In dem Beitrag werden das Konzept und erste Realisierungsergebnisse eines Monitors vorgestellt, der Zustand und Entwicklung von Siedlungs- und Freiraumstruktur in Deutschland beschreibt. Grundlage ist das ATKIS Basis-DLM, dessen Geobasisdaten einer gesetzlichen Fortschreibung unterliegen. Dieses digitale Landschaftsmodell ist der aktuellste und genauste topographische Datensatz, der flächendeckend für Deutschland vorliegt. Die hochauflösenden GIS-Daten ermöglichen erstmals die Berechnung sehr kleinräumiger Kennzahlen und Indikatoren der Flächennutzung für die gesamte Fläche der Bundesrepublik Deutschland. Das geplante Indikatorensystem umfasst die Themenbereiche Siedlung, Freiraum, Bevölkerung, Landschafts- und Naturschutz sowie Verkehr. Es soll, in Ergänzung zu bestehenden flächenstatistischen Berichtssystemen, den urbanen Nutzungswandel und den damit einhergehenden Druck auf Freiräume und Schutzgebiete, insbesondere unter Nachhaltigkeitsaspekten, beschreiben. Die Ergebnisse der komplexen Berechnungen werden im Internet bereitgestellt. Ein Überblicks- und ein Detail-Viewer ermöglichen eine einfache Visualisierung der raumbezogenen Indikatoren und Entwicklungsphänomene. Der Monitor und die damit verbundenen methodischen Entwicklungen sind Aufgabe des Forschungsbereichs „Monitor der Siedlungs- und Freiraumentwicklung“ des Leibniz-Instituts für ökologische Raumentwicklung.

Der Monitor der Siedlungs- und Freiraumentwicklung, kurz: „IÖR-Monitor“ informiert seit 2010 im Internet über die Entwicklung der Siedlungs- und Freiraumstruktur in Deutschland. Dabei befindet er sich in stetiger Weiterentwicklung, sowohl was das Indikatorenset betrifft, als auch die Visualisierungs- und die Analysemöglichkeiten. Der Beitrag beschreibt die neuesten Entwicklungen des Übersichts-Viewers und gibt einen Einblick in den entstehenden Detail-Viewer.

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The acquisition of data and its analysis has become a common yet critical task in many areas of modern economy and research. Unfortunately, the ever-increasing scale of datasets has long outgrown the capacities and abilities humans can muster to extract information from them and gain new knowledge. For this reason, research areas like data mining and knowledge discovery steadily gain importance. The algorithms they provide for the extraction of knowledge are mandatory prerequisites that enable people to analyze large amounts of information. Among the approaches offered by these areas, clustering is one of the most fundamental. By finding groups of similar objects inside the data, it aims to identify meaningful structures that constitute new knowledge. Clustering results are also often used as input for other analysis techniques like classification or forecasting. As clustering extracts new and unknown knowledge, it obviously has no access to any form of ground truth. For this reason, clustering results have a hypothetical character and must be interpreted with respect to the application domain. This makes clustering very challenging and leads to an extensive and diverse landscape of available algorithms. Most of these are expert tools that are tailored to a single narrowly defined application scenario. Over the years, this specialization has become a major trend that arose to counter the inherent uncertainty of clustering by including as much domain specifics as possible into algorithms. While customized methods often improve result quality, they become more and more complicated to handle and lose versatility. This creates a dilemma especially for amateur users whose numbers are increasing as clustering is applied in more and more domains. While an abundance of tools is offered, guidance is severely lacking and users are left alone with critical tasks like algorithm selection, parameter configuration and the interpretation and adjustment of results. This thesis aims to solve this dilemma by structuring and integrating the necessary steps of clustering into a guided and feedback-driven process. In doing so, users are provided with a default modus operandi for the application of clustering. Two main components constitute the core of said process: the algorithm management and the visual-interactive interface. Algorithm management handles all aspects of actual clustering creation and the involved methods. It employs a modular approach for algorithm description that allows users to understand, design, and compare clustering techniques with the help of building blocks. In addition, algorithm management offers facilities for the integration of multiple clusterings of the same dataset into an improved solution. New approaches based on ensemble clustering not only allow the utilization of different clustering techniques, but also ease their application by acting as an abstraction layer that unifies individual parameters. Finally, this component provides a multi-level interface that structures all available control options and provides the docking points for user interaction. The visual-interactive interface supports users during result interpretation and adjustment. For this, the defining characteristics of a clustering are communicated via a hybrid visualization. In contrast to traditional data-driven visualizations that tend to become overloaded and unusable with increasing volume/dimensionality of data, this novel approach communicates the abstract aspects of cluster composition and relations between clusters. This aspect orientation allows the use of easy-to-understand visual components and makes the visualization immune to scale related effects of the underlying data. This visual communication is attuned to a compact and universally valid set of high-level feedback that allows the modification of clustering results. Instead of technical parameters that indirectly cause changes in the whole clustering by influencing its creation process, users can employ simple commands like merge or split to directly adjust clusters. The orchestrated cooperation of these two main components creates a modus operandi, in which clusterings are no longer created and disposed as a whole until a satisfying result is obtained. Instead, users apply the feedback-driven process to iteratively refine an initial solution. Performance and usability of the proposed approach were evaluated with a user study. Its results show that the feedback-driven process enabled amateur users to easily create satisfying clustering results even from different and not optimal starting situations.

Groundwater is the only fresh water resource in the semi-arid to hyper-arid Western Dead Sea catchment. Due to exploitation of groundwater the water level is decreasing in the surrounding Cretaceous aquifer system and sustainable water management is needed in order to prevent the progressive yields and contamination of those water resources. In addition, the water level of the Dead Sea decreases dramatically by at least one meter per year. This is connected to channel off the water from the Jordan River to supply intensive agriculture in the semi-arid to hyper-arid region. Hydrological and hydro-geological analysis and modelling in arid regions, like the study area, frequently suffer from data scarcity and uncertainties regarding rainfall and discharge measurements. The study showed that spatial and temporal interpolations as well as additional methods (e.g. empirical relationships and simultaneous numerical approaches) were suitable tools to overcome data shortage for modelling. Water balances are the result of a calibrated model and are the basis for sustainable management of surface and subsurface water resources. The present study investigates beside the hydrological characterisation of selected sub-catchments (wadis) also the hydro-geology of the Judean limestone aquifer and calculates a comprehensive water balance of the entire western flank of the Dead Sea by the application of two numerical open source codes: OpenGeoSys (OGS) and J2000g. The calibrated two-dimensional hydrological model J2000g provides a 33 years time series of temporal and spatial distributed groundwater recharge for the numerical groundwater flow model of OGS. The mean annual groundwater recharge of 139.9 · 10^6 m^3ˑ a^-1 is nearly completely depleted by abstractions from pumping wells close to the replenishment area in the Judea Mountains.

Public displays are increasingly used in public transport to present information such as departure and arrival times or network maps. Since this information is displayed generically, users often have problems to find the specific information they need. We propose context-aware visualizations on public displays to support passengers by improving personalized information access. Several visualizations for this domain were identified, for example, highlighting individual route or pricing information by fading out the background or increasing readability by font size adaptation. To investigate the influence of adapted content on the user we tested prototypical presentations that show personalized information concerning personal trips. In our user study with 20 participants we analyzed these visualizations to compare their efficiency in contrast to non-adaptive content by measuring time to perform specific tasks. This work presents the results of our user study. They show that especially highlighted information supports the user in finding personalized information faster.

Background: A large number of life science ontologies has been developed to support different application scenarios such as gene annotation or functional analysis. The continuous accumulation of new insights and knowledge affects specific portions in ontologies and thus leads to their adaptation. Therefore, it is valuable to study which ontology parts have been extensively modified or remained unchanged. Users can monitor the evolution of an ontology to improve its further development or apply the knowledge in their applications.

This thesis is about visualizing a kind of data that is trivial to process by computers but difficult to imagine by humans because nature does not allow for intuition with this type of information: high-dimensional data. Such data often result from representing observations of objects under various aspects or with different properties. In many applications, a typical, laborious task is to find related objects or to group those that are similar to each other. One classic solution for this task is to imagine the data as vectors in a Euclidean space with object variables as dimensions. Utilizing Euclidean distance as a measure of similarity, objects with similar properties and values accumulate to groups, so-called clusters, that are exposed by cluster analysis on the high-dimensional point cloud. Because similar vectors can be thought of as objects that are alike in terms of their attributes, the point cloud\''s structure and individual cluster properties, like their size or compactness, summarize data categories and their relative importance. The contribution of this thesis is a novel analysis approach for visual exploration of high-dimensional point clouds without suffering from structural occlusion. The work is based on implementing two key concepts: The first idea is to discard those geometric properties that cannot be preserved and, thus, lead to the typical artifacts. Topological concepts are used instead to shift away the focus from a point-centered view on the data to a more structure-centered perspective. The advantage is that topology-driven clustering information can be extracted in the data\''s original domain and be preserved without loss in low dimensions. The second idea is to split the analysis into a topology-based global overview and a subsequent geometric local refinement. The occlusion-free overview enables the analyst to identify features and to link them to other visualizations that permit analysis of those properties not captured by the topological abstraction, e.g. cluster shape or value distributions in particular dimensions or subspaces. The advantage of separating structure from data point analysis is that restricting local analysis only to data subsets significantly reduces artifacts and the visual complexity of standard techniques. That is, the additional topological layer enables the analyst to identify structure that was hidden before and to focus on particular features by suppressing irrelevant points during local feature analysis. This thesis addresses the topology-based visual analysis of high-dimensional point clouds for both the time-invariant and the time-varying case. Time-invariant means that the points do not change in their number or positions. That is, the analyst explores the clustering of a fixed and constant set of points. The extension to the time-varying case implies the analysis of a varying clustering, where clusters appear as new, merge or split, or vanish. Especially for high-dimensional data, both tracking---which means to relate features over time---but also visualizing changing structure are difficult problems to solve.

Bei der Entwickelung von System-on-Chip (SoC) Debuggern ist es leider hinreichend oft erforderlich den Debugger selbst auf mögliche Fehler zu untersuchen. Da alle ernstzunehmenden Debugger konstruktionsbedingt selbst ein eingebettetes System darstellen, erwächst die Notwendigkeit eine einfache und sicher kontrollierbare Diagnose-Hardware zu entwerfen, welche den Zugang zur Funktionsweise des Debuggers über seine Ausgänge erschließt. Derzeitig ist der Test Access Port (TAP nach IEEE 1149.1-Standard) für viele Integratoren die Grundlage für den Zugriff auf ihre instanzierte Hardware. Selbst in forschungsorientierten Multi- Core System-on-Chip Architekturen wie dem ARM11MP der Firma ARM wird dieses Verfahren noch immer eingesetzt. In unserem Beitrag möchten wir ein Spezialwerkzeug zur Analyse des TAPKommunikationsprotokolles vorstellen, welches den Einsatz teurer Analysetechnik (Logik- Analysatoren) unnötig werden lässt und darüber hinaus eine komfortable, weitergehende Unterstützung für Multi-Core-Systeme bietet. Aufbauend auf der Problematik der Abtastung und Erfassung der Signalzustände am TAP mittels FPGA wird auf die verschiedenen Visualisierungs- und Analyseaspekte der TAPProtokollphasen in einer Multi-Core-Prozessor-Zielsystemumgebung eingegangen. Die hier vorgestellte Lösung ist im Rahmen eines FuE-Verbundprojektes enstanden. Das Vorhaben wird im Rahmen der Technologieförderung mit Mitteln des Europäischen Fonds für regionale Entwicklung (EFRE) 2000-2006 und mit Mitteln des Freistaates Sachsen gefördert.