PhD Course - An Introduction to Data Visualization and Data Visualization Technology

February 17

Organizer(s):

Søren Knudsen (Assistant Professor at Digital Design)
Miguel González Duque (PhD Student at Digital Design)
Luca Rossi (Associate Professor at Digital Design)

Lecturer(s):

Søren Knudsen

Date(s) of the course: February 17, 2022.

Time: 9-17 (lunch break 12-13)

Room: 3A20

Course description:

In this course, we introduce data visualization as a design and research field. We approach the area from a technical perspective and for example consider automation and scalability in visualization.

The course focuses on three areas: 1) general visualization theory, 2) visualization grammars and other technical contributions in visualization, and 3) specific contributions in visualization that relate to student’s research field such as artificial intelligence (AI), machine learning (ML), natural language processing (NLP), and database management systems (DBMS).

Students that participate in the course might for example motivated to use data visualization techniques to analyse data in a separate field of research, to disseminate research findings more effectively in papers and supplemental material (such as companion websites), or to contribute to data visualization research itself.

Intended Learning Outcomes:

Outline fundamental concepts and design principles in data visualization.
Construct interactive data visualizations that relate to the student’s research field using the concepts and design principles discussed in the course.
Interpret, deconstruct, and critique data visualizations in the research literature pertinent to the student’s research field, and propose improvements on them.

Reading List

Reading list:

Chapters 1-5 from

T. Munzner, Visualization Analysis and Design, 1st ed. A K Peters/CRC Press, 2014. doi: 10.1201/b17511.

Papers:

I expect students to spend between 30 minutes on each before the course day; they select a few papers to focus on in their reflection and might spend more time reading these as part of this.

M. Bostock, V. Ogievetsky, and J. Heer, “D³ Data-Driven Documents,” IEEE Transactions on Visualization and Computer Graphics, vol. 17, no. 12, pp. 2301–2309, Dec. 2011, doi: 10.1109/TVCG.2011.185.

Kanit Wongsuphasawat et al., “Voyager 2: Augmenting Visual Analysis with Partial View Specifications,” in Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems - CHI ’17, Denver, Colorado, USA, 2017, pp. 2648–2659. doi: 10.1145/3025453.3025768.

H. Kim, R. Rossi, A. Sarma, D. Moritz, and J. Hullman, “An Automated Approach to Reasoning About Task- Oriented Insights in Responsive Visualization,” arXiv:2107.08141 [cs], Jul. 2021, Accessed: Dec. 13, 2021. [Online]. Available: http://arxiv.org/abs/2107.08141

H. Lin, D. Moritz, and J. Heer, “Dziban: Balancing Agency & Automation in Visualization Design via Anchored Recommendations,” in Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, Honolulu HI USA, Apr. 2020, pp. 1–12. doi: 10.1145/3313831.3376880.

J. Mackinlay, “Automating the Design of Graphical Presentations of Relational Information,” ACM Trans. Graph., vol. 5, no. 2, pp. 110–141, Apr. 1986, doi: 10.1145/22949.22950.

D. Moritz et al., “Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco,” IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 1, pp. 438–448, Jan. 2019, doi: 10.1109/TVCG.2018.2865240.

A. Satyanarayan, D. Moritz, K. Wongsuphasawat, and J. Heer, “Vega-Lite: A Grammar of Interactive Graphics,” IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 1, pp. 341–350, Jan. 2017, doi: 10.1109/TVCG.2016.2599030.

A. Satyanarayan, R. Russell, J. Hoffswell, and J. Heer, “Reactive Vega: A Streaming Dataflow Architecture for Declarative Interactive Visualization,” IEEE Trans. Visual. Comput. Graphics, vol. 22, no. 1, pp. 659–668, Jan. 2016, doi: 10.1109/TVCG.2015.2467091.

A. Satyanarayan, K. Wongsuphasawat, and J. Heer, “Declarative Interaction Design for Data Visualization,” in Proc UIST, 2014, pp. 669–678. doi: 10.1145/2642918.2647360.

C. Stolte, D. Tang, and P. Hanrahan, “Polaris: a system for query, analysis, and visualization of multidimensional relational databases,” IEEE Transactions on Visualization and Computer Graphics, vol. 8, no. 1, pp. 52–65, Mar. 2002, doi: 10.1109/2945.981851.

J. VanderPlas et al., “Altair: Interactive Statistical Visualizations for Python,” Journal of Open Source Software, vol. 3, no. 32, p. 1057, Dec. 2018, doi: 10.21105/joss.01057.

C. Weaver, “Cross-Filtered Views for Multidimensional Visual Analysis,” IEEE Transactions on Visualization and Computer Graphics, vol. 16, no. 2, pp. 192–204, Mar. 2010, doi: 10.1109/TVCG.2009.94.

K. Wongsuphasawat, D. Moritz, A. Anand, J. Mackinlay, B. Howe, and J. Heer, “Towards a General-purpose Query Language for Visualization Recommendation,” in Proceedings of the Workshop on Human-In-the- Loop Data Analytics, New York, NY, USA, 2016, p. 4:1-4:6. doi: 10.1145/2939502.2939506.

A. Wu et al., “AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualization,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–1, 2021, doi: 10.1109/TVCG.2021.3099002.

Optional reading

J. Heer, S. K. Card, and J. A. Landay, “prefuse: a toolkit for interactive information visualization,” in Proceedings of the SIGCHI conference on Human factors in computing systems - CHI ’05, Portland, Oregon, USA, 2005, p. 421. doi: 10.1145/1054972.1055031.

H. Kim, D. Moritz, and J. Hullman, “Design Patterns and Trade-Offs in Responsive Visualization for Communication,” Computer Graphics Forum, vol. 40, no. 3, pp. 459–470, Jun. 2021, doi: 10.1111/cgf.14321.

Lauro Lins, James T. Klosowski, and Carlos Scheidegger, “Nanocubes for Real-Time Exploration of Spatiotemporal Datasets,” 2013.

D. Moritz, B. Howe, and J. Heer, “Falcon: Balancing Interactive Latency and Resolution Sensitivity for Scalable Linked Visualizations,” p. 11, 2019.

D. Ren, B. Lee, and T. Höllerer, “Stardust: Accessible and Transparent GPU Support for Information Visualization Rendering,” Computer Graphics Forum, vol. 36, no. 3, pp. 179–188, Jun. 2017, doi: 10.1111/cgf.13178.

D. Sacha, M. Kraus, D. A. Keim, and M. Chen, “VIS4ML: An Ontology for Visual Analytics Assisted Machine Learning,” IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 1, pp. 385–395, Jan. 2019, doi: 10.1109/TVCG.2018.2864838.

A. Satyanarayan and J. Heer, “Lyra: An Interactive Visualization Design Environment: Lyra: An Interactive Visualization Design Environment,” Computer Graphics Forum, vol. 33, no. 3, pp. 351–360, Jun. 2014, doi: 10.1111/cgf.12391.

Programme

Preliminary programme:

Morning:
9:00	-	9:30	Welcome, brief introductions + motivations for participating
9:30	-	10:30	Introduction to visualization theory and concepts
			Coffee break
10:45	-	11:15	Breakout discussions on theory and relevance for own work
11:15	-	12:00	Plenum discussion and overview of afternoon
Afternoon:
13:00	-	14:20	Grammars, specifications, and automation in visualization
			Coffee break
14:35	-	15:25	Web technologies and d3.js
			Coffee break
15:40	-	16:20	Breakout discussions on tools and relevance for own work
16:20	-	17:00	Plenum discussion and overview of technical exploration(s) for reflection

Prerequisites:

We expect participants to have basic programming skills, as well as experience in a programming language like Python, R or JavaScript.

Exam:

Participants will explore technical data visualization ideas, possibilities, and approaches based on the course topic that relate to one of their own current or recent past research projects. This might be in the form of creating visualization prototypes that explore new ideas or by improving data visualizations from existing work.

They will hand in a reflection on the course teaching in relation to the technical exploration(s). This reflection might for example address, what from the course the student could or will apply in this project, how they might do it, and what they might gain.

The reflection should take the form of a 3 normal page (7200 characters including spaces) document submitted as pdf by email to the lecturer(s). The technical explorations should be attached and described very briefly in an appendix (up to 300 characters including spaces per idea).

Credits: 1 ECTS point

Number of hours the student is expected to use on the course:

Participation: 7 hours
Preparation: 10 hours
Exam Reflection: 12 hours

Deadline for registration: February 10, 2022

How to sign up: Participants sign up by email to vis-phd-course-feb-2022-org@o365team.itu.dk