Vorlesung: 6171 V Data Visualization - Details

Vorlesung: 6171 V Data Visualization - Details

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Veranstaltungsname Vorlesung: 6171 V Data Visualization
Untertitel
Veranstaltungsnummer 6171 V
Semester SoSe 26
Aktuelle Anzahl der Teilnehmenden 111
erwartete Teilnehmendenanzahl 35
Heimat-Einrichtung Juniorprofessur für Kognitive Sensorsysteme
Veranstaltungstyp Vorlesung in der Kategorie Lehre (mit Prüfung)
Nächster Termin Montag, 13.04.2026 12:00 - 14:00 Uhr, Ort: (ITZ) SR 002
Art/Form
Teilnehmende
M.Sc. Artificial Intelligence Engineering, Modul Group “Artifical Intelligence Methods"
M.Sc. Computer Science, Module Group “Intelligent Technical Systems“
Voraussetzungen
none
Lernorganisation
60 h presence, 60 h exercises, 60 h revisiting lecture materials
Leistungsnachweis
Portfolio

The grade for Data Visualization will be based on the practical implementation work and the theoretical knowledge.
The performance evaluation of the exercise course (6171 UE) focusses on the implemented programming tasks in terms of the selected visualization paper. For a positive assessment, the students must present the selected article and their implementation concepts. The implementation quality, functionality, and usability as well as the documentation of code and functionality will be evaluated. Finally, the students will present and demonstrate their final implementation. The score for the grading results is given as follows:
• 1st presentation of the article incl. implementation concept (10 points)
• Implementation of the article’s underlying technique (45 points)
• Functionality and usability (15 points)
• Documentation of code + functionality (10 points)
• 2nd presentation and demonstration of the implementation (20 points)

The lecture part (6171 V) will be evaluated in an oral exam (approx. 15 min) of the presented lecture content, in which the achievement of the teaching objectives will be checked.

For a positive evaluation of Data Visualization, both exercise and lecture have to be completed positively.
SWS
2V+2Ü
Literatur
• Chen, Hauser, Rheingans, Scheuermann: Foundations of Data Visualization, 2019
• Telea: Data Visualization – Principles and Practice, Second Edition AK Peters Verlag, 2014.
• Munzer: Visualization Analysis and Design, AK Peters Verlag, 2014.
• Hansen, Johnson: The Visualization Handbook, 2005
• Hansen, Chen, Johnson, Kaufman, Hagen: Scientific Visualization - Uncertainty, Multifield, Biomedical, and Scalable Visualization, 2014
• Kim Marriott, Falk Schreiber, Tim Dwyer, Karsten Klein, Nathalie Henry Riche, Takayuki Itoh, Wolfgang Stuerzlinger, Bruce H. Thomas, Immersive Analytics, 2018
• Keim, Kohlhammer, Ellis, Mansmann: Mastering the Information Age - Solving Problems with Visual Analytics, 2010
• Ward, Grinstein, Keim: Interactive Data Visualization: Foundations, Techniques, and Applications, 2010.
• Ware: Information Visualization, Second Edition: Perception for Design, 2004
• Aigner, Miksch, Schumann, Tominski: Visualization of Time‐Oriented Data, Springer Verlag, 2011.
• Further materials in the lecture slides
Hinweise zur Anrechenbarkeit
https://www.fim.uni-passau.de/studium/anrechenbarkeit/
Turnus
every summer semester
ECTS-Punkte
6

Modulzuordnungen

  • Universität Passau
    • Master Artificial Intelligence Engineering (Version WiSe 2021) (Hauptfach)
      • Abschluss MR AIE > Gesamtkonto MR AIE > Wahlpflichtbereich > Modulgruppe "Artificial Intelligence Methods"
    • Master Informatik (Version SoSe 2016) (Hauptfach)

Kommentar/Beschreibung

Data Visualization consists of a theoretical lecture part and a practical exercise part.

The lecture part on Data Visualization contains of a series of lectures which take place throughout the semester. In these lectures advanced data visualization topics will be covered, such as visualization of graph data or spatio-temporal data, immersive analytics, cross virtuality analytics or visual analysis of non-destructive testing data in material sciences.
The lecture introduces bridging data visualization concepts and further covers the following aspects in two parts:
1) Revisiting Important Visualization Areas:
  • Scientific Visualization comprises volume and flow visualization. Volume visualization on the one hand is focused on direct and indirect techniques but also explores underlying techniques such as ray casting for direct volume rendering, simple and advanced transfer functions, as well as iso-surfacing for indirect volume rendering. Flow visualization on the other discusses visual metaphors and techniques for direct and indirect flow visualization. Advanced concepts in terms of topology visualization will be discussed.
  • Information visualization: While information visualization targets the visualization of abstract data, with a specialization on networks and graph data.
  • Visual analytics and visual data science seek to facilitate analytical reasoning by interactive visual interfaces. It involves the use of visualization and interaction techniques to explore and analyze large, complex, and dynamic datasets, with the goal of gaining insights, making discoveries, and supporting decision making.

2) Advanced Visualization Concepts:
  • Visualization Design: This chapter explores models for visualization design in order to justify the choices made when applying vis techniques in a (novel) application area, e.g., relating the visual encodings and interaction techniques to the requirements of the target task.
  • Biomedical visualization encompasses novel topics and approaches to enhance the understanding of biological and medical concepts and data. It involves data visualization of medical images (such as MRI or CT scans) or biological processes (such as the movement of molecules in the body).
  • Tensor visualization is the process of representing tensors (multi-dimensional arrays of data) in a visual format. This chapter will present visualization concepts mainly focusing on tensors of second order as well as respective abstraction concepts. Novel tensor analysis techniques, concepts for analyzing tensor field topology, and new tensor visualization methods will be discussed
  • Immersive (IA) and Cross Virtuality Analytics (XVA) are novel topics in visualization requiring suitable visual metaphors and interaction concepts for in depth analyses. IA is using engaging, embodied analysis tools to support data understanding and decision making. XVA is concerned with systems for data visualization and analysis that seamlessly integrate different visual metaphors and devices along the entire RVC to support multiple users with transitional and collaborative interfaces analysis that seamlessly integrate different visual metaphors and devices along the entire RVC to support multiple users with transitional and collaborative interfaces
  • Visual Computing in Materials Science: Visual computing has become highly attractive for boosting research endeavors in the materials science domain. Using visual computing, a multitude of different phenomena may now be studied, at various scales, dimensions, or using different modalities. This was simply impossible. Visual computing techniques generate novel insights to understand, discover, design, and use complex material systems of interest.
  • Visualization and Decision-Making Design Under Uncertainty: Visualization is a core component of any decision or risk analysis process. Respective tools for this purpose are becoming increasingly accessible. In addition, the visual literacy of the general public has been increasing due to the pervasiveness of visualizations in everyday life. As the appetite for decision making tools grows, so does the need to convey error, confidence, missing, or conflicting data visually.

For a positive grade, both lecture and exercise must be completed positively!