Research

By Florian Feigl, BSc

Table of Contents

Big Data Algorithms

We perform basic research on the design and analysis of prior-free algorithms with a strong focus on theoretical and mathematical aspects. Motivated by the end of Moore’s Law and the prevalence of “big” and “fast” data, we mainly work an distributed and dynamic algorithms. Our main expertise lies in the domain of graphs, which are an abstract model for all kinds of networks.

Computational Geometry

The Computational Geometry and Applications Lab at the Department of Computer Science (FB Computerwissenschaften) of the  University of Salzburg is directed by Martin Held. This page contains links to surveys (and color images) of some of the research topics which my students and I had or have been working on during the last few years. Mostly, only application-oriented work with a geometric flavor is included. The reader is referred to my papers for work on algorithms and data structures that is focussed more on theory than on application.

Computational Systems

We are the Computational Systems Group at the University of Salzburg, Austria. We work on the problem of how to engineer systems software rigorously and how to apply that knowledge to teach computer science in ways that may eventually make computer science more accessible to more people.

We teach basic principles of computer science on bachelor and masters level covering all relevant levels of computer systems and their connection, from the lowest levels of machine architecture via programming languages, compilers, and virtual machines which includes algorithms, data structures, complexity, and computability, to the highest levels of cloud computing.

In our classes, we use Selfie, an educational system of a self-compiling C compiler, a self-executing RISC-V emulator, and a self-hosting RISC-V hypervisor that comes with source code, slides, and an autograder.

Database Systems

The Database Research Group is part of the Department of Computer Science at the University of Salzburg, Austria.

In our research, we deal with all aspects of data management. We are attracted by applications that are heavily based on data but cannot leverage current systems due to the rich set of queries they need. The focus of our research is on queries over complex objects and massive data collections, data cleaning and integration, indexing techniques, query processing and optimization, distributed data management, and numerical computations in databases. Our research is triggered by problems that arise in concrete applications, for example, process mining, digital humanities, or cognitive neuroscience. The results of our research are new algorithms with performance guarantees, which are implemented and evaluated on the motivating application.

Efficient Algorithms

Our research group focuses on different algorithmic aspects of modern models of computation, such as parallel and distributed computing. Parallel and distributed algorithms are used to solve large, computationally intensive problems in science and engineering. We work on the interface between discrete mathematics, theoretical computer science, and parallel aspects of computing. We combine various techniques from these fields to design efficient solutions for problems such as information dissemination, distributed communication, network exploration, load balancing, and graph models for large real world networks.

Software Engineering

Research activities are guided by a mind set. Our evidence is that most programs and thus the systems in which software plays a crucial role for their correct working, are unnecessarily complex, often to a point at which they become unmanagable. Thus, we strive for concepts, methods and tools that help in the construction of lean software. This includes all aspects of software construction, in particular

  • software design and implementation
  • software reuse and composition
  • programming methodology

A pragmatic research approach requires a focus on certain domain areas. Currently we have chosen socalled cyber-physcial systems, in particular those that help reduce greenhouse gas emissions. In the past few years we have implemented an autonomous train on open tracks. The goal is to make the numerous side tracks that exist more attractive again by offering ca. 10 minute intervals between smaller trains. This is only economically feasible if the trains drive autonomously with obstacle detection. The tracks cannot be enclosed, at least not in an economic way, as is the case, for example, in the context of subways or of autonomous people movers on airports.