Spatial Interaction for Immersive Mixed-Reality Visualizations

Growing amounts of data, both in personal and professional settings, have caused an increased interest in data visualization and visual analytics. Especially for inherently three-dimensional data, immersive technologies such as virtual and augmented reality and advanced, natural interaction techniques have been shown to facilitate data analysis. Furthermore, in such use cases, the physical environment often plays an important role, both by directly influencing the data and by serving as context for the analysis. Therefore, there has been a trend to bring data visualization into new, immersive environments and to make use of the physical surroundings, leading to a surge in mixed-reality visualization research. One of the resulting challenges, however, is the design of user interaction for these often complex systems. In my thesis, I address this challenge by investigating interaction for immersive mixed-reality visualizations regarding three core research questions: 1) What are promising types of immersive mixed-reality visualizations, and how can advanced interaction concepts be applied to them? 2) How does spatial interaction benefit these visualizations and how should such interactions be designed? 3) How can spatial interaction in these immersive environments be analyzed and evaluated? To address the first question, I examine how various visualizations such as 3D node-link diagrams and volume visualizations can be adapted for immersive mixed-reality settings and how they stand to benefit from advanced interaction concepts. For the second question, I study how spatial interaction in particular can help to explore data in mixed reality. There, I look into spatial device interaction in comparison to touch input, the use of additional mobile devices as input controllers, and the potential of transparent interaction panels. Finally, to address the third question, I present my research on how user interaction in immersive mixed-reality environments can be analyzed directly in the original, real-world locations, and how this can provide new insights. Overall, with my research, I contribute interaction and visualization concepts, software prototypes, and findings from several user studies on how spatial interaction techniques can support the exploration of immersive mixed-reality visualizations.Zunehmende Datenmengen, sowohl im privaten als auch im beruflichen Umfeld, führen zu einem zunehmenden Interesse an Datenvisualisierung und visueller Analyse. Insbesondere bei inhärent dreidimensionalen Daten haben sich immersive Technologien wie Virtual und Augmented Reality sowie moderne, natürliche Interaktionstechniken als hilfreich für die Datenanalyse erwiesen. Darüber hinaus spielt in solchen Anwendungsfällen die physische Umgebung oft eine wichtige Rolle, da sie sowohl die Daten direkt beeinflusst als auch als Kontext für die Analyse dient. Daher gibt es einen Trend, die Datenvisualisierung in neue, immersive Umgebungen zu bringen und die physische Umgebung zu nutzen, was zu einem Anstieg der Forschung im Bereich Mixed-Reality-Visualisierung geführt hat. Eine der daraus resultierenden Herausforderungen ist jedoch die Gestaltung der Benutzerinteraktion für diese oft komplexen Systeme. In meiner Dissertation beschäftige ich mich mit dieser Herausforderung, indem ich die Interaktion für immersive Mixed-Reality-Visualisierungen im Hinblick auf drei zentrale Forschungsfragen untersuche: 1) Was sind vielversprechende Arten von immersiven Mixed-Reality-Visualisierungen, und wie können fortschrittliche Interaktionskonzepte auf sie angewendet werden? 2) Wie profitieren diese Visualisierungen von räumlicher Interaktion und wie sollten solche Interaktionen gestaltet werden? 3) Wie kann räumliche Interaktion in diesen immersiven Umgebungen analysiert und ausgewertet werden? Um die erste Frage zu beantworten, untersuche ich, wie verschiedene Visualisierungen wie 3D-Node-Link-Diagramme oder Volumenvisualisierungen für immersive Mixed-Reality-Umgebungen angepasst werden können und wie sie von fortgeschrittenen Interaktionskonzepten profitieren. Für die zweite Frage untersuche ich, wie insbesondere die räumliche Interaktion bei der Exploration von Daten in Mixed Reality helfen kann. Dabei betrachte ich die Interaktion mit räumlichen Geräten im Vergleich zur Touch-Eingabe, die Verwendung zusätzlicher mobiler Geräte als Controller und das Potenzial transparenter Interaktionspanels. Um die dritte Frage zu beantworten, stelle ich schließlich meine Forschung darüber vor, wie Benutzerinteraktion in immersiver Mixed-Reality direkt in der realen Umgebung analysiert werden kann und wie dies neue Erkenntnisse liefern kann. Insgesamt trage ich mit meiner Forschung durch Interaktions- und Visualisierungskonzepte, Software-Prototypen und Ergebnisse aus mehreren Nutzerstudien zu der Frage bei, wie räumliche Interaktionstechniken die Erkundung von immersiven Mixed-Reality-Visualisierungen unterstützen können

Eye Tracking Support for Visual Analytics Systems

Visual analytics (VA) research provides helpful solutions for interactive visual data analysis when exploring large and complex datasets. Due to recent advances in eye tracking technology, promising opportunities arise to extend these traditional VA approaches. Therefore, we discuss foundations for eye tracking support in VA systems. We first review and discuss the structure and range of typical VA systems. Based on a widely used VA model, we present five comprehensive examples that cover a wide range of usage scenarios. Then, we demonstrate that the VA model can be used to systematically explore how concrete VA systems could be extended with eye tracking, to create supportive and adaptive analytics systems. This allows us to identify general research and application opportunities, and classify them into research themes. In a call for action, we map the road for future research to broaden the use of eye tracking and advance visual analytics

Investigating the Impacts of AR, AI, and Website Optimization on Ecommerce Sales Growth

E-commerce has evolved into a vital element of modern life by giving customers a quick and easy way to buy products and services online. Businesses increasingly focus on building their online presence in order to remain competitive, which represents a huge change as a result of the growth of e-commerce. Utilizing artificial intelligence (AI), augmented reality (AR), and website optimization is one of the primary ways firms are aiming to improve their e-commerce operations at the moment. While AR can improve product recommendations and the visual component of online shopping by giving customers a more immersive experience, AI can be used to tailor the user experience and boost personalization. On the other side, website optimization can assist companies in enhancing the user experience and raising conversion rates. Businesses can make better choices about how to implement these variables into their operations by knowing how they affect e-commerce sales. This study used data from 190 global e-commerce sites to empirically examine the effects of using AI, AR, and website optimization on the increase of e-commerce sales. The study used a multiple regression analysis to look at how these factors and the rise of e-commerce relate to one another. The study's findings demonstrated that every element had a favorable and significant impact on the increase of e-commerce sales. This suggests that companies investing in artificial intelligence, augmented reality, and website optimization can anticipate a comparable rise in revenue. These results suggest that companies wishing to enhance their e-commerce operations should think about investing in AI, AR, and website optimization. They may improve client satisfaction this way, boost conversion rates, and eventually boost sales. &nbsp

Visualizing genome and systems biology: technologies, tools, implementation techniques and trends, past, present and future.

"Α picture is worth a thousand words." This widely used adage sums up in a few words the notion that a successful visual representation of a concept should enable easy and rapid absorption of large amounts of information. Although, in general, the notion of capturing complex ideas using images is very appealing, would 1000 words be enough to describe the unknown in a research field such as the life sciences? Life sciences is one of the biggest generators of enormous datasets, mainly as a result of recent and rapid technological advances; their complexity can make these datasets incomprehensible without effective visualization methods. Here we discuss the past, present and future of genomic and systems biology visualization. We briefly comment on many visualization and analysis tools and the purposes that they serve. We focus on the latest libraries and programming languages that enable more effective, efficient and faster approaches for visualizing biological concepts, and also comment on the future human-computer interaction trends that would enable for enhancing visualization further

Cultural Heritage Storytelling, Engagement and Management in the Era of Big Data and the Semantic Web

The current Special Issue launched with the aim of further enlightening important CH areas, inviting researchers to submit original/featured multidisciplinary research works related to heritage crowdsourcing, documentation, management, authoring, storytelling, and dissemination. Audience engagement is considered very important at both sites of the CH production–consumption chain (i.e., push and pull ends). At the same time, sustainability factors are placed at the center of the envisioned analysis. A total of eleven (11) contributions were finally published within this Special Issue, enlightening various aspects of contemporary heritage strategies placed in today’s ubiquitous society. The finally published papers are related but not limited to the following multidisciplinary topics:Digital storytelling for cultural heritage;Audience engagement in cultural heritage;Sustainability impact indicators of cultural heritage;Cultural heritage digitization, organization, and management;Collaborative cultural heritage archiving, dissemination, and management;Cultural heritage communication and education for sustainable development;Semantic services of cultural heritage;Big data of cultural heritage;Smart systems for Historical cities – smart cities;Smart systems for cultural heritage sustainability

Sports in Digital Era

The thesis's primary purpose is to demonstrate the growth of the digital era on the sports industry for awareness and better management. Moreover, it aims to explain the digital technology revolution and its effect on physical activities and sports. The paper presents a social analysis of sports regarding the effects of the IV Industrial Revolution, driven by an unprecedented level of development in materials sciences, digital technology, and biology. The future views on the evolution of the sports industry and options for the sports manager in the phase of digital transition are illustrated. The conclusion summarizes the implications and represents the direction of the sports industry.O objetivo desta tese é demonstrar o crescimento da era digital na indrústia desportiva para a consciencialização e uma melhor gestão. Além disso, visa explicar a revolução tecnológica digital e a sua influência na atividade física e desporto. O documento apresenta uma análise social do desporto em relação aos efeitos da IV Revolução Industrial, impulsionada pelo sem precedente nível de desenvolvimento nas ciências materiais, tecnologia digital e biologia. O futuro da evolução da indústria do desporto e as opções dos gestores desportivos na fase de transição do digital. A conclusão resume as implicações e reflete a direção da indústria do desporto

Augmented Reality and Health Informatics: A Study based on Bibliometric and Content Analysis of Scholarly Communication and Social Media

Healthcare outcomes have been shown to improve when technology is used as part of patient care. Health Informatics (HI) is a multidisciplinary study of the design, development, adoption, and application of IT-based innovations in healthcare services delivery, management, and planning. Augmented Reality (AR) is an emerging technology that enhances the user’s perception and interaction with the real world. This study aims to illuminate the intersection of the field of AR and HI. The domains of AR and HI by themselves are areas of significant research. However, there is a scarcity of research on augmented reality as it applies to health informatics. Given both scholarly research and social media communication having contributed to the domains of AR and HI, research methodologies of bibliometric and content analysis on scholarly research and social media communication were employed to investigate the salient features and research fronts of the field. The study used Scopus data (7360 scholarly publications) to identify the bibliometric features and to perform content analysis of the identified research. The Altmetric database (an aggregator of data sources) was used to determine the social media communication for this field. The findings from this study included Publication Volumes, Top Authors, Affiliations, Subject Areas and Geographical Locations from scholarly publications as well as from a social media perspective. The highest cited 200 documents were used to determine the research fronts in scholarly publications. Content Analysis techniques were employed on the publication abstracts as a secondary technique to determine the research themes of the field. The study found the research frontiers in the scholarly communication included emerging AR technologies such as tracking and computer vision along with Surgical and Learning applications. There was a commonality between social media and scholarly communication themes from an applications perspective. In addition, social media themes included applications of AR in Healthcare Delivery, Clinical Studies and Mental Disorders. Europe as a geographic region dominates the research field with 50% of the articles and North America and Asia tie for second with 20% each. Publication volumes show a steep upward slope indicating continued research. Social Media communication is still in its infancy in terms of data extraction, however aggregators like Altmetric are helping to enhance the outcomes. The findings from the study revealed that the frontier research in AR has made an impact in the surgical and learning applications of HI and has the potential for other applications as new technologies are adopted

Visual Analytics for the Exploratory Analysis and Labeling of Cultural Data

Cultural data can come in various forms and modalities, such as text traditions, artworks, music, crafted objects, or even as intangible heritage such as biographies of people, performing arts, cultural customs and rites. The assignment of metadata to such cultural heritage objects is an important task that people working in galleries, libraries, archives, and museums (GLAM) do on a daily basis. These rich metadata collections are used to categorize, structure, and study collections, but can also be used to apply computational methods. Such computational methods are in the focus of Computational and Digital Humanities projects and research. For the longest time, the digital humanities community has focused on textual corpora, including text mining, and other natural language processing techniques. Although some disciplines of the humanities, such as art history and archaeology have a long history of using visualizations. In recent years, the digital humanities community has started to shift the focus to include other modalities, such as audio-visual data. In turn, methods in machine learning and computer vision have been proposed for the specificities of such corpora. Over the last decade, the visualization community has engaged in several collaborations with the digital humanities, often with a focus on exploratory or comparative analysis of the data at hand. This includes both methods and systems that support classical Close Reading of the material and Distant Reading methods that give an overview of larger collections, as well as methods in between, such as Meso Reading. Furthermore, a wider application of machine learning methods can be observed on cultural heritage collections. But they are rarely applied together with visualizations to allow for further perspectives on the collections in a visual analytics or human-in-the-loop setting. Visual analytics can help in the decision-making process by guiding domain experts through the collection of interest. However, state-of-the-art supervised machine learning methods are often not applicable to the collection of interest due to missing ground truth. One form of ground truth are class labels, e.g., of entities depicted in an image collection, assigned to the individual images. Labeling all objects in a collection is an arduous task when performed manually, because cultural heritage collections contain a wide variety of different objects with plenty of details. A problem that arises with these collections curated in different institutions is that not always a specific standard is followed, so the vocabulary used can drift apart from another, making it difficult to combine the data from these institutions for large-scale analysis. This thesis presents a series of projects that combine machine learning methods with interactive visualizations for the exploratory analysis and labeling of cultural data. First, we define cultural data with regard to heritage and contemporary data, then we look at the state-of-the-art of existing visualization, computer vision, and visual analytics methods and projects focusing on cultural data collections. After this, we present the problems addressed in this thesis and their solutions, starting with a series of visualizations to explore different facets of rap lyrics and rap artists with a focus on text reuse. Next, we engage in a more complex case of text reuse, the collation of medieval vernacular text editions. For this, a human-in-the-loop process is presented that applies word embeddings and interactive visualizations to perform textual alignments on under-resourced languages supported by labeling of the relations between lines and the relations between words. We then switch the focus from textual data to another modality of cultural data by presenting a Virtual Museum that combines interactive visualizations and computer vision in order to explore a collection of artworks. With the lessons learned from the previous projects, we engage in the labeling and analysis of medieval illuminated manuscripts and so combine some of the machine learning methods and visualizations that were used for textual data with computer vision methods. Finally, we give reflections on the interdisciplinary projects and the lessons learned, before we discuss existing challenges when working with cultural heritage data from the computer science perspective to outline potential research directions for machine learning and visual analytics of cultural heritage data