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 database research group develops algorithms for data-intensive applications in database and information systems with a particular focus on similarity search queries over large data collections, for example, similarity matching of sets, strings, and trees, efficient indexes for distance computations, and top-k queries.

Other fields of research include the distribution of large scale database queries using fast networks and remote direct memory access, load balancing algorithms for distributed frameworks like MapReduce, and queries in geographic information systems.
The research results are new algorithms with performance guarantees, which are implemented and empirically evaluated on both benchmark datasets and on the motivating application. Key performance parameters are runtime, memory efficiency, and network traffic; the performance measurements require direct and exclusive access to the hardware of the machines.

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
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
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