| 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 | 81 | 2017 |
| Putting pandas in a box S Hagedorn, S Kläbe, KU Sattler Conference on Innovative Data Systems Research (CIDR);(Online), 15, 2021 | 36 | 2021 |
| Big Spatial Data Processing Frameworks: Feature and Performance Evaluation. S Hagedorn, P Götze, KU Sattler EDBT, 490-493, 2017 | 33 | 2017 |
| Efficient spatio-temporal event processing with STARK S Hagedorn Deutsche Nationalbibliothek, 2017 | 25 | 2017 |
| 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 | 18 | 2020 |
| Complex event processing on linked stream data O Saleh, S Hagedorn, KU Sattler Datenbank-Spektrum 15 (2), 119-129, 2015 | 15 | 2015 |
| 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 | 13 | 2016 |
| Applying machine learning models to scalable dataframes with grizzly S Kläbe, S Hagedorn BTW 2021, 2021 | 10 | 2021 |
| When sweet and cute isn't enough anymore: Solving scalability issues in Python Pandas with Grizzly. S Hagedorn CIDR, 2020 | 8 | 2020 |
| Sparqling pig-processing linked data with pig latin S Hagedorn, K Hose, KU Sattler Datenbanksysteme für Business, Technologie und Web (BTW 2015), 2015 | 8 | 2015 |
| Resource Planning for SPARQL Query Execution on Data Sharing Platforms. S Hagedorn, K Hose, KU Sattler, J Umbrich COLD 1264, 2014 | 8 | 2014 |
| Efficient parallel processing of analytical queries on linked data S Hagedorn, KU Sattler On the Move to Meaningful Internet Systems: OTM 2013 Conferences …, 2013 | 8 | 2013 |
| Discovery querying in linked open data S Hagedorn, KU Sattler Proceedings of the Joint EDBT/ICDT 2013 Workshops, 38-44, 2013 | 7 | 2013 |
| Accelerating Python UDFs in Vectorized Query Execution S Kläbe, R DeSantis, S Hagedorn, KU Sattler 12th Conference on Innovative Data Systems Research, CIDR, 2022 | 6 | 2022 |
| Stream processing platforms for analyzing big dynamic data S Hagedorn, P Götze, O Saleh, KU Sattler it-Information Technology 58 (4), 195-205, 2016 | 6 | 2016 |
| 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 | 5 | 2018 |
| 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 | 4 | 2014 |
| 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 | 3 | 2021 |
| Conquering a Panda's weaker self-Fighting laziness with laziness. S Hagedorn, S Kläbe, KU Sattler EDBT, 670-673, 2021 | 3 | 2021 |
| Processing large raster and vector data in apache spark S Hagedorn, O Birli, KU Sattler Gesellschaft für Informatik, Bonn, 2019 | 3 | 2019 |
