Applications are invited for a PhD fellowship/scholarship at Graduate School of Science and Technology, Aarhus University, Denmark, within the Computer Science program. The position is available from 1 February 2019 or later.
Research area and project description:
Progressive Visual Analytics (PVA) is currently revolutionizing big data analysis. Instead of crunching a whole dataset at once, PVA breaks a dataset down into smaller chunks and processes them in order of importance. This way, after each chunk, we can already output an in-progress result on which to base early analytical decisions long before the dataset is processed in its entirety. You probably know this concept from slowly transmitted images or maps online that are already shown while still being refined. PVA is like that, but for long-running data analysis and visualization operations.
This PhD project sets out to apply PVA to visual analysis techniques that follow the focus+context principle – i.e., by interacting with a visualization, users specify a point or region of interest (the focus) and its periphery of lesser interest (the context). Following the focus+context principle yields a natural, user-driven starting point for data partitioning and ordering strategies for PVA that is expected to outperform sampling-based methods, which partition the data without taking the user into account. It will be your task to explore this new combination of “Focused-PVA” to merge the swiftness and responsiveness of PVA with the interactive, user-driven nature of the focus+context principle.
Research questions to pursue in this project are:
- What can we imply from the focus/context information in terms of suitable data partitioning schemes and processing strategies for PVA? How does the kind of visualization in which the focus is specified influence these strategies?
- In which way can we leverage Focused-PVA to instantiate, for example, Progressive Lenses, Progressive Portals, or Progressive Probes on top of different visualizations?
- How to generalize the above ideas from a binary distinction between focus and context regions to a continuous distance-based concept of importance? How to extend this concept to two or more focal regions?
Work on this PhD topic will be conducted in close collaboration with the DABAI project (https://dabai.dk), which provides the datasets and analysis scenarios on which the developed visualizations are tested. In addition, this research topic is embedded in a larger ongoing research effort to develop a visual analytics concept and system that will center on progressive visualizations using smart lenses or locally inserted “in situ visualizations”.
Qualifications and specific competences:
To apply for the position, you must have a relevant Master’s degree and excellent computer programming skills. Prior experience in at least one of the following areas is of advantage: data visualization, data science, computer graphics, human-computer interaction, and database technologies.
You are expected to bring or develop the necessary soft skills for working in teams, as well as for managing and communicating your research progress. The same holds for the necessary hard skills in software development and scientific writing.
Applicants seeking further information are invited to contact:
Assoc. Prof. Hans-Jörg Schulz
Department of Computer Science
University of Aarhus
8200 Aarhus N