Hasso-Plattner-InstitutSDG am HPI
Hasso-Plattner-InstitutDSG am HPI

Selected Topics in Visual Analytics (Sommersemester 2018)

Dozent: Prof. Dr. Jürgen Döllner (Computergrafische Systeme) , Dr. Benjamin Hagedorn (Computergrafische Systeme) , Dr. Jan Klimke (Computergrafische Systeme)

Allgemeine Information

  • Semesterwochenstunden: 4
  • ECTS: 6
  • Benotet: Ja
  • Einschreibefrist: 20.04.2018
  • Lehrform: Seminar / Projekt
  • Belegungsart: Wahlpflichtmodul
  • Maximale Teilnehmerzahl: 12

Studiengänge, Modulgruppen & Module

IT-Systems Engineering MA
  • IT-Systems Engineering
    • HPI-ITSE-A Analyse
  • IT-Systems Engineering
    • HPI-ITSE-E Entwurf
  • IT-Systems Engineering
    • HPI-ITSE-K Konstruktion
  • IT-Systems Engineering
    • HPI-ITSE-M Maintenance
  • HCGT: Human Computer Interaction & Computer Graphics Technology
    • HPI-HCGT-K Konzepte und Methoden
  • HCGT: Human Computer Interaction & Computer Graphics Technology
    • HPI-HCGT-S Spezialisierung
  • HCGT: Human Computer Interaction & Computer Graphics Technology
    • HPI-HCGT-T Techniken und Werkzeuge
  • SAMT: Software Architecture & Modeling Technology
    • HPI-SAMT-K Konzepte und Methoden
  • SAMT: Software Architecture & Modeling Technology
    • HPI-SAMT-S Spezialisierung
  • SAMT: Software Architecture & Modeling Technology
    • HPI-SAMT-T Techniken und Werkzeuge


Visual Analytics "[is] the science of analytical reasoning facilitated by interactive visual interfaces" (Thomas, Cook: Illuminating the Path, 2004) it covers concepts, techniques, and processes from different scientific areas: information visualization, computer graphics, data processing, management, and statistical analysis. Here one key element of Visual Analytics is to provide users massive datasets (e.g., sensor data, financial transactions, or data about software systems structure and behavior) visually and in an interactive manner. By exploiting the strengths of the human visual system (e.g., preattentive processing and pattern recognition) structures, correlations, and patterns in massive datasets can be recognized and assessed.

Students are expected to cope with a specific aspect or technique in the area of Visual Analytics, including a prototypical implementation for massive spatio-temporal datasets.

Examples for thematic areas include:

  • Integration, processing, and visualization of massive sensor datasets
  • Outlier and pattern recognition in spatio-temporal data
  • Mapping techniques for Visual Analytics
  • Visualization Techniques for spatio-temporal data
  • Layout techniques and hierarchy building for spatio-temporal data
  • Assisting User Interfaces for Visual Analytics
  • Interaction techniques for Visual Analytics
  • Virtual/Augmented Reality for Visual Analytics
  • Event processing, visualization, and notification
  • Uncertainty and validity visualization
  • Multi-View Dashboards
  • Evaluation of Visualizations and connceted User Interfaces

The topics provided contain a strong relation to current research activities at the computer graphics systems group. Partially, an existing Visual Analytics framework can be used as a basis for implementation. Course material including topic presentations are available on CGS Moodle.

After successful completion of the course, many of the topics provide the possibility to extend the work into an academic publication, a master thesis, or to continue working on the topic as a working student.


The course is primarily designed for students in the master’s program „IT-Systems Engineering“ or computer science. Knowledge in (web-based) software engineering, programming, computer graphics, and visualization are of advantage.


A set of basic related literature is provided to participants on a per topic basis. Further, it is expected that an extended literature research is conducted independently. A general literature list will be provided using the CGS Moodle

Lern- und Lehrformen

Subject of the course is to work on the topics and connected research questions. For this, an independent research in literature and existing solutions or implementation, as well as design and implementation of a prototypic implementation has to be conducted.

Further abilities to deep dive into a research topic, to systematically present the topic, and to build an implementation solution using software engineering techniques should be trained.

Topics are generally assigned individually. Some of the topics can also be tackled in teams of 2 students (coordinated with supervisors).


  • 1/8 initial presentation about foundations and related work (~20 min)
  • 2/8 final presentation
  • 3/8 software project implementation and documentation. Quality of implementation including:  disassembly into functional and reusable components (e.g., nodeJS modules, libraries, database extension, service interfaces, APIs, etc.), code quality, test coverage and quality of test cases, documentations
  • 2/8 Paper


There will only be selected appointments for the course for presentations during the semester. The work itself will be conducted individually. Appointments for status updates with supervisors will be assigned individually with each participant.

The first appointment for this course is Monday, April 9th 2018 at 15:15 in H-2.57. This will include topic presentations.