| 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 | 211 | 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 | 117 | 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 | 100 | 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 | 81 | 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 | 81 | 2012 |
| Efficient in-memory indexing with generalized prefix trees M Boehm, B Schlegel, PB Volk, U Fischer, D Habich, W Lehner Datenbanksysteme für Business, Technologie und Web (BTW), 2011 | 71 | 2011 |
| 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 | 62 | 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 | 49 | 2018 |
| 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 | 48 | 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, 2017 | 45 | 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 | 44 | 2014 |
| Data management in machine learning systems M Boehm, A Kumar, J Yang Synthesis Lectures on Data Management 11 (1), 1-173, 2019 | 43 | 2019 |
| 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 | 36 | 2019 |
| Declarative machine learning-a classification of basic properties and types M Boehm, AV Evfimievski, N Pansare, B Reinwald arXiv preprint arXiv:1605.05826, 2016 | 27 | 2016 |
| 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 | 25 | 2018 |
| 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 | 25 | 2011 |
| 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 | 23 | 2016 |
| Cost-based vectorization of instance-based integration processes M Boehm, D Habich, S Preissler, W Lehner, U Wloka Information Systems 36 (1), 3-29, 2011 | 23 | 2011 |
| Context-aware parameter estimation for forecast models L Dannecker, R Schulze, M Boehm, W Lehner US Patent 9,361,273, 2016 | 22 | 2016 |
| Dipbench toolsuite: A framework for benchmarking integration systems M Bohm, D Habich, W Lehner, U Wloka 2008 IEEE 24th International Conference on Data Engineering, 1596-1599, 2008 | 22 | 2008 |
Wolfgang LehnerTechnische Universität DresdenVerified email at tu-dresden.de
