Geodateninfrastrukturen (GDI) erfahren in den letzten Jahren immer weitere Verbreitung durch die Schaffung neuer Standards zum Austausch von Geodaten. Die vom Open Geospatial Consortium (OGC), einem Zusammenschluss aus Forschungseinrichtungen und privaten Firmen, entwickelten offenen Beschreibungen von Dienste-Schnittstellen verbessern die Interoperabilität in GDI. OGC-konforme Geodienste werden momentan hauptsächlich zur Aufnahme, Verwaltung, Prozessierung und Visualisierung von Geodaten verwendet. Durch das vermehrte Aufkommen von Geodiensten steigt die Verfügbarkeit von Geodaten. Gleichzeitig hält der Trend zur Generierung immer größerer Datenmengen beispielsweise durch wissenschaftliche Simulationen an (Unwin et al., 2006). Dieser führt zu einem wachsenden Bedarf an Funktionalität zur effektiven Exploration und Analyse von Geodaten, da komplexe Zusammenhänge in großen Datenbeständen untersucht und relevante Informationen heraus gefiltert werden müssen. Dazu angewendete Techniken werden im Forschungsfeld Visual Analytics (Visuelle Analyse) umfassend beschrieben. Die visuelle Analyse beschäftigt sich mit der Entwicklung von Werkzeugen und Techniken zur automatisierten Analyse und interaktiven Visualisierung zum Verständnis großer und komplexer Datensätze (Keim et al., 2008). Bei aktuellen Web-basierten Anwendungen zur Exploration und Analyse handelt es sich hauptsächlich um Client-Server-Systeme, die auf fest gekoppelten Datenbanken arbeiten. Mit den wachsenden Fähigkeiten von Geodateninfrastrukturen steigt das Interesse, Funktionalitäten zur Datenanalyse in einer GDI anzubieten. Das Zusammenspiel von bekannten Analysetechniken und etablierten Standards zur Verarbeitung von Geodaten kann dem Nutzer die Möglichkeit geben, in einer Webanwendung interaktiv auf ad hoc eingebundenen Geodaten zu arbeiten. Damit lassen sich mittels aktueller Technologien Einsichten in komplexe Daten gewinnen, ihnen zugrunde liegende Zusammenhänge verstehen und Aussagen zur Entscheidungsunterstützung ableiten. In dieser Arbeit wird die Eignung der OGC WMS GetFeatureInfo-Operation zur Analyse raum-zeitlicher Geodaten in einer GDI untersucht. Der Schwerpunkt liegt auf der dynamischen Generierung von Diagrammen unter Nutzung externer Web Map Service (WMS) als Datenquellen. Nach der Besprechung von Grundlagen zur Datenmodellierung und GDIStandards, wird auf relevante Aspekte der Datenanalyse und Visualisierung von Diagrammen eingegangen. Die Aufstellung einer Task Taxonomie dient der Untersuchung, welche raumzeitlichen Analysen sich durch die GetFeatureInfo-Operation umsetzen lassen. Es erfolgt die Konzeption einer Systemarchitektur zur Umsetzung der Datenanalyse auf verteilten Geodaten. Zur Sicherstellung eines konsistenten und OGC-konformen Datenaustauschs zwischen den Systemkomponenenten, wird ein GML-Schema erarbeitet. Anschließend wird durch eine prototypischen Implementierung die Machbarkeit der Diagramm-basierten Analyse auf Klimasimulationsdaten des ECHAM5-Modells verifiziert. / Spatial data infrastructures (SDI) have been subject to a widening dispersion in the last decade, through the development of standards for the exchange of geodata. The open descriptions of service interfaces, developed by the OGC, a consortium from research institutions and private sector companies, alter interoperability in SDI. Until now, OGC-conform geoservices are mainly utilised for the recording, management, processing and visualisation of geodata. Through the ongoing emergence of spatial data services there is a rise in the availability of geodata. At the same time, the trend of the generation of ever increasing amounts of data, e. g. by scientific simulation (Unwin et al., 2006), continues. By this, the need for capabilities to effectively explore and analyse geodata is growing. Complex relations in huge data need to be determined and relevant information extracted. Techniques, which are capable of this, are being described extensively by Visual Analytics. This field of research engages in the development of tools and techniques for automated analysis and interactive visualisation of huge and complex data (Keim et al., 2008). Current web-based applications for the exploration and analysis are usually established as Client-Server approaches, working on a tightly coupled data storage (see subsection 3.3). With the growing capabilities of SDI, there is an increasing interest in offering functionality for data analysis. The combination of widely used analysis techniques and well-established standards for the treatment of geodata may offer the possibility of working interactively on ad hoc integrated data. This will allow insights into large amounts of complex data, understand natural interrelations and derive knowledge for spatial decision support by the use of state-of-the-art technologies. In this paper, the capabilities of the OGC WMS GetFeatureInfo operation for the analysis of spatio-temporal geodata in a SDI are investigated. The main focus is on dynamic generation of diagrams by the use of distributed WMS as a data storage. After the review of basics in data modelling and SDI-standards, relevant aspects of data analysis and visualisation of diagrams are treated. The compilation of a task taxonomy aids in the determination of realisable spatio-temporal analysis tasks by use of the GetFeatureInfo operation. In the following, conceptual design of a multi-layered system architecture to accomplish data analysis on distributed datasets, is carried out. In response to one of the main issues, a GML-schema is developed to ensure consistent and OGC-conform data exchange among the system components. To verify the feasibility of integration of diagram-based analysis in a SDI, a system prototype is developed to explore ECHAM5 climate model data.

Geodateninfrastrukturen (GDI) erfahren in den letzten Jahren immer weitere Verbreitung durch die Schaffung neuer Standards zum Austausch von Geodaten. Die vom Open Geospatial Consortium (OGC), einem Zusammenschluss aus Forschungseinrichtungen und privaten Firmen, entwickelten offenen Beschreibungen von Dienste-Schnittstellen verbessern die Interoperabilität in GDI. OGC-konforme Geodienste werden momentan hauptsächlich zur Aufnahme, Verwaltung, Prozessierung und Visualisierung von Geodaten verwendet. Durch das vermehrte Aufkommen von Geodiensten steigt die Verfügbarkeit von Geodaten. Gleichzeitig hält der Trend zur Generierung immer größerer Datenmengen beispielsweise durch wissenschaftliche Simulationen an (Unwin et al., 2006). Dieser führt zu einem wachsenden Bedarf an Funktionalität zur effektiven Exploration und Analyse von Geodaten, da komplexe Zusammenhänge in großen Datenbeständen untersucht und relevante Informationen heraus gefiltert werden müssen. Dazu angewendete Techniken werden im Forschungsfeld Visual Analytics (Visuelle Analyse) umfassend beschrieben. Die visuelle Analyse beschäftigt sich mit der Entwicklung von Werkzeugen und Techniken zur automatisierten Analyse und interaktiven Visualisierung zum Verständnis großer und komplexer Datensätze (Keim et al., 2008). Bei aktuellen Web-basierten Anwendungen zur Exploration und Analyse handelt es sich hauptsächlich um Client-Server-Systeme, die auf fest gekoppelten Datenbanken arbeiten. Mit den wachsenden Fähigkeiten von Geodateninfrastrukturen steigt das Interesse, Funktionalitäten zur Datenanalyse in einer GDI anzubieten. Das Zusammenspiel von bekannten Analysetechniken und etablierten Standards zur Verarbeitung von Geodaten kann dem Nutzer die Möglichkeit geben, in einer Webanwendung interaktiv auf ad hoc eingebundenen Geodaten zu arbeiten. Damit lassen sich mittels aktueller Technologien Einsichten in komplexe Daten gewinnen, ihnen zugrunde liegende Zusammenhänge verstehen und Aussagen zur Entscheidungsunterstützung ableiten. In dieser Arbeit wird die Eignung der OGC WMS GetFeatureInfo-Operation zur Analyse raum-zeitlicher Geodaten in einer GDI untersucht. Der Schwerpunkt liegt auf der dynamischen Generierung von Diagrammen unter Nutzung externer Web Map Service (WMS) als Datenquellen. Nach der Besprechung von Grundlagen zur Datenmodellierung und GDIStandards, wird auf relevante Aspekte der Datenanalyse und Visualisierung von Diagrammen eingegangen. Die Aufstellung einer Task Taxonomie dient der Untersuchung, welche raumzeitlichen Analysen sich durch die GetFeatureInfo-Operation umsetzen lassen. Es erfolgt die Konzeption einer Systemarchitektur zur Umsetzung der Datenanalyse auf verteilten Geodaten. Zur Sicherstellung eines konsistenten und OGC-konformen Datenaustauschs zwischen den Systemkomponenenten, wird ein GML-Schema erarbeitet. Anschließend wird durch eine prototypischen Implementierung die Machbarkeit der Diagramm-basierten Analyse auf Klimasimulationsdaten des ECHAM5-Modells verifiziert. / Spatial data infrastructures (SDI) have been subject to a widening dispersion in the last decade, through the development of standards for the exchange of geodata. The open descriptions of service interfaces, developed by the OGC, a consortium from research institutions and private sector companies, alter interoperability in SDI. Until now, OGC-conform geoservices are mainly utilised for the recording, management, processing and visualisation of geodata. Through the ongoing emergence of spatial data services there is a rise in the availability of geodata. At the same time, the trend of the generation of ever increasing amounts of data, e. g. by scientific simulation (Unwin et al., 2006), continues. By this, the need for capabilities to effectively explore and analyse geodata is growing. Complex relations in huge data need to be determined and relevant information extracted. Techniques, which are capable of this, are being described extensively by Visual Analytics. This field of research engages in the development of tools and techniques for automated analysis and interactive visualisation of huge and complex data (Keim et al., 2008). Current web-based applications for the exploration and analysis are usually established as Client-Server approaches, working on a tightly coupled data storage (see subsection 3.3). With the growing capabilities of SDI, there is an increasing interest in offering functionality for data analysis. The combination of widely used analysis techniques and well-established standards for the treatment of geodata may offer the possibility of working interactively on ad hoc integrated data. This will allow insights into large amounts of complex data, understand natural interrelations and derive knowledge for spatial decision support by the use of state-of-the-art technologies. In this paper, the capabilities of the OGC WMS GetFeatureInfo operation for the analysis of spatio-temporal geodata in a SDI are investigated. The main focus is on dynamic generation of diagrams by the use of distributed WMS as a data storage. After the review of basics in data modelling and SDI-standards, relevant aspects of data analysis and visualisation of diagrams are treated. The compilation of a task taxonomy aids in the determination of realisable spatio-temporal analysis tasks by use of the GetFeatureInfo operation. In the following, conceptual design of a multi-layered system architecture to accomplish data analysis on distributed datasets, is carried out. In response to one of the main issues, a GML-schema is developed to ensure consistent and OGC-conform data exchange among the system components. To verify the feasibility of integration of diagram-based analysis in a SDI, a system prototype is developed to explore ECHAM5 climate model data.

With the continued development towards a digitalized and data-driven world, the importance of visual data analysis is increasing as well. Visual data analysis enables people to interactively explore and reason on certain data through the combined use of multiple visualizations. This is relevant for a wide range of application domains, including personal, professional, and public ones. In parallel, a ubiquity of modern devices with very heterogeneous characteristics has spawned. These devices, such as smartphones, tablets, or digital whiteboards, can enable more flexible workflows during our daily work, for example, while on-the-go, in meetings, or at home. One way to enable flexible workflows is the combination of multiple devices in so-called device ecologies. This thesis investigates how such a combined usage of devices can facilitate the visual data analysis of multivariate data sets. For that, new approaches for both visualization and interaction are presented here, allowing to make full use of the dynamic nature of device ecologies. So far, the literature on these aspects is limited and lacks a broader consideration of data analysis in device ecologies. This doctoral thesis presents investigations into three main parts, each addressing one research question: (i) how visualizations can be adapted for heterogeneous devices, (ii) how device pairings can be used to support data exploration workflows, and (iii) how visual data analysis can be supported in fully dynamic device ecologies. For the first part, an extended analytical investigation of the notion of responsive visualization is contributed. This investigation is then complemented by the introduction of a novel matrix-based visualization approach that incorporates such responsive visualizations as local focus regions. For the two other parts, multiple conceptual frameworks are presented that are innovative combinations of visualization and interaction techniques. In the second part, such work is conducted for two selected display pairings, the extension of smartwatches with display-equipped watchstraps and the contrary combination of smartwatch and large display. For these device ensembles, it is investigated how analysis workflows can be facilitated. Then, in the third part, it is explored how interactive mechanisms can be used for flexibly combining and coordinating devices by utilizing spatial arrangements, as well as how the view distribution process can be supported through automated optimization processes. This thesis’s extensive conceptual work is accompanied by the design of prototypical systems, qualitative evaluations, and reviews of existing literature.

Information visualization (InfoVis) makes data accessible in a graphical form, enables visual and interactive data exploration, and is becoming increasingly important in our data-driven world - InfoVis empowers people from various domains to truly benefit from abstract and vast amounts of data. Although they often target desktop environments, nowadays, data visualizations are also used on omnipresent mobile devices, such as smartphones and tablets. However, most mobile devices are personal digital companions, typically visualizing moderately complex data (e.g., fitness, health, finances, weather, public transport data) on a single and very compact display, making it inherently hard to show the full range or simultaneously different perspectives of data. The research in this thesis engages with these aspects by striving for novel mobile device interactions that enable data analysis with more than a single device, more than a single visualization view, and more than a single user. At the core of this dissertation are four realized projects that can be connected by the following research objectives: (i) Facilitating data visualization beyond the casual exploration of personal data, (ii) Integrating mobile devices in multi-device settings for InfoVis, and (iii) Exploiting the mobility and spatiality of mobile devices for InfoVis. To address the first objective, my research mainly concentrates on interactions with multivariate data represented in multiple coordinated views (MCV). To address the second objective, I consider two different device settings in my work: One part investigates scenarios where one or more people sit at a regular table and analyze data in MCV that are distributed across several mobile devices (mobile devices on a table). The other part focuses on scenarios in which a wall-sized display shows large-scale MCV and mobile devices enable interactions with the visualizations from varying positions and distances (mobile devices in 3D space). The settings also allow to look at different purposes and roles of mobile devices during data exploration. To address the third objective, I examine different spatial device interactions. This includes placing and organizing multiple mobile devices in meaningful spatial arrangements and also pointing interaction that combines touch and spatial device input. Overall, with my research, I apply an exploratory approach and develop a range of techniques and studies that contribute to the understanding of how mobile devices can be used not only for typical personal visualization but also in more professional settings as part of novel and beyond-the-desktop InfoVis environments.:Publications ... ix List of Figures ... xix List of Tables ... xx 1. Introduction ... 1 1.1. Research Objectives and Questions ... 5 1.2. Methodological Approach ... 8 1.3. Scope of the Thesis ... 10 1.4. Thesis Outline & Contributions ... 13 2. Background & Related Work ... 15 2.1. Data Visualization on a Mobile Device ... 16 2.1.1. Revisiting Differences of Data Visualization for Desktops and Mobiles ... 16 2.1.2. Visualization on Handheld Devices: PDAs to Smartphones ... 18 2.1.3. Visualization on Tablet Computers ... 20 2.1.4. Visualization on Smartwatches and Fitness Trackers ... 21 2.1.5. Mobile Data Visualization and Adjacent Topics ... 22 2.2. Cross-Device Data Visualization ... 24 2.2.1. General Components of Cross-Device Interaction ... ... 24 2.2.2. Cross-Device Settings with Large Displays ... 26 2.2.3. Cross-Device Settings with Several Mobile Devices ... 27 2.2.4. Augmented Displays ... 29 2.2.5. Collaborative Data Analysis ... 30 2.2.6. Technological Aspects ... 31 2.3. Interaction for Visualization ... 32 2.3.1. Touch Interaction for InfoVis ... 33 2.3.2. Spatial Interaction for InfoVis ... 36 2.4. Summary ... 38 3. VisTiles: Combination & Spatial Arrangement of Mobile Devices ... 41 3.1. Introduction ... 43 3.2. Dynamic Layout and Coordination ... 45 3.2.1. Design Space: Input and Output ... 46 3.2.2. Tiles: View Types and Distribution ... 46 3.2.3. Workspaces: Coordination of Visualizations ... 47 3.2.4. User-defined View Layout ... 49 3.3. Smart Adaptations and Combinations ... 49 3.3.1. Expanded Input Design Space ... 50 3.3.2. Use of Side-by-Side Arrangements ... 50 3.3.3. Use of Continuous Device Movements ... 53 3.3.4. Managing Adaptations and Combinations ... 54 3.4. Realizing a Working Prototype of VisTiles ... 55 3.4.1. Phase I: Proof of Concept ... 55 3.4.2. Phase II: Preliminary User Study ... 56 3.4.3. Phase III: Framework Revision and Final Prototype ... 59 3.5. Discussion ... 63 3.5.1. Limitations of the Technical Realization ... 63 3.5.2. Understanding the Use of Space and User Behavior ... 64 3.5.3. Divide and Conquer: Single-Display or Multi-Display? ... 64 3.5.4. Space to Think: Physical Tiles or Virtual Tiles? ... 65 3.6. Chapter Summary & Conclusion ... 66 4. Marvis: Mobile Devices and Augmented Reality ... 69 4.1. Introduction ... 71 4.2. Related Work: Augmented Reality for Information Visualization ... 74 4.3. Design Process & Design Rationale ... 75 4.3.1. Overview of the Development Process ... 75 4.3.2. Expert Interviews in the Design Phase ... 76 4.3.3. Design Choices & Rationales ... 78 4.4. Visualization and Interaction Concepts ... 79 4.4.1. Single Mobile Device with Augmented Reality ... 79 4.4.2. Two and More Mobile Devices with Augmented Reality ... 83 4.5. Prototype Realization ... 86 4.5.1. Technical Implementation and Setup ... 87 4.5.2. Implemented Example Use Cases ... 88 4.6. Discussion ... 94 4.6.1. Expert Reviews ... 94 4.6.2. Lessons Learned ... 95 4.7. Chapter Summary & Conclusion ... 98 5. FlowTransfer: Content Sharing Between Phones and a Large Display ... 101 5.1. Introduction ... 103 5.2. Related Work ... 104 5.2.1. Interaction with Large Displays ... 104 5.2.2. Interactive Cross-Device Data Transfer ... 105 5.2.3. Distal Pointing ... 106 5.3. Development Process and Design Goals ... 106 5.4. FlowTransfer’s Pointing Cursor and Transfer Techniques ... 108 5.4.1. Distance-dependent Pointing Cursor ... 109 5.4.2. Description of Individual Transfer Techniques ... 110 5.5. Technical Implementation and Setup ... 115 5.6. User Study ... 115 5.6.1. Study Design and Methodology ... 115 5.6.2. General Results ... 117 5.6.3. Results for Individual Techniques ... 117 5.7. Design Space for Content Sharing Techniques ... 119 5.8. Discussion ... 120 5.8.1. Design Space Parameters and Consequences ... 121 5.8.2. Interaction Design ... 121 5.8.3. Content Sharing-inspired Techniques for Information Visual- ization ... 122 5.9. Chapter Summary & Conclusion ... 123 6. Divico: Touch and Pointing Interaction for Multiple Coordinated Views ... 125 6.1. Introduction ... 127 6.2. Bringing Large-Scale MCV to Wall-Sized Displays ... 129 6.3. Interaction Design for Large-Scale MCV ... 130 6.3.1. Interaction Style and Vocabulary ... 131 6.3.2. Interaction with Visual Elements of Views ... 132 6.3.3. Control of Analysis Tools ... 134 6.3.4. Interaction with Visualization Views ... 134 6.4. Data Set and Prototype Implementation ... 135 6.5. User Study: Goals and Methodology ... 136 6.5.1. Participants ... 137 6.5.2. Apparatus ... 137 6.5.3. Procedure and Tasks ... 138 6.5.4. Collected and Derived Data ... 139 6.6. Results: User Behavior and Usage Patterns ... 140 6.6.1. Data Analysis Method ... 140 6.6.2. Analysis of User Behavior and Movement ... 140 6.6.3. Analysis of Collaboration Aspects ... 142 6.6.4. Analysis of Application Usage ... 145 6.7. Discussion ... 146 6.7.1. Setup ... 146 6.7.2. Movement ... 147 6.7.3. Distance and Interaction Modality ... 147 6.7.4. Device Usage ... 148 6.7.5. MCV Aspects ... 149 6.8. Chapter Summary & Conclusion ... 149 7. Discussion and Conclusion ... 151 7.1. Summary of the Chapters ... 151 7.2. Contributions ... 152 7.2.1. Beyond Casual Exploration of Personal Data ... 153 7.2.2. Multi-Device Settings ... 154 7.2.3. Spatial Interaction ... 156 7.3. Facets of Mobile Device Interaction for InfoVis ... 157 7.3.1. Mobile Devices ... 158 7.3.2. Interaction ... 160 7.3.3. Data Visualization ... 161 7.3.4. Situation ... 162 7.4. Limitations, Open Questions, and Future Work ... 162 7.4.1. Technical Realization ... 163 7.4.2. Extent of Visual Data Analysis ... 164 7.4.3. Natural Movement in the Spectrum of Explicit and Implicit User Input ... 165 7.4.4. Novel Setups & Future Devices ... 166 7.5. Closing Remarks ... 167 Bibliography ... 169 A. Appendix for ViTiles ... 219 A.1. Examples of Early Sketches and Notes ... 219 A.2. Color Scheme for Visualizations ... 220 A.3. Notes Sheet with Interview Procedure ... 221 A.4. Demographic Questionaire ... 222 A.5. Examplary MCV Images for Explanation ... 223 B. Appendix for Marvis ... 225 B.1. Participants’ Expertise ... 225 B.2. Notes Sheet with Interview Procedure ... 226 B.3. Sketches of Ideas by the Participants ... 227 B.4. Grouped Comments from Expert Interviews (Design Phase) ... 228 C. Appendix for FlowTransfer ... 229 C.1. State Diagram for the LayoutTransfer Technique ... 229 C.2. User Study: Demographic Questionnaire ... 230 C.3. User Study: Techniques Questionnaire ... 231 D. Appendix for Divico ... 235 D.1. User Study: Demographic Information ... 235 D.2. User Study: Expertise Information ... 237 D.3. User Study: Training Questionnaire ... 239 D.4. User Study: Final Questionnaire ... 241 D.5. Study Tasks ... 245 D.5.1. Themed Exploration Phase ... 245 D.5.2. Open Exploration Phase ... 246 D.6. Grouping and Categorization of Protocol Data ... 246 D.7. Usage of Open-Source Tool GIAnT for Video Coding Analysis ... 248 D.8. Movement of Participants (Themed Exploration Phase) ... 250 D.9. Movement of Participants (Open Exploration Phase) ... 254 E. List of Co-supervised Student Theses ... 259 / Informationsvisualisierung (InfoVis) macht Daten in grafischer Form zugänglich, ermöglicht eine visuelle und interaktive Datenexploration und wird in unserer von Daten bestimmten Welt immer wichtiger. InfoVis ermöglicht es Menschen in verschiedenen Anwendungsbereichen, aus den abstrakten und enormen Datenmengen einen echten Nutzen zu ziehen. Obwohl sie häufig auf Desktop-Umgebungen ausgerichtet sind, werden Datenvisualisierungen heutzutage auch auf den allseits präsenten Mobilgeräten wie Smartphones und Tablets eingesetzt. Die meisten Mobilgeräte sind jedoch persönliche digitale Begleiter, die in der Regel mäßig komplexe Daten (z.B. Fitness-, Gesundheits-, Finanz-, Wetter-, Nahverkehrsdaten) auf einem einzigen und sehr kompakten Display visualisieren, wodurch es grundsätzlich schwierig ist, die gesamte Bandbreite von bzw. gleichzeitig mehrere Blickwinkel auf Daten darzustellen. Die in dieser Arbeit vorgestellte Forschung greift diese Aspekte auf und versucht, neuartige Mobilgeräte-Interaktionen zu untersuchen, die eine Datenanalyse mit mehr als nur einem Gerät, mehr als nur einer Visualisierung und mehr als nur einem Benutzer ermöglichen. Im Mittelpunkt dieser Dissertation stehen vier durchgeführte Projekte, die sich anhand der folgenden Forschungsziele miteinander verbinden lassen: (i) Datenvisualisierung jenseits der einfachen Exploration persönlicher Daten ermöglichen, (ii) Mobilgeräte für InfoVis in geräteübergreifende Umgebungen einbinden und (iii) die Beweglichkeit und Räumlichkeit von Mobilgeräten für InfoVis ausnutzen. Um auf das erste Ziel hinzuarbeiten, liegt der Schwerpunkt meiner Forschung auf der Interaktion mit multivariaten Daten, die in mehreren miteinander verknüpften Visualisierungen (engl. multiple coordinated views, kurz MCV) abgebildet werden. Um das zweite Ziel zu adressieren, werden in meiner Arbeit zwei grundlegend unterschiedliche Gerätekonfigurationen behandelt: Der eine Teil befasst sich mit Szenarien, in denen eine oder mehrere Personen an einem Tisch sitzen, um Daten mit MCV zu analysieren, wobei die Ansichten auf mehrere Mobilgeräte verteilt sind (Mobilgeräte auf einem Tisch). Der andere Teil beschäftigt sich mit Szenarien, in denen ein wandgroßes Display eine große Anzahl von MCV anzeigt, während Mobilgeräte die Interaktion mit diesen Ansichten aus unterschiedlichen Positionen und Entfernungen ermöglichen (Mobilgeräte im 3D-Raum). Die Gerätekonfigurationen erlauben es zudem, verschiedene Einsatzzwecke und Rollen von mobilen Geräten während der Datenexploration zu untersuchen. Um auf das dritte Ziel hinzuwirken, untersuche ich mehrere räumliche Geräteinteraktionen. Dies umfasst die Platzierung und Anordnung mehrerer Mobilgeräte in sinnvollen räumlichen Konstellationen sowie Pointing-Interaktion die Touch- und räumliche Geräteeingaben miteinander kombiniert. Allgemein betrachtet wende ich in meiner Forschung einen explorativen Ansatz an. Ich entwickle eine Reihe von Techniken und führe Untersuchungen durch, die zu einem besseren Verständnis beitragen, wie Mobilgeräte nicht nur für typische persönliche Visualisierungen, sondern auch in einem eher professionellen Umfeld als Teil neuartiger InfoVis-Umgebungen jenseits klassischer Desktop-Arbeitsplätze eingesetzt werden können.:Publications ... ix List of Figures ... xix List of Tables ... xx 1. Introduction ... 1 1.1. Research Objectives and Questions ... 5 1.2. Methodological Approach ... 8 1.3. Scope of the Thesis ... 10 1.4. Thesis Outline & Contributions ... 13 2. Background & Related Work ... 15 2.1. Data Visualization on a Mobile Device ... 16 2.1.1. Revisiting Differences of Data Visualization for Desktops and Mobiles ... 16 2.1.2. Visualization on Handheld Devices: PDAs to Smartphones ... 18 2.1.3. Visualization on Tablet Computers ... 20 2.1.4. Visualization on Smartwatches and Fitness Trackers ... 21 2.1.5. Mobile Data Visualization and Adjacent Topics ... 22 2.2. Cross-Device Data Visualization ... 24 2.2.1. General Components of Cross-Device Interaction ... ... 24 2.2.2. Cross-Device Settings with Large Displays ... 26 2.2.3. Cross-Device Settings with Several Mobile Devices ... 27 2.2.4. Augmented Displays ... 29 2.2.5. Collaborative Data Analysis ... 30 2.2.6. Technological Aspects ... 31 2.3. Interaction for Visualization ... 32 2.3.1. Touch Interaction for InfoVis ... 33 2.3.2. Spatial Interaction for InfoVis ... 36 2.4. Summary ... 38 3. VisTiles: Combination & Spatial Arrangement of Mobile Devices ... 41 3.1. Introduction ... 43 3.2. Dynamic Layout and Coordination ... 45 3.2.1. Design Space: Input and Output ... 46 3.2.2. Tiles: View Types and Distribution ... 46 3.2.3. Workspaces: Coordination of Visualizations ... 47 3.2.4. User-defined View Layout ... 49 3.3. Smart Adaptations and Combinations ... 49 3.3.1. Expanded Input Design Space ... 50 3.3.2. Use of Side-by-Side Arrangements ... 50 3.3.3. Use of Continuous Device Movements ... 53 3.3.4. Managing Adaptations and Combinations ... 54 3.4. Realizing a Working Prototype of VisTiles ... 55 3.4.1. Phase I: Proof of Concept ... 55 3.4.2. Phase II: Preliminary User Study ... 56 3.4.3. Phase III: Framework Revision and Final Prototype ... 59 3.5. Discussion ... 63 3.5.1. Limitations of the Technical Realization ... 63 3.5.2. Understanding the Use of Space and User Behavior ... 64 3.5.3. Divide and Conquer: Single-Display or Multi-Display? ... 64 3.5.4. Space to Think: Physical Tiles or Virtual Tiles? ... 65 3.6. Chapter Summary & Conclusion ... 66 4. Marvis: Mobile Devices and Augmented Reality ... 69 4.1. Introduction ... 71 4.2. Related Work: Augmented Reality for Information Visualization ... 74 4.3. Design Process & Design Rationale ... 75 4.3.1. Overview of the Development Process ... 75 4.3.2. Expert Interviews in the Design Phase ... 76 4.3.3. Design Choices & Rationales ... 78 4.4. Visualization and Interaction Concepts ... 79 4.4.1. Single Mobile Device with Augmented Reality ... 79 4.4.2. Two and More Mobile Devices with Augmented Reality ... 83 4.5. Prototype Realization ... 86 4.5.1. Technical Implementation and Setup ... 87 4.5.2. Implemented Example Use Cases ... 88 4.6. Discussion ... 94 4.6.1. Expert Reviews ... 94 4.6.2. Lessons Learned ... 95 4.7. Chapter Summary & Conclusion ... 98 5. FlowTransfer: Content Sharing Between Phones and a Large Display ... 101 5.1. Introduction ... 103 5.2. Related Work ... 104 5.2.1. Interaction with Large Displays ... 104 5.2.2. Interactive Cross-Device Data Transfer ... 105 5.2.3. Distal Pointing ... 106 5.3. Development Process and Design Goals ... 106 5.4. FlowTransfer’s Pointing Cursor and Transfer Techniques ... 108 5.4.1. Distance-dependent Pointing Cursor ... 109 5.4.2. Description of Individual Transfer Techniques ... 110 5.5. Technical Implementation and Setup ... 115 5.6. User Study ... 115 5.6.1. Study Design and Methodology ... 115 5.6.2. General Results ... 117 5.6.3. Results for Individual Techniques ... 117 5.7. Design Space for Content Sharing Techniques ... 119 5.8. Discussion ... 120 5.8.1. Design Space Parameters and Consequences ... 121 5.8.2. Interaction Design ... 121 5.8.3. Content Sharing-inspired Techniques for Information Visual- ization ... 122 5.9. Chapter Summary & Conclusion ... 123 6. Divico: Touch and Pointing Interaction for Multiple Coordinated Views ... 125 6.1. Introduction ... 127 6.2. Bringing Large-Scale MCV to Wall-Sized Displays ... 129 6.3. Interaction Design for Large-Scale MCV ... 130 6.3.1. Interaction Style and Vocabulary ... 131 6.3.2. Interaction with Visual Elements of Views ... 132 6.3.3. Control of Analysis Tools ... 134 6.3.4. Interaction with Visualization Views ... 134 6.4. Data Set and Prototype Implementation ... 135 6.5. User Study: Goals and Methodology ... 136 6.5.1. Participants ... 137 6.5.2. Apparatus ... 137 6.5.3. Procedure and Tasks ... 138 6.5.4. Collected and Derived Data ... 139 6.6. Results: User Behavior and Usage Patterns ... 140 6.6.1. Data Analysis Method ... 140 6.6.2. Analysis of User Behavior and Movement ... 140 6.6.3. Analysis of Collaboration Aspects ... 142 6.6.4. Analysis of Application Usage ... 145 6.7. Discussion ... 146 6.7.1. Setup ... 146 6.7.2. Movement ... 147 6.7.3. Distance and Interaction Modality ... 147 6.7.4. Device Usage ... 148 6.7.5. MCV Aspects ... 149 6.8. Chapter Summary & Conclusion ... 149 7. Discussion and Conclusion ... 151 7.1. Summary of the Chapters ... 151 7.2. Contributions ... 152 7.2.1. Beyond Casual Exploration of Personal Data ... 153 7.2.2. Multi-Device Settings ... 154 7.2.3. Spatial Interaction ... 156 7.3. Facets of Mobile Device Interaction for InfoVis ... 157 7.3.1. Mobile Devices ... 158 7.3.2. Interaction ... 160 7.3.3. Data Visualization ... 161 7.3.4. Situation ... 162 7.4. Limitations, Open Questions, and Future Work ... 162 7.4.1. Technical Realization ... 163 7.4.2. Extent of Visual Data Analysis ... 164 7.4.3. Natural Movement in the Spectrum of Explicit and Implicit User Input ... 165 7.4.4. Novel Setups & Future Devices ... 166 7.5. Closing Remarks ... 167 Bibliography ... 169 A. Appendix for ViTiles ... 219 A.1. Examples of Early Sketches and Notes ... 219 A.2. Color Scheme for Visualizations ... 220 A.3. Notes Sheet with Interview Procedure ... 221 A.4. Demographic Questionaire ... 222 A.5. Examplary MCV Images for Explanation ... 223 B. Appendix for Marvis ... 225 B.1. Participants’ Expertise ... 225 B.2. Notes Sheet with Interview Procedure ... 226 B.3. Sketches of Ideas by the Participants ... 227 B.4. Grouped Comments from Expert Interviews (Design Phase) ... 228 C. Appendix for FlowTransfer ... 229 C.1. State Diagram for the LayoutTransfer Technique ... 229 C.2. User Study: Demographic Questionnaire ... 230 C.3. User Study: Techniques Questionnaire ... 231 D. Appendix for Divico ... 235 D.1. User Study: Demographic Information ... 235 D.2. User Study: Expertise Information ... 237 D.3. User Study: Training Questionnaire ... 239 D.4. User Study: Final Questionnaire ... 241 D.5. Study Tasks ... 245 D.5.1. Themed Exploration Phase ... 245 D.5.2. Open Exploration Phase ... 246 D.6. Grouping and Categorization of Protocol Data ... 246 D.7. Usage of Open-Source Tool GIAnT for Video Coding Analysis ... 248 D.8. Movement of Participants (Themed Exploration Phase) ... 250 D.9. Movement of Participants (Open Exploration Phase) ... 254 E. List of Co-supervised Student Theses ... 259