Visual analytics

In recent years, Visual Analytics has transformed how we approach data-driven decision making across many industries. As organizations increasingly turn to data to guide their strategies, being able to clearly visualize complex information has become more important than ever. This rising demand has led to the creation of a wide variety of tools and libraries, each designed to suit different users and skill levels. Because of their easy drag-and-drop functionality, programs like Microsoft Power BI, Tableau, and Excel are growing in popularity. Since they can generate insightful visuals without any programming, which is gaining a lot of attention in the field of Business Intelligence, these applications are especially helpful for professionals who lack technical skills. In contrast, Plotly, GGPlot and D3 are charting libraries that cater to programmers by offering options for more sophisticated statistical analysis, advanced levels of data manipulation, and even greater customization for sophisticated visualizations. Prior conducted researches marked disparities among these tools, particularly with respect to how well their design supports data exploration in comparison to data presentation. Nevertheless, and as the field progresses, today’s tools for Visual Analytics are more comprehensive and robust than in the past, providing new features and enhancements to keep pace with user demand.This thesis sets out to carry out a detailed comparative analysis of some of the most widely used visualization tools in the field today. The goal is to evaluate their core strengths, limitations, and overall performance in the context of data visualization. Primary visualization criteria, including usability, data preparation requirements, and other pertinent aspects, will be the main focus of the evaluation. A clustering approach will be used to analyze the features of the tools, classifying them according to similarities in their features. To make sure the overview is thorough and balanced, both more recent, cutting-edge solutions and older, more established platforms will be covered. The goal of this thesis is, through these findings, provide guidance to the users regarding the tools that best address their specific requirements. Additionally, the analysis is anticipated to highlight questions that need further exploration and identify solutions aimed at improving visualization technologies in general, particularly as these technologies face new challenges posed by large-scale data.

We introduce Thovea, a THree-layer information diffusiOn Visual analytics systEm for lArge networks. This three-layered approach is designed to simultaneously investigate several diffusion processes---with each layer representing a different level of abstraction. Our method supports a top-down exploration strategy: starting from a high-level overview and drilling down to individual node details. This design provides a comprehensive understanding of the diffusion process(es) at hand, while enabling a detailed exploration down to distinct infection chains. We integrate suitable network layouts and representation methodologies into each level, aiming to support a scalable and agile exploration of information diffusion processes and networks. Information Diffusion investigates how information spreads over a tightly connected network and is relevant to many domains, such as modeling fake news spreading, pathogen contagion, or malware infections. While different modeling approaches exist in the literature, the visualization of information diffusion processes still constitutes an under-investigated problem. To gain a comprehensive overview, we present a survey and analysis of the current state-of-the-art in visual analytics techniques employed in representing and understanding diffusion processes happening over networks. We introduce a taxonomy that categorizes and structures the selected approaches while generalizing across application domains. Existing contributions are often tailored to niche domain-specific problems and lack generalizability. Visual scalability is also a challenge, as current research still struggles to effectively handle networks with thousands of nodes and edges, limiting their practical applicability. We evaluate our system by (i) presenting two case studies and (ii) conducting a quantitative value-driven estimation using the ICE-T methodology. The latter confirms the value of Thovea with a global average score of 5.82, where each component has been awarded a score greater than 5. The highest scored components are Essence and Insight with a score of 6.2 each.