Data Formulator: AI-powered Concept-driven Visualization Authoring

With most modern visualization tools, authors need to transform their data into tidy formats to create visualizations they want. Because this requires experience with programming or separate data processing tools, data transformation remains a barrier in visualization authoring. To address this challenge, we present a new visualization paradigm, concept binding, that separates high-level visualization intents and low-level data transformation steps, leveraging an AI agent. We realize this paradigm in Data Formulator, an interactive visualization authoring tool. With Data Formulator, authors first define data concepts they plan to visualize using natural languages or examples, and then bind them to visual channels. Data Formulator then dispatches its AI-agent to automatically transform the input data to surface these concepts and generate desired visualizations. When presenting the results (transformed table and output visualizations) from the AI agent, Data Formulator provides feedback to help authors inspect and understand them. A user study with 10 participants shows that participants could learn and use Data Formulator to create visualizations that involve challenging data transformations, and presents interesting future research directions

The State of the Art in Multilayer Network Visualization

Modelling relationship between entities in real-world systems with a simple graph is a standard approach. However, realityis better embraced as several interdependent subsystems (or layers). Recently, the concept of a multilayer network model hasemerged from the field of complex systems. This model can be applied to a wide range of real-world data sets. Examples ofmultilayer networks can be found in the domains of life sciences, sociology, digital humanities and more. Within the domainof graph visualization, there are many systems which visualize data sets having many characteristics of multilayer graphs.This report provides a state of the art and a structured analysis of contemporary multilayer network visualization, not only forresearchers in visualization, but also for those who aim to visualize multilayer networks in the domain of complex systems, as wellas those developing systems across application domains. We have explored the visualization literature to survey visualizationtechniques suitable for multilayer graph visualization, as well as tools, tasks and analytic techniques from within applicationdomains. This report also identifies the outstanding challenges for multilayer graph visualization and suggests future researchdirections for addressing them

Saliency Prediction in the Data Visualization Design Process

L'abstract è presente nell'allegato / the abstract is in the attachmen

A Tale of Two Approaches: Comparing Top-Down and Bottom-Up Strategies for Analyzing and Visualizing High-Dimensional Data

The proliferation of high-throughput and sensory technologies in various fields has led to a considerable increase in data volume, complexity, and diversity. Traditional data storage, analysis, and visualization methods are struggling to keep pace with the growth of modern data sets, necessitating innovative approaches to overcome the challenges of managing, analyzing, and visualizing data across various disciplines. One such approach is utilizing novel storage media, such as deoxyribonucleic acid~(DNA), which presents efficient, stable, compact, and energy-saving storage option. Researchers are exploring the potential use of DNA as a storage medium for long-term storage of significant cultural and scientific materials. In addition to novel storage media, scientists are also focussing on developing new techniques that can integrate multiple data modalities and leverage machine learning algorithms to identify complex relationships and patterns in vast data sets. These newly-developed data management and analysis approaches have the potential to unlock previously unknown insights into various phenomena and to facilitate more effective translation of basic research findings to practical and clinical applications. Addressing these challenges necessitates different problem-solving approaches. Researchers are developing novel tools and techniques that require different viewpoints. Top-down and bottom-up approaches are essential techniques that offer valuable perspectives for managing, analyzing, and visualizing complex high-dimensional multi-modal data sets. This cumulative dissertation explores the challenges associated with handling such data and highlights top-down, bottom-up, and integrated approaches that are being developed to manage, analyze, and visualize this data. The work is conceptualized in two parts, each reflecting the two problem-solving approaches and their uses in published studies. The proposed work showcases the importance of understanding both approaches, the steps of reasoning about the problem within them, and their concretization and application in various domains

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

Timeline design for visualising cultural heritage data

This thesis is concerned with the design of data visualisations of digitised museum, archive and library collections, in timelines. As cultural institutions digitise their collections—converting texts, objects, and artworks to electronic records—the volume of cultural data available grows. There is a growing perception, though, that we need to get more out of this data. Merely digitising does not automatically make collections accessible, discoverable and comprehensible, and standard interfaces do not necessarily support the types of interactions users wish to make. Data visualisations—this thesis focuses on interactive visual representations of data created with software—allow us to see an overview of, observe patterns in, and showcase the richness of, digitised collections. Visualisation can support analysis, exploration and presentation of collections for different audiences: research, collection administration, and the general public. The focus here is on visualising cultural data by time: a fundamental dimension for making sense of historical data, but also one with unique strangeness. Through cataloguing, cultural institutions define the meaning and value of items in their collections and the structure within which to make sense of them. By visualising threads in cataloguing data through time, can historical narratives be made visible? And is the data alone enough to tell the stories that people wish to tell? The intended audience for this research is cultural heritage institutions. This work sits at the crossroads between design, cultural heritage (particularly museology), and computing—drawing on the fields of digital humanities, information visualisation and human computer-interaction which also live in these overlapping spaces. This PhD adds clarity around the question of what cultural visualisation is (and can be) for, and highlights issues in the visualisation of qualitative or nominal data. The first chapter lays out the background, characterising cultural data and its visualisation. Chapter two walks through examples of existing cultural timeline visualisations, from the most handcrafted displays to automated approaches. At this point, the research agenda and methodology are set out. The next five chapters document a portfolio of visualisation projects, designing and building novel prototype timeline visualisations with data from the Wellcome Library and Victoria & Albert Museum, London, Cooper Hewitt Smithsonian Design Museum, New York City, and the Nordic Museum, Stockholm. In the process, a range of issues are identified for further discussion. The final chapters reflect on these projects, arguing that automated timeline visualisation can be a productive way to explore and present historical narratives in collection data, but a range of factors govern what is possible and useful. Trust in cultural data visualisation is also discussed. This research argues that visualising cultural data can add value to the data both for users and for data-holding institutions. However, that value is likely to be best achieved by customising a visualisation design to the dataset, audience and use case. Keywords: cultural heritage data; historical data; cultural analytics; cultural informatics; humanities visualisation; generous interfaces; digital humanities; design; information design; interface design; data visualisation; information visualisation; time; timeline; history; historiography; museums; museology; archives; chronographics

Cognition-Based Evaluation of Visualisation Frameworks for Exploring Structured Cultural Heritage Data

It is often claimed that Information Visualisation (InfoVis) tools improve the audience’s engagement with the display of cultural heritage (CH) collections, open up CH content to new audiences and support teaching and learning through interactive experiences. But there is a lack of studies systematically evaluating these claims, particularly from the perspective of modern educational theory. As far as the author is aware no experimental investigation has been undertaken until now, that attempts to measure deeper levels of user engagement and learning with InfoVis tools. The investigation of this thesis complements InfoVis research by initiating a human-centric approach since little previous research has attempted to incorporate and integrate human cognition as one of the fundamental components of InfoVis. In this thesis, using Bloom’s taxonomy of learning objectives as well as individual learning characteristics (i.e. cognitive preferences), I have evaluated the visitor experience of an art collection both with and without InfoVis tools (between subjects design). Results indicate that whilst InfoVis tools have some positive effect on the lower levels of learning, they are less effective for higher levels. In addition, this thesis shows that InfoVis tools seem to be more effective when they match specific cognitive preferences. These results have implications for both the designers of tools and for CH venues in terms of expectation of effectiveness and exhibition design; the proposed cognitive based evaluation framework and the results of this investigation could provide a valuable baseline for assessing the effectiveness of visitors’ interaction with the artifacts of online and physical exhibitions where InfoVis tools such as Timelines and Maps along with storytelling techniques are being used