| GPU-accelerated Database Systems: Survey and Open Challenges S Breß, M Heimel, N Siegmund, L Bellatreche, G Saake Transactions on Large-Scale Data and Knowledge-Centered Systems (TLDKS), 2014 | 121 | 2014 |
| Analyzing efficient stream processing on modern hardware S Zeuch, BD Monte, J Karimov, C Lutz, M Renz, J Traub, S Breß, T Rabl, ... Proceedings of the VLDB Endowment 12 (5), 516-530, 2019 | 102 | 2019 |
| Why it is time for a HyPE: A hybrid query processing engine for efficient GPU coprocessing in DBMS S Breß, G Saake Proceedings of the VLDB Endowment 6 (12), 1398-1403, 2013 | 96 | 2013 |
| Efficient Co-Processor Utilization in Database Query Processing S Breß, F Beier, H Rauhe, KU Sattler, E Schallehn, G Saake Information Systems, 2013 | 82 | 2013 |
| Robust query processing in co-processor-accelerated databases S Breß, H Funke, J Teubner Proceedings of the 2016 International Conference on Management of Data, 1891 …, 2016 | 79 | 2016 |
| Pipelined query processing in coprocessor environments H Funke, S Breß, S Noll, V Markl, J Teubner Proceedings of the 2018 International Conference on Management of Data, 1603 …, 2018 | 78 | 2018 |
| The design and implementation of CoGaDB: A column-oriented GPU-accelerated DBMS S Breß Datenbank-Spektrum 14, 199-209, 2014 | 78 | 2014 |
| The nebulastream platform: Data and application management for the internet of things S Zeuch, A Chaudhary, B Del Monte, H Gavriilidis, D Giouroukis, ... arXiv preprint arXiv:1910.07867, 2019 | 67 | 2019 |
| Efficient Window Aggregation with General Stream Slicing. J Traub, PM Grulich, AR Cuéllar, S Breß, A Katsifodimos, T Rabl, V Markl EDBT 19, 97-108, 2019 | 66 | 2019 |
| Estimating join selectivities using bandwidth-optimized kernel density models M Kiefer, M Heimel, S Breß, V Markl Proceedings of the VLDB Endowment 10 (13), 2085-2096, 2017 | 65 | 2017 |
| Pump up the volume: Processing large data on GPUs with fast interconnects C Lutz, S Breß, S Zeuch, T Rabl, V Markl Proceedings of the 2020 ACM SIGMOD International Conference on Management of …, 2020 | 64 | 2020 |
| Generating custom code for efficient query execution on heterogeneous processors S Breß, B Köcher, H Funke, S Zeuch, T Rabl, V Markl The VLDB Journal 27, 797-822, 2018 | 64 | 2018 |
| Scotty: Efficient window aggregation for out-of-order stream processing J Traub, PM Grulich, AR Cuellar, S Breß, A Katsifodimos, T Rabl, V Markl 2018 IEEE 34th International Conference on Data Engineering (ICDE), 1300-1303, 2018 | 52 | 2018 |
| An intermediate representation for optimizing machine learning pipelines A Kunft, A Katsifodimos, S Schelter, S Breß, T Rabl, V Markl Proceedings of the VLDB Endowment 12 (11), 1553-1567, 2019 | 51 | 2019 |
| A Framework for Cost based Optimization of Hybrid CPU/GPU Query Plans in Database Systems S Breß, I Geist, E Schallehn, M Mory, G Saake Control and Cybernetics 41 (4), 715–742, 2012 | 48* | 2012 |
| Toward Hardware-Sensitive Database Operations. D Broneske, S Breß, M Heimel, G Saake EDBT, 229-234, 2014 | 45 | 2014 |
| Ocelot/hype: Optimized data processing on heterogeneous hardware S Breß, B Köcher, M Heimel, V Markl, M Saecker, G Saake Proceedings of the VLDB Endowment 7 (13), 1609-1612, 2014 | 43 | 2014 |
| Optimized on-demand data streaming from sensor nodes J Traub, S Breß, T Rabl, A Katsifodimos, V Markl Proceedings of the 2017 Symposium on Cloud Computing, 586-597, 2017 | 41 | 2017 |
| Grizzly: Efficient stream processing through adaptive query compilation PM Grulich, B Sebastian, S Zeuch, J Traub, J Bleichert, Z Chen, T Rabl, ... Proceedings of the 2020 ACM SIGMOD International Conference on Management of …, 2020 | 38 | 2020 |
| Automatic Selection of Processing Units for Coprocessing in Databases S Breß, F Beier, H Rauhe, E Schallehn, KU Sattler, G Saake Advances in Databases and Information Systems, 57-70, 2012 | 38 | 2012 |
Volker MarklTechnische Universität BerlinVerified email at tu-berlin.de
