Lecture: Interactive-Visual Data Analysis (L&E)

General Description 

This course introduces fundamental concepts and techniques of interactive-visual data analysis (IVDA). The main focus is on the combination of automatic data analysis methods with interactive visual interfaces, as well as on their interplay to facilitate data analysis goals. As such, IVDA is particularly suited to leverage the strengths of both humans and machines in a human-in-the-loop data analysis process. Associated research fields are Information Visualization, Visual Analytics, Interactive Data Science, and Interactive Machine Learning.

Learning Outcome

In the first part, students will learn basic characteristics of data types and data attributes (WHAT), as well as data analysis tasks (WHY). Further, students will learn basic design skills about HOW data can be transformed into visual structures and which types of visualization techniques are meaningful design choices for given data types and analysis at hand. Students will also learn fundamental interaction techniques, as well as concepts for the composition of views in data analysis systems.

In the second part, students will gain a deep understanding about how data analysis can benefit from both having a human and a (machine learning) model in the loop, following the goal to gain knowledge from data. Along these lines, students will learn about the strengths (and weaknesses) of human and machines, as well as about combining these complementary strengths effectively, as described in Visual Analytics methodology. In detail, students will learn examples for interactive data preprocessing, for human-centered unsupervised machine learning, as well as for human-centered semi-supervised and supervised machine learning. Finally, the course introduces approaches that allow training personalized machine learning models and conduct personal and human-centered data analytics.

Target Groups

This module is designed for MA students (POC, DS). There are no enforced prerequisites. It would be possible for students in other disciplines to take this course with some programming background. It is useful if students have already passed the Data Visualization Concepts lecture, but there are no enforced prerequisites.

Required Reading

Visualization Analysis and Design, Tamara Munzner (A K Peters Visualization Series, CRC Press, 2014) is the course textbook. Required reading also includes selected papers, as outlined below.

Required Reading (pre-class)

• VAD Book Chapter 1. What's Vis, and Why Do It?

In-class Agenda

• Welcome to the class!
• Why take this course? - some considerations
• Course logistics - organizational stuff
• Introduction - Introduction to Interactive Visual Data Analysis
• Vis-Fails                                                     

Further Reading

• [VIS Design Principles]: Semiology of Graphics, Jacques Bertin, Gauthier-Villars 1967, EHESS 1998
• [VIS Design Principles]: The Visual Display of Quantitative Information. Edward R. Tufte. Graphics Press, 1983.

Required Reading (pre-class)

• VAD Book Chapter 2: What: Data Abstraction
• The eyes have it: A task by data type taxonomy for information visualizations - B. Shneiderman - 1996

In-class Agenda (Lecture)

• What to visualize? Data - an introduction
• What: Dataset Types - tables, networks, time series, fields, geometry, text, sets
• What: Data Attributes - nominal, ordinal, and numerical

In-class Agenda (Exercise)

• Examples of mapping data attributes into the visual space
• Gestalt principles of design
• Assessment 1 kickoff
• Guest Lecture Talk

Further Reading

• [Dataset Types]: Information Visualization and Visual Data Mining - Daniel Keim - 2002
• [Time Series Data]: Visualization of Time-Oriented Data - Wolfgang Aigner, Silvia Miksch, Heidrun Schumann, Christian Tominski. Springer - 2011

Required Reading (pre-class)

• Research directions in data wrangling: Visualizations and transformations for usable and credible data - Kandel S, Heer J, Plaisant C, Kennedy J, Van Ham F, Riche NH, Weaver C, Lee B, Brodbeck D, Buono P. (2011)

In-class Agenda (Lecture)

• Aspect of Dirty Data - Identification and Curation
• Data Transformations - Making data usable and useful
• Visual Preprocessing - Examples of Vis tool usage in applications

In-class Agenda (Exercise)

• Assessment feedback
• Visualization of distribution
• Semantics vs Syntactics
• Dirty Data: time series and text preprocessing
• Tools for data wrangling
• Assessment 2 kickoff

Required Reading (pre-class)

• VAD Book Chapter 3. Why: Task Abstraction

In-class Agenda

• Why: Analysis Tasks - Actions and Targets      
• Application Example - Data Relation Exploration
• Data and Task Abstraction - A Four-Level Analysis Framework   

In-class Agenda (Exercise)

• Data source exploration for project work
• Project pitch
• Data scientist and Data Baton
• Nested Model application
• Assessment 2 feedback
• Assessment 3 kickoff                    

Further Reading

• [The Nested Model]: A Nested Model for Visualization Design and Validation. Tamara Munzner, 2009
• [Data and Task]: Information Visualization and Visual Data Mining. Daniel Kaim, 2002

In-class Agenda

• Project group work  

In-class Agenda (Exercise)

• Project pitch presentation
• Assessment 4 kickoff                    

Required Reading (pre-class)

• VAD Book Chapter 4. Analysis: Four Levels for Validation

In-class Agenda

• Introduction - About Patterns and Models
• Knowledge Generation - Patterns, Models, and Analytical Reasoning
• Data Transform. Processes - The InfoVis and the KDD Process
• Humans and Machines - Strengths and Weaknesses
• Visual Analytics - Synthesis

In-class Agenda (Exercise)

• Project presentations
• Kickoff Assessment 5                               

Further Reading

• Book: Illuminating the Path: The Research and Development Agenda for Visual Analytics. Thomas, J. and Cook, K. National Visualization and Analytics Center, 2005
• Paper:Visual Analytics: Definition, Process, and Challenges. D Keim, G Andrienko, JD Fekete, C Gorg, J Kohlhammer, G Melancon, 2008

Required Reading (pre-class)

• VAD Book Chapter 5. Marks and Channels
• VAD Book Chapter 6. Rules of Thumb

In-class Agenda

• Marks - Basic Graphical Elements
• Channels - Visual Variables
• Visual Encoding Example - Scenario: Stocks Data
• Visualization Guidelines - Perception, Color, and Rules of Thumb

  Decoding of Visualizations - Chart Decomposition

In-class Agenda (Exercise)

• Design of Visual Encodings
• Color Usage for Attribute Types
• 8 minutes madness
• Kickoff Assessment 6                              

Further Reading

• [VIS Design Principles]: Semiology of Graphics, Jacques Bertin, Gauthier-Villars 1967, EHESS 1998
• [VIS Design Principles]: The Visual Display of Quantitative Information. Edward R. Tufte. Graphics Press, 1983.

Required Reading (pre-class)

• VAD Book Chapter 11. Manipulate View
• VAD Book Chapter 12. Facet into Multiple Views
• VAD Book Chapter 14. Embed: Focus+Context

In-class Agenda

• Interaction Design - Engaging in a dialog with the data
• Interaction Techniques - Overview of atomic Interactions
• View Composition - Leveraging interaction techniques

In-class Agenda (Exercise)

• Kickoff Assessment 7
• Guest Lecture Talk                            

Further Reading

• [ND2002] Norman, D., The design of everyday things: Revised and expanded edition. Basic books. 2013
• [TS2020]: Interactive Visual Data Analysis. Christian Tominski and Heidrun Schumann. AK Peters Visualization Series. CRC Press. 2020

Recommended Reading

• VAD Book Chapter 7: Arrange Tables
• VAD Book Chapter 8: Arrange Spatial Data
• VAD Book Chapter 9: Arrange Networks and Trees

In-class Agenda (Lecture)

Advanced visualization techniques for...

• Multivariate Data
• Networks & Graphs
• Trees & Hierarchies
• Time Series
• Geographical Data
• Other Data Types

In-class Agenda (Exercise)

• Applications of advanced visualization techniques
• Game: Detection of techniques in a real-world video

Further Reading

Web-based overviews of techniques for...

• Multivariate Data
• Networks & Graphs
• Trees & Hierarchies
• Time Series
• Geographical Data
• Text Data

Required Reading (pre-class)

• VAD Book Chapter 13. Reduce Items and Attributes

In-class Agenda

• Unsupervised ML - The two "Work Horses"
• Clustering - Finding Groups in Datasets
• Dimensionality Reduction - Reducing the Number of Attributes
• Visual Data Exploration - Using Self-Organizing Maps (SOM)

In-class Agenda (Exercise)

• Guest Lecture Talk - Search and Exploration in Digital Document Spaces
• ML4VIS - Using ML to Support VIS       
• Kickoff Assessment 8

Further Reading

• VAD Book
• Wang Q, Chen Z, Wang Y, Qu H. A Survey on ML4VIS: Applying Machine Learning Advances to Data Vis. TVCG. 2021
• Sedlmair, Aupetit: Data-driven Evaluation of Visual Quality Measures. EuroVis. 2015
• Bernard, Hutter, Zeppelzauer, Sedlmair, Munzner: ProSeCo: Visual Analysis of Class Separation Measures and Dataset Characteristics. G&C (2021)

Required Reading (pre-class)

• Hohman F, Kahng M, Pienta R, Chau DH. Visual analytics in deep learning: An interrogative survey for the next frontiers. IEEE Transactions on Visualization and Computer Graphics (TVCG). 2018.

In-class Agenda

• Supervised ML - The two "Work Horses"
• Explainable AI Special - Why something happens in ML Models
• Interactive ML Application - VIAL: Visual Interactive Data Labeling

In-class Agenda (Exercise)

• VIS4ML - Using ML to Support VIS
• Human-in-the-loop - When & How
• Guest Talk Lecture: Interactive Explainable AI                      

Further Reading

• Bernard J, Zeppelzauer M, Lehmann M, Müller M, Sedlmair M. Towards User‐Centered Active Learning Algorithms. Computer Graphics Forum. 2018.
• Sacha D, Kraus M, Keim DA, Chen M. Vis4ml: An ontology for visual analytics assisted machine learning. IEEE transactions on visualization and computer graphics. 2018
• Amershi S, Cakmak M, Knox WB, Kulesza T. Power to the people: The role of humans in interactive machine learning. AI Magazine. 2014

Required Reading (pre-class)

--none--

In-class Agenda

• Human-Centered AI
• Human-Centered Similarity + live demo
• Human-Centered Classification + live demo
• Human-Centered Regression + real-world application

In-class Agenda (Exercise)

• Knowledge Generation Model by Sacha et al.
• Human-centered regression
• Vote for the worst visualization
• Guest Talk Lecture: Interactive VIS for Clinical ML                  

Further Reading

• Shneiderman B. Human-centered artificial intelligence: Reliable, safe & trustworthy. International Journal of Human–Computer Interaction. 2020
• Personalized Visual-Interactive Music Classification. Christian Ritter, Christian Altenhofen, Matthias Zeppelzauer, Arjan Kuijper, Tobias Schreck, and Jürgen Bernard, EuroVA @ EuroVis (EuroGraphics), 2018
• Bernard, J., Ritter, C., Sessler, D., Zeppelzauer, M., Kohlhammer, J., Fellner, D. Visual-Interactive Similarity Search for Complex Objects by Example of Soccer Player Analysis. IVAPP/VISIGRAPP, 2017
• A Visual Active Learning System for the Assessment of Patient Well-Being in Prostate Cancer Research. Bernard, J., Sessler, D., Bannach, A., May, T., Kohlhammer, J. IEEE VIS Workshop on Visual Analytics in Healthcare (VAHC), 2015

In-Class Agenda

• Airbnb Income Prediction System
• AirPenguin: Finding a suitable Airbnb in Zurich
• World Happiness and its key determinants
• Playing and Understanding Pokémon How You Desire To!
• Search and explore the characteristic behavior of similar popular tracks
• Climate Change Resources: International Investment Flow
• LowESS: Depression Dataset Exploration [Lecture Award: Best Student Vote]
• Exploratory Aanalysis and Browsing of the OkCupid Dataset
• VI Tool to Support Used Car Purchasing Decisions
• Temperature Data Visualization Tool
• Employees’ Burnout Rate
• Stroke prediction dashboard [Lecture Award: Impact on Society]
• VIS Tool for US Accidents
• Visualization of Climate Vulnerability and Social Equity
• Mushroom Labeling
• A Visual-Interactive Tool for CDC 500 Cities Project [Lecture Award: Impact on Research]
• eNYrgy: Visualizing Energy Efficiency of NYC Buildings
• Predicting Market Prices for Airbnb
• An IVDA Tool for Earth Surface Temperature Analysis [Lecture Award: Impact on Business]