Matthias Boehm

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 35

Top Topics

Machine Learning

Top Venues

Proc. VLDB Endow.

SIGMOD Conference

Proc. ACM Manag. Data

Publications

Matthias Boehm, Nesime Tatbul
Special issue on "Machine learning and databases". VLDB J. 33 (4) (2024)
Jiwon Chang, Christina Dionysio, Fatemeh Nargesian, Matthias Boehm
PLUTUS: Understanding Data Distribution Tailoring for Machine Learning. SIGMOD Conference Companion (2024)
David Justen, Daniel Ritter, Campbell Fraser, Andrew Lamb, Nga Tran, Allison Lee, Thomas Bodner, Mhd Yamen Haddad, Steffen Zeuch, Volker Markl, Matthias Boehm
POLAR: Adaptive and Non-invasive Join Order Selection via Plans of Least Resistance. Proc. VLDB Endow. 17 (6) (2024)
Matthias Boehm, Matteo Interlandi, Chris Jermaine
Optimizing Tensor Computations: From Applications to Compilation and Runtime Techniques. SIGMOD Conference Companion (2023)
Georgia Koutrika, Jun Yang, Manos Athanassoulis, Kostas Stefanidis, Ju Fan, Abdul Quamar, Yuanyan Tian, Alekh Jindal, Carsten Binnig, Jennie Rogers, Senjuti Basu Roy, Steven Euijong Whang, Matthias Boehm, Aaron J. Elmore, Vasilis Efthymiou, Xiao Hu, Xiaofang Zhou, Alan D. Fekete
Front Matter. Proc. VLDB Endow. 16 (12) (2023)
Matthias Boehm, Madelon Hulsebos, Shreya Shankar, Paroma Varma
Seventh Workshop on Data Management for End-to-End Machine Learning (DEEM). SIGMOD Conference Companion (2023)
Saeed Fathollahzadeh, Matthias Boehm
GIO: Generating Efficient Matrix and Frame Readers for Custom Data Formats by Example. Proc. ACM Manag. Data 1 (2) (2023)
Patrick Damme, Matthias Boehm
Enabling Integrated Data Analysis Pipelines on Heterogeneous Hardware through Holistic Extensibility. BTW (2023)
Shafaq Siddiqi, Roman Kern, Matthias Boehm
SAGA: A Scalable Framework for Optimizing Data Cleaning Pipelines for Machine Learning Applications. Proc. ACM Manag. Data 1 (3) (2023)
Sebastian Baunsgaard, Matthias Boehm
AWARE: Workload-aware, Redundancy-exploiting Linear Algebra. Proc. ACM Manag. Data 1 (1) (2023)
Aristotelis Vontzalidis, Stratos Psomadakis, Constantinos Bitsakos, Mark Dokter, Kevin Innerebner, Patrick Damme, Matthias Boehm, Florina M. Ciorba, Ahmed Eleliemy, Vasileios Karakostas, Ales Zamuda, Dimitrios Tsoumakos
DAPHNE Runtime: Harnessing Parallelism for Integrated Data Analysis Pipelines. Euro-Par Workshops (2023)
Matthias Boehm, Paroma Varma, Doris Xin
DEEM'22: Data Management for End-to-End Machine Learning. SIGMOD Conference (2022)
Patrick Damme, Marius Birkenbach, Constantinos Bitsakos, Matthias Boehm, Philippe Bonnet, Florina M. Ciorba, Mark Dokter, Pawel Dowgiallo, Ahmed Eleliemy, Christian Faerber, Georgios I. Goumas, Dirk Habich, Niclas Hedam, Marlies Hofer, Wenjun Huang, Kevin Innerebner, Vasileios Karakostas, Roman Kern, Tomaz Kosar, Alexander Krause, Daniel Krems, Andreas Laber, Wolfgang Lehner, Eric Mier, Marcus Paradies, Bernhard Peischl, Gabrielle Poerwawinata, Stratos Psomadakis, Tilmann Rabl, Piotr Ratuszniak, Pedro Silva, Nikolai Skuppin, Andreas Starzacher, Benjamin Steinwender, Ilin Tolovski, Pinar Tözün, Wojciech Ulatowski, Yuanyuan Wang, Izajasz P. Wrosz, Ales Zamuda, Ce Zhang, Xiaoxiang Zhu
DAPHNE: An Open and Extensible System Infrastructure for Integrated Data Analysis Pipelines. CIDR (2022)
Arnab Phani, Lukas Erlbacher, Matthias Boehm
UPLIFT: Parallelization Strategies for Feature Transformations in Machine Learning Workloads. Proc. VLDB Endow. 15 (11) (2022)
Sebastian Baunsgaard, Matthias Boehm, Kevin Innerebner, Mito Kehayov, Florian Lackner, Olga Ovcharenko, Arnab Phani, Tobias Rieger, David Weissteiner, Sebastian Benjamin Wrede
Federated Data Preparation, Learning, and Debugging in Apache SystemDS. CIKM (2022)
Svetlana Sagadeeva, Matthias Boehm
SliceLine: Fast, Linear-Algebra-based Slice Finding for ML Model Debugging. SIGMOD Conference (2021)
Arnab Phani, Benjamin Rath, Matthias Boehm
LIMA: Fine-grained Lineage Tracing and Reuse in Machine Learning Systems. SIGMOD Conference (2021)
Sebastian Baunsgaard, Matthias Boehm, Ankit Chaudhary, Behrouz Derakhshan, Stefan Geißelsöder, Philipp M. Grulich, Michael Hildebrand, Kevin Innerebner, Volker Markl, Claus Neubauer, Sarah Osterburg, Olga Ovcharenko, Sergey Redyuk, Tobias Rieger, Alireza Rezaei Mahdiraji, Sebastian Benjamin Wrede, Steffen Zeuch
ExDRa: Exploratory Data Science on Federated Raw Data. SIGMOD Conference (2021)
Matthias Boehm, Iulian Antonov, Sebastian Baunsgaard, Mark Dokter, Robert Ginthör, Kevin Innerebner, Florijan Klezin, Stefanie N. Lindstaedt, Arnab Phani, Benjamin Rath, Berthold Reinwald, Shafaq Siddiqui, Sebastian Benjamin Wrede
SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle. CIDR (2020)
Prithviraj Sen, Marina Danilevsky, Yunyao Li, Siddhartha Brahma, Matthias Boehm, Laura Chiticariu, Rajasekar Krishnamurthy
Learning Explainable Linguistic Expressions with Neural Inductive Logic Programming for Sentence Classification. EMNLP (1) (2020)
Matthias Boehm
Technical Perspective: Declarative Recursive Computation on an RDBMS. SIGMOD Rec. 49 (1) (2020)
Johanna Sommer, Matthias Boehm, Alexandre V. Evfimievski, Berthold Reinwald, Peter J. Haas
MNC: Structure-Exploiting Sparsity Estimation for Matrix Expressions. SIGMOD Conference (2019)
Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald
Compressed linear algebra for declarative large-scale machine learning. Commun. ACM 62 (5) (2019)
Matthias Boehm, Iulian Antonov, Mark Dokter, Robert Ginthör, Kevin Innerebner, Florijan Klezin, Stefanie N. Lindstaedt, Arnab Phani, Benjamin Rath
SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle. CoRR (2019)
Matthias Boehm, Alexandre V. Evfimievski, Berthold Reinwald
Efficient Data-Parallel Cumulative Aggregates for Large-Scale Machine Learning. BTW (2019)
Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald
Compressed linear algebra for large-scale machine learning. VLDB J. 27 (5) (2018)
Niketan Pansare, Michael Dusenberry, Nakul Jindal, Matthias Boehm, Berthold Reinwald, Prithviraj Sen
Deep Learning with Apache SystemML. CoRR (2018)
Matthias Boehm, Berthold Reinwald, Dylan Hutchison, Alexandre V. Evfimievski, Prithviraj Sen
On Optimizing Operator Fusion Plans for Large-Scale Machine Learning in SystemML. CoRR (2018)
Matthias Boehm, Berthold Reinwald, Dylan Hutchison, Prithviraj Sen, Alexandre V. Evfimievski, Niketan Pansare
On Optimizing Operator Fusion Plans for Large-Scale Machine Learning in SystemML. Proc. VLDB Endow. 11 (12) (2018)
Tarek Elgamal, Shangyu Luo, Matthias Boehm, Alexandre V. Evfimievski, Shirish Tatikonda, Berthold Reinwald, Prithviraj Sen
SPOOF: Sum-Product Optimization and Operator Fusion for Large-Scale Machine Learning. CIDR (2017)
Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald
Scaling Machine Learning via Compressed Linear Algebra. SIGMOD Rec. 46 (1) (2017)
Arun Kumar, Matthias Boehm, Jun Yang
Data Management in Machine Learning: Challenges, Techniques, and Systems. SIGMOD Conference (2017)
Matthias Boehm, Michael Dusenberry, Deron Eriksson, Alexandre V. Evfimievski, Faraz Makari Manshadi, Niketan Pansare, Berthold Reinwald, Frederick Reiss, Prithviraj Sen, Arvind Surve, Shirish Tatikonda
SystemML: Declarative Machine Learning on Spark. Proc. VLDB Endow. 9 (13) (2016)
Matthias Boehm, Alexandre V. Evfimievski, Niketan Pansare, Berthold Reinwald
Declarative Machine Learning - A Classification of Basic Properties and Types. CoRR (2016)
Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald
Compressed Linear Algebra for Large-Scale Machine Learning. Proc. VLDB Endow. 9 (12) (2016)
Matthias Boehm
Costing Generated Runtime Execution Plans for Large-Scale Machine Learning Programs. CoRR (2015)
Botong Huang, Matthias Boehm, Yuanyuan Tian, Berthold Reinwald, Shirish Tatikonda, Frederick R. Reiss
Resource Elasticity for Large-Scale Machine Learning. SIGMOD Conference (2015)
Arash Ashari, Shirish Tatikonda, Matthias Boehm, Berthold Reinwald, Keith Campbell, John Keenleyside, P. Sadayappan
On optimizing machine learning workloads via kernel fusion. PPOPP (2015)
Matthias Boehm, Shirish Tatikonda, Berthold Reinwald, Prithviraj Sen, Yuanyuan Tian, Douglas Burdick, Shivakumar Vaithyanathan
Hybrid Parallelization Strategies for Large-Scale Machine Learning in SystemML. Proc. VLDB Endow. 7 (7) (2014)
Matthias Boehm, Douglas Burdick, Alexandre V. Evfimievski, Berthold Reinwald, Prithviraj Sen, Shirish Tatikonda, Yuanyuan Tian
Compiling machine learning algorithms with SystemML. SoCC (2013)