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
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
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
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
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
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
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
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
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
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
Context-aware parameter estimation for forecast models
L Dannecker, R Schulze, M Boehm, W Lehner
US Patent 9,361,273, 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
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
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
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
Data management in machine learning systems
M Boehm, A Kumar, J Yang
Synthesis Lectures on Data Management 11 (1), 1-173, 2019
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
Model-Driven Generation and Optimization of Complex Integration Processes.
M Böhm, U Wloka, D Habich, W Lehner
ICEIS (1), 131-136, 2008
Towards Self-Optimization of Message Transformation Processes.
M Böhm, D Habich, U Wloka, J Bittner, W Lehner
ADBIS Research Communications, 1-10, 2007
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
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