Lecture: Introduction to Interactive-Visual Data Analysis

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, 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 an 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 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 only minimal programming background. It is useful if students have already passed the Data Visualization Concepts lecture but once again there are no enforced prerequisites.

This course does not teach visualization libraries: most students will pick up Tableau, D3 (Javascript), ggplot (R), or python-based visualization tooling on their own.

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.

start with the required reading (VAD book) instead

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

In-class Application Demo

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
• VAD book Chapter 3: Why: Task Abstraction

In-class Agenda

• What to visualize?: data types
• Why visualize?: analysis tasks
• Exercises/Questions

In-class Application Demo

Further Reading

• [Data and Tasks]: The eyes have it: A task by data type taxonomy for information visualizations - B. Shneiderman - 1996
• [VIS Introduction]: Readings in Information Visualization: Using Vision To Think, Chapter 1 - Stuart K. Card, Jock Mackinlay, and Ben Shneiderman. Morgan Kaufmann - 1999
• [Data and Tasks]: Information Visualization and Visual Data Mining - Daniel Keim - 2002
• [Interaction Tasks]: Toward a Deeper Understanding of the Role of Interaction in Information Visualization - Yi, Kang, Stasko, Jacko – 2007
• [Time Series Data and Tasks]: Visualization of Time-Oriented Data - Wolfgang Aigner, Silvia Miksch, Heidrun Schumann, Chris Tominski. Springer - 2011

Further Reading

• [IEEE VIS Best Paper] IRVINE: Using Interactive Clustering and Labeling to Analyze Correlation Patterns: A Design Study from the Manufacturing of Electrical Engines. Joscha Eirich, Jakob Bonart, Dominik Jäckle, Michael Sedlmair, Ute Schmid, Kai Fischbach, Tobias Schreck, Jürgen Bernard. IEEE VIS, TVCG, 2021.

Required Reading (pre-class)

• VAD book Chapter 7: Arrange Tables
• VAD book Chapter 8: Arrange Spatial Data
• VAD book Chapter 9: Arrange Networks and Trees
• Polaris: A System for Query, Analysis and Visualization of Multidimensional Relational Databases. Chris Stolte and Pat Hanrahan. Proceedings of IEEE InfoVis 2000. [research paper, intellectual foundation of the Tableau software]

In-class Agenda

• Live presentation of homework one-pagers
• Overview of advanced visualization techniques
• In-class Application Demo

In-class Application Demo: LineUp

Further Reading

• [Multivariate Data]: Samuel Gratzl, Alexander Lex, Nils Gehlenborg, Hanspeter Pfister, Marc Streit - LineUp: Visual Analysis of Multi-Attribute Rankings - IEEE TVCG, 2013

Required Reading (pre-class)

• VAD book Chapter 4: Analysis: Four Levels for Validation
• Paper: Anamaria Crisan; Brittany Fiore-Gartland; Melanie Tory. Passing the Data Baton: A Retrospective Analysis on Data Science Work and Workers. IEEE Transactions on Visualization and Computer Graphics. 2021.

In-class Agenda

• Data Scientists & Users
• Validation of VIS Designs using the Nested Model
• Design Studies
• In-class Application Demo

In-class Application Demo: IRVINE

Further Reading

• Paper: Sedlmair M, Meyer M, Munzner T. Design study methodology: Reflections from the trenches and the stacks. IEEE Transactions on Visualization and Computer Graphics. 2012.

Required Reading (pre-class)

• Paper: Knowledge Generation Model for Visual Analytics. Dominik Sacha, Andreas Stoffel, Florian Stoffel, Bum Chul Kwon, Member, Geoffrey Ellis and Daniel A. Keim. IEEE Transactions on Visualization and Computer Graphics. 2014.

In-class Agenda

• In-class Application Demo
• Knowledge Generation Process
• Data Transformation Processes
• Humans and Machines
• Synthesis: Visual Analytics

In-class Application Demo: Topic Modelling with SpecEx

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)

• Paper: 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. Information Visualization. 2011

In-class Agenda

• Aspects of Dirty Data
• Data Transformations
• Visual Preprocessing Applied
• In-class Application Demo

In-class Application Demo: Preprocessing of multivariate time series

Further Reading

• Paper: Visplause: Visual Data Quality Assessment - Arbesser, Spechtenhauser, Mühlbacher, Piringer. TVCG. 2017
• Paper: Visual-Interactive Preprocessing of Multivariate Time Series Data - Bernard, J., Hutter, M., Reinemuth, H., Pfeifer, H., Bors, C., Kohlhammer, J. Computer Graphics Forum (CGF). 2019

Required Reading (pre-class)

• VAD book Chapter 13: Reduce Items and Attributes

In-class Agenda

• "ML4VIS" with an emphasis on data-centered VIS challenges
• Visual Analytics based on Clustering
• Visual Analytics based on Dimensionality Reduction
• Self-organizing Maps (SOM) special
• In-class Application Demo

In-class Application Demo

Further Reading

• Paper: Visual Cluster Analysis of Trajectory Data With Interactive Kohonen Maps. Schreck, T., Bernard, J., Von Landesberger, T., Kohlhammer, J. Information Visualization, Palgrave Macmillan, 2009
• Paper: MotionExplorer: Exploratory Search in Human Motion Capture Data Based on Hierarchical Aggregation. Bernard, J., Wilhelm, N., Krüger, B., May, T., Schreck, T., Kohlhammer, J. TVCG, 2013
• Paper: Sacha D, Kraus M, Bernard J, Behrisch M, Schreck T, Asano Y, Keim DA. Somflow: Guided exploratory cluster analysis with self-organizing maps and analytic provenance. TVCG, 2018

Required Reading (pre-class)

• Paper: 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 Special
• VIS4ML - Using VIS/VA to support ML
• Live Application - VIAL – Visual Interactive Data Labeling
• In-class Application Demo

In-class Application Demo: Visual Observation of Data Labeling Runs

Further Reading

• Paper: T. Mühlbacher, H. Piringer. A partition-based framework for building and validating regression models. IEEE Transactions on Visualization and Computer Graphics (TVCG). 2013
• Paper: J. Bernard, M. Zeppelzauer, M. Sedlmair, and W. Aigner: VIAL – A Unified Process for Visual-Interactive Labeling. The Visual Computer Journal (TVCJ). 2018.

Required Reading (pre-class)

• none

In-class Agenda

• Human-Centered Artificial Intelligence (HCAI)
• Introduction to Human-Centered Data Analysis
• Types of Explicit User Feedback
• In-class Application Demo

In-class Application Demo: Personalized Similarity and Personalized Classification

Further Reading

• Book: Ben Shneiderman. Human-Centered AI. Oxford University Press. 2022
• Paper: 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

• Paper: Personalized Visual-Interactive Music Classification. Christian Ritter, Christian Altenhofen, Matthias Zeppelzauer, Arjan Kuijper, Tobias Schreck, and Jürgen Bernard, EuroVA @ EuroVis (EuroGraphics). 2018