The thesis deals with analysing and designing an effective dashboard. By way of introduction the topics of Business Intelligence, data storages and data quality are described. Then, an overview of analytical outputs is given and the process of their formation is outlined. The basic principles of an effective design of management reports are described, with emphasis laid on the end user. The thesis focuses on important factors of efficiency, such as applicability of transferred information and the form of their transfer. The practical part concentrates on the improvement of the efficiency of a particular dashboard. Besides the rules of a correct design, knowledge of cognitive psychology is also used for this purpose. Upon an analysis of deficiencies of an existing dashboard, proposed changes are incorporated. Changes resulting in more effective transfer of information via a dashboard are based on a theoretical basis of the previous section.

The thesis is focused on the development of JSF framework providing components for complex data visualizations. Its objective consists of the implementation of the API for creating JSF components rendering complex charts based on the JavaScript library C3.js and subsequent implementation of a set of chart components using this API. The contribution of this thesis is a library providing a tool for creating new JSF components based on C3.js together with a set of ready to use components. It begins with research of relevant JSF libraries, following with API analysis and design. Based on that is API implemented, in which a component set is then created. As a part of this work is a user guide, API reference guide and presentation web of implemented components.

[pt] A interpretação de dados sísmicos é de fundamental importância para a industria de óleo e gás. Uma vez que esses tipos de dados possuem um caráter volumétrico, não é tão simples se identificar e selecionar atributos presentes em sua estrutura 3D. Além disso, a grande presença de ruídos e concavidades acentuadas nesse tipo de dado aumenta a complexidade de sua manipulação e visualização. Devido a essas características, a geometria do dado é muito complexa, sendo necessários modelos de iluminação mais realísticos para realizar a iluminação do volume sísmico. Este trabalho consiste em realizar a visualização volumétrica de dados sísmicos baseada no algoritmo de traçado de raios, utilizando um modelo de iluminação por oclusão direcional, calculando a contribuição de luz ambiente que chega a cada elemento do volume. Desta forma, conseguimos realçar a geometria do dado sísmico, sobretudo onde as concavidades e falhas são mais acentuadas. O algoritmo proposto foi inteiramente implementado em placa gráfica, permitindo manipulação a taxas interativas, sem a necessidade de pré-processamento. / [en] The interpretation of seismic volumetric data has a major importance for the oil and gas industry. Since these data types have a volumetric character mode, identify and select attributes present in this struct become a difficult task. Furthemore, the high-frequecy noise and depth information typically found in this type of data, increasesthe complexity of their manipulation and visualization. Due to these characteristics, the geometry of 3D sismic data is very complexy and is necessary more realistic light model to perfom the illumnination of the seismic volume. This work consists of performing a volumetric visualization of seismic data based on ray tracing algorithm, using an illumination model by directional occlusion, computing the ambiente light attenuated by the elements in the light trajetory for all elements in the volume. Thus, we emphasize the geometry of the seismic data, especially the depth cues and spatial relationship. The proposed algorithm was fully implemented on graphics card, allowing at interactive rates, without any pre-processing.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / In this paper, we proposed a method that allows us to formulate and evaluate process mining indicators through questions related to the process traceability, and to bring about a clear understanding of the process variables through data visualization techniques. This proposal identifies bottlenecks and violations of policies that arise due to the difficulty of carrying out measurements and analysis for the improvement of process quality assurance and process transformation. The proposal validation was carried out in a health clinic in Lima (Peru) with data obtained from an information system that supports the surgery block process. Finally, the results contribute to the optimization of decision-making by the medical staff involved in the surgery block process. / Revisión por pares

The master’s thesis submitted deals with realtime rendering of three-dimensional CAD data using Vulkan API. The thesis also covers possibilities of connecting the API with VRUT, complex solution for renderig developed by ŠKODA AUTO a.s. Design of such connection is presented in form of Vulkan rendering plugin for VRUT application. Performance of the designed module is compared with another rendering module, based on different rendering technologies.

This work deals with the 2D and 3D visualization of simulation outputs from the k-Wave toolbox. This toolbox, designed to accurately model the propagation of ultrasound waves in the human body, usually generates immense amounts of output data (up to hundreds of GB). That is why new methods for both the visualization and the effective data representation are necessary to be developed to help users to easily understand the simulation results. This thesis elaborates on the data format, simulation outputs are stored in, with the use of the HDF5 library and looking for the best way to quickly read the simulation data. Finally, the thesis presents the design and the implementation of the console-based application for big simulation data pre-processing and the GUI-based application for interactive visualization of the pre-processed data. The most significant features of these applications are downsampling data, changing the format of storing, viewing 2D sections, planar and volumetric visualization and animation of the simulation process. The proposed implementation allows parts of the simulation domain to be visualised within tens of milliseconds even if the simulation domain comprises GBs of data - This significantly streamlines the work of scientists and clinicians in the field of HIFU.

Microorganisms are all around us. Some of them even live in our body and are essential for our healthy being. Study of microbial communities based on their genetic content has become very popular with the development of new technologies, which enable easy reading of DNA or RNA. The key role of these studies is usually to characterize significant microbial patterns of an environment. However, currently used visualization tools have many drawbacks for such analyses. The subject of this thesis is to design a R/Bioconductor package for simple creation of bipartite graphs from microbial data. This type of visualization brings many advantages for microbiome analysis. Benefits of bipartite graphs are further demonstrated by analysis of main parameters affecting computer processing of microbial data.

Automotive diagnosis data are useful for the automotive OEMs and third parties in order to analyze the vehicle performance and driving behavior. This data can be access and read in real time situation using the On-Board Diagnosis system which is located inside the vehicle and is accessible by its socket. However, as the diagnosis data are in real time and not being stored, analyzing them is not easy. Therefore, in this project, a program on Raspberry Pi will be developed which is going to read diagnosis data and store them in a cloud database. The cloud database will handle the data storage and keep the data for further analysis and evaluations. The database will be accessible everywhere as it is using cloud technology. On the other hand, in order to provide easy and meaningful access to the data, a web application is developed in order to visualize the data by means of Graphs, Texts, and maps.

As a result of the digitalization of industrial processes, huge amounts of data are generated in organizations every second. Developing solutions that properly represent this data is critical for making it useful to the organization's stakeholders. Data visualization is a field within computer science that can be utilized to address this demand by enabling the development of appropriate methods for showing the data output of Digital Twins of the Organization (DTO). A recently proposed architecture for a resilient DTO using the EA Blueprint Pattern provides a promising basis for implementing DTOs that are capable of evolving alongside a dynamic organization. However, there is a lack of implementation of any prototypes of the proposed architecture's Information Architecture, which is responsible for dealing with data and visualizing them. Therefore, this thesis aims to develop a visualization prototype, aligned with the proposed architecture. The thesis achieves this goal by first establishing a theoretical basis in data visualization and applicable visualization techniques through a brief review of research articles. After a systematic selection process of visualization tools, the Python frameworks Plotly and Dash are chosen for designing and implementing a visualization prototype. The final design consists of a Python application containing various features and implementing multiple classes which make the prototype easily modifiable and thus able to evolve. In an industrial use case, the prototype is demonstrated to be fulfilling its requirements. Using the observations from the use case, it is finally concluded that the prototype is, in fact, sufficiently aligned with the proposed DTO architecture.

Enabling human understanding of high-dimensional (HD) data is critical for scientific research but highly challenging. To deal with large datasets, probabilistic-based non-linear DR models, like UMAP and t-SNE, lead the performance on reducing the high dimensionality. However, considering the trade-off between global and local structure preservation and the randomness initialized for computation, applying non-linear models in different parameter settings to unknown high-dimensional structure data may return different 2D visual forms. Much critical neighborhood relationship may be falsely imposed, and uncertainty may be introduced into the low-dimensional embedding visualizations, so-called distortion. In this work, a survey has been conducted to illustrate the most state-of-the-art layout enrichment works for interpreting dimensionality reduction methods and results. Responding to the lack of visual interpretation techniques to probabilistic-based DR methods, we propose a visualization technique called ManiGraph, which facilitates users to explore multi-view 2D embeddings via mesoscopic structure graphs. A dynamic mesoscopic structure first subsets HD data by a hexagonal grid in visual space from non-linear embedding (e.g., UMAP). Then, it measures the regional adapted trustworthiness/continuity and visualizes the restored missing and highlighted false connections between subsets from high-dimensional space to the low-dimensional in a node-linkage manner. The visualization helps users understand and interpret the distortion from both visualization and model stages. We further demonstrate the user cases tested on intuitive 3D toy datasets, fashion-MNIST, and single-cell RNA sequencing with domain experts in unsupervised scenarios. This work will potentially benefit the data science community, from toolkit users to DR algorithm developers.<br>