| Systemml: Declarative machine learning on spark M Boehm, MW Dusenberry, D Eriksson, AV Evfimievski, FM Manshadi, ... Proceedings of the VLDB Endowment 9 (13), 1425-1436, 2016 | 265 | 2016 |
| Data management in machine learning: Challenges, techniques, and systems A Kumar, M Boehm, J Yang Proceedings of the 2017 ACM International Conference on Management of Data …, 2017 | 186 | 2017 |
| Hybrid parallelization strategies for large-scale machine learning in systemml M Boehm, S Tatikonda, B Reinwald, P Sen, Y Tian, DR Burdick, ... Proceedings of the VLDB Endowment 7 (7), 553-564, 2014 | 113 | 2014 |
| Compressed linear algebra for large-scale machine learning A Elgohary, M Boehm, PJ Haas, FR Reiss, B Reinwald Proceedings of the VLDB Endowment 9 (12), 960-971, 2016 | 92 | 2016 |
| Data management in the mirabel smart grid system M Boehm, L Dannecker, A Doms, E Dovgan, B Filipič, U Fischer, ... Proceedings of the 2012 Joint EDBT/ICDT Workshops, 95-102, 2012 | 89 | 2012 |
| Efficient in-memory indexing with generalized prefix trees M Boehm, B Schlegel, PB Volk, U Fischer, D Habich, W Lehner Gesellschaft für Informatik eV, 2011 | 83 | 2011 |
| SystemDS: A declarative machine learning system for the end-to-end data science lifecycle M Boehm, I Antonov, S Baunsgaard, M Dokter, R Ginthör, K Innerebner, ... arXiv preprint arXiv:1909.02976, 2019 | 79 | 2019 |
| Resource elasticity for large-scale machine learning B Huang, M Boehm, Y Tian, B Reinwald, S Tatikonda, FR Reiss Proceedings of the 2015 ACM SIGMOD International Conference on Management of …, 2015 | 76 | 2015 |
| On optimizing operator fusion plans for large-scale machine learning in systemml M Boehm, B Reinwald, D Hutchison, AV Evfimievski, P Sen arXiv preprint arXiv:1801.00829, 2018 | 74 | 2018 |
| Data management in machine learning systems M Boehm, A Kumar, J Yang Morgan & Claypool Publishers, 2019 | 70 | 2019 |
| Sliceline: Fast, linear-algebra-based slice finding for ml model debugging S Sagadeeva, M Boehm Proceedings of the 2021 international conference on management of data, 2290 …, 2021 | 60 | 2021 |
| On optimizing machine learning workloads via kernel fusion A Ashari, S Tatikonda, M Boehm, B Reinwald, K Campbell, J Keenleyside, ... ACM SIGPLAN Notices 50 (8), 173-182, 2015 | 60 | 2015 |
| SPOOF: Sum-Product Optimization and Operator Fusion for Large-Scale Machine Learning. T Elgamal, S Luo, M Boehm, AV Evfimievski, S Tatikonda, B Reinwald, ... CIDR 2 (6), 25, 2017 | 56 | 2017 |
| SystemML's Optimizer: Plan Generation for Large-Scale Machine Learning Programs. M Boehm, DR Burdick, AV Evfimievski, B Reinwald, FR Reiss, P Sen, ... IEEE Data Eng. Bull. 37 (3), 52-62, 2014 | 49 | 2014 |
| Pipelined approach to fused kernels for optimization of machine learning workloads on graphical processing units A Ashari, M Boehm, KW Campbell, A Evfimievski, JD Keenleyside, ... US Patent 9,972,063, 2018 | 37 | 2018 |
| Daphne: An open and extensible system infrastructure for integrated data analysis pipelines P Damme, M Birkenbach, C Bitsakos, M Boehm, P Bonnet, F Ciorba, ... Conference on Innovative Data Systems Research, 2022 | 36 | 2022 |
| Hybrid parallelization strategies for machine learning programs on top of MapReduce M Boehm, D Burdick, B Reinwald, P Sen, S Tatikonda, Y Tian, ... US Patent 9,286,044, 2016 | 35 | 2016 |
| Lima: Fine-grained lineage tracing and reuse in machine learning systems A Phani, B Rath, M Boehm Proceedings of the 2021 International Conference on Management of Data, 1426 …, 2021 | 33 | 2021 |
| Context-aware parameter estimation for forecast models in the energy domain L Dannecker, R Schulze, M Böhm, W Lehner, G Hackenbroich Scientific and Statistical Database Management: 23rd International …, 2011 | 33 | 2011 |
| Compressed linear algebra for large-scale machine learning A Elgohary, M Boehm, PJ Haas, FR Reiss, B Reinwald The VLDB Journal 27 (5), 719-744, 2018 | 32 | 2018 |
Wolfgang LehnerTechnische Universität DresdenVerified email at tu-dresden.de
