Advanced Data Visualization

Recommended Prerequisites

Data Visualization.

Teaching Methods

The unit includes theoretical lectures where the fundamental concepts, principles and techniques and there are presented and explained in detail.
Lectures of practical nature play the role of strengthening the connection between theoretic knowledge and its practical application. For that purpose the course promotes the development of projects that allow the application of the fundamental concepts and the development of the necessary competences for the development of visualizations for large volumes of data and their integration in processes and applications of data science.

Learning Outcomes

This course presents and discusses advanced data visualization tools and techniques, focusing on the development of Large-Scale Visualization applications. Advanced techniques for the visualization of georeferenced and network data will also be addressed. Moreover, state of the art interaction techniques will be discussed and implemented.

By the end of the course, the student will have theoretical knowledge and practical experience on the development of visualizations for Big Data scenarios, being able to design, implement, test and validate solutions for real world scenarios that require Large-Scale Visualization.

 

Main competencies to be developed are:

–analysis and synthesis, problem solving

–critical thinking

-practical application of the theoretical knowledge; research

Secondary competences are:

–organizing and planning

–work in teams

–autonomous learning, creativity    

Work Placement(s)

No

Syllabus

Large-Scale Visualization
. Big Data: the 3Vs (volume, velocity, variety)
. Heterogeneous data sources and data types
. Architectures for Large Scale Visualization
. Developing InfoVis Applications
. Interaction for large scale visualization
. Visualization methods

Perception for Design
. Model of visual processing
. Lightness, brightness, contrast, constancy
. Color
. Visual Salience / Finding Information
. Static and Moving Patterns
. Space Perception
. Visual Thinking Processes

Advanced Visualization Techniques
. Georeferenced data
Vector fields
Tensor fields
. Network Data
- Multilevel graph layouts
- Semantic substrate
- Node grouping and filtering
- Dynamic Layouts
- Concept maps and mind maps
- Edge bundling techniques
- Representing flow
- Representing change in networks
. Interaction
- Data Lenses
- Semantic zoom

Head Lecturer(s)

Evgheni Polisciuc

Assessment Methods

Assessment
Exam: 20.0%
Project: 80.0%

Bibliography

Munzner, T.: Visualization Analysis and Design, 2014.

Ware, C.: Information Visualization: Perception for Design, 2nd ed., 2004.

Wang, L., Wang, G., & Alexander, C. A. (2015). Big data and visualization: methods, challenges and technology progress. Digital Technologies, 1(1), 33-38.


Meirelles, I: Design for Information: An Introduction to the Histories, Theories, and Best Practices Behind Effective Information Visualizations. Rockport Publishers, 2013

T. Finke and S. Manger, Informotion: Animated Infographics. Gestalten Verlag, 2012
M. Lima, Visual Complexity: Mapping Patterns of Information. Princeton Architectural Press, 2011

Sagiroglu, S., & Sinanc, D. (2013, May). "Big data: A review". In 2013 International Conference on Collaboration Technologies and Systems (CTS) (pp. 42-47). IEEE.

Kitchin, R., & McArdle, G. (2016). "What makes Big Data, Big Data? Exploring the ontological characteristics of 26 datasets".