Database Research Cluster

University of Salzburg

Salzburg | Website

Core facility (CF)

Short Description

The research infrastructure consists of a network of 15 high-end server systems from Supermicro with multi core CPUs based on the AMD64 architecture. It can be used for calculations and test setups in the fields of software development and algorithm research. The nodes are connected with 10Gbit/s ethernet and 2x 56Gbit/s (per computing node) Infiniband FDR and can be used for dedicated low level programming and development of RDMA capable software.

Additionally applications and operating systems can be run in an isolated and abstracted way using virtualisation and container technologies. The research infrastructure allows to satisfy diverse research needs. Memory- and CPU-intensive research projects as well as distributed computing projects utilizing multiple nodes at the same time can be run on the available infrastructure. Available configurations range from systems with 96GB up to 1TB RAM and 12 to 64 cores per node. Fast storage systems using RAID with double parity and backup systems in different locations allow safe storage of research data.

A Continuous Integration (CI) system for rapid development and distribution of research software to all nodes is available.

Contact Person

Prof. DI Dr. Nikolaus Augsten

Research Services

Systems and algorithms for processing data
Storing and querying large data
Approximate query processing
Spatio-temporal database systems
GIS enabled databases
Interconnected nodes with 2x56Gbit/s Infiniband (per computing node) and 10Gbit/s ethernet technology
Direct Infiniband hardware access for RDMA-enabled software and services
Abstraction and Isolation of running systems using virtualisation and container technologies

Methods & Expertise for Research Infrastructure

The research infrastructure is designed for research in the field of data engineering with the aim of solving efficiency problems in data processing. In this branch of research, new algorithms are developed, implemented and empirically evaluated. The empirical evaluation requires precise runtime measurements, measurements of memory consumption and network traffic. This usually requires exclusive and physical access (bare metal) to individual servers or a cluster of servers. The use is characterized by frequently changing configurations in order to be able to carry out tests under different conditions.

The research infrastructure is technically professionally administered and offers support services for the execution of experiments, e.g. versioned storage of experimental setups and large experimental data, E2EE for sensitive research data, fully automatic provisioning of the cluster nodes, as well as databases for measurement results. Researchers are supported and advised by technical staff when setting up their experiments. From a scientific point of view, there is a strong wealth of experience in design and the empirical evaluation of single-core, multi-core and parallel as well as distributed shared-nothing algorithms.

Prof. DI Dr. Nikolaus Augsten
Fachbereich Computerwissenschaften
0043 662 8044 6347
nikolaus.augsten@sbg.ac.at
https://dbresearch.uni-salzburg.at/
Please contact us via science.plus@sbg.ac.at, or contact the responsible person for this section, mentioned in the contact field
Humboldt-University Berlin
Johannes Gutenberg University Mainz (JGU)
Technical University Munich
Celonis SE, Munich
Findologic GmbH, Salzburg
Salzburg Research GmbH
Spring: Scalable Process Mining
2021-2024
Celonis SE, Munich; Univ. Salzburg
Industrieförderung

Fast and Flexible Tree Edit Distance (FFTED) Projekt
2017-2021
Univ. Salzburg
Fonds zur Förderung der wissenschaftlichen Forschung: FWF https://ffted.dbresearch.uni-salzburg.at/

FWF Doctoral College GIScience
2015-2019
Univ. Salzburg
Fonds zur Förderung der wissenschaftlichen Forschung: FWF https://dk-giscience.zgis.net/

Synonyme für Suchmaschinen
2018-2019
Univ. Salzburg, Findologic GmbH
Österreichische Forschungsförderungsgesellschaft mbH

Fast and Flexible Tree Edit Distance (FFTED) Projekt
2017-2021
Univ. Prof. Dipl.-Ing. Nikolaus Augsten, PhD
Fonds zur Förderung der wissenschaftlichen Forschung: FWF
https://ffted.dbresearch.uni-salzburg.at/

FWF Doctoral College GIScience
2015-2019
Nikolaus Augsten, Euro Beinat, Stefan Lang, Franz Neubauer, Anette Bartsch, Thomas Blaschke, Michael Leitner, Josef Strobl
Fonds zur Förderung der wissenschaftlichen Forschung: FWF
https://dk-giscience.zgis.net/

Synonyme für Suchmaschinen
2018-2019
Univ. Prof. Dipl.-Ing. Nikolaus Augsten, PhD
Findologic GmbH, Österreichische Forschungsförderungsgesellschaft mbH
Oksana Dolmatova (Univ. Zurich), Nikolaus Augsten (Univ. Salzburg), Michael H. Böhlen (Univ. Zurich):
A Relational Matrix Algebra and its Implementation in a Column Store. SIGMOD Conference 2020: 2573-2587
https://doi.org/10.1145/3318464.3389747

Thomas Hütter (Univ. Salzburg), Maximilian H. Ganser (Univ. Salzburg), Manuel Kocher (Univ. Salzburg), Merima Halkic (Univ. Salzburg), Sabine Agatha (Univ. Salzburg), Nikolaus Augsten (Univ. Salzburg):
DeSignate: detecting signature characters in gene sequence alignments for taxon diagnoses. BMC Bioinform. 21(1): 151 (2020)
https://doi.org/10.1186/s12859-020-3498-6

Set Similarity Joins on MapReduce: An Experimental Survey
2018
Fabian Fier (Humboldt-Universität zu Berlin), Nikolaus Augsten (Univ. Salzburg), Panagiotis Bouros (Johannes Gutenberg University Mainz), Ulf Leser (Humboldt-Universität zu Berlin), Johann-Christoph Freytag (Humboldt-Universität zu Berlin) PVLDB 11(10): 1110-1122
https://doi.org/10.14778/3231751.3231760

An Empirical Evaluation of Set Similarity Join Techniques
2016
Willi Mann (Univ. Salzburg), Nikolaus Augsten (Univ. Salzburg), Panagiotis Bouros (Aarhus Univ., Denmark) PVLDB 9(9): 636-647
https://doi.org/10.14778/2947618.2947620

On-the-fly token similarity joins in relational databases
2014
Nikolaus Augsten (Univ. Salzburg), Armando Miraglia (VU Amsterdam), Thomas Neumann (TU München), Alfons Kemper (TU München) SIGMOD Conference 2014: 1495-1506
https://doi.org/10.1145/2588555.2610530

Set Similarity Joins on MapReduce: An Experimental Survey
2018
Fabian Fier, Nikolaus Augsten, Panagiotis Bouros, Ulf Leser, Johann-Christoph Freytag
PVLDB 11(10): 1110-1122
https://doi.org/10.14778/3231751.3231760

Tree edit distance: Robust and memory-efficient
2016
Mateusz Pawlik, Nikolaus Augsten
Inf. Syst. 56: 157-173
https://doi.org/10.1016/j.is.2015.08.004

An Empirical Evaluation of Set Similarity Join Techniques
2016
Willi Mann, Nikolaus Augsten, Panagiotis Bouros
PVLDB 9(9): 636-647
https://doi.org/10.14778/2947618.2947620

On-the-fly token similarity joins in relational databases
2014
Nikolaus Augsten, Armando Miraglia, Thomas Neumann, Alfons Kemper
SIGMOD Conference 2014: 1495-1506
https://doi.org/10.1145/2588555.2610530