Chair for Computer Science 6 (Data Management)


The Chair of Computer Science 6 (Data Management) was founded in 1979 by appointing Prof. Dr. Hartmut Wedekind. After his retirement, Prof. Dr. Klaus Meyer-Wegener was appointed in 2001 as the new head. Since April 2007, the associated professorship is staffed with Prof. Dr. Richard Lenz.

The chair is engaged in research on the foundations of data management and the application-driven deployment of data-management technologies. We strive to immediately transfer our research results and developed concepts into industrial practice in the context of projects with partners from economy and public services. Both research and project activities together are important cornerstones of student education.

Database systems have been established as an indispensible tool for the fault-tolerant and consistent management of large datasets. There is a growing need to integrate huge datasets from heterogeneous, in particular external data sources into the data management of organisations ("Big Data"). This motivates application-oriented research in evolutionary information systems, schema inference, and data quality. As far as fundamental research is concerned, the chair works on technologies that accelerate the processing of database queries and on the functional enhancement with data-stream processing. Furthermore, it supports the various approaches to data management in the digital humanities and social sciences.

Martensstraße 3
91058 Erlangen

Research Fields

Database Systems
Data Integration
Data Management in the Digital Humanities
Data Quality
Datastream Systems
Evolutionary Information Systems
Process Management

Related Project(s)

(DFG Priority Programme (SPP) 2037 - Scalable Data Management for Future Hardware):
ReProVide: Query Optimisation and Near-Data Processing on Reconfigurable SoCs for Big Data Analysis
Prof. Dr.-Ing. Klaus Meyer-Wegener; Dr.-Ing. Stefan Wildermann; Prof. Dr.-Ing. Jürgen Teich
(28/08/2017 - 31/08/2020)

(EFRE EIASY-Opt - Competence and Analysis Project for the "Data-driven Process and Production Optimization with the help of Data Mining and Big Data"):
E|ASY-Opt INF6: REAPER: A Framework for Materializing and Reusing Deep-Learning Models
Prof. Dr.-Ing. Klaus Meyer-Wegener
(01/01/2017 - 31/12/2020)

KYQ: Know Your Queries!
Prof. Dr.-Ing. Klaus Meyer-Wegener
(01/04/2015 - 31/12/2018)

DAMSEL: Assessment of Data Management Systems
Prof. Dr.-Ing. Klaus Meyer-Wegener
(01/09/2014 - 31/12/2018)

(FOR 1508: Dynamisch adaptierbare Anwendungen zur Fledermausortung mittels eingebetteter kommunizierender Sensorsysteme):
BATS-TP3: Cross-system Optimization of Data-stream Queries
Prof. Dr.-Ing. Klaus Meyer-Wegener
(01/08/2012 - 31/12/2018)

Publications (Download BibTeX)

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Schwab, P., Wahl, A.M., Matschinske, J.O., & Meyer-Wegener, K. (2019). Query-Driven Data Minimization with the DataEconomist. In 2019 (pp. 614-617). Lisbon, Portugal, PT: 78457 Konstanz, Germany: University of Konstanz University Library.
Meyer-Wegener, K. (2019). Wie funktioniert die Blockchain? Datenbank-Spektrum, 19(1), 67-71.
Sigl, M. (2019). Don't Fear the REAPER: A Framework for Materializing and Reusing Deep-Learning Models. In IEEE Computer Society (Eds.), Proceedings of the International Conference on Data Engineering. Macau SAR, China, MO.
Duda, N., Nowak, T., Hartmann, M., Schadhauser, M., Cassens, B., Wägemann, P.,... Kölpin, A. (2018). BATS: Adaptive Ultra Low Power Sensor Network for Animal Tracking. Sensors.
Schwab, P., Wahl, A.M., Meyer-Wegener, K., & Matschinske, J.O. (2018). Towards Query-Driven Data Minimization. In Proc. Conf. "Lernen, Wissen, Daten, Analysen" (pp. 335-338). Mannheim, Germany, DE: CEUR-WS.
Becher, A., Beena Gopalakrishnan Nair, L., Broneske, D., Drewes, T., Gurumurthy, B., Meyer-Wegener, K.,... Wildermann, S. (2018). Integration of FPGAs in Database Management Systems: Challenges and Opportunities. Datenbank-Spektrum.
Wahl, A.M., Endler, G., Schwab, P.K., Rith, J., Herbst, S., & Lenz, R. (2018). A graph-based framework for analyzing SQL query logs. Association for Computing Machinery, Inc.
Wahl, A.M., Sauerhammer, C., Schwab, P., Herbst, S., & Lenz, R. (2018). Query-Driven Data Profiling with OCEANProfile. In Proceedings of the Twelfth International Workshop on Real-Time Business Intelligence and Analytics (BIRTE 2018). Rio de Janeriro, BR.
Wahl, A.M., Sauerhammer, C., Schwab, P., Herbst, S., & Lenz, R. (2018). Query-Driven Data Profiling with OCEANProfile. In ACM (Eds.), Proceedings of the International Workshop on Real-Time Business Intelligence and Analytics. Rio de Janeiro, BR: ACM.
Wahl, A.M., Endler, G., Schwab, P., Herbst, S., Rith, J., & Lenz, R. (2018). Crossing an OCEAN of queries: Analyzing SQL query logs with OCEANLog. In Proceedings of the 30th International Conference on Scientific and Statistical Database Management, SSDBM 2018. Association for Computing Machinery.
Wahl, A.M., Schwab, P., & Lenz, R. (2018). Minimally-Intrusive Augmentation of Data Science Workflows. In Proceedings of the Lernen. Wissen. Daten. Analysen. (LWDA 2018). Mannheim, DE.
Wahl, A.M., & Lenz, R. (2017). Analyzing SQL Query Logs using Multi-Relational Graphs. In Proc. Conf. "Lernen, Wissen, Daten, Analysen". Rostock, Germany, DE: CEUR-WS.
Guhlemann, S., Petersohn, U., & Meyer-Wegener, K. (2017). Reducing the Distance Calculations when Searching an M-Tree. Datenbank-Spektrum, 17(2), 155-167.
Wahl, A.M., Endler, G., Schwab, P., Herbst, S., & Lenz, R. (2017). Anfrage-getriebener Wissenstransfer zur Unterstützung von Datenanalysten. Stuttgart, DE: Springer.
Wahl, A.M., Endler, G., Schwab, P., Herbst, S., & Lenz, R. (2017). We Can Query More than We Can Tell: Facilitating Collaboration Through Query-Driven Knowledge-Sharing. Portland, Oregon, US: ACM.
Freiburg, R., Büttner, S., Glasze, G., Hagenhoff, S., Meyer-Wegener, K., & Prokosch, H.-U. (2017). D@tenflut: Fünf Vorträge.Erlanger Universitätstage Amberg/Ansbach 2016. Erlangen: FAU University Press.
Wahl, A.M., Endler, G., Schwab, P., Herbst, S., & Lenz, R. (2017). Query-Driven Knowledge-Sharing for Data Integration and Collaborative Data Science. In New Trends in Databases and Information Systems. Nicosia, CY.
Meyer-Wegener, K. (2017). Erstmal einfach alles speichern: Big Data als Aufgabe für die Informatik. In Freiburg, Rudolf (Eds.), D@tenflut : Erlanger Universitätstage Ansbach 2016 (pp. 99-115). Erlangen: FAU University Press.
Schwab, P., Wahl, A.M., Lenz, R., & Meyer-Wegener, K. (2016). Query-driven Data Integration (Short Paper). In Proc. Conf. "Lernen, Wissen, Daten, Analysen" (pp. 206-211). Potsdam, Germany, DE: CEUR-WS.
Kolominsky-Rabas, P., Kriza, C., Djanatliev, A., Meier, F., Uffenorde, S., Radeleff, J.,... Adamson, P. (2016). Health economic impact of a pulmonary artery pressure sensor for heart failure telemonitoring: a dynamic simulation. Telemedicine Journal and E-Health, 22(10), 798-808.

Last updated on 2019-24-04 at 10:16