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Matthias Boehm
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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
2692016
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
1872017
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
1142014
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
952016
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
892012
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
832011
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
812019
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
762015
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
742018
Data management in machine learning systems
M Boehm, A Kumar, J Yang
Springer Nature, 2022
712022
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
632021
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
602015
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
562017
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
502014
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
382022
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
372018
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
362016
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
342021
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
332018
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
332011
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