Task-based Adaptation of Graphical Content in Smart Visual Interfaces

To be effective visual representations must be adapted to their respective context of use, especially in so-called Smart Visual Interfaces striving to present specifically those information required for the task at hand. This thesis proposes a generic approach that facilitate the automatic generation of task-specific visual representations from suitable task descriptions. It is discussed how the approach is applied to four principal content types raster images, 2D vector and 3D graphics as well as data visualizations, and how existing display techniques can be integrated into the approach.Effektive visuelle Repräsentationen müssen an den jeweiligen Nutzungskontext angepasst sein, insbesondere in sog. Smart Visual Interfaces, welche anstreben, möglichst genau für die aktuelle Aufgabe benötigte Informationen anzubieten. Diese Arbeit entwirft einen generischen Ansatz zur automatischen Erzeugung aufgabenspezifischer Darstellungen anhand geeigneter Aufgabenbeschreibungen. Es wird gezeigt, wie dieser Ansatz auf vier grundlegende Inhaltstypen Rasterbilder, 2D-Vektor- und 3D-Grafik sowie Datenvisualisierungen anwendbar ist, und wie existierende Darstellungstechniken integrierbar sind

Doctor of Philosophy

dissertationDataflow pipeline models are widely used in visualization systems. Despite recent advancements in parallel architecture, most systems still support only a single CPU or a small collection of CPUs such as a SMP workstation. Even for systems that are specifically tuned towards parallel visualization, their execution models only provide support for data-parallelism while ignoring taskparallelism and pipeline-parallelism. With the recent popularization of machines equipped with multicore CPUs and multi-GPU units, these visualization systems are undoubtedly falling further behind in reaching maximum efficiency. On the other hand, there exist several libraries that can schedule program executions on multiple CPUs and/or multiple GPUs. However, due to differences in executing a task graph and a pipeline along with their APIs being considerably low-level, it still remains a challenge to integrate these run-time libraries into current visualization systems. Thus, there is a need for a redesigned dataflow architecture to fully support and exploit the power of highly parallel machines in large-scale visualization. The new design must be able to schedule executions on heterogeneous platforms while at the same time supporting arbitrarily large datasets through the use of streaming data structures. The primary goal of this dissertation work is to develop a parallel dataflow architecture for streaming large-scale visualizations. The framework includes supports for platforms ranging from multicore processors to clusters consisting of thousands CPUs and GPUs. We achieve this in our system by introducing the notion of Virtual Processing Elements and Task-Oriented Modules along with a highly customizable scheduler that controls the assignment of tasks to elements dynamically. This creates an intuitive way to maintain multiple CPU/GPU kernels yet still provide coherency and synchronization across module executions. We have implemented these techniques into HyperFlow which is made of an API with all basic dataflow constructs described in the dissertation, and a distributed run-time library that can be used to deploy those pipelines on multicore, multi-GPU and cluster-based platforms

Analysis domain model for shared virtual environments

The field of shared virtual environments, which also encompasses online games and social 3D environments, has a system landscape consisting of multiple solutions that share great functional overlap. However, there is little system interoperability between the different solutions. A shared virtual environment has an associated problem domain that is highly complex raising difficult challenges to the development process, starting with the architectural design of the underlying system. This paper has two main contributions. The first contribution is a broad domain analysis of shared virtual environments, which enables developers to have a better understanding of the whole rather than the part(s). The second contribution is a reference domain model for discussing and describing solutions - the Analysis Domain Model

AN EXPEDITIOUS PARAMETRIC APPROACH FOR CITY INFORMATION MODELING AND FINITE ELEMENT ANALYSIS

Abstract. The mitigation of seismic risk passes through the assessment of seismic hazard of urban fabrics on a given territory. Statistical methods and damage probability matrices are currently used to facilitate seismic safety knowledge and assessment operations. These methods, despite being fast and low cost, often return results that differ from reality and prone to the expertise of the operator. Indeed, in order to have more accurate information it is necessary to conduct Finite Element Analysis (FEA). However, this type of analysis requires considerable surveying and modeling time and therefore are not easily applied to the urban scale. The key to implement this analysis at the territorial scale lies in the way of acquisition of urban data (geometric and informative) and their management within appropriate modeling environments that allow their treatment. Currently, the information modeling paradigms used for urban data collection and management are either time and resource consuming (HBIM) or overly simplified (GIS). In this research we investigate the potential of City Information Modeling (CIM) in a parametric environment (with reference to CityGML standards) combined with urban survey procedures. Aim of the work presented here is the definition of a parametric modeling protocol that allows, in a short time, the acquisition, modeling and finite element structural analysis of urban aggregates

Linked Open Data - Creating Knowledge Out of Interlinked Data: Results of the LOD2 Project

Database Management; Artificial Intelligence (incl. Robotics); Information Systems and Communication Servic

Real-time Human Eye Resolution Ray Tracing in Mixed Reality

Mixed reality applications require natural visualizations. Ray tracing is one of the candidates for this purpose. Real-time ray tracing is slowly becoming a reality in consumer market mixed and virtual reality. This is happening due to development in display technologies and computer hardware. Some of these examples are foveated rendering enabled high resolution displays, like Varjo mixed reality headset, and parallel computing enablers, like GPUs getting ray tracing hardware acceleration enablers, such as for example Nvidia's RTX. Currently, the challenge in ray tracing is resource need especially in mixed reality where low latency is wanted and with human eye resolution where high resolution needs are obvious. In this paper, we design and implement a novel foveated ray tracing solution called Human Eye Resolution Ray Tracer (HERR) that achieves real-time frame rates in human eye resolution in mixed reality.Peer reviewe

PERICLES Deliverable 4.3:Content Semantics and Use Context Analysis Techniques

The current deliverable summarises the work conducted within task T4.3 of WP4, focusing on the extraction and the subsequent analysis of semantic information from digital content, which is imperative for its preservability. More specifically, the deliverable defines content semantic information from a visual and textual perspective, explains how this information can be exploited in long-term digital preservation and proposes novel approaches for extracting this information in a scalable manner. Additionally, the deliverable discusses novel techniques for retrieving and analysing the context of use of digital objects. Although this topic has not been extensively studied by existing literature, we believe use context is vital in augmenting the semantic information and maintaining the usability and preservability of the digital objects, as well as their ability to be accurately interpreted as initially intended.PERICLE