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 | 117 | 2019 |
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 | 82 | 2019 |
Rhino: Efficient Management of Very Large Distributed State for Stream Processing Engines B Del Monte, S Zeuch, T Rabl, V Markl SIGMOD '20: Proceedings of the 2020 ACM SIGMOD International Conference on …, 2020 | 62 | 2020 |
Nebulastream: Complex analytics beyond the cloud S Zeuch, ET Zacharatou, S Zhang, X Chatziliadis, A Chaudhary, ... Open Journal of Internet Of Things (OJIOT) 6 (1), 66-81, 2020 | 38 | 2020 |
Rethinking Stateful Stream Processing with RDMA B Del Monte, S Zeuch, T Rabl, V Markl SIGMOD'22: Proceedings of the 2022 ACM International Conference on …, 2022 | 12 | 2022 |
On-the-fly Reconfiguration of Query Plans for Stateful Stream Processing Engines A Bartnik, B Del Monte, T Rabl, V Markl Datenbanksysteme für Business, Technologie und Web (BTW 2019), 2018 | 10 | 2018 |
A scalable GPU-enabled framework for training deep neural networks B Del Monte, R Prodan 2016 2nd International Conference on Green High Performance Computing …, 2016 | 8 | 2016 |
Efficient Migration of Very Large Distributed State for Scalable Stream Processing B Del Monte Proceedings of the VLDB 2017 PhD Workshop co-located with the 43rd …, 2017 | 6 | 2017 |
Towards unifying query interpretation and compilation PM Grulich, A Lepping, DPA Nugroho, B Del Monte, V Pandey, S Zeuch, ... CIDR, 2023 | 2 | 2023 |
PROTEUS: Scalable Online Machine Learning for Predictive Analytics and Real-Time Interactive Visualization B Del Monte, J Karimov, A Rezaei Mahdiraji, T Rabl, V Markl | 1 | 2017 |
Hardware-conscious techniques for efficient and reliable stateful stream processing B Del Monte Technische Universität Berlin, 2022 | | 2022 |