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ORCIDMatthias Boehm 0001
Matthias Böhm 0001
Person information
- affiliation: TU Berlin, Germany
- affiliation (former): Graz University of Technology, Austria
- affiliation (former): IBM Research, Almaden
- affiliation (former): Dresden University of Technology
Other persons with the same name
- Matthias Boehm 0002 — University of Osnabrück, Germany
- Matthias Böhm 0002 — University of Regensburg, Germany
- Matthias Böhm 0003 — University of Rostock
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2020 – today
- 2024
[j20]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): 1350-1363 (2024)- 2023
- [j19]Sebastian Baunsgaard
, Matthias Boehm:
AWARE: Workload-aware, Redundancy-exploiting Linear Algebra. Proc. ACM Manag. Data 1(1): 2:1-2:28 (2023) - [j18]Saeed Fathollahzadeh, Matthias Boehm:
GIO: Generating Efficient Matrix and Frame Readers for Custom Data Formats by Example. Proc. ACM Manag. Data 1(2): 120:1-120:26 (2023) - [j17]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): 218:1-218:26 (2023) - [j16]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) - [c48]Manisha Luthra, Andreas Kipf, Matthias Böhm:
A Tutorial Workshop on ML for Systems and Systems for ML. BTW 2023: 707-708 - [c47]Patrick Damme, Matthias Boehm:
Enabling Integrated Data Analysis Pipelines on Heterogeneous Hardware through Holistic Extensibility. BTW 2023: 815-818 - [c46]Matthias Boehm, Matteo Interlandi, Chris Jermaine:
Optimizing Tensor Computations: From Applications to Compilation and Runtime Techniques. SIGMOD Conference Companion 2023: 53-59 - [c45]Matthias Boehm, Madelon Hulsebos, Shreya Shankar, Paroma Varma:
Seventh Workshop on Data Management for End-to-End Machine Learning (DEEM). SIGMOD Conference Companion 2023: 305-306 - 2022
- [j15]Arnab Phani, Lukas Erlbacher, Matthias Boehm:
UPLIFT: Parallelization Strategies for Feature Transformations in Machine Learning Workloads. Proc. VLDB Endow. 15(11): 2929-2938 (2022) - [c44]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 - [c43]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: 4813-4817 - [c42]Matthias Boehm, Paroma Varma, Doris Xin:
DEEM'22: Data Management for End-to-End Machine Learning. SIGMOD Conference 2022: 2548-2549 - [e2]Matthias Boehm, Paroma Varma, Doris Xin:
DEEM '22: Proceedings of the Sixth Workshop on Data Management for End-To-End Machine Learning Philadelphia, PA, USA, 12 June 2022. ACM 2022, ISBN 978-1-4503-9375-1 [contents] - 2021
- [c41]Arnab Phani, Benjamin Rath, Matthias Boehm:
LIMA: Fine-grained Lineage Tracing and Reuse in Machine Learning Systems. SIGMOD Conference 2021: 1426-1439 - [c40]Svetlana Sagadeeva, Matthias Boehm:
SliceLine: Fast, Linear-Algebra-based Slice Finding for ML Model Debugging. SIGMOD Conference 2021: 2290-2299 - [c39]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: 2450-2463 - [e1]Matthias Boehm, Julia Stoyanovich, Steven Whang:
Proceedings of the Fifth Workshop on Data Management for End-To-End Machine Learning, In conjunction with the 2021 ACM SIGMOD/PODS Conference, DEEM@SIGMOD 2021, Virtual Event, China, 20 June, 2021. ACM 2021, ISBN 978-1-4503-8486-5 [contents] - 2020
- [j14]Matthias Boehm:
Technical Perspective: Declarative Recursive Computation on an RDBMS. SIGMOD Rec. 49(1): 42 (2020) - [c38]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 - [c37]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: 4211-4221
2010 – 2019
- 2019
- [b2]Matthias Boehm, Arun Kumar, Jun Yang:
Data Management in Machine Learning Systems. Synthesis Lectures on Data Management, Morgan & Claypool Publishers 2019, ISBN 978-3-031-00741-5 - [j13]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): 83-91 (2019) - [c36]Matthias Boehm, Alexandre V. Evfimievski, Berthold Reinwald:
Efficient Data-Parallel Cumulative Aggregates for Large-Scale Machine Learning. BTW 2019: 267-286 - [c35]Johanna Sommer, Matthias Boehm, Alexandre V. Evfimievski, Berthold Reinwald, Peter J. Haas:
MNC: Structure-Exploiting Sparsity Estimation for Matrix Expressions. SIGMOD Conference 2019: 1607-1623 - [r1]Matthias Boehm:
Apache SystemML. Encyclopedia of Big Data Technologies 2019 - [i5]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 abs/1909.02976 (2019) - 2018
- [j12]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): 1755-1768 (2018) - [j11]Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald:
Compressed linear algebra for large-scale machine learning. VLDB J. 27(5): 719-744 (2018) - [i4]Matthias Boehm, Berthold Reinwald, Dylan Hutchison, Alexandre V. Evfimievski, Prithviraj Sen:
On Optimizing Operator Fusion Plans for Large-Scale Machine Learning in SystemML. CoRR abs/1801.00829 (2018) - [i3]Niketan Pansare, Michael Dusenberry, Nakul Jindal, Matthias Boehm, Berthold Reinwald, Prithviraj Sen:
Deep Learning with Apache SystemML. CoRR abs/1802.04647 (2018) - 2017
- [j10]Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald:
Scaling Machine Learning via Compressed Linear Algebra. SIGMOD Rec. 46(1): 42-49 (2017) - [c34]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 - [c33]Arun Kumar, Matthias Boehm, Jun Yang:
Data Management in Machine Learning: Challenges, Techniques, and Systems. SIGMOD Conference 2017: 1717-1722 - 2016
- [j9]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): 960-971 (2016) - [j8]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): 1425-1436 (2016) - [i2]Matthias Boehm, Alexandre V. Evfimievski, Niketan Pansare, Berthold Reinwald:
Declarative Machine Learning - A Classification of Basic Properties and Types. CoRR abs/1605.05826 (2016) - 2015
- [c32]Arash Ashari, Shirish Tatikonda, Matthias Boehm, Berthold Reinwald, Keith Campbell, John Keenleyside, P. Sadayappan:
On optimizing machine learning workloads via kernel fusion. PPoPP 2015: 173-182 - [c31]Botong Huang, Matthias Boehm, Yuanyuan Tian, Berthold Reinwald, Shirish Tatikonda, Frederick R. Reiss:
Resource Elasticity for Large-Scale Machine Learning. SIGMOD Conference 2015: 137-152 - [i1]Matthias Boehm:
Costing Generated Runtime Execution Plans for Large-Scale Machine Learning Programs. CoRR abs/1503.06384 (2015) - 2014
- [j7]Matthias Böhm, Douglas R. Burdick, Alexandre V. Evfimievski, Berthold Reinwald, Frederick R. Reiss, Prithviraj Sen, Shirish Tatikonda, Yuanyuan Tian:
SystemML's Optimizer: Plan Generation for Large-Scale Machine Learning Programs. IEEE Data Eng. Bull. 37(3): 52-62 (2014) - [j6]Matthias Böhm, Dirk Habich, Wolfgang Lehner:
On-demand re-optimization of integration flows. Inf. Syst. 45: 1-17 (2014) - [j5]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): 553-564 (2014) - [c30]Peter D. Kirchner, Matthias Böhm, Berthold Reinwald, Daby M. Sow, J. Michael Schmidt, Deepak S. Turaga, Alain Biem:
Large Scale Discriminative Metric Learning. IPDPS Workshops 2014: 1656-1663 - 2013
- [j4]Ulrike Fischer, Lars Dannecker, Laurynas Siksnys, Frank Rosenthal, Matthias Böhm, Wolfgang Lehner:
Towards Integrated Data Analytics: Time Series Forecasting in DBMS. Datenbank-Spektrum 13(1): 45-53 (2013) - [c29]Matthias Boehm, Douglas Burdick, Alexandre V. Evfimievski, Berthold Reinwald, Prithviraj Sen, Shirish Tatikonda, Yuanyuan Tian:
Compiling machine learning algorithms with SystemML. SoCC 2013: 57:1 - 2012
- [j3]Thomas Kissinger, Benjamin Schlegel, Matthias Böhm, Dirk Habich, Wolfgang Lehner:
A high-throughput in-memory index, durable on flash-based SSD: insights into the winning solution of the SIGMOD programming contest 2011. SIGMOD Rec. 41(3): 44-50 (2012) - [c28]Lars Dannecker, Elena Vasilyeva, Matthias Böhm, Wolfgang Lehner, Gregor Hackenbroich:
Efficient Integration of External Information into Forecast Models from the Energy Domain. ADBIS 2012: 139-152 - [c27]Matthias Böhm, Lars Dannecker, Andreas Doms, Erik Dovgan, Bogdan Filipic, Ulrike Fischer, Wolfgang Lehner, Torben Bach Pedersen, Yoann Pitarch, Laurynas Siksnys, Tea Tusar:
Data management in the MIRABEL smart grid system. EDBT/ICDT Workshops 2012: 95-102 - [c26]Lars Dannecker, Matthias Böhm, Wolfgang Lehner, Gregor Hackenbroich:
Partitioning and Multi-core Parallelization of Multi-equation Forecast Models. SSDBM 2012: 106-123 - [c25]Ulrike Fischer, Matthias Böhm, Wolfgang Lehner, Torben Bach Pedersen:
Optimizing Notifications of Subscription-Based Forecast Queries. SSDBM 2012: 449-466 - 2011
- [b1]Matthias Böhm:
Cost-based optimization of integration flows. Dresden University of Technology, 2011, pp. 1-227 - [j2]Matthias Böhm, Dirk Habich, Steffen Preissler, Wolfgang Lehner, Uwe Wloka:
Cost-based vectorization of instance-based integration processes. Inf. Syst. 36(1): 3-29 (2011) - [j1]Matthias Böhm, Wolfgang Lehner, Christof Fetzer:
Resiliency-Aware Data Management. Proc. VLDB Endow. 4(12): 1462-1465 (2011) - [c24]Lars Dannecker, Matthias Böhm, Wolfgang Lehner, Gregor Hackenbroich:
Forcasting Evolving Time Series of Energy Demand and Supply. ADBIS 2011: 302-315 - [c23]Ulrike Fischer, Matthias Böhm, Wolfgang Lehner:
Offline Design Tuning for Hierarchies of Forecast Models. BTW 2011: 167-186 - [c22]Matthias Böhm, Benjamin Schlegel, Peter Benjamin Volk, Ulrike Fischer, Dirk Habich, Wolfgang Lehner:
Efficient In-Memory Indexing with Generalized Prefix Trees. BTW 2011: 227-246 - [c21]Dirk Habich, Matthias Böhm, Maik Thiele, Benjamin Schlegel, Ulrike Fischer, Hannes Voigt, Wolfgang Lehner:
Next Generation Database Programming and Execution Environment. DBPL 2011 - [c20]Lars Dannecker, Robert Schulze, Matthias Böhm, Wolfgang Lehner, Gregor Hackenbroich:
Context-Aware Parameter Estimation for Forecast Models in the Energy Domain. SSDBM 2011: 491-508 - 2010
- [c19]Matthias Böhm, Dirk Habich, Wolfgang Lehner:
Multi-process Optimization Via Horizontal Message Queue Partitioning. ICEIS (1) 2010: 5-14 - [c18]Matthias Böhm, Dirk Habich, Wolfgang Lehner:
Multi-flow Optimization via Horizontal Message Queue Partitioning. ICEIS 2010: 31-47 - [c17]Ulrike Fischer, Frank Rosenthal, Matthias Böhm, Wolfgang Lehner:
Indexing forecast models for matching and maintenance. IDEAS 2010: 26-31
2000 – 2009
- 2009
- [c16]Matthias Böhm, Dirk Habich, Steffen Preissler, Wolfgang Lehner, Uwe Wloka:
Cost-Based Vectorization of Instance-Based Integration Processes. ADBIS 2009: 253-269 - [c15]Matthias Böhm, Dirk Habich, Wolfgang Lehner, Uwe Wloka:
Systemübergreifende Kostennormalisierung für Integrationsprozesse. BTW 2009: 67-86 - [c14]Matthias Böhm, Uwe Wloka, Dirk Habich, Wolfgang Lehner:
GCIP: exploiting the generation and optimization of integration processes. EDBT 2009: 1128-1131 - [c13]Hannes Voigt, Steffen Preißler, Matthias Böhm, Wolfgang Lehner:
Anfragegetriebene Indizierung räumlicher Daten. GI Jahrestagung 2009: 2056-2070 - [c12]Matthias Böhm, Dirk Habich, Steffen Preissler, Wolfgang Lehner, Uwe Wloka:
Vectorizing Instance-Based Integration Processes. ICEIS 2009: 40-52 - [c11]Matthias Böhm, Dirk Habich, Wolfgang Lehner, Uwe Wloka:
Invisible Deployment of Integration Processes. ICEIS 2009: 53-65 - 2008
- [c10]Matthias Böhm, Dirk Habich, Wolfgang Lehner, Uwe Wloka:
An Advanced Transaction Model for Recovery Processing of Integration Processes. ADBIS (local proceedings) 2008: 90-105 - [c9]Matthias Böhm, Jürgen Bittner, Dirk Habich, Wolfgang Lehner, Uwe Wloka:
Improving Data Independence, Efficiency and Functional Flexibility of Integration Platforms. CAiSE Forum 2008: 97-100 - [c8]Matthias Böhm, Uwe Wloka, Dirk Habich, Wolfgang Lehner:
Workload-based optimization of integration processes. CIKM 2008: 1479-1480 - [c7]Matthias Böhm, Dirk Habich, Wolfgang Lehner, Uwe Wloka:
DIPBench: An independent benchmark for Data-Intensive Integration Processes. ICDE Workshops 2008: 214-221 - [c6]Matthias Böhm, Dirk Habich, Wolfgang Lehner, Uwe Wloka:
DIPBench Toolsuite: A Framework for Benchmarking Integration Systems. ICDE 2008: 1596-1599 - [c5]Matthias Böhm, Uwe Wloka, Dirk Habich, Wolfgang Lehner:
Model-Driven Generation and Optimization of Complex Integration Processes. ICEIS (1) 2008: 131-136 - [c4]Matthias Böhm, Uwe Wloka, Dirk Habich, Wolfgang Lehner:
Message Indexing for Document-Oriented Integration Processes. ICEIS (1) 2008: 137-142 - [c3]Matthias Böhm, Dirk Habich, Wolfgang Lehner, Uwe Wloka:
Model-Driven Development of Complex and Data-Intensive Integration Processes. MBSDI 2008: 31-42 - 2007
- [c2]Matthias Böhm, Dirk Habich, Uwe Wloka, Jürgen Bittner, Wolfgang Lehner:
Towards Self-Optimization of Message Transformation Processes. ADBIS Research Communications 2007 - [c1]Matthias Böhm, Jürgen Bittner, Uwe Wloka, Dirk Habich, Wolfgang Lehner:
Ein Nachrichtentransformationsmodell für komplexe Transformationsprozesse in datenzentrischen Anwendungsszenarien. BTW 2007: 562-581
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