Visualization

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.

Time is a complex dimension, especially when trying to visualize it. In the last ten years a lot of approaches to display and interact with temporal data have been published. They range from linear timeline visualizations to novel ideas employing visual metaphors and even clustering techniques to support the user in exploring large-scale data sets. The diversity of the proposed methods has raised the awareness that a common categorization needs to be defined to efficiently evaluate the usability and interactivity of information visualization tools.

Natural and cultural cycles, like day and night, weekday and weekend, years and seasons, define our life and become perceivable in every analysis of human behavior. This work documents reimplementation and extension of Groove (granular overview overlay), a visualization technique to gain insights on several levels of detail in complex, time-oriented data at a single glance. A powerful framework was implemented, dealing with common data-related tasks and providing an extensible visualization and interaction pipeline. Based on that framework, a visualization adapting the calendar-analogy was implemented to show the frameworks benefits and resulting easements for future studies. In the conclusion we sketch possible future extensions and the usability of the Groove-Framework.