DOCSLIB.ORG

Supporting Asynchronous Collaboration for Interactive Visualization

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Supporting Asynchronous Collaboration for Interactive Visualization Supporting Asynchronous Collaboration for Interactive Visualization by Jeffrey Michael Heer B.S. (University of California, Berkeley) 2001 M.S. (University of California, Berkeley) 2004 A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Computer Science in the GRADUATE DIVISION of the UNIVERSITY OF CALIFORNIA, BERKELEY Committee in charge: Professor Maneesh Agrawala, Chair Doctor Stuart K. Card Professor Marti A. Hearst Professor Joseph M. Hellerstein Fall 2008 The dissertation of Jeffrey Michael Heer is approved: _____________________________________________________ Chair Date _____________________________________________________ Date _____________________________________________________ Date _____________________________________________________ Date University of California, Berkeley Fall 2008 Supporting Asynchronous Collaboration for Interactive Visualization Copyright Fall 2008 by Jeffrey Michael Heer 1 ABSTRACT Supporting Asynchronous Collaboration for Interactive Visualization by Jeffrey Michael Heer Doctor of Philosophy in Computer Science University of California, Berkeley Professor Maneesh Agrawala, Chair Interactive visualizations leverage human visual processing to increase the scale of information with which we can effectively work. However, most visualization research to date relies on a single-user model, overlooking the social nature of visual media. Visualizations are used not only to explore and analyze data, but to communicate findings. People may disagree on how to interpret data and contribute contextual knowledge. Furthermore, some data sets are so large that thorough exploration by a single person is unlikely. Such scenarios arise regularly in scientific collaboration, business intelligence, and public data consumption. This thesis recasts interactive visualizations as not just analytic tools, but social spaces supporting collective data analysis. To this aim, I introduce theoretical design considerations guiding the invention of social visual analysis tools and present the design, implementation, and evaluation of interactive systems based on these principles. The first such system is sense.us, a web site supporting asynchronous collaboration across a variety of visualization types. The site supports view sharing, discussion, graphical annotation, and social navigation and includes novel interaction elements. User studies of the system reveal emergent patterns of social data analysis, cycles of 2 observation and hypothesis, and the complementary roles of social navigation and data-driven exploration. Based on design considerations and lessons learned from sense.us, this dissertation also introduces new techniques to support collaborative interaction around visualizations. The scented widgets system embeds visualizations of social activity in common user interface controls to enhance collective information foraging. A generalized selection framework represents collaborative annotations as declarative queries over visualized data, enabling annotation of dynamic data across multiple visualization views. Interactive query relaxation enables users to further generalize selections along data dimensions of interest. New graphical histories for visualization support analysis and accelerate collaborative sharing of findings, and a framework for animated transitions better communicates the relationship between views in an analysis session. As evidenced in a series of evaluative studies, these components enable teams to collaborate more effectively as they conduct visual data analysis. _____________________________________________________________ Professor Maneesh Agrawala Dissertation Chair i For my parents, Michael and Star Heer ii Table of Contents 1 Introduction 1 1.1 Thesis Problem and Approach 3 1.2 Thesis Contributions 4 1.3 Thesis Outline 5 1.3.1 Principles and Systems for Collaborative Visual Analysis 6 1.3.2 Interface Techniques supporting Social Data Analysis 6 2 Related Work 8 2.1 Information Visualization 8 2.1.1 Graphical Perception 9 2.1.2 Interaction and Sensemaking 10 2.2 Social Software and Collaborative Visualization 11 2.2.1 Collaborative and Distributed Visualization 12 2.2.2 Asynchronous Collaboration with Visualizations 13 3 Design Considerations for Collaborative Visual Analytics 16 3.1 Division and Allocation of Work 17 3.1.1 The Information Visualization Reference Model 18 3.1.2 The Sensemaking Model 19 3.2 Common Ground and Awareness 23 3.2.1 Discussion Models 24 3.2.2 Activity Awareness 25 3.2.3 Social Navigation 25 3.3 Reference and Deixis 26 3.3.1 Graphical Annotation 27 iii 3.3.2 Brushing and Dynamic Queries 27 3.3.3 Ambiguity of Reference 28 3.4 Incentives and Engagement 29 3.4.1 Personal Relevance 29 3.4.2 Social-Psychological Incentives 30 3.4.3 Game Play 30 3.5 Identity, Trust, and Reputation 31 3.5.1 Identity Presentation 31 3.5.2 Reputation Formation 32 3.6 Group Dynamics 33 3.6.1 Group Management 33 3.6.2 Group Size 34 3.6.3 Group Diversity 35 3.7 Consensus and Decision Making 35 3.7.1 Consensus and Discussion 36 3.7.2 Information Distribution 37 3.7.3 Presentation and Story-Telling 37 3.8 Conclusion and Future Directions 38 4 sense.us: A Web Application for Collaborative Visual Analysis 41 4.1 sense.us: A Site for Collaborative Visual Analysis 41 4.1.1 View Sharing 44 4.1.2 Doubly-Linked Discussion 44 4.1.3 Pointing via Graphical Annotation 45 4.1.4 Collecting and Linking Views 46 4.1.5 Awareness and Social Navigation 47 4.1.6 Unobtrusive Collaboration 48 4.2 Implementation Notes 48 4.2.1 Application Bookmarking 48 4.2.2 Doubly-Linked Discussions 49 4.2.3 Graphical Annotation 49 4.3 Usage Observation of sense.us 50 4.3.1 Commentary: Observation, Question, and Hypothesis 52 4.3.2 Graphical Annotation: Pointing and Play 58 4.3.3 Visitation and Navigation: Voyagers and Voyeurs 59 4.3.4 Doubly-Linked Discussions 60 iv 4.3.5 User Experience 61 4.4 Discussion 61 4.4.1 Embedded Social Navigation Cues 62 4.4.2 Enhanced Commentary and Navigation Features 62 4.4.3 Annotations 63 4.4.4 Bookmark Trails and Story-Telling 63 4.5 Summary 64 5 Scented Widgets: Improving Navigation Cues with Embedded Visualizations 65 5.1 Related Work on Navigation Cues 67 5.2 Design Considerations for Scented Widgets 69 5.2.1 Information Scent Metrics 69 5.2.2 Navigation and the Display of Visual Scent 70 5.2.3 Visual Encodings 70 5.2.4 Modifying Widgets 71 5.2.5 Design Guidelines and Feature Requirements 71 5.3 Implementation of Scented Widgets 73 5.3.1 Rendering and Interaction 42 5.3.2 Scent Configuration and Widget Groups 75 5.3.3 Data Management 75 5.3.4 Usage Example 76 5.4 Applications 78 5.4.1 HomeFinder with Histogram Sliders 78 5.4.2 Collaborative Authoring with Activity Indicators 78 5.4.3 Social Data Analysis with Social Navigation 80 5.5 Evaluation of Social Navigation Cues 81 5.5.1 Experiment Design 81 5.5.2 Results: Revisitation 82 5.5.3 Results: Unique Discoveries 83 5.5.4 Results: User Preferences 85 5.5.5 Discussion 86 5.6 Future Work 87 5.7 Conclusion 88 v 6 Generalized Selection via Interactive Query Relaxation 89 6.1 Related Work on Reference and Interactive Querying 91 6.1.1 Selection Techniques and Reference 91 6.1.2 Dynamic Queries, Brushing, and Linking 92 6.1.3 Query Relaxation 93 6.2 Example: Information Visualization 94 6.2.1 Basic Brushing and Selection 96 6.2.2 Selection Reuse 96 6.2.3 Data-Aware Annotation 98 6.2.4 Query Relaxation: Generalizing to Related Selections 98 6.2.5 Alternate Output Modalities 100 6.3 Example: Vector Graphics Editor 100 6.4 Implementation of Generalized Selection 101 6.4.1 Initialization 102 6.4.2 Query Generation 102 6.4.3 Query Visualization 103 6.4.4 Query Relaxation 103 6.4.5 Query Reuse 107 6.5 Evaluation 108 6.5.1 Methods 108 6.5.2 Results 109 6.6 Discussion and Summary 112 7 Graphical Histories for Visual Analysis 114 7.1 Design Space Analysis of Interaction Histories 116 7.1.1 History Models 116 7.1.2 Visual Representation of History 119 7.1.3 Operations on History 120 7.1.4 Design Space Summary 121 7.2 Graphical History in Tableau: A Case Study 122 7.2.1 The Tableau Visual Analysis System 122 7.2.2 A Re-designed History Model 123 7.2.3 Visual Design of the History Interface 124 7.2.4 Navigating and Managing History 127 7.2.5 Operating on History 130 vi 7.3 Using History to Evaluate Visualization Design 131 7.3.1 Analyzing Individual Usage with Behavior Graphs 132 7.3.2 Analyzing Aggregate Usage 134 7.3.3 Findings 134 7.4 Summary and Future Work 136 8 Animated Transitions in Statistical Data Graphics 138 8.1 Animation: A Double-Edged Sword 139 8.1.1 Principles for Animation Design 140 8.1.2 Animation in Information Visualization 141 8.2 Transitions between Statistical Data Graphics 142 8.2.1 A Taxonomy of Transition Types 143 8.2.2 Design Considerations for Animated Transitions 145 8.3 Animated Data Graphics in DynaVis 147 8.3.1 Implementation Notes 150 8.4 Evaluation of Animated Transitions 152 8.4.1 Experiment 1: Object Tracking 152 8.4.2 Experiment 2: Estimating Changing Values 155 8.4.3 Subjective Preferences 158 8.5 Discussion 158 8.5.1 Animation Improves Graphical Perception 158 8.5.2 Trade-Offs Between Design Principles 159 8.5.3 The Case for Staging 160 8.5.4 The Effects of Axis Rescaling: Avoid If Possible 160 8.5.5 The Intricacies of the Donut: Smaller Wedges Are Better? 160 8.6 Summary 161 9 Conclusion 163 9.1 Review of Thesis Contributions 163 9.2 Recent Developments 165 9.3 Limitations and Future Work 166 9.3.1 Synthesizing Collaborative Contributions 167 9.3.2 Pointing, Naming, and Reference 167 9.3.3 Computation as Collaborator 168 9.3.4 Supporting the Information Life-Cycle 168 vii 9.3.5 Evaluating Social Data Analysis 169 9.3.6 Social Data Analysis Applications 170 9.4 Closing Remarks 171 Bibliography 172 viii List of Figures Figure 2.1 Space-time matrix for classifying collaborative applications.
Load more

Recommended publications
  • UC Berkeley UC Berkeley Electronic Theses and Dissertations
    UC Berkeley UC Berkeley Electronic Theses and Dissertations Title Perceptual and Context Aware Interfaces on Mobile Devices Permalink https://escholarship.org/uc/item/7tg54232 Author Wang, Jingtao Publication Date 2010 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California Perceptual and Context Aware Interfaces on Mobile Devices by Jingtao Wang A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Computer Science in the Graduate Division of the University of California, Berkeley Committee in charge: Professor John F. Canny, Chair Professor Maneesh Agrawala Professor Ray R. Larson Spring 2010 Perceptual and Context Aware Interfaces on Mobile Devices Copyright 2010 by Jingtao Wang 1 Abstract Perceptual and Context Aware Interfaces on Mobile Devices by Jingtao Wang Doctor of Philosophy in Computer Science University of California, Berkeley Professor John F. Canny, Chair With an estimated 4.6 billion units in use, mobile phones have already become the most popular computing device in human history. Their portability and communication capabil- ities may revolutionize how people do their daily work and interact with other people in ways PCs have done during the past 30 years. Despite decades of experiences in creating modern WIMP (windows, icons, mouse, pointer) interfaces, our knowledge in building ef- fective mobile interfaces is still limited, especially for emerging interaction modalities that are only available on mobile devices. This dissertation explores how emerging sensors on a mobile phone, such as the built-in camera, the microphone, the touch sensor and the GPS module can be leveraged to make everyday interactions easier and more efficient.
    [Show full text]
  • Focus+Context Sphere Visualization for Interactive Large Graph Exploration
    © ACM, 2017. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in: Du, F., Cao, N., Lin, Y.-R., Xu, P., Tong, H. (2017). iSphere: Focus+Context Sphere Visualization for Interactive Large Graph Exploration. In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI 2017) http://doi.org/10.1145/3025453.3025628 iSphere: Focus+Context Sphere Visualization for Interactive Large Graph Exploration Fan Du Nan Cao Yu-Ru Lin University of Maryland Tongji University University of Pittsburgh [email protected] [email protected] [email protected] Panpan Xu Hanghang Tong Hong Kong University of Arizona State University Science and Technology [email protected] [email protected] a b c Figure 1. The node-link diagram shown in (a) the zoomable plane and the focus+context displays, (b) hyperbolic display and (c) iSphere display. ABSTRACT Author Keywords Interactive exploration plays a critical role in large graph visu- Graph visualization; graph exploration; focus+context. alization. Existing techniques, such as zoom-and-pan on a 2D plane and hyperbolic browser facilitate large graph exploration ACM Classification Keywords by showing both the details of a focal area and its surrounding H.5.2 User Interfaces: Graphical user interfaces (GUI) context that guides the exploration process. However, existing techniques for large graph exploration are limited in either INTRODUCTION providing too little context or presenting graphs with too much Interactive exploration is one of the major approaches for nav- distortion.
    [Show full text]
  • A Model of Inheritance for Declarative Visual Programming Languages
    An Abstract Of The Dissertation Of Rebecca Djang for the degree of Doctor of Philosophy in Computer Science presented on December 17, 1998. Title: Similarity Inheritance: A Model of Inheritance for Declarative Visual Programming Languages. Abstract approved: Margaret M. Burnett Declarative visual programming languages (VPLs), including spreadsheets, make up a large portion of both research and commercial VPLs. Spreadsheets in particular enjoy a wide audience, including end users. Unfortunately, spreadsheets and most other declarative VPLs still suffer from some of the problems that have been solved in other languages, such as ad-hoc (cut-and-paste) reuse of code which has been remedied in object-oriented languages, for example, through the code-reuse mechanism of inheritance. We believe spreadsheets and other declarative VPLs can benefit from the addition of an inheritance-like mechanism for fine-grained code reuse. This dissertation first examines the opportunities for supporting reuse inherent in declarative VPLs, and then introduces similarity inheritance and describes a prototype of this model in the research spreadsheet language Forms/3. Similarity inheritance is very flexible, allowing multiple granularities of code sharing and even mutual inheritance; it includes explicit representations of inherited code and all sharing relationships, and it subsumes the current spreadsheet mechanisms for formula propagation, providing a gradual migration from simple formula reuse to more sophisticated uses of inheritance among objects. Since the inheritance model separates inheritance from types, we investigate what notion of types is appropriate to support reuse of functions on different types (operation polymorphism). Because it is important to us that immediate feedback, which is characteristic of many VPLs, be preserved, including feedback with respect to type errors, we introduce a model of types suitable for static type inference in the presence of operation polymorphism with similarity inheritance.
    [Show full text]
  • Introduction to Information Visualization.Pdf
    Introduction to Information Visualization Riccardo Mazza Introduction to Information Visualization 123 Riccardo Mazza University of Lugano Switzerland ISBN: 978-1-84800-218-0 e-ISBN: 978-1-84800-219-7 DOI: 10.1007/978-1-84800-219-7 British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Control Number: 2008942431 c Springer-Verlag London Limited 2009 Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publish- ers, or in the case of reprographic reproduction in accordance with the terms of licences issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers. The use of registered names, trademarks, etc., in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant laws and regulations and therefore free for general use. The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made. Printed on acid-free paper Springer Science+Business Media springer.com To Vincenzo and Giulia Preface Imagine having to make a car journey. Perhaps you’re going to a holiday resort that you’re not familiar with.
    [Show full text]
  • Escalabilidad Y Uso De Técnicas Foco+Contexto En Atlas Médicos 3D
    Escalabilidad y Uso de T´ecnicas Foco+Contexto en Atlas M´edicos 3D Trabajo de Tesis presentado al Departamento de Ingenier´ıa de Sistemas y Computaci´on por Oscar Ariza Asesor: Pablo Figueroa Ph.D. Para optar al t´ıtulo de Maestr´ıa en Ingenier´ıa de Sistemas Facultad de Ingenier´ıa - Departamento de Ingenier´ıa de Sistemas y Computaci´on Universidad de Los Andes Julio 2006 Escalabilidad y Uso de T´ecnicas Foco+Contexto en Atlas M´edicos 3D Aprobado por: Pablo Figueroa Ph.D., Asesor Jos´eTiberio Hern´andez Ph.D. Gabriel Ma˜nana Ph.D.c Fecha de Aprobaci´on Tr`es bon les gˆateaux d’avoine... Merci petit pingouin! iii Reconocimientos Este trabajo cont´ocon el apoyo financiero y acad´emico de la Facultad de Medi- cina de la Universidad de Los Andes, especialmente del profesor Gustavo Valbuena M.D. Ph.D. quien aport´osus conocimientos y experiencia sobre anatom´ıa y fisiolog´ıa del ri˜n´on humano, sobre software educativo para medicina y sobre los requerimien- tos, necesidades y caracter´ısticas que deb´ıan ser tratados en el proyecto. Gabriel Mart´ınez y Santiago Leal, estudiantes de Facultad de Artes de la Univer- sidad de Los Andes, colaboraron con el dise˜no e implementaci´on de los modelos anat´omicos del ri˜n´on humano que conformaron el contenido 3D del proyecto. iv Resumen Este trabajo muestra los esfuerzos realizados alrededor de la construc- ci´on de un ambiente interactivo con integraci´on de contenidos 3D y 2D, permitiendo explorar jerarqu´ıas de objetos tridimensionales a diferentes niveles de detalle.
    [Show full text]
  • IAT 355 Visual Analytics Space: View Transformations
    IAT 355 Visual Analytics Space: View Transformations Lyn Bartram So much data, so little space: 1 • Rich data (many dimensions) • Huge amounts of data • Overplotting [Few] • patterns and relations across sets • Visual fragmentation • Decoding too many different visual forms IAT 355 | View transformations 2 Recall: Dimensional division • “splitting” dimensions across multiple linked views • Small multiples • Trellis displays • Scatterplot matrices IAT 355 | View transformations 3 Small multiples • use the same basic graphic or chart to display difference slices of a data set • rich, multi-dimensional data without trying to cram all that information into a single, overly-complex chart. • Singular design reduces decoding effort. • E. Tufte “The Visual Display of Quantitative Information,” p. 42 and “Envisioning Information,” p. 29 IAT 355 | View transformations 4 Small multiples IAT 355 | View transformations 5 Small multiples • Small multiples to convey n-dims. IAT 355 | View transformations 6 Horizon graphs IAT 355 | View transformations 7 Trellis plots IAT 355 | View transformations 8 Scatter plot matrices IAT 355 | View transformations 9 Multiple Views • “Guidelines for Using Multiple Views in Information Visualization” • Baldonado, Woodruff and Kichinsky AVI 00 IAT 355 | View transformations 10 Multiple Views: 8 Guidelines • Rule of Diversity: • Use multiple views when there is a diversity of attributes • Rule of Complementarity: • Multiple views should bring out correlations and/or disparities • Rule of Decomposition: “Divide and conquer”. • Help users visualize relevant chunks of complex data • Rule of Parsimony: • Use multiple views minimally IAT 355 | View transformations 11 8 Guidelines Cont’d • Rule of Space/Time Resource • Optimization: Balance spatial and temporal benefits of presenting and using the views • Rule of Self Evidence: • Use cues to make relationships apparent.
    [Show full text]
  • Information Visualization Primer December 6, 2011 (Information) Visualization
    CS 53000 Introduction to Scientific Visualization Information Visualization Primer December 6, 2011 (Information) Visualization Problem: • HUGE Datasets: How to understand them? Solution • Take better advantage of human perceptual system • Convert information into a graphical representation. Issues • How to convert abstract information into graphical form? • Do visualizations do a better job than other methods? CS530CS - Introduction53000 - Introduction to Scientific to Scientific Visualization Visualization - 12/06/2011 Visualization Success Stories CS530CS - Introduction53000 - Introduction to Scientific to Scientific Visualization Visualization - 12/06/2011 The Power of Visualization 1. Start out going Southwest on ELLSWORTH AVE Towards BROADWAY by turning right. 2: Turn RIGHT onto BROADWAY. 3. Turn RIGHT onto QUINCY ST. 4. Turn LEFT onto CAMBRIDGE ST. 5. Turn SLIGHT RIGHT onto MASSACHUSETTS AVE. 6. Turn RIGHT onto RUSSELL ST. CS530CS - Introduction53000 - Introduction to Scientific to Scientific Visualization Visualization - 12/06/2011 The Power of Visualization Line drawing tool by Maneesh Agrawala http://graphics.stanford.edu/~maneesh/ CS530CS - Introduction53000 - Introduction to Scientific to Scientific Visualization Visualization - 12/06/2011 London Subway www.londontransport.co.uk/tube CS530CS - Introduction53000 - Introduction to Scientific to Scientific Visualization Visualization - 12/06/2011 From E. Tufte The Visual Display of Napolean’s MarchQuantitative Information Minard graphic size of army latitude temperature direction longitude
    [Show full text]
  • Lecture 11: Navigation Information Visualization CPSC 533C, Fall 2007
    Lecture 11: Navigation Information Visualization CPSC 533C, Fall 2007 Tamara Munzner UBC Computer Science 17 October 2007 Readings Covered Ware, Chap 10: Interacting With Visualizations (2nd half) Tufte, Chap 2: Macro/Micro Space-Scale Diagrams: Understanding Multiscale Interfaces George Furnas and Ben Bederson, Proc SIGCHI 95. Smooth and Efficient Zooming and Panning. Jack J. van Wijk and Wim A.A. Nuij, Proc. InfoVis 2003, p. 15-22 OrthoZoom Scroller: 1D Multi-Scale Navigation. Catherine Appert and Jean-Daniel Fekete. Proc. SIGCHI 06, pp 21-30. Further Reading Speed-Dependent Automatic Zooming for Browsing Large Documents Takeo Igarashi and Ken Hinckley, Proc. UIST 00, pp. 139-148. Pad++: A Zooming Graphical Interface for Exploring Alternate Interface Physics Ben Bederson, and James D Hollan, Proc UIST 94. Rapid Controlled Movement Through a Virtual 3D Workspace Jock Mackinlay, Stuart Card, and George Robertson. Proc SIGGRAPH ’90, pp 171-176. Effective View Navigation, George W. Furnas, Proc. SIGCHI 97, pp. 367-374 Critical Zones in Desert Fog: Aids to Multiscale Navigation, Susanne Jul and George W. Furnas, Proc. UIST 98 Design Guidelines for Landmarks to Support Navigation in Virtual Environments Norman G. Vinson, Proc. SIGCHI 99. Tuning and testing scrolling interfaces that automatically zoom Andy Cockburn, Joshua Savage, Andrew Wallace. Proc CHI 05. What Kind of Motion? I rigid I rotate/pan/zoom I easy to understand I object shape static, positions change I morph/change/distort I object evolves I beating heart, thunderstorm, walking
    [Show full text]
  • Thesisdanielvelasquez 1.Pdf
    Abstract Over the last decade, researchers have been interested in personalised recommender sys- tems as they have detected the many profitable applications they have. As a direct effect many algorithms and different techniques have been developed, some of them combining user and item categorisation techniques. The purpose of this work is to bring the capabilities of such sys- tems to the context of museums and art galleries and combine them with advanced visualisation techniques. This will be done in order to help the users to explore a large dataset of European artworks from the 11th until the 19th century while taking advantage of a recommendation engine that will help them finding interesting artworks rapidly. To pursue the aforementioned goals, an existing application that already contains such advanced visualisation techniques and the dataset has been enhanced with recommendation capabilities. The recommendations are based on semantic user profiling techniques extracted unobtrusively from users. Using semantic user profiling to provide recommendations enables the possibility to have a hybrid recommendation algorithm with content-based and collaborative features. The rec- ommendation algorithm extracts the preferences from users through their interactions with the system and uses them to find related content that might be interesting for the user. An offline experiment has been conducted in order to assess the relative accuracy of the recommendations and the user categorisation based on semantic user profiling. The offline experiment reveals the advantages of combining such techniques and shows promise on a theoretical level on how to bring recommendations in such a restricted and relatively scarcely explored environment like the applications for museums and art galleries.
    [Show full text]
  • CHI 2013 Conference Program
    CCHANGINGI2013 PERSPECTIVES CONFERENCE PROGRAM The 31st Annual CHI Conference on Human Factors in Computing Systems 27 APRIL - 2 MAY 2013 • PARIS • FRANCE ORIENTATION MAP 352 351 LEVEL 3 362 361 HAVANE 343 342 A BORDEAUX 253 252 B LEVEL 2 252 A 251 BLEU 243 H A LL 242 B M A ILL 242 A OT 241 MAILLOT MEZZANINE LEVEL 1 GRAND AMPHITHÉÂTRE REGISTRATION LEVEL 0 PORTE MAILLOT LEVEL -1 Additional maps are available on pages 67-68: • Detailed map of Hall Maillot with Exhibitor booths and Interactivity hands-on demonstrations • Map of the area around the Palais des Congrès WELCOME FROM THE CHAIRS Bienvenue and Welcome to CHI 2013 CHI 2013 is located in the Palais des Congrès in central Paris, a Course program and invited talks from SIGCHI’s award winners: few blocks from the Arc de Triomphe. Often described as the most George Robertson, Jacob Nielsen, and Sara Czaja. This year, RepliCHI beautiful city in the world, Paris is home to world-class museums, joins the Honorable Mention and Best Paper awards, to recognize excellent food and breath-taking architecture, just steps away or a excellence in the research process. We also host student research, short ride on the metro. design, and game competitions, provocative alt.chi presentations and last-minute SIGs for discussing current topics. CHI is the premier international conference on human-computer interaction, offering a central forum for sharing innovative interactive Interactivity hands-on demonstrations showcase the best of technologies that shape people’s lives. CHI gathers a multidisciplinary interactive technology, with both advances in research and artistic community from around the world.
    [Show full text]
  • Ware Chapter 10 - Spatial Navigation Rapid Controlled Movement Spatial Navigation Design Guidelines for VE Landmarks
    Readings Covered Further Reading What Kind of Motion? Speed-Dependent Automatic Zooming for Browsing Large Documents Takeo Igarashi and Ken Hinckley, Proc. UIST 00, pp. 139-148. Lecture 11: Navigation I rigid Ware, Chap 10: Interacting With Visualizations (2nd half) Pad++: A Zooming Graphical Interface for Exploring Alternate I rotate/pan/zoom Information Visualization Interface Physics Ben Bederson, and James D Hollan, Proc UIST 94. I easy to understand CPSC 533C, Fall 2007 Tufte, Chap 2: Macro/Micro Rapid Controlled Movement Through a Virtual 3D Workspace Jock I object shape static, positions change Space-Scale Diagrams: Understanding Multiscale Interfaces George Mackinlay, Stuart Card, and George Robertson. Proc SIGGRAPH morph/change/distort Furnas and Ben Bederson, Proc SIGCHI 95. ’90, pp 171-176. I I object evolves Tamara Munzner Smooth and Efficient Zooming and Panning. Jack J. van Wijk and Effective View Navigation, George W. Furnas, Proc. SIGCHI 97, pp. I beating heart, thunderstorm, walking person Wim A.A. Nuij, Proc. InfoVis 2003, p. 15-22 367-374 I multiscale/ZUI UBC Computer Science OrthoZoom Scroller: 1D Multi-Scale Navigation. Catherine Appert Critical Zones in Desert Fog: Aids to Multiscale Navigation, Susanne I object appearance changes by viewpoint and Jean-Daniel Fekete. Proc. SIGCHI 06, pp 21-30. Jul and George W. Furnas, Proc. UIST 98 17 October 2007 I focus+context Design Guidelines for Landmarks to Support Navigation in Virtual I carefully chosen distortion Environments Norman G. Vinson, Proc. SIGCHI 99. Tuning and
    [Show full text]
  • Overview and Detail + Focus and Context
    Overview and Detail + Focus and Context CS 7450 - Information Visualization September 28, 2016 John Stasko Learning Objectives • Explain motivation behind providing overview & detail • Provide examples of zooming visualization applications and describe benefits and limitations of such applications • Describe different methods of providing overview & detail • Define concept of focus+context and fisheye view • Explain components of fisheye view and how its equation is calculated • Describe different fisheye data visualization applications • Understand limitations of fisheye approach Fall 2016 CS 7450 2 1 Fundamental Problem • Scale - Many data sets are too large to visualize on one screen May simply be too many cases May be too many variables May only be able to highlight particular cases or particular variables, but viewer’s focus may change from time to time Fall 2016 CS 7450 3 Large Scale • One of the fundamental challenges in information visualization How to allow end-user to work with, navigate through, and generally analyze a set of data that is too large to fit in the display Potential solutions lie in Representation Interaction Both Fall 2016 CS 7450 4 2 One Solution :^) You can just buy more pixels Problem: You’ll always eventually run out of pixels Fall 2016 CS 7450 5 Overview • Providing an overview of the data set can be extremely valuable Helps present overall patterns Assists user with navigation and search Orients activities • Generally start with overview Shneiderman mantra Fall 2016 CS 7450 6 3 Details • Viewers
    [Show full text]