Maximilian Langohr
Maximilian Langohr, M. Sc.
Department of Computer Science
Chair of Computer Science 6 (Data Management)
Martensstr. 3
91058 Erlangen
Germany
- Phone number: +49 9131 85-27800
- Email: maximilian.langohr@fau.de
- Website: https://www.cs6.tf.fau.de/person/maximilian-langohr/
Maximilian Langohr is a former member of the research staff (August 2024-July 2024). Since August 2024, he is an external Ph.D. candidate.
Research Projects
ReProVide
This project is funded by the German Research Foundation (DFG) within the Priority Program SPP 2037 “Scalable Data Management for Future Hardware”.
The goal of this project is to provide novel hardware and optimisation techniques for scalable, high-performance processing of Big Data. We particularly target huge data sets with flexible schemata (row-oriented, column-oriented, document-oriented, irregular, and/or non-indexed) as well as data streams as found in click-stream analysis, enterprise sources like emails, software logs and discussion-forum archives, as well as produced by sensors in IoT and Industrie 4.0. In this realm, the project investigates the potential of hardware-reconfigurable, FPGA-based SoCs for near-data processing where computations are pushed towards such heterogeneous data sources. Based on FPGA technology and in particular on their dynamic reconfiguration, we propose a generic architecture called ReProVide for low-cost processing of database queries.
The concepts are intended to enable the integration of FPGA-based acceleration support into available SQL, NoSQL, and in-memory database management systems (DBMSs) as well as stream-processing frameworks. Our intention is to attach volatile and non-volatile storage directly to ReProVide nodes, which will not only contain cleansed and integrated data sets, but can also be used for temporarily or persistently storing uncleaned data from new data sources and data streams.
Publications
2023
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SKYSHARK: A Benchmark with Real-world Data for Line-rate Stream Processing with FPGAs
Lernen, Wissen, Daten, Analysen (LWDA) (Marburg, 09/10/2023 - 11/10/2023)
In: Leyer M, Wichmann J (ed.): Lernen, Wissen, Daten, Analysen (LWDA) Conference Proceedings, Marburg, Germany, October 9-11, 2023 2023
Open Access: https://ceur-ws.org/Vol-3630/LWDA2023-paper9.pdf
URL: https://ceur-ws.org/Vol-3630/LWDA2023-paper9.pdf
BibTeX: Download
2021
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Performance Evaluation of Policy-Based SQL Query Classification for Data-Privacy Compliance
In: Datenbank-Spektrum (2021)
ISSN: 1618-2162
DOI: 10.1007/s13222-021-00385-9
URL: https://link.springer.com/article/10.1007/s13222-021-00385-9
BibTeX: Download
2020
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Konzeption und prototypische Implementierung einer regelbasierten Anfrageklassifizierung zur Einhaltung des Datenschutzes (Master thesis, 2020)
BibTeX: Download - , , :
A Framework for DSL-Based Query Classification Using Relational and Graph-Based Data Models
3rd Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA) (Portland, OR, 14/06/2020 - 14/06/2020)
In: ACM (ed.): GRADES-NDA'20: Proceedings of the 3rd Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA) 2020
DOI: 10.1145/3398682.3399167
URL: https://dl.acm.org/doi/abs/10.1145/3398682.3399167
BibTeX: Download - , , :
We Know What You Did Last Session: Policy-Based Query Classification for Data-Privacy Compliance With the DataEconomist
SSDBM 2020: 32nd International Conference on Scientific and Statistical Database Management (Vienna, Virtual Conference, 07/07/2020 - 09/07/2020)
DOI: 10.1145/3400903.3401692
BibTeX: Download
2019
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Query-Driven Enforcement of Rule-Based Policies for Data-Privacy Compliance
LWDA 2019 Lernen, Wissen, Daten, Analysen (Berlin, Germany, 30/09/2019 - 02/10/2019)
In: Proc. Conf. "Lernen, Wissen, Daten, Analysen" 2019
Open Access: http://ceur-ws.org/Vol-2454/paper_56.pdf
URL: http://ceur-ws.org/Vol-2454/paper_56.pdf
BibTeX: Download