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Stefan Hagedorn
Stefan Hagedorn
PhD Student, TU Ilmenau
Verified email at tu-ilmenau.de - Homepage
Title
Cited by
Cited by
Year
The STARK framework for spatio-temporal data analytics on spark
S Hagedorn, P Gotze, KU Sattler
Datenbanksysteme für Business, Technologie und Web (BTW 2017), 2017
852017
Putting pandas in a box
S Hagedorn, S Kläbe, KU Sattler
Conference on Innovative Data Systems Research (CIDR);(Online), 15, 2021
392021
Big Spatial Data Processing Frameworks: Feature and Performance Evaluation.
S Hagedorn, P Götze, KU Sattler
EDBT, 490-493, 2017
332017
Efficient spatio-temporal event processing with STARK
S Hagedorn
Deutsche Nationalbibliothek, 2017
252017
A gray-box modeling methodology for runtime prediction of apache spark jobs
H Al-Sayeh, S Hagedorn, KU Sattler
Distributed and Parallel Databases 38, 819-839, 2020
192020
Complex event processing on linked stream data
O Saleh, S Hagedorn, KU Sattler
Datenbank-Spektrum 15 (2), 119-129, 2015
152015
Piglet: interactive and platform transparent analytics for rdf & dynamic data
S Hagedorn, KU Sattler
Proceedings of the 25th International Conference Companion on World Wide Web …, 2016
132016
Applying machine learning models to scalable dataframes with grizzly
S Kläbe, S Hagedorn
BTW 2021, 2021
122021
When sweet and cute isn't enough anymore: Solving scalability issues in Python Pandas with Grizzly.
S Hagedorn
CIDR, 2020
92020
Accelerating Python UDFs in Vectorized Query Execution
S Kläbe, R DeSantis, S Hagedorn, KU Sattler
12th Conference on Innovative Data Systems Research, CIDR, 2022
82022
Sparqling pig-processing linked data with pig latin
S Hagedorn, K Hose, KU Sattler
Datenbanksysteme für Business, Technologie und Web (BTW 2015), 2015
82015
Resource Planning for SPARQL Query Execution on Data Sharing Platforms.
S Hagedorn, K Hose, KU Sattler, J Umbrich
COLD 1264, 2014
82014
Efficient parallel processing of analytical queries on linked data
S Hagedorn, KU Sattler
On the Move to Meaningful Internet Systems: OTM 2013 Conferences …, 2013
82013
Discovery querying in linked open data
S Hagedorn, KU Sattler
Proceedings of the Joint EDBT/ICDT 2013 Workshops, 38-44, 2013
72013
Cost-based sharing and recycling of (intermediate) results in dataflow programs
S Hagedorn, KU Sattler
European Conference on Advances in Databases and Information Systems, 185-199, 2018
62018
Stream processing platforms for analyzing big dynamic data
S Hagedorn, P Götze, O Saleh, KU Sattler
it-Information Technology 58 (4), 195-205, 2016
62016
LODHub—A platform for sharing and integrated processing of linked open data
S Hagedorn, KU Sattler
2014 IEEE 30th International Conference on Data Engineering Workshops, 260-262, 2014
52014
Exploration of Approaches for In-Database ML
S Kläbe, S Hagedorn, KU Sattler
Proceedings of the 26th International Conference on Extending Database …, 2023
32023
When bears get machine support: Applying machine learning models to scalable dataframes with grizzly
S Kläbe, S Hagedorn
Datenbanksysteme für Business, Technologie und Web (BTW 2021) 13.–17 …, 2021
32021
Conquering a Panda's weaker self-Fighting laziness with laziness.
S Hagedorn, S Kläbe, KU Sattler
EDBT, 670-673, 2021
32021
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