Matthias Boehm

66 papers A 4B 5Misc 5Journal 32Unranked 15

Year	Rank	Type	Title / Venue / Authors
2026	B	`conf`	CAMEO: Autocorrelation-Preserving Line Simplification for Lossy Time Series Compression. EDBT Carlos Enrique Muñiz-Cuza, Matthias Boehm, Torben Bach Pedersen
2026	B	`conf`	MetricLib: A Modular and Extensible Toolkit for Evaluation of Medical ML Datasets. EDBT Martin Seyferth, Katinka Becker, Tobias Schaeffter, Daniel Schwabe, Matthias Boehm
2026	J	`jnl`	Saga++: A Scalable Framework for Optimizing Data Cleaning Pipelines for Machine Learning Applications. ACM Trans. Database Syst. Shafaq Siddiqi, Arnab Phani, Roman Kern, Matthias Boehm
2026	J	`jnl`	SemPipes - Optimizable Semantic Data Operators for Tabular Machine Learning Pipelines. CoRR Olga Ovcharenko, Matthias Boehm, Sebastian Schelter
2026	B	`conf`	TerseTS: A Framework for Time Series Compression. EDBT Carlos Enrique Muñiz-Cuza, Søren Kejser Jensen, Tom Louis Klein, Sabina Bakhtiiarova, Matthias Boehm, Torben Bach Pedersen
2025	J	`jnl`	CAMEO: Autocorrelation-Preserving Line Simplification for Lossy Time Series Compression. CoRR Carlos Enrique Muñiz-Cuza, Matthias Boehm, Torben Bach Pedersen
2025	J	`jnl`	CatDB: Data-catalog-guided, LLM-based Generation of Data-centric ML Pipelines. Proc. VLDB Endow. Saeed Fathollahzadeh, Essam Mansour, Matthias Boehm
2025	—	`conf`	Demonstrating CatDB: LLM-based Generation of Data-centric ML Pipelines. SIGMOD Conference Companion Saeed Fathollahzadeh, Essam Mansour, Matthias Boehm
2025	J	`jnl`	Enter the Warp: Fast and Adaptive Data Transfer with XDBC. Proc. VLDB Endow. Haralampos Gavriilidis, Joel Ziegler, Midhun Kaippillil Venugopalan, Benedikt Didrich, Matthias Boehm, Volker Markl
2025	J	`jnl`	Fast and Scalable Data Transfer Across Data Systems. Proc. ACM Manag. Data Haralampos Gavriilidis, Kaustubh Beedkar, Matthias Boehm, Volker Markl
2025	Misc	`conf`	Fast, Parameter-free Time Series Anomaly Detection. BTW Kristiyan Blagov, Carlos Enrique Muñiz-Cuza, Matthias Boehm
2025	J	`jnl`	Front Matter. Proc. VLDB Endow. Matthias Boehm, Reynold Cheng, Xin Luna Dong, Themis Palpanas, Nesime Tatbul
2025	Misc	`conf`	Incremental SliceLine for Iterative ML Model Debugging under Updates. BTW Frederic Caspar Zoepffel, Christina Dionysio, Matthias Boehm
2025	J	`jnl`	Join Cardinality Estimation with OmniSketches. CoRR David Justen, Matthias Boehm
2025	B	`conf`	MEMPHIS: Holistic Lineage-based Reuse and Memory Management for Multi-backend ML Systems. EDBT Arnab Phani, Matthias Boehm
2025	J	`jnl`	Morphing-based Compression for Data-centric ML Pipelines. CoRR Sebastian Baunsgaard, Matthias Boehm
2025	J	`jnl`	Morphing-based Compression for Data-centric ML Pipelines. Proc. VLDB Endow. Sebastian Baunsgaard, Matthias Boehm
2025	—	`ed.`	Proceedings of the Workshops of the EDBT/ICDT 2025 Joint Conference co-located with the EDBT/ICDT 2025 Joint Conference, Barcelona, Spain, March 25, 2025. EDBT/ICDT Workshops Matthias Boehm, Khuzaima Daudjee
2025	Misc	`conf`	Scalable Computation of Shapley Additive Explanations. BTW Louis Le Page, Christina Dionysio, Matthias Boehm
2025	J	`jnl`	Technical Perspective: TASHEEH: Repairing Row-Structure in Raw CSV Files. SIGMOD Rec. Matthias Boehm
2024	—	`conf`	PLUTUS: Understanding Data Distribution Tailoring for Machine Learning. SIGMOD Conference Companion Jiwon Chang, Christina Dionysio, Fatemeh Nargesian, Matthias Boehm
2024	J	`jnl`	POLAR: Adaptive and Non-invasive Join Order Selection via Plans of Least Resistance. Proc. VLDB Endow. David Justen, Daniel Ritter, Campbell Fraser, Andrew Lamb, Nga Tran, Allison Lee, Thomas Bodner, Mhd Yamen Haddad, Steffen Zeuch, Volker Markl, Matthias Boehm
2024	J	`jnl`	Resource-Efficient Machine Learning (Dagstuhl Seminar 24311). Dagstuhl Reports Oana Balmau, Matthias Boehm, Ana Klimovic, Peter R. Pietzuch, Pinar Tözün
2024	J	`jnl`	Special issue on "Machine learning and databases". VLDB J. Matthias Boehm, Nesime Tatbul
2024	—	`conf`	Version Control for Speech Corpora. KONVENS Vlad Dumitru, Matthias Boehm, Martin Hagmüller, Barbara Schuppler
2023	J	`jnl`	AWARE: Workload-aware, Redundancy-exploiting Linear Algebra. Proc. ACM Manag. Data Sebastian Baunsgaard, Matthias Boehm
2023	—	`conf`	DAPHNE Runtime: Harnessing Parallelism for Integrated Data Analysis Pipelines. Euro-Par Workshops 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
2023	Misc	`conf`	Enabling Integrated Data Analysis Pipelines on Heterogeneous Hardware through Holistic Extensibility. BTW Patrick Damme, Matthias Boehm
2023	J	`jnl`	Front Matter. Proc. VLDB Endow. 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
2023	J	`jnl`	GIO: Generating Efficient Matrix and Frame Readers for Custom Data Formats by Example. Proc. ACM Manag. Data Saeed Fathollahzadeh, Matthias Boehm
2023	—	`conf`	Optimizing Tensor Computations: From Applications to Compilation and Runtime Techniques. SIGMOD Conference Companion Matthias Boehm, Matteo Interlandi, Chris Jermaine
2023	J	`jnl`	SAGA: A Scalable Framework for Optimizing Data Cleaning Pipelines for Machine Learning Applications. Proc. ACM Manag. Data Shafaq Siddiqi, Roman Kern, Matthias Boehm
2023	—	`conf`	Seventh Workshop on Data Management for End-to-End Machine Learning (DEEM). SIGMOD Conference Companion Matthias Boehm, Madelon Hulsebos, Shreya Shankar, Paroma Varma
2022	A	`conf`	DAPHNE: An Open and Extensible System Infrastructure for Integrated Data Analysis Pipelines. CIDR 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
2022	—	`ed.`	DEEM '22: Proceedings of the Sixth Workshop on Data Management for End-To-End Machine Learning Philadelphia, PA, USA, 12 June 2022 DEEM@SIGMOD Matthias Boehm, Paroma Varma, Doris Xin
2022	—	`conf`	DEEM'22: Data Management for End-to-End Machine Learning. SIGMOD Conference Matthias Boehm, Paroma Varma, Doris Xin
2022	A	`conf`	Federated Data Preparation, Learning, and Debugging in Apache SystemDS. CIKM Sebastian Baunsgaard, Matthias Boehm, Kevin Innerebner, Mito Kehayov, Florian Lackner, Olga Ovcharenko, Arnab Phani, Tobias Rieger, David Weissteiner, Sebastian Benjamin Wrede
2022	J	`jnl`	UPLIFT: Parallelization Strategies for Feature Transformations in Machine Learning Workloads. Proc. VLDB Endow. Arnab Phani, Lukas Erlbacher, Matthias Boehm
2021	—	`conf`	ExDRa: Exploratory Data Science on Federated Raw Data. SIGMOD Conference 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
2021	—	`conf`	LIMA: Fine-grained Lineage Tracing and Reuse in Machine Learning Systems. SIGMOD Conference Arnab Phani, Benjamin Rath, Matthias Boehm
2021	—	`ed.`	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 DEEM@SIGMOD Matthias Boehm, Julia Stoyanovich, Steven Whang
2021	—	`conf`	SliceLine: Fast, Linear-Algebra-based Slice Finding for ML Model Debugging. SIGMOD Conference Svetlana Sagadeeva, Matthias Boehm
2020	—	`conf`	Learning Explainable Linguistic Expressions with Neural Inductive Logic Programming for Sentence Classification. EMNLP (1) Prithviraj Sen, Marina Danilevsky, Yunyao Li, Siddhartha Brahma, Matthias Boehm, Laura Chiticariu, Rajasekar Krishnamurthy
2020	A	`conf`	SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle. CIDR 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
2020	J	`jnl`	Technical Perspective: Declarative Recursive Computation on an RDBMS. SIGMOD Rec. Matthias Boehm
2019	—	`ch.`	Apache SystemML. Encyclopedia of Big Data Technologies Matthias Boehm
2019	J	`jnl`	Compressed linear algebra for declarative large-scale machine learning. Commun. ACM Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald
2019	—	`book`	Data Management in Machine Learning Systems Matthias Boehm, Arun Kumar, Jun Yang
2019	Misc	`conf`	Efficient Data-Parallel Cumulative Aggregates for Large-Scale Machine Learning. BTW Matthias Boehm, Alexandre V. Evfimievski, Berthold Reinwald
2019	—	`conf`	MNC: Structure-Exploiting Sparsity Estimation for Matrix Expressions. SIGMOD Conference Johanna Sommer, Matthias Boehm, Alexandre V. Evfimievski, Berthold Reinwald, Peter J. Haas
2019	J	`jnl`	SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle. CoRR Matthias Boehm, Iulian Antonov, Mark Dokter, Robert Ginthör, Kevin Innerebner, Florijan Klezin, Stefanie N. Lindstaedt, Arnab Phani, Benjamin Rath
2018	J	`jnl`	Compressed linear algebra for large-scale machine learning. VLDB J. Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald
2018	J	`jnl`	Deep Learning with Apache SystemML. CoRR Niketan Pansare, Michael Dusenberry, Nakul Jindal, Matthias Boehm, Berthold Reinwald, Prithviraj Sen
2018	J	`jnl`	On Optimizing Operator Fusion Plans for Large-Scale Machine Learning in SystemML. CoRR Matthias Boehm, Berthold Reinwald, Dylan Hutchison, Alexandre V. Evfimievski, Prithviraj Sen
2018	J	`jnl`	On Optimizing Operator Fusion Plans for Large-Scale Machine Learning in SystemML. Proc. VLDB Endow. Matthias Boehm, Berthold Reinwald, Dylan Hutchison, Prithviraj Sen, Alexandre V. Evfimievski, Niketan Pansare
2017	—	`conf`	Data Management in Machine Learning: Challenges, Techniques, and Systems. SIGMOD Conference Arun Kumar, Matthias Boehm, Jun Yang
2017	A	`conf`	SPOOF: Sum-Product Optimization and Operator Fusion for Large-Scale Machine Learning. CIDR Tarek Elgamal, Shangyu Luo, Matthias Boehm, Alexandre V. Evfimievski, Shirish Tatikonda, Berthold Reinwald, Prithviraj Sen
2017	J	`jnl`	Scaling Machine Learning via Compressed Linear Algebra. SIGMOD Rec. Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald
2016	J	`jnl`	Compressed Linear Algebra for Large-Scale Machine Learning. Proc. VLDB Endow. Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald
2016	J	`jnl`	Declarative Machine Learning - A Classification of Basic Properties and Types. CoRR Matthias Boehm, Alexandre V. Evfimievski, Niketan Pansare, Berthold Reinwald
2016	J	`jnl`	SystemML: Declarative Machine Learning on Spark. Proc. VLDB Endow. Matthias Boehm, Michael Dusenberry, Deron Eriksson, Alexandre V. Evfimievski, Faraz Makari Manshadi, Niketan Pansare, Berthold Reinwald, Frederick Reiss, Prithviraj Sen, Arvind Surve, Shirish Tatikonda
2015	J	`jnl`	Costing Generated Runtime Execution Plans for Large-Scale Machine Learning Programs. CoRR Matthias Boehm
2015	B	`conf`	On optimizing machine learning workloads via kernel fusion. PPoPP Arash Ashari, Shirish Tatikonda, Matthias Boehm, Berthold Reinwald, Keith Campbell, John Keenleyside, P. Sadayappan
2015	—	`conf`	Resource Elasticity for Large-Scale Machine Learning. SIGMOD Conference Botong Huang, Matthias Boehm, Yuanyuan Tian, Berthold Reinwald, Shirish Tatikonda, Frederick R. Reiss
2014	J	`jnl`	Hybrid Parallelization Strategies for Large-Scale Machine Learning in SystemML. Proc. VLDB Endow. Matthias Boehm, Shirish Tatikonda, Berthold Reinwald, Prithviraj Sen, Yuanyuan Tian, Douglas Burdick, Shivakumar Vaithyanathan
2013	—	`conf`	Compiling machine learning algorithms with SystemML. SoCC Matthias Boehm, Douglas Burdick, Alexandre V. Evfimievski, Berthold Reinwald, Prithviraj Sen, Shirish Tatikonda, Yuanyuan Tian