Parallel Rendering and Large Data Visualization

We are living in the big data age: An ever increasing amount of data is being produced through data acquisition and computer simulations. While large scale analysis and simulations have received significant attention for cloud and high-performance computing, software to efficiently visualise large data sets is struggling to keep up. Visualization has proven to be an efficient tool for understanding data, in particular visual analysis is a powerful tool to gain intuitive insight into the spatial structure and relations of 3D data sets. Large-scale visualization setups are becoming ever more affordable, and high-resolution tiled display walls are in reach even for small institutions. Virtual reality has arrived in the consumer space, making it accessible to a large audience. This thesis addresses these developments by advancing the field of parallel rendering. We formalise the design of system software for large data visualization through parallel rendering, provide a reference implementation of a parallel rendering framework, introduce novel algorithms to accelerate the rendering of large amounts of data, and validate this research and development with new applications for large data visualization. Applications built using our framework enable domain scientists and large data engineers to better extract meaning from their data, making it feasible to explore more data and enabling the use of high-fidelity visualization installations to see more detail of the data.Comment: PhD thesi

Development of a Powerwall-based solution for the manual flagging of radio astronomy data from eMerlin

This project was created with the intention of establishing an optimisation method for the manual flagging of interferometric data of the eMerlin radio astronomy array, using a Powerwall as a visualisation tool. The complexity of this process which is due to the amount of variables and parameters demands a deep understanding of the data treatment. Once the data is achieved by the antennas the signals are correlated. This process generates undesired signals which mostly coming from radio frequency interference. Also when the calibration is performed some values can mislead the expected outcome. Although the flagging is supported with algorithms this method is not one hundred percent accurate. That is why visual inspection is still required. The possibility to use a Powerwall as a visualisation system allows different and new dynamics in terms of the interaction of the analyst with the information required to make the flagging

A Federated Computational Workflow for Analysis of DISKOS Digital Palynological Slides

A novel federated computational workflow for analyzing digital palynological slide images is implemented in this thesis. The slide data files, typically exceeding 3GB, present significant data mobility and computation challenges. The novel distributed computational framework is implemented to address privacy concerns and the challenges associated with moving large data. The idea is to move computational to the data location, optimally utilizing local computational capacity and reducing data movement. Trained deep-learning models deployed in a containerized environment leveraging the Docker technology are integrated in the workflow with a user-friendly interface, and users can run processes with the trained models. The workflow processes include reading slide image files, generating tiled images, and identifying and removing undesirable tiles such as blank tiles. Object detection with the watershed segmentation algorithm identifies tiles with potential microfossils. The identified dinoflagellates are classified with a trained convolution neural network (CNN) model. The classification results are sent to the host and shared with the users. The federated computational approach effectively addresses the challenges related to moving and handling large palynological slide images, creating a more efficient, scalable, and distributed pipeline. Collaborative efforts involving domain experts for model training with more annotated slide images will improve the effectiveness of the workflow

A Federated Computational Workflow for Analysis of DISKOS Digital Palynological Slides.

A novel federated computational workflow for analyzing digital palynological slide images is implemented in this thesis. The slide data files, typically exceeding 3GB, present significant data mobility and computation challenges. The novel distributed computational framework is implemented to address privacy concerns and the challenges associated with moving large data. The idea is to move computational to the data location, optimally utilizing local computational capacity and reducing data movement. Trained deep-learning models deployed in a containerized environment leveraging the Docker technology are integrated in the workflow with a user-friendly interface, and users can run processes with the trained models.\\ The workflow processes include reading slide image files, generating tiled images, and identifying and removing undesirable tiles such as blank tiles. Object detection with the watershed segmentation algorithm identifies tiles with potential microfossils. The identified dinoflagellates are classified with a trained convolution neural network (CNN) model. The classification results are sent to the host and shared with the users. The federated computational approach effectively addresses the challenges related to moving and handling large palynological slide images, creating a more efficient, scalable, and distributed pipeline. Collaborative efforts involving domain experts for model training with more annotated slide images will improve the effectiveness of the workflow

Distributed D3: A web-based distributed data visualisation framework for Big Data

The influx of Big Data has created an ever-growing need for analytic tools targeting towards the acquisition of insights and knowledge from large datasets. Visual perception as a fundamental tool used by humans to retrieve information from the outside world around us has its unique ability to distinguish patterns pre-attentively. Visual analytics via data visualisations is therefore a very powerful tool and has become ever more important in this era. Data-Driven Documents (D3.js) is a versatile and popular web-based data visualisation library that has tended to be the standard toolkit for visualising data in recent years. However, the library is technically inherent and limited in capability by the single thread model of a single browser window in a single machine, and therefore not able to deal with large datasets. The main objective of this thesis is to overcome this limitation and address possible challenges by developing the Distributed D3 framework that employs distributed mechanism to enable the possibility of delivering web-based visualisations for large-scale data, which also allows to effectively utilise the graphical computational resources of the modern visualisation environments. As a result, the first contribution is that the integrated version of Distributed D3 framework has been developed for the Data Observatory. The work proves the concept of Distributed D3 is feasible in reality and also enables developers to collaborate on large-scale data visualisations by using it on the Data Observatory. The second contribution is that the Distributed D3 has been optimised by investigating the potential bottlenecks for large-scale data visualisation applications. The work finds the key performance bottlenecks of the framework and shows an improvement of the overall performance by 35.7% after optimisations, which improves the scalability and usability of Distributed D3 for large-scale data visualisation applications. The third contribution is that the generic version of Distributed D3 framework has been developed for the customised environments. The work improves the usability and flexibility of the framework and makes it ready to be published in the open-source community for further improvements and usages.Open Acces

Perception and Mitigation of Artifacts in a Flat Panel Tiled Display System

Flat panel displays continue to dominate the display market. Larger, higher resolution flat panel displays are now in demand for scientific, business, and entertainment purposes. Manufacturing such large displays is currently difficult and expensive. Alternately, larger displays can be constructed by tiling smaller flat panel displays. While this approach may prove to be more cost effective, appropriate measures must be taken to achieve visual seamlessness and uniformity. In this project we conducted a set of experiments to study the perception and mitigation of image artifacts in tiled display systems. In the first experiment we used a prototype tiled display to investigate its current viability and to understand what critical perceptible visual artifacts exist in this system. Based on word frequencies of the survey responses, the most disruptive artifacts perceived were ranked. On the basis of these findings, we conducted a second experiment to test the effectiveness of image processing algorithms designed to mitigate some of the most distracting artifacts without changing the physical properties of the display system. Still images were processed using several algorithms and evaluated by observers using magnitude scaling. Participants in the experiment noticed statistically significant improvement in image quality from one of the two algorithms. Similar testing should be conducted to evaluate the effectiveness of the algorithms on video content. While much work still needs to be done, the contributions of this project should enable the development of an image processing pipeline to mitigate perceived artifacts in flat panel display systems and provide the groundwork for extending such a pipeline to realtime applications

Optimization of Display-Wall Aware Applications on Cluster Based Systems

Actualment, els sistemes d'informació i comunicació que treballen amb grans volums de dades requereixen l'ús de plataformes que permetin una representació entenible des del punt de vista de l'usuari. En aquesta tesi s'analitzen les plataformes Cluster Display Wall, usades per a la visualització de dades massives, i es treballa concretament amb la plataforma Liquid Galaxy, desenvolupada per Google. Mitjançant la plataforma Liquid Galaxy, es realitza un estudi de rendiment d'aplicacions de visualització representatives, identificant els aspectes de rendiment més rellevants i els possibles colls d'ampolla. De forma específica, s'estudia amb major profunditat un cas representatiu d'aplicació de visualització, el Google Earth. El comportament del sistema executant Google Earth s'analitza mitjançant diferents tipus de test amb usuaris reals. Per a aquest fi, es defineix una nova mètrica de rendiment, basada en la ratio de visualització, i es valora la usabilitat del sistema mitjançant els atributs tradicionals d'efectivitat, eficiència i satisfacció. Adicionalment, el rendiment del sistema es modela analíticament i es prova la precisió del model comparant-ho amb resultats reals.Nowadays, information and communication systems that work with a high volume of data require infrastructures that allow an understandable representation of it from the user's point of view. This thesis analyzes the Cluster Display Wall platforms, used to visualized massive amounts of data, and specifically studies the Liquid Galaxy platform, developed by Google. Using the Liquid Galaxy platform, a performance study of representative visualization applications was performed, identifying the most relevant aspects of performance and possible bottlenecks. Specifically, we study in greater depth a representative case of visualization application, Google Earth. The system behavior while running Google Earth was analyzed through different kinds of tests with real users. For this, a new performance metric was defined, based on the visualization ratio, and the usability of the system was assessed through the traditional attributes of effectiveness, efficiency and satisfaction. Additionally, the system performance was analytically modeled and the accuracy of the model was tested by comparing it with actual results.Actualmente, los sistemas de información y comunicación que trabajan con grandes volúmenes de datos requieren el uso de plataformas que permitan una representación entendible desde el punto de vista del usuario. En esta tesis se analizan las plataformas Cluster Display Wall, usadas para la visualización de datos masivos, y se trabaja en concreto con la plataforma Liquid Galaxy, desarrollada por Google. Mediante la plataforma Liquid Galaxy, se realiza un estudio de rendimiento de aplicaciones de visualización representativas, identificando los aspectos de rendimiento más relevantes y los posibles cuellos de botella. De forma específica, se estudia en mayor profundidad un caso representativo de aplicación de visualización, el Google Earth. El comportamiento del sistema ejecutando Google Earth se analiza mediante diferentes tipos de test con usuarios reales. Para ello se define una nueva métrica de rendimiento, basada en el ratio de visualización, y se valora la usabilidad del sistema mediante los atributos tradicionales de efectividad, eficiencia y satisfacción. Adicionalmente, el rendimiento del sistema se modela analíticamente y se prueba la precisión del modelo comparándolo con resultados reales

Doctor of Philosophy

dissertationInteractive editing and manipulation of digital media is a fundamental component in digital content creation. One media in particular, digital imagery, has seen a recent increase in popularity of its large or even massive image formats. Unfortunately, current systems and techniques are rarely concerned with scalability or usability with these large images. Moreover, processing massive (or even large) imagery is assumed to be an off-line, automatic process, although many problems associated with these datasets require human intervention for high quality results. This dissertation details how to design interactive image techniques that scale. In particular, massive imagery is typically constructed as a seamless mosaic of many smaller images. The focus of this work is the creation of new technologies to enable user interaction in the formation of these large mosaics. While an interactive system for all stages of the mosaic creation pipeline is a long-term research goal, this dissertation concentrates on the last phase of the mosaic creation pipeline - the composition of registered images into a seamless composite. The work detailed in this dissertation provides the technologies to fully realize interactive editing in mosaic composition on image collections ranging from the very small to massive in scale