List of publications
A Modular Software Architecture for Processing of Big Geospatial Data in the Cloud
Computers & Graphics
In this paper we propose a software architecture that allows for processing of large geospatial data sets in the cloud. Our system is modular and flexible and supports multiple algorithm design paradigms such as MapReduce, in-memory computing or agent-based programming. It contains a web-based user interface where domain experts (e.g. GIS analysts or urban planners) can define high-level processing workflows using a domain-specific language (DSL). The workflows are passed through a number of components including a parser, interpreter, and a service called job manager. These components use declarative and procedural knowledge encoded in rules to generate a processing chain specifying the execution of the workflows on a given cloud infrastructure according to the constraints defined by the user. The job manager evaluates this chain, spawns processing services in the cloud and monitors them. The services communicate with each other through a distributed file system that is scalable and fault-tolerant. Compared to previous work describing cloud infrastructures and architectures we focus on the processing of big heterogeneous geospatial data. In addition to that, we do not rely on only one specific programming model or a certain cloud infrastructure but support several ones. Combined with the possibility to control the processing through DSL-based workflows, this makes our architecture very flexible and configurable. We do not only see the cloud as a means to store and distribute large data sets but also as a way to harness the processing power of distributed computing environments for large-volume geospatial data sets. The proposed architecture design has been developed for the IQmulus research project funded by the European Commission. The paper concludes with the evaluation results from applying our solution to two example workflows from this project.
A Visual-Interactive System for Prostate Cancer Cohort Analysis
IEEE Computer Graphics and Applications
Data-centered research is becoming increasingly important in prostate cancer research, where a long-term goal is a sound prognosis prior to surgery. The proposed visual-interactive system, developed in close collaboration with medical researchers, helps physicians efficiently and effectively visualize single and multiple patient histories at a glance.
Ambient Intelligence from Senior Citizens' Perspectives: Understanding Privacy Concerns, Technology Acceptance, and Expectations
European Conference on Ambient Intelligence (AmI) <12, 2015, Athens, Greece>
Especially for seniors, Ambient Intelligence can provide assistance in daily living and emergency situations, for example by automatically recognizing critical situations. The use of such systems may involve trade-offs with regard to privacy, social stigmatization, and changes of the well-known living environment. This raises the question of how older adults perceive restrictions of privacy, accept technology, and which requirements are placed on Ambient Intelligent systems. In order to better understand the related concerns and expectations, we surveyed 60 senior citizens. The results show that experience with Ambient Intelligence increases technology acceptance and reduces fears regarding privacy violations and insufficient system reliability. While participants generally tolerate a monitoring of activities in their home, including bathrooms, they do not accept commercial service providers as data recipients. A comparison between four exemplary systems shows that camera-based solutions are perceived with much greater fears than wearable emergency solutions. Burglary detection was rated as similarly important assigned as health features, whereas living comfort features were considered less useful.
An Adaptive Acceleration Structure for Screen-space Ray Tracing
Proceedings of the 7th Conference on High-Performance Graphics 2015
High-Performance Graphics (HPG) <7, 2015, Los Angeles, CA, USA>
We propose an efficient acceleration structure for real-time screenspace ray tracing. The hybrid data structure represents the scene geometry by combining a bounding volume hierarchy with local planar approximations. This enables fast empty space skipping while tracing and yields exact intersection points for the planar approximation. In combination with an occlusion-aware ray traversal our algorithm is capable to quickly trace even multiple depth layers. Compared to prior work, our technique improves the accuracy of the results, is more general, and allows for advanced image transformations, as all pixels can cast rays to arbitrary directions. We demonstrate real-time performance for several applications, including depth-of-field rendering, stereo warping, and screen-space ray traced reflections.
Botential: Localizing On-Body Gestures by Measuring Electrical Signatures on the Human Skin
International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI) <17, 2015, Copenhagen, Denmark>
We present Botential, an on-body interaction method for a wearable input device that can identify the location of onbody tapping gestures, using the entire human body as an interactive surface to expand the usually limited interaction space in the context of mobility. When the sensor is being touched, Botential identifies a body part's unique electric signature, which depends on its physiological and anatomical compositions. This input method exhibits a number of advantages over previous approaches, which include: 1) utilizing the existing signal the human body already emits, to accomplish input with various body parts, 2) the ability to also sense soft and long touches, 3) an increased sensing range that covers the whole body, and 4) the ability to detect taps and hovering through clothes.
Content First - A Concept for Industrial Augmented Reality Maintenance Applications using Mobile Devices
ACM Multimedia Systems Conference (MMSys) <6, 2015, Portland, OR, USA>
Although AR has a long history in the area of maintenance and service-support in industry, there still is a lack of lightweight, yet practical solutions for handheld AR systems in everyday workflows. Attempts to support complex maintenance tasks with AR still miss reliable tracking techniques, simple ways to be integrated into existing maintenance environments, and practical authoring solutions, which minimize costs for specialized content generation. We present a general, customisable application framework, allowing to employ AR and VR techniques in order to support technicians in their daily tasks. In contrast to other systems, we do not aim to replace existing support systems such as traditional manuals. Instead we integrate well-known AR- and novel presentation techniques with existing instruction media. To this end practical authoring solutions are crucial and hence we present an application development system based on web-standards such as HTML,CSS and X3D.
Exploring Hierarchical Visualization Designs Using Phylogenetic Trees
Visualization and Data Analysis 2015
IS&T/SPIE Conference on Visualization and Data Analysis (VDA) <15, 2015, San Francisco, CA, USA>
Ongoing research on information visualization has produced an ever-increasing number of visualization designs. Despite this activity, limited progress has been made in categorizing this large number of information visualizations. This makes understanding their common design features challenging, and obscures the yet unexplored areas of novel designs. With this work, we provide categorization from an evolutionary perspective, leveraging a computational model to represent evolutionary processes, the phylogenetic tree. The result - a phylogenetic tree of a design corpus of hierarchical visualizations - enables better understanding of the various design features of hierarchical information visualizations, and further illuminates the space in which the visualizations lie, through support for interactive clustering and novel design suggestions. We demonstrate these benefits with our software system, where a corpus of two-dimensional hierarchical visualization designs is constructed into a phylogenetic tree. This software system supports visual interactive clustering and suggesting for novel designs; the latter capacity is also demonstrated via collaboration with an artist who sketched new designs using our system.
MobilityGraphs: Visual Analysis of Mass Mobility Dynamics via Spatio-Temporal Graphs and Clustering
IEEE Transactions on Visualization and Computer Graphics
Learning more about people mobility is an important task for official decision makers and urban planners. Mobility data sets characterize the variation of the presence of people in different places over time as well as movements (or flows) of people between the places. The analysis of mobility data is challenging due to the need to analyze and compare spatial situations (i.e., presence and flows of people at certain time moments) and to gain an understanding of the spatio-temporal changes (variations of situations over time). Traditional flow visualizations usually fail due to massive clutter. Modern approaches offer limited support for investigating the complex variation of the movements over longer time periods. We propose a visual analytics methodology that solves these issues by combined spatial and temporal simplifications. We have developed a graph-based method, called MobilityGraphs, which reveals movement patterns that were occluded in flow maps. Our method enables the visual representation of the spatio-temporal variation of movements for long time series of spatial situations originally containing a large number of intersecting flows. The interactive system supports data exploration from various perspectives and at various levels of detail by interactive setting of clustering parameters. The feasibility our approach was tested on aggregated mobility data derived from a set of geolocated Twitter posts within the Greater London city area and mobile phone call data records in Abidjan, Ivory Coast. We could show that MobilityGraphs support the identification of regular daily and weekly movement patterns of resident population.
Notes on Discrete Gaussian Scale Space.
Journal of Mathematical Imaging and Vision
Gaussian scale space is a well-known linear multi-scale representation for continuous signals. The exploration of its so-called deep structure by tracing critical points over scale has various theoretical applications and allows for the construction of a scale space hierarchy tree. However, implementation issues arise, caused by discretization and quantization errors. In order to develop more robust scale space based algorithms, the discrete nature of computer processed signals has to be taken into account. We propose suitable neighborhoods, boundary conditions, and sampling methods. In analogy to prevalent approaches and inspired by Lindeberg's scale space primal sketch, a discretized diffusion equation is derived, including requirements imposed by the chosen neighborhood and boundary condition. The resulting discrete scale space respects important topological invariants such as the Euler number, a key criterion for the successful implementation of algorithms operating on critical points in its deep structure. Relevant properties of the discrete diffusion equation and the Eigenvalue decomposition of its Laplacian kernel are discussed and a fast and robust sampling method is proposed. We finally discuss properties of topological graphs under the influence of smoothing, setting the stage for more robust deep structure extraction algorithms.
Opportunities for Activity Recognition using Ultrasound Doppler Sensing on Unmodified Mobile Phones
iWOAR 2015 - 2nd international Workshop on Sensor-based Activity Recognition and Interaction
International Workshop on Sensor-based Activity Recognition (iWOAR) <2, 2015, Rostock, Germany>
Nowadays activity recognition on smartphones is ubiquitously applied, for example to monitor personal health. The smartphone's sensors act as a foundation to provide information on movements, the user's location or direction. Incorporating ultrasound sensing using the smartphone's native speaker and microphone provides additional means for perceiving the environment and humans. In this paper, we outline possible usage scenarios for this new and promising sensing modality. Based on a custom implementation, we provide results on various experiments to assess the opportunities for activity recognition systems. We discuss various limitations and possibilities when wearing the smartphone on the human body. In stationary deployments, e.g. while placed on a night desk, our implementation is able to detect movements in proximities up to 2m as well as discern several gestures performed above the phone.
Pushing the Limits of 3D Color Printing: Error Diffusion with Translucent Materials
ACM Transactions on Graphics
Accurate color reproduction is important in many applications of 3D printing, from design prototypes to 3D color copies or portraits. Although full color is available via other technologies, multi-jet printers have greater potential for graphical 3D printing, in terms of reproducing complex appearance properties. However, to date these printers cannot produce full color, and doing so poses substantial technical challenges, from the shear amount of data to the translucency of the available color materials. In this article, we propose an error diffusion halftoning approach to achieve full color with multi-jet printers, which operates on multiple isosurfaces or layers within the object. We propose a novel traversal algorithm for voxel surfaces, which allows the transfer of existing error diffusion algorithms from 2D printing. The resulting prints faithfully reproduce colors, color gradients and fine-scale details.
The Visual Exploration of Aggregate Similarity for Multi-dimensional Clustering
IVAPP 2015. Proceedings
International Conference on Information Visualization Theory and Applications (IVAPP) <6, 2015, Berlin, Germany>
We present a visualisation prototype for the support of a novel approach to clustering called TRIAGE. TRIAGE uses aggregation functions which are more adaptable and flexible than the weighted mean for similarity modelling. While TRIAGE has proven itself in practice, the use of complex similarity models makes the interpretation of TRIAGE clusterings challenging. We address this challenge by providing analysts with a linked, matrix-based visualisation of all relevant data attributes. We employ data sampling and matrix seriation to support both effective overviews and fluid, interactive exploration using the same visual metaphor for heterogeneous attributes. The usability of our prototype is demonstrated and assessed with the help of real-world usage scenarios from the cyber-security domain.
Visual Analysis of Contagion in Networks
Contagion is a process whereby the collapse of a node in a network leads to the collapse of neighboring nodes and thereby sets off a chain reaction in the network. It thus creates a special type of time-dependent network. Such processes are studied in various applications, for example, in financial network analysis, infection diffusion prediction, supply-chain management, or gene regulation. Visual analytics methods can help analysts examine contagion effects. For this purpose, network visualizations need to be complemented with specific features to illustrate the contagion process. Moreover, new visual analysis techniques for comparison of contagion need to be developed. In this paper, we propose a system geared to the visual analysis of contagion. It includes the simulation of contagion effects as well as their visual exploration. We present new tools able to compare the evolution of the different contagion processes. In this way, propagation of disturbances can be effectively analyzed. We focus on financial networks; however, our system can be applied to other use cases as well.