not suddenly change their positions or, while zooming out, a vanished label must not appear again. In this thesis, we present efficient algorithms that dynamically label point and line features in interactive maps. We try to label as many features as possible while we prohibit labels that overlap, jump, and flicker. We have implemented all our approaches and tested them on real-world data. We conclude that our algorithms are indeed real-time capable.},
  subject      = {Computerkartografie},
  language  = {en}
}
@phdthesis{Wawrowsky2007,
  author    = {Wawrowsky, Kolja Alexander},
  title     = {Analysis and Visualization in Multidimensional Microscopy},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-23867},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2007},
  abstract  = {The live sciences currently undergo a paradigm shift to computer aided discoveries. Discoveries in the live sciences were historically made by either direct observation or as a result of chemical assays. Today we see a growing shift toward computer aided analysis and visualization. This gradual process happens in microscopy. Multidimensional laser scanning microscopy can acquire very complex multichannel data from fixed or live specimen. New probes such as visible fluorescent proteins let us observe the expression of genes and track protein localization. Ion sensitive dyes change intensity with the concentration of ions in the cell. The laser scanning confocal allows us to record these processes in three dimensions over time. This work demonstrates the application of software analysis to multidimensional microscopy data. We introduce methods for volume investigation, ion flux analysis and molecular modeling. The visualization methods are based on a multidimensional data model to accommodate complex datasets. The software uses vector processing and multiple processors to accelerate volume rendering and achieve interactive rendering. The algorithms are based on human visual perception and allow the observer a wide range of mixed render modes. The software was used to reconstruct the pituitary development in zebrafish and observe the degeneration of neurons after injury in a mouse model. Calicum indicator dyes have long been used to study calcium fluxes. We optimized the imaging method to minimize impact on the cell. Live cells were imaged continuously for 45 minutes and subjected to increasing does of a drug. We correlated the amplitude of calcium oscillations to increasing doses of a drug and obtain single cell dose response curves. Because this method is very sensitive and measures single cell responses it has potential in drug discovery and characterization. Microtubules form a dynamic cytoskeleton, which is responsible for cell shape, intracellular transport and has an integral role in mitosis. A hallmark of microtubule organization is lateral interactions. Microtubules are bundles by proteins into dense structures. To estimate the contribution of this bundling process, we created a fractal model of microtubule organization. This model demonstrates that morphology of complex microtubule arrays can be explained by bundling alone. In summary we showed that advances in software for visualization, data analysis and modeling lead to new discoveries.},
  subject      = {Konfokale Mikroskopie},
  language  = {en}
}
@phdthesis{Atzmueller2006,
  author    = {Atzm{\"u}ller, Martin},
  title     = {Knowledge-Intensive Subgroup Mining - Techniques for Automatic and Interactive Discovery},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-21004},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2006},
  abstract  = {Data mining has proved its significance in various domains and applications. As an important subfield of the general data mining task, subgroup mining can be used, e.g., for marketing purposes in business domains, or for quality profiling and analysis in medical domains. The goal is to efficiently discover novel, potentially useful and ultimately interesting knowledge. However, in real-world situations these requirements often cannot be fulfilled, e.g., if the applied methods do not scale for large data sets, if too many results are presented to the user, or if many of the discovered patterns are already known to the user. This thesis proposes a combination of several techniques in order to cope with the sketched problems: We discuss automatic methods, including heuristic and exhaustive approaches, and especially present the novel SD-Map algorithm for exhaustive subgroup discovery that is fast and effective. For an interactive approach we describe techniques for subgroup introspection and analysis, and we present advanced visualization methods, e.g., the zoomtable that directly shows the most important parameters of a subgroup and that can be used for optimization and exploration. We also describe various visualizations for subgroup comparison and evaluation in order to support the user during these essential steps. Furthermore, we propose to include possibly available background knowledge that is easy to formalize into the mining process. We can utilize the knowledge in many ways: To focus the search process, to restrict the search space, and ultimately to increase the efficiency of the discovery method. We especially present background knowledge to be applied for filtering the elements of the problem domain, for constructing abstractions, for aggregating values of attributes, and for the post-processing of the discovered set of patterns. Finally, the techniques are combined into a knowledge-intensive process supporting both automatic and interactive methods for subgroup mining. The practical significance of the proposed approach strongly depends on the available tools. We introduce the VIKAMINE system as a highly-integrated environment for knowledge-intensive active subgroup mining. Also, we present an evaluation consisting of two parts: With respect to objective evaluation criteria, i.e., comparing the efficiency and the effectiveness of the subgroup discovery methods, we provide an experimental evaluation using generated data. For that task we present a novel data generator that allows a simple and intuitive specification of the data characteristics. The results of the experimental evaluation indicate that the novel SD-Map method outperforms the other described algorithms using data sets similar to the intended application concerning the efficiency, and also with respect to precision and recall for the heuristic methods. Subjective evaluation criteria include the user acceptance, the benefit of the approach, and the interestingness of the results. We present five case studies utilizing the presented techniques: The approach has been successfully implemented in medical and technical applications using real-world data sets. The method was very well accepted by the users that were able to discover novel, useful, and interesting knowledge.},
  subject      = {Data Mining},
  language  = {en}
}