Research Visual Analytics Visualization Research Work

Transcription

1 Research Visual Analytics Visualization Research Work Silvia Miksch Silvia Miksch: Short History Linz

2 Backbone of Research Project Ecosystem Content Part 1: Visual Computing Doctoral College Motivation & Contextualization Visual Analytics of Time-Oriented Data Challenges & Opportunities Part 2: How Does Research Work Research &Teaching Scientific Writing Scientific Reviewing Funding EXPAND Visual Computing Conclusion Content Part 1: Motivation & Contextualization Visual Analytics of Time-Oriented Data Challenges & Opportunities Part 2: How Does Research Work Research &Teaching Scientific Writing Scientific Reviewing Funding Visualization Success Story [Tufte, 1997] adapted from [Hearst, 2004] Mystery: What is causing a cholera epidemic in London in 1854? Conclusion

3 London

4 + * + * * to do to do to do * + * * to do to do to do * + * * to do to do to do Which Information to Tackle Change over Time Data Patient Data * * * Guidelines + + Patient Data Patient Data Patient Data Guidelines Guidelines Guidelines Users Tasks to do to do to do time Data & Information Big Data time-oriented, multivariate, irregular sampled, having different temporal granularities, qualitative, quantitative, etc. structured and unstructured enriched by meta data Motivation: Main Problems Data Unmanageable Information Overload variables On the one hand, a huge amount of highly structured data and information is available in working situations and the daily life,... On the other hand, different kinds of data and information analysis methods were developed to gain more insights (information and knowledge gains). Missing Integration of Various (Heterogeneous) Information Sources Various Interdisciplinary Methods time Missing Involvement of Users and their Tasks

5 Visualization for Problem Solving Analytical Methods Screen Resolution: 1024 * 768 = Yearly Measurements of Water Level in Low.Austria: Number of Cellular Phones in Austria (2005): Transmitted s Every Hours (World-Wide): Whole Data often not Presentable 1. Applying Analytical Methods (Data Reduction) 2. Visualization of Most Important Data and Information Analytical Methods Statistics, Machine Learning & Data Mining 1... Amt der NÖ Landesregierung, Abt. WA5 - Hydrologie, CIA Factbook, How Much Information?, UC Berkeley, today: peta (10 15 ) tomorrow: exa (10 18 )& zeta (10 21 ) Interactions Visual Analytics Past Today Only passive Observations Representation not Changeable one fits all Active Examination with Visualizations Dynamically Adaptable and Modifiable Different Users, Tasks, and Aims James Thomas & Kristin A. Cook NVAC (National Visualization and Analytics Center), Seattle, USA Visual Analytics is the science of analytical reasoning facilitated by interactive visual interfaces [Thomas & Cook 2005] [Keim, et al. 2010]

6 Visual Analytics Process [Keim, et al., 2008] Knowledge Generation Model for VA [Sacha, et al., 2014] Knowledge Generation Model for VA [Sacha, et al., 2014] Time has a Complex Structure

7 Visual Analytics of Time-Oriented Data User-Centered Design data characterizing time & time-oriented data visualizing time-oriented data interacting with time analyzing time-oriented data goals/tasks Interactive Visual Analytics Methods appropriateness user/audience Content Part 1: Motivation & Contextualization Visual Analytics of Time-Oriented Data Challenges & Opportunities Part 2: How Does Research Work Research &Teaching Scientific Writing Scientific Reviewing Funding Conclusion

8 CareCruiser Project Interactive Exploration of Effects of Therapeutic Actions on a Patient s Condition Wolfgang Aigner, Theresia Gschwandtner, Katharina Kaiser, Silvia Miksch, Andreas Seyfang [Gschwandtner, et al., ] CareCruiser Users Medical Experts & Physicians Data Patient Data and Treatment Plans Data: multivariate, abstract Time: linear, instant Task Exploring the Effects of Clinical Actions on a Patient s Condition [Gschwandtner, et al., 2010, 2011] Communicating Different Aspects with Multiple Views Communicating Different Aspects with Multiple Views

9 Communicating Different Aspects with Multiple Views Communicating Different Aspects with Multiple Views Visualizing Temporal Aspects Visualizing temporal aspects Treatment Plans and Patient Data Patient Parameter Chart Treatment Plans and Patient Data Patient Parameter Chart: Possible Value Range maximum value mimimum value

10 Visualizing temporal aspects Aligning Treatment Plans or Clinical Actions Treatment Plans and Patient Data Patient Parameter Chart: Intended Value Range intended value range 38 Interacting with Time CareCruiser Video [Gschwandtner, et al. : CareCruiser]

11 Interacting with Time [Gschwandtner, et al. : CareCruiser] Lessons Learned Visual Analytics: patient data in combination with applied treatment plans and clinical actions Step-wise interactive exploration of effects New insights Generation of hypotheses Evaluation (different points of view): Collaboration with medical expert guided design Heuristic Usability evaluation technical point of view Case study domain point of view [Lammarsch, et al., 2011, 2013], [Bögl, et al., 2011, 2013] Modeling Hypotheses with Visual Analytics Methods to Analyze the Past and Forecast the Future Wolfgang Aigner, Markus Bögl, Tim Lammarsch, Silvia Miksch, Alexander Rind Peter Filzmoser TiMoVA Visual Analytics for Model Selection in Time Series Analysis Users Experts in time series analysis Data Data: univariate, abstract Time: instant Tasks Time series transformation and model selection

12 Example: Statistical SW Tool Gretl Preview-Video [Bögl, et al., 2011, 2013] Usage Scenario Model Selection TiMoVA VA Prototype Time Series Line Plot [Bögl, et al., 2011, 2013] Definition of [Bögl, et al., 2011, 2013] Autocorrelation Function (ACF) Residual Analysis [Shumway and Stoffer, 2011]

13 TiMoVA VA Prototype [Bögl, et al., 2011, 2013] Usage Scenario Model Selection Time Series Line Plot [Bögl, et al., 2011, 2013] Autocorrelation Function (ACF) Residual Analysis Lessons Learned Visual Analytics of Time Oriented Data Evaluation Applied usage scenarios based on the requirements Formative evaluation during design and implementation phase Demonstration session internally and with two external experts Supports and guides domain experts by Model order selection inside the relevant plot Immediate visual feedback of the model residuals Visualization of the model transitions Short visual feedback cycles

14 Visual Analytics of Time-Oriented Data Information Discovery Process Usability & Utility Insights Studies Knowledge Respositories Infrastructure for Reusable Components TimeBench: A Data Model and Software Library Infrastructure to Facilitate Evaluation EvalBench: A Software Library for Visualization Evaluation [Rind, et al., 2013] Multiple Granularities Different Time Primitives Temporal Indeterminacy Science of Interaction Role and Value of Interactivity Supported by the Austrian BMWFJ via CVAST, a Laura Bassi Centre of Excellence (#822746), and by the Austrian Science Fund (FWF) via HypoVis (#P22883) [Aigner, et al., 2013] Reduces implementation effort for evaluation features Consistent and reproducible execution of study protocols Integrates well with existing visualization prototypes Free and open source software (@GitHub) How to use? [Aigner, et al., 2013] Supports: Controlled Experiments Interaction Logging Laboratory Questionnaires Heuristic Evaluations Insight Diaries

15 How to use? [Aigner, et al., 2013] How to use? [Aigner, et al., 2013] Define task lists for sessions Define task lists for sessions Implement EvaluationDelegate interface How to use? [Aigner, et al., 2013] Content Part 1: Define task lists for sessions Implement EvaluationDelegate interface Motivation & Contextualization Visual Analytics of Time-Oriented Data Challenges & Opportunities Part 2: How Does Research Work Research &Teaching Scientific Writing Scientific Reviewing Funding Conclusion

16 Challenges (Time-Oriented) Data Scale and Complexity Heterogeneous Data (Meta Data, Semantics, Multiple Sources, etc.) Data Quality & Uncertainty Data Provenance Application Challenges in Visual Analytics User Meeting Users Needs High Degree of Interactivity (Temporal Dimensions) Evaluation (Qualitative & Quantitative) Challenges (Time-Oriented) Data Scale and Complexity Heterogeneous Data (Meta Data, Semantics, Multiple Sources, etc.) Data Quality & Uncertainty Data Provenance Application Challenges in Visual Analytics User Meeting Users Needs High Degree of Interactivity (Temporal Dimensions) Evaluation (Qualitative & Quantitative) Design Technology Design Technology Guidance on how to Design and Develop Visual Analytics Systems Provide Reusable Infrastructure Guidance on how to Design and Develop Visual Analytics Systems Provide Reusable Infrastructure [VisMaster Challenges 2010] [VisMaster Challenges 2010] Challenges: Visual Analytics Process [Keim, et al., 2008] Conclusion Visual Analytics Detect the Expected and Discover the Unexpected data goals/tasks Interactive Visual Analytics Methods appropriateness user/audience

17 Thanks to Alan Albert Alessio Alexander Alexander Alime Amin Andreas Andreas Annette Arghad Barbara Barbara Ben Bilal Brain Burcu Carlo Catherine Christian Christian Christian Claudio Daniel David Dorna Edeltraud Eduard Elisabeth Elpida Elske Eva Fabian Felix Florian Florian Frank Franz Gennady Georg Georg Gerhard Gerhilde Guiseppe Hanna Heidrun Helga Helwig Ingrid Jarke Jim Jimmy Johannes Jörn Jürgen Kai Karl Katharina Klaus Krist Luca Lukas Manfred Mar Margit Maria Markus Markus Martin Martin Matt Michael Michael Michael Mikko Monika Monika Mor Nada Natalie Nikolaus Otto Panagiotis Paolo Paolo Patrick Peter Peter Peter Rene Rita Robert Robert Robert Roberto Ruth Sabine Salvo Samson Silvana Simone Sophie Stefan Stefan Stephan Susanne Sylvia Taowei David Theresia Thomas Tim Tom Werner Wolfgang Yuval... and many students and co-workers Wolfgang Aigner Silvia Miksch Heidrun Schumann Christian Tominski Visualization of Time-Oriented Data with a foreword by Ben Shneiderman Springer 1st Edition., 2011, XVIII, 286 p. 221 illus., 198 in color. Hardcover, ISBN Due: June 2011 Table of Contents Introduction Historical Background Time & Time-Oriented Data Visualization Aspects Interaction Support Analytical Support Survey of Visualization Techniques Conclusion Horizon Graph [Reijner, 2005]

18 Content Part 1: Motivation & Contextualization Visual Analytics of Time-Oriented Data Challenges & Opportunities Part 2: Part 2 HOW DOES RESEARCH WORK How Does Research Work Research &Teaching Scientific Writing Scientific Reviewing Funding Conclusion [Hevner et al. 2004] WARUM habe ich WAS gemacht, WIE habe ich es gemacht und mit WELCHEM ERGEBNIS. 7

19 [Hevner et al. 2004] [Hevner et al. 2004] Research & Teaching Bachelor Course Publications Conferences Journals Master Courses Supervision of Bachelor Students Supervision of Master Students Contributions in &Books Supervision of PhD Students

20 >> Title, Affiliation << [Simon L. Peyton Jones, 2004 presentation] >> References << Peer Reviewing Process Roles Different in each community Different... PC IPC Different for Journals Conferenes Timely and Complex Process Steps... Example EuroVis 2012 & VisWeek VU Informationsdesign und Visualisierung 8

21 Primary and Secondary Reviewers 8 8 Review Forms Review Forms 8 8

22 Review Forms Review Forms 8 8 Review Forms Ethics Guidelines Be Timely Protect Ideas Avoid Conflict of Interest Be Specific Be Helpful Be Tactful (In Summary)

23 Funding Backbone of Research Project Ecosystem Research needs funding Basic Research Doctoral College... Applied/Coooerative Reserch Visual Computing EXPAND Visual Computing Thanks to (Intern)ational Collaborations Alan Albert Alessio Alexander Alexander Alime Amin Andreas Andreas Annette Arghad Barbara Barbara Ben Bilal Brain Burcu Carlo Catherine Christian Christian Christian Claudio Daniel David Dorna Edeltraud Eduard Elisabeth Elpida Elske Eva Fabian Felix Florian Florian Frank Franz Gennady Georg Georg Gerhard Gerhilde Guiseppe Hanna Heidrun Helga Helwig Ingrid Jarke Jim Jimmy Johannes Jörn Jürgen Kai Karl Katharina Klaus Krist Luca Lukas Manfred Mar Margit Maria Markus Markus Martin Martin Matt Michael Michael Michael Mikko Monika Monika Mor Nada Natalie Nikolaus Otto Panagiotis Paolo Paolo Patrick Peter Peter Peter Rene Rita Robert Robert Robert Roberto Ruth Sabine Salvo Samson Silvana Simone Sophie Stefan Stefan Stephan Susanne Sylvia Taowei David Theresia Thomas Tim Tom Werner Wolfgang Yuval... and many students and co-workers Content Part 1: Motivation & Contextualization Visual Analytics of Time-Oriented Data Challenges & Opportunities Part 2: How Does Research Work Research &Teaching Scientific Writing Scientific Reviewing Funding Conclusion

24 TIPS: How to Do Research Part 3 TIPS & TRICKS 9 TIPS: How to Do Research TIPS: How to Do Research

25 TIPS: How to Do Research TIPS: How to Do Research siehe TIPS: How to Do Research

26 My Favorite Book for years... Lyn Dupré: BUGS in Writing: a Guide to Debugging Your Prose, Addison-Wesley, and William Strunk Jr., E. B. White The Elements of Style Longman, New York; 4th edition 1999 WARUM habe ich WAS gemacht, WIE habe ich es gemacht und mit WELCHEM ERGEBNIS. 1 1