Dieter W. Fellner

Visual analysis of large graphs : State-of-the-art and future research challenges

The analysis of large graphs plays a prominent role in various fields of research and is relevant in many important application areas. Effective visual analysis of graphs requires appropriate visual presentations in combination with respective user interaction facilities and algorithmic graph analysis methods. How to design appropriate graph analysis systems depends on many factors, including the type of graph describing the data, the analytical task at hand and the applicability of graph analysis methods. The most recent surveys of graph visualization and navigation techniques cover techniques that had been introduced until 2000 or concentrate only on graph layouts published until 2002. Recently, new techniques have been developed covering a broader range of graph types, such as time‐varying graphs. Also, in accordance with ever growing amounts of graph‐structured data becoming available, the inclusion of algorithmic graph analysis and interaction techniques becomes increasingly important. In this State‐of‐the‐Art Report, we survey available techniques for the visual analysis of large graphs. Our review first considers graph visualization techniques according to the type of graphs supported. The visualization techniques form the basis for the presentation of interaction approaches suitable for visual graph exploration. As an important component of visual graph analysis, we discuss various graph algorithmic aspects useful for the different stages of the visual graph analysis process. We also present main open research challenges in this field.

A scalable architecture for the HTML5/X3D integration model X3DOM

We present a scalable architecture, which implements and further evolves the HTML/X3D integration model X3DOM introduced in [Behr et al. 2009]. The goal of this model is to integrate and update declarative X3D content directly in the HTML DOM tree. The model was previously presented in a very abstract and generic way by only suggesting implementation strategies. The available open-source x3dom.js architecture provides concrete solutions to the previously open points and extents the generic model if necessary. The outstanding feature of the architecture is to provide a single declarative interface to application developers and at the same time support of various backends through a powerful fallback-model. This fallback-model does not provide a single implementation strategy for the runtime and rendering module but supports different methods transparently. This includes native browser implementations and X3D-plugins as well as a WebGL-based scene-graph, which allows running the content without the need for installing additional plugins on all browsers that support WebGL. The paper furthermore discusses generic aspects of the architecture like encoding and introspection, but also provides details concerning two backends. It shows how the system interfaces with X3D-plugins and WebGL and also discusses implementation specific features and limitations.

Irregular lattices for complex shape grammar facade parsing

High-quality urban reconstruction requires more than multi-view reconstruction and local optimization. The structure of facades depends on the general layout, which has to be optimized globally. Shape grammars are an established method to express hierarchical spatial relationships, and are therefore suited as representing constraints for semantic facade interpretation. Usually inference uses numerical approximations, or hard-coded grammar schemes. Existing methods inspired by classical grammar parsing are not applicable on real-world images due to their prohibitively high complexity. This work provides feasible generic facade reconstruction by combining low-level classifiers with mid-level object detectors to infer an irregular lattice. The irregular lattice preserves the logical structure of the facade while reducing the search space to a manageable size. We introduce a novel method for handling symmetry and repetition within the generic grammar. We show competitive results on two datasets, namely the Paris 2010 and the Graz 50. The former includes only Hausmannian, while the latter includes Classicism, Biedermeier, Historicism, Art Nouveau and post-modern architectural styles.

Efficient GPU Data Structures and Methods to Solve Sparse Linear Systems in Dynamics Applications

We present graphics processing unit (GPU) data structures and algorithms to efficiently solve sparse linear systems that are typically required in simulations of multi‐body systems and deformable bodies. Thereby, we introduce an efficient sparse matrix data structure that can handle arbitrary sparsity patterns and outperforms current state‐of‐the‐art implementations for sparse matrix vector multiplication. Moreover, an efficient method to construct global matrices on the GPU is presented where hundreds of thousands of individual element contributions are assembled in a few milliseconds. A finite‐element‐based method for the simulation of deformable solids as well as an impulse‐based method for rigid bodies are introduced in order to demonstrate the advantages of the novel data structures and algorithms. These applications share the characteristic that a major computational effort consists of building and solving systems of linear equations in every time step. Our solving method results in a speed‐up factor of up to 13 in comparison to other GPU methods.

Thinking Penguin: Multimodal Brain–Computer Interface Control of a VR Game

In this paper, we describe a multimodal brain-computer interface (BCI) experiment, situated in a highly immersive CAVE. A subject sitting in the virtual environment controls the main character of a virtual reality game: a penguin that slides down a snowy mountain slope. While the subject can trigger a jump action via the BCI, additional steering with a game controller as a secondary task was tested. Our experiment profits from the game as an attractive task where the subject is motivated to get a higher score with a better BCI performance. A BCI based on the so-called brain switch was applied, which allows discrete asynchronous actions. Fourteen subjects participated, of which 50% achieved the required performance to test the penguin game. Comparing the BCI performance during the training and the game showed that a transfer of skills is possible, in spite of the changes in visual complexity and task demand. Finally and most importantly, our results showed that the use of a secondary motor task, in our case the joystick control, did not deteriorate the BCI performance during the game. Through these findings, we conclude that our chosen approach is a suitable multimodal or hybrid BCI implementation, in which the user can even perform other tasks in parallel.

Trajectory-based visual analysis of large financial time series data

Visual Analytics seeks to combine automatic data analysis with visualization and human-computer interaction facilities to solve analysis problems in applications characterized by occurrence of large amounts of complex data. The financial data analysis domain is a promising field for research and application of Visual Analytics technology, as it prototypically involves the analysis of large data volumes in solving complex analysis tasks. We introduce a Visual Analytics system for supporting the analysis of large amounts of financial time-varying indicator data. A system, driven by the idea of extending standard technical chart analysis from one to two-dimensional indicator space, is developed. The system relies on an unsupervised clustering algorithm combined with an appropriately designed movement data visualization technique. Several analytical views on the full market and specific assets are offered for the user to navigate, to explore, and to analyze. The system includes automatic screening of the potentially large visualization space, preselecting possibly interesting candidate data views for presentation to the user. The system is applied to a large data set of time varying 2-D stock market data, demonstrating its effectiveness for visual analysis of financial data. We expect the proposed techniques to be beneficial in other application areas as well.

On spatial quantization of color images

Image quantization and digital halftoning, two fundamental image processing problems, are generally performed sequentially and, in most cases, independent of each other. Color reduction with a pixel-wise defined distortion measure and the halftoning process with its local averaging neighborhood typically optimize different quality criteria or, frequently, follow a heuristic approach without reference to any quantitative quality measure. In this paper, we propose a new model to simultaneously quantize and halftone color images. The method is based on a rigorous cost-function approach which optimizes a quality criterion derived from a simplified model of human perception. It incorporates spatial and contextual information into the quantization and thus overcomes the artificial separation of quantization and halftoning. Optimization is performed by an efficient multiscale procedure which substantially alleviates the computational burden. The quality criterion and the optimization algorithms are evaluated on a representative set of artificial and real-world images showing a significant image quality improvement compared to standard color reduction approaches. Applying the developed cost function, we also suggest a new distortion measure for evaluating the overall quality of color reduction schemes.

The arrigo showcase reloaded—towards a sustainable link between 3D and semantics

It is still a big technical problem to establish a relation between a shape and its meaning in a sustainable way. We present a solution with a markup method that allows for labeling parts of a 3D object very much like labeling parts of a hypertext. A 3D markup can serve both as hyperlink and as link anchor, which is the key to bidirectional linking between 3D objects and Web documents. Our focus is on a sustainable 3D software infrastructure for application scenarios ranging from email and Internet over authoring and browsing semantic networks to interactive museum presentations. We demonstrate the workflow and the effectiveness of our tools by redoing the Arrigo 3D Showcase. We are working towards a best practice example for information modeling in cultural heritage.

Generative parametric design of Gothic window tracery

Gothic architecture, and especially window tracery, exhibits quite complex geometric shape configurations by combining only a few basic geometric patterns, namely circles and straight lines. They are combined using only a limited set of operations, such as intersection, offsetting, and extrusions. The reason lies in the process how these objects have been physically realized, i.e., through construction with compass and ruler. Consequently, Gothic architecture is a great, although challenging, domain for parametric modeling. We present some principles of this long-standing domain, together with some delicate details, and show how the constructions of some prototypic Gothic windows can be formalized using our Generative Modeling Language (GML). The emphasis of this procedural approach is on modularization, so that complex configurations can be obtained from combining elementary constructions. Different combinations of specific parametric features can be grouped together, which leads to the concept of styles. They permit to differentiate between the basic shape and its appearance, i.e., a particular ornamental decoration. This leads to an extremely compact representation for a whole class of shapes, which can nevertheless be quickly evaluated to obtain a connected manifold mesh of a particular window instance. The resulting mesh may also contain free-form surface parts, represented as subdivision surfaces.

Visual Analysis of Large Graphs

The analysis of large graphs plays a prominent role in various fields of research and is relevant in many important application areas. Effective visual analysis of graphs requires appropriate visual presentations in combination with respective user interaction facilities and algorithmic graph analysis methods. How to design appropriate graph analysis systems depends on many factors, including the type of graph describing the data, the analytical task at hand, and the applicability of graph analysis methods. The most recent surveys of graph visualization and navigation techniques were presented by Herman et al. [HMM00] and Diaz [DPS02]. The first work surveyed the main techniques for visualization of hierarchies and graphs in general that had been introduced until 2000. The second work concentrated on graph layouts introduced until 2002. Recently, new techniques have been developed covering a broader range of graph types, such as time-varying graphs. Also, in accordance with ever growing amounts of graph-structured data becoming available, the inclusion of algorithmic graph analysis and interaction techniques becomes increasingly important. In this State-of-the-Art Report, we survey available techniques for the visual analysis of large graphs. Our review firstly considers graph visualization techniques according to the type of graphs supported. The visualization techniques form the basis for the presentation of interaction approaches suitable for visual graph exploration. As an important component of visual graph analysis, we discuss various graph algorithmic aspects useful for the different stages of the visual graph analysis process.