| 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 | 79 | 2016 |
| 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 | 63 | 2014 |
| 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 | 50 | 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 | 49 | 2011 |
| 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 | 48 | 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 | 42 | 2017 |
| 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 | 38 | 2015 |
| 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 | 28 | 2014 |
| 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 | 24 | 2017 |
| Context-aware parameter estimation for forecast models L Dannecker, R Schulze, M Boehm, W Lehner US Patent 9,361,273, 2016 | 21 | 2016 |
| DIPBench Toolsuite: A Framework for Benchmarking Integration Systems. M Böhm, D Habich, W Lehner, U Wloka ICDE, 1596-1599, 2008 | 21 | 2008 |
| Context-aware parameter estimation for forecast models in the energy domain L Dannecker, R Schulze, M Böhm, W Lehner, G Hackenbroich International Conference on Scientific and Statistical Database Management …, 2011 | 20 | 2011 |
| 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 | 18 | 2015 |
| 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 | 17 | 2016 |
| Declarative machine learning-a classification of basic properties and types M Boehm, AV Evfimievski, N Pansare, B Reinwald arXiv preprint arXiv:1605.05826, 2016 | 16 | 2016 |
| Towards integrated data analytics: Time series forecasting in DBMS U Fischer, L Dannecker, L Siksnys, F Rosenthal, M Boehm, W Lehner Datenbank-Spektrum 13 (1), 45-53, 2013 | 16 | 2013 |
| Towards Self-Optimization of Message Transformation Processes. M Böhm, D Habich, U Wloka, J Bittner, W Lehner ADBIS Research Communications, 1-10, 2007 | 15 | 2007 |
| Model-Driven Generation and Optimization of Complex Integration Processes. M Böhm, U Wloka, D Habich, W Lehner ICEIS (1), 131-136, 2008 | 14 | 2008 |
| Forcasting evolving time series of energy demand and supply L Dannecker, M Böhm, W Lehner, G Hackenbroich East European Conference on Advances in Databases and Information Systems …, 2011 | 13 | 2011 |
| 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 | 12 | 2018 |
Wolfgang LehnerTechnische Universität DresdenVerified email at tu-dresden.de
