User hints for optimisation processes

Innovative improvements in the area of Human-Computer Interaction and User Interfaces have en-abled intuitive and effective applications for a variety of problems. On the other hand, there has also been the realization that several real-world optimization problems still cannot be totally auto-mated. Very often, user interaction is necessary for refining the optimization problem, managing the computational resources available, or validating or adjusting a computer-generated solution. This thesis investigates how humans can help optimization methods to solve such difficult prob-lems. It presents an interactive framework where users play a dynamic and important role by pro-viding hints. Hints are actions that help to insert domain knowledge, to escape from local minima, to reduce the space of solutions to be explored, or to avoid ambiguity when there is more than one optimal solution. Examples of user hints are adjustments of constraints and of an objective function, focusing automatic methods on a subproblem of higher importance, and manual changes of an ex-isting solution. User hints are given in an intuitive way through a graphical interface. Visualization tools are also included in order to inform about the state of the optimization process. We apply the User Hints framework to three combinatorial optimization problems: Graph Clus-tering, Graph Drawing and Map Labeling. Prototype systems are presented and evaluated for each problem. The results of the study indicate that optimization processes can benefit from human interaction. The main goal of this thesis is to list cases where human interaction is helpful, and provide an ar-chitecture for supporting interactive optimization. Our contributions include the general User Hints framework and particular implementations of it for each optimization problem. We also present a general process, with guidelines, for applying our framework to other optimization problems

Collaborative geographic visualization

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia do Ambiente, perfil Gestão e Sistemas AmbientaisThe present document is a revision of essential references to take into account when developing ubiquitous Geographical Information Systems (GIS) with collaborative visualization purposes. Its chapters focus, respectively, on general principles of GIS, its multimedia components and ubiquitous practices; geo-referenced information visualization and its graphical components of virtual and augmented reality; collaborative environments, its technological requirements, architectural specificities, and models for collective information management; and some final considerations about the future and challenges of collaborative visualization of GIS in ubiquitous environment

User Interfaces and Difference Visualizations for Alternatives

Designers often create multiple iterations to evaluate alternatives. Todays computer-based tools do not support such easy exploration of a design space, despite the fact that such support has been advocated. This dissertation is centered on this. I begin by investigating the effectiveness of various forms of difference visualizations and support for merging changes within a system targeted at diagrams with node and edge attributes. I evaluated the benefits of the introduced difference visualization techniques in two user studies. I found that the basic side-by-side juxtaposition visualization was not effective and also not well received. For comparing diagrams with matching node positions, participants preferred the side-by-side option with a difference layer. For diagrams with non-matching positions animation was beneficial, but the combination with a difference layer was preferred. Thus, the difference layer technique was useful and a good complement to animation. I continue by investigating if explicit support for design alternatives better supports exploration and creativity in a generative design system. To investigate the new techniques to better support exploration, I built a new system that supports parallel exploration of alternative designs and generation of new structural combinations. I investigate the usefulness of my prototype in two user studies and interviews. The results and feedback suggest and confirm that supporting design alternatives explicitly enables designers to work more creatively. Generative models are often represented as DAGs (directed acyclic graphs) in a dataflow programming environment. Existing approaches to compare such DAGs do not generalize to multiple alternatives. Informed by and building on the first part of my dissertation, I introduce a novel user interface that enables visual differencing and editing alternative graphsspecifically more than two alternatives simultaneously, something that has not been presented before. I also explore multi-monitor support to demonstrate that the difference visualization technique scales well to up to 18 alternatives. The novel jamming space feature makes organizing alternatives on a 23 monitor system easier. To investigate the usability of the new difference visualization method I conducted an exploratory interview with three expert designers. The received comments confirmed that it meets their design goals

Exploratory Cluster Analysis from Ubiquitous Data Streams using Self-Organizing Maps

This thesis addresses the use of Self-Organizing Maps (SOM) for exploratory cluster analysis over ubiquitous data streams, where two complementary problems arise: first, to generate (local) SOM models over potentially unbounded multi-dimensional non-stationary data streams; second, to extrapolate these capabilities to ubiquitous environments. Towards this problematic, original contributions are made in terms of algorithms and methodologies. Two different methods are proposed regarding the first problem. By focusing on visual knowledge discovery, these methods fill an existing gap in the panorama of current methods for cluster analysis over data streams. Moreover, the original SOM capabilities in performing both clustering of observations and features are transposed to data streams, characterizing these contributions as versatile compared to existing methods, which target an individual clustering problem. Also, additional methodologies that tackle the ubiquitous aspect of data streams are proposed in respect to the second problem, allowing distributed and collaborative learning strategies. Experimental evaluations attest the effectiveness of the proposed methods and realworld applications are exemplified, namely regarding electric consumption data, air quality monitoring networks and financial data, motivating their practical use. This research study is the first to clearly address the use of the SOM towards ubiquitous data streams and opens several other research opportunities in the future

Scalable Profiling and Visualization for Characterizing Microbiomes

Metagenomics is the study of the combined genetic material found in microbiome samples, and it serves as an instrument for studying microbial communities, their biodiversities, and the relationships to their host environments. Creating, interpreting, and understanding microbial community profiles produced from microbiome samples is a challenging task as it requires large computational resources along with innovative techniques to process and analyze datasets that can contain terabytes of information. The community profiles are critical because they provide information about what microorganisms are present in the sample, and in what proportions. This is particularly important as many human diseases and environmental disasters are linked to changes in microbiome compositions. In this work we propose novel approaches for the creation and interpretation of microbial community profiles. This includes: (a) a cloud-based, distributed computational system that generates detailed community profiles by processing large DNA sequencing datasets against large reference genome collections, (b) the creation of Microbiome Maps: interpretable, high-resolution visualizations of community profiles, and (c) a machine learning framework for characterizing microbiomes from the Microbiome Maps that delivers deep insights into microbial communities. The proposed approaches have been implemented in three software solutions: Flint, a large scale profiling framework for commercial cloud systems that can process millions of DNA sequencing fragments and produces microbial community profiles at a very low cost; Jasper, a novel method for creating Microbiome Maps, which visualizes the abundance profiles based on the Hilbert curve; and Amber, a machine learning framework for characterizing microbiomes using the Microbiome Maps generated by Jasper with high accuracy. Results show that Flint scales well for reference genome collections that are an order of magnitude larger than those used by competing tools, while using less than a minute to profile a million reads on the cloud with 65 commodity processors. Microbiome maps produced by Jasper are compact, scalable representations of extremely complex microbial community profiles with numerous demonstrable advantages, including the ability to display latent relationships that are hard to elicit. Finally, experiments show that by using images as input instead of unstructured tabular input, the carefully engineered software, Amber, can outperform other sophisticated machine learning tools available for classification of microbiomes

Visual analytics methods for retinal layers in optical coherence tomography data

Optical coherence tomography is an important imaging technology for the early detection of ocular diseases. Yet, identifying substructural defects in the 3D retinal images is challenging. We therefore present novel visual analytics methods for the exploration of small and localized retinal alterations. Our methods reduce the data complexity and ensure the visibility of relevant information. The results of two cross-sectional studies show that our methods improve the detection of retinal defects, contributing to a deeper understanding of the retinal condition at an early stage of disease.Die optische Kohärenztomographie ist ein wichtiges Bildgebungsverfahren zur Früherkennung von Augenerkrankungen. Die Identifizierung von substrukturellen Defekten in den 3D-Netzhautbildern ist jedoch eine Herausforderung. Wir stellen daher neue Visual-Analytics-Methoden zur Exploration von kleinen und lokalen Netzhautveränderungen vor. Unsere Methoden reduzieren die Datenkomplexität und gewährleisten die Sichtbarkeit relevanter Informationen. Die Ergebnisse zweier Querschnittsstudien zeigen, dass unsere Methoden die Erkennung von Netzhautdefekten in frühen Krankheitsstadien verbessern

In Situ Visualization of Performance Data in Parallel CFD Applications

This thesis summarizes the work of the author on visualization of performance data in parallel Computational Fluid Dynamics (CFD) simulations. Current performance analysis tools are unable to show their data on top of complex simulation geometries (e.g. an aircraft engine). But in CFD simulations, performance is expected to be affected by the computations being carried out, which in turn are tightly related to the underlying computational grid. Therefore it is imperative that performance data is visualized on top of the same computational geometry which they originate from. However, performance tools have no native knowledge of the underlying mesh of the simulation. This scientific gap can be filled by merging the branches of HPC performance analysis and in situ visualization of CFD simulations data, which shall be done by integrating existing, well established state-of-the-art tools from each field. In this threshold, an extension for the open-source performance tool Score-P was designed and developed, which intercepts an arbitrary number of manually selected code regions (mostly functions) and send their respective measurements – amount of executions and cumulative time spent – to the visualization software ParaView – through its in situ library, Catalyst –, as if they were any other flow-related variable. Subsequently the tool was extended with the capacity to also show communication data (messages sent between MPI ranks) on top of the CFD mesh. Testing and evaluation are done with two industry-grade codes: Rolls-Royce’s CFD code, Hydra, and Onera, DLR and Airbus’ CFD code, CODA. On the other hand, it has been also noticed that the current performance tools have limited capacity of displaying their data on top of three-dimensional, framed (i.e. time-stepped) representations of the cluster’s topology. Parallel to that, in order for the approach not to be limited to codes which already have the in situ adapter, it was extended to take the performance data and display it – also in codes without in situ – on a three-dimensional, framed representation of the hardware resources being used by the simulation. Testing is done with the Multi-Grid and Block Tri-diagonal NAS Parallel Benchmarks (NPB), as well as with Hydra and CODA again. The benchmarks are used to explain how the new visualizations work, while real performance analyses are done with the industry-grade CFD codes. The proposed solution is able to provide concrete performance insights, which would not have been reached with the current performance tools and which motivated beneficial changes in the respective source code in real life. Finally, its overhead is discussed and proven to be suitable for usage with CFD codes. The dissertation provides a valuable addition to the state of the art of highly parallel CFD performance analysis and serves as basis for further suggested research directions

Developing a Framework for Heterotopias as Discursive Playgrounds: A Comparative Analysis of Non-Immersive and Immersive Technologies

The discursive space represents the reordering of knowledge gained through accumulation. In the digital age, multimedia has become the language of information, and the space for archival practices is provided by non-immersive technologies, resulting in the disappearance of several layers from discursive activities. Heterotopias are unique, multilayered epistemic contexts that connect other systems through the exchange of information. This paper describes a process to create a framework for Virtual Reality, Mixed Reality, and personal computer environments based on heterotopias to provide absent layers. This study provides virtual museum space as an informational terrain that contains a "world within worlds" and presents place production as a layer of heterotopia and the subject of discourse. Automation for the individual multimedia content is provided via various sorting and grouping algorithms, and procedural content generation algorithms such as Binary Space Partitioning, Cellular Automata, Growth Algorithm, and Procedural Room Generation. Versions of the framework were comparatively evaluated through a user study involving 30 participants, considering factors such as usability, technology acceptance, and presence. The results of the study show that the framework can serve diverse contexts to construct multilayered digital habitats and is flexible for integration into professional and daily life practices

Interactive web-based visualization

The visualization of large amounts of data, which cannot be easily copied for processing on a user’s local machine, is not yet a fully solved problem. Remote visualization represents one possible solution approach to the problem, and has long been an important research topic. Depending on the device used, modern hardware, such as high-performance GPUs, is sometimes not available. This is another reason for the use of remote visualization. Additionally, due to the growing global networking and collaboration among research groups, collaborative remote visualization solutions are becoming more important. The additional use of collaborative visualization solutions is eventually due to the growing global networking and collaboration among research groups. The attractiveness of web-based remote visualization is greatly increased by the wide availability of web browsers on almost all devices; these are available today on all systems - from desktop computers to smartphones. In order to ensure interactivity, network bandwidth and latency are the biggest challenges that web-based visualization algorithms have to solve. Despite the steady improvements in available bandwidth, these improvements are still significantly slower than, for example, processor performance, resulting in increasing the impact of this bottleneck. For example, visualization of large dynamic data in low-bandwidth environments can be challenging because it requires continuous data transfer. However, bandwidth improvement alone cannot improve the latency because it is also affected by factors such as the distance between server and client and network utilization. To overcome these challenges, a combination of techniques is needed to customize the individual processing steps of the visualization pipeline, from efficient data representation to hardware-accelerated rendering on the client side. This thesis first deals with related work in the field of remote visualization with a particular focus on interactive web-based visualization and then presents techniques for interactive visualization in the browser using modern web standards such as WebGL and HTML5. These techniques enable the visualization of dynamic molecular data sets with more than one million atoms at interactive frame rates using GPU-based ray casting. Due to the limitations which exist in a browser-based environment, the concrete implementation of the GPU-based ray casting had to be customized. Evaluation of the resulting performance shows that GPU-based techniques enable the interactive rendering of large data sets and achieve higher image quality compared to polygon-based techniques. In order to reduce data transfer times and network latency, and improve rendering speed, efficient approaches for data representation and transmission are used. Furthermore, this thesis introduces a GPU-based volume-ray marching technique based on WebGL 2.0, which uses progressive brick-wise data transfer, as well as multiple levels of detail in order to achieve interactive volume rendering of datasets stored on a server. The concepts and results presented in this thesis contribute to the further spread of interactive web-based visualization. The algorithmic and technological advances that have been achieved form a basis for further development of interactive browser-based visualization applications. At the same time, this approach has the potential for enabling future collaborative visualization in the cloud.Die Visualisierung großer Datenmengen, welche nicht ohne Weiteres zur Verarbeitung auf den lokalen Rechner des Anwenders kopiert werden können, ist ein bisher nicht zufriedenstellend gelöstes Problem. Remote-Visualisierung stellt einen möglichen Lösungsansatz dar und ist deshalb seit langem ein relevantes Forschungsthema. Abhängig vom verwendeten Endgerät ist moderne Hardware, wie etwa performante GPUs, teilweise nicht verfügbar. Dies ist ein weiterer Grund für den Einsatz von Remote-Visualisierung. Durch die zunehmende globale Vernetzung und Kollaboration von Forschungsgruppen gewinnt kollaborative Remote-Visualisierung zusätzlich an Bedeutung. Die Attraktivität web-basierter Remote-Visualisierung wird durch die weitreichende Verfügbarkeit von Web-Browsern auf nahezu allen Endgeräten enorm gesteigert; diese sind heutzutage auf allen Systemen - vom Desktop-Computer bis zum Smartphone - vorhanden. Bei der Gewährleistung der Interaktivität sind Bandbreite und Latenz der Netzwerkverbindung die größten Herausforderungen, welche von web-basierten Visualisierungs-Algorithmen gelöst werden müssen. Trotz der stetigen Verbesserungen hinsichtlich der verfügbaren Bandbreite steigt diese signifikant langsamer als beispielsweise die Prozessorleistung, wodurch sich die Auswirkung dieses Flaschenhalses immer weiter verstärkt. So kann beispielsweise die Visualisierung großer dynamischer Daten in Umgebungen mit geringer Bandbreite eine Herausforderung darstellen, da kontinuierlicher Datentransfer benötigt wird. Dennoch kann die alleinige Verbesserung der Bandbreite keine entsprechende Verbesserung der Latenz bewirken, da diese zudem von Faktoren wie der Distanz zwischen Server und Client sowie der Netzwerkauslastung beeinflusst wird. Um diese Herausforderungen zu bewältigen, wird eine Kombination verschiedener Techniken für die Anpassung der einzelnen Verarbeitungsschritte der Visualisierungspipeline benötigt, angefangen bei effizienter Datenrepräsentation bis hin zu hardware-beschleunigtem Rendering auf der Client-Seite. Diese Doktorarbeit befasst sich zunächst mit verwandten Arbeiten auf dem Gebiet der Remote-Visualisierung mit besonderem Fokus auf interaktiver web-basierter Visualisierung und präsentiert danach Techniken für die interaktive Visualisierung im Browser mit Hilfe moderner Web-Standards wie WebGL und HTML5. Diese Techniken ermöglichen die Visualisierung dynamischer molekularer Datensätze mit mehr als einer Million Atomen bei interaktiven Frameraten durch die Verwendung GPU-basierten Raycastings. Aufgrund der Einschränkungen, welche in einer Browser-basierten Umgebung vorliegen, musste die konkrete Implementierung des GPU-basierten Raycastings angepasst werden. Die Evaluation der daraus resultierenden Performanz zeigt, dass GPU-basierte Techniken das interaktive Rendering von großen Datensätzen ermöglichen und eine im Vergleich zu Polygon-basierten Techniken höhere Bildqualität erreichen. Zur Verringerung der Übertragungszeiten, Reduktion der Latenz und Verbesserung der Darstellungsgeschwindigkeit werden effiziente Ansätze zur Datenrepräsentation und übertragung verwendet. Des Weiteren wird in dieser Doktorarbeit eine GPU-basierte Volumen-Ray-Marching-Technik auf Basis von WebGL 2.0 eingeführt, welche progressive blockweise Datenübertragung verwendet, sowie verschiedene Detailgrade, um ein interaktives Volumenrendering von auf dem Server gespeicherten Datensätzen zu erreichen. Die in dieser Doktorarbeit präsentierten Konzepte und Resultate tragen zur weiteren Verbreitung von interaktiver web-basierter Visualisierung bei. Die erzielten algorithmischen und technologischen Fortschritte bilden eine Grundlage für weiterführende Entwicklungen von interaktiven Visualisierungsanwendungen auf Browser-Basis. Gleichzeitig hat dieser Ansatz das Potential, zukünftig kollaborative Visualisierung in der Cloud zu ermöglichen