Matthias Boehm

发表

Compressed linear algebra for declarative large-scale machine learning

Frederick Reiss, Peter J. Haas, Berthold Reinwald, 2019, Commun. ACM.

Offline Design Tuning for Hierarchies of Forecast Models

Wolfgang Lehner, Matthias Boehm, Ulrike Fischer, 2011, BTW.

Efficient Data-Parallel Cumulative Aggregates for Large-Scale Machine Learning

Berthold Reinwald, Alexandre V. Evfimievski, Matthias Boehm, 2019, BTW.

Towards Integrated Data Analytics: Time Series Forecasting in DBMS

Wolfgang Lehner, Laurynas Siksnys, Matthias Boehm, 2012, Datenbank-Spektrum.

Hybrid Parallelization Strategies for Large-Scale Machine Learning in SystemML

Shirish Tatikonda, Berthold Reinwald, Yuanyuan Tian, 2014, Proc. VLDB Endow..

Data Management in Machine Learning: Challenges, Techniques, and Systems

Jun Yang, Matthias Boehm, Arun Kumar, 2017, SIGMOD Conference.

Costing Generated Runtime Execution Plans for Large-Scale Machine Learning Programs pdf

Matthias Boehm, 2015, ArXiv.

Workload-based optimization of integration processes

Wolfgang Lehner, Dirk Habich, Matthias Boehm, 2008, CIKM '08.

On Optimizing Operator Fusion Plans for Large-Scale Machine Learning in SystemML pdf

Berthold Reinwald, Alexandre V. Evfimievski, Matthias Boehm, 2018, Proc. VLDB Endow..

Cost-Based Vectorization of Instance-Based Integration Processes

Wolfgang Lehner, Dirk Habich, Matthias Boehm, 2009, ADBIS.

Improving Data Independence, Efficiency and Functional Flexibility of Integration Platforms

Wolfgang Lehner, Dirk Habich, Matthias Boehm, 2008, CAiSE Forum.

Vectorizing Instance-Based Integration Processes

Wolfgang Lehner, Dirk Habich, Matthias Boehm, 2009, ICEIS.

Model-Driven Development of Complex and Data-Intensive Integration Processes

Wolfgang Lehner, Dirk Habich, Matthias Boehm, 2008, MBSDI.

Partitioning and Multi-core Parallelization of Multi-equation Forecast Models

Wolfgang Lehner, Matthias Boehm, Lars Dannecker, 2012, SSDBM.

Scaling Machine Learning via Compressed Linear Algebra

Frederick Reiss, Peter J. Haas, Berthold Reinwald, 2017, SGMD.

Data management in the MIRABEL smart grid system

Torben Bach Pedersen, Wolfgang Lehner, Laurynas Siksnys, 2012, EDBT-ICDT '12.

Compressed linear algebra for large-scale machine learning

Frederick Reiss, Peter J. Haas, Berthold Reinwald, 2016, The VLDB Journal.

Resource Elasticity for Large-Scale Machine Learning

Shirish Tatikonda, Frederick Reiss, Berthold Reinwald, 2015, SIGMOD Conference.

Apache SystemML

Matthias Boehm, Matthias Boehm, 2019, Encyclopedia of Big Data Technologies.

MNC: Structure-Exploiting Sparsity Estimation for Matrix Expressions

Peter J. Haas, Berthold Reinwald, Alexandre V. Evfimievski, 2019, SIGMOD Conference.

A high-throughput in-memory index, durable on flash-based SSD: insights into the winning solution of the SIGMOD programming contest 2011

Wolfgang Lehner, Dirk Habich, Matthias Boehm, 2012, SGMD.

Deep Learning with Apache SystemML pdf

Berthold Reinwald, Matthias Boehm, Prithviraj Sen, 2018, ArXiv.

Learning Explainable Linguistic Expressions with Neural Inductive Logic Programming for Sentence Classification

Rajasekar Krishnamurthy, Matthias Boehm, Prithviraj Sen, 2020, EMNLP.

D3.1 State-of-the-art report on data collection and analysis

Torben Bach Pedersen, Christian S. Jensen, Laurynas Siksnys, 2010 .

SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle pdf

Stefanie Lindstaedt, Matthias Boehm, Mark Dokter, 2020, CIDR.

Model-Driven Generation and Optimization of Complex Integration Processes

Wolfgang Lehner, Dirk Habich, Matthias Boehm, 2008, ICEIS.

On-demand re-optimization of integration flows

Wolfgang Lehner, Dirk Habich, Matthias Boehm, 2014, Inf. Syst..

Efficient In-Memory Indexing with Generalized Prefix Trees

Wolfgang Lehner, Dirk Habich, Peter Benjamin Volk, 2011, BTW.

Next Generation Database Programming and Execution Environment

Wolfgang Lehner, Dirk Habich, Maik Thiele, 2011, DBPL.

Cost-based vectorization of instance-based integration processes

Wolfgang Lehner, Dirk Habich, Matthias Boehm, 2009, Inf. Syst..

Large Scale Discriminative Metric Learning

Berthold Reinwald, Alain Biem, Deepak S. Turaga, 2014, 2014 IEEE International Parallel & Distributed Processing Symposium Workshops.

Multi-flow Optimization via Horizontal Message Queue Partitioning

Wolfgang Lehner, Dirk Habich, Matthias Boehm, 2010, ICEIS.

Systemübergreifende Kostennormalisierung für Integrationsprozesse

Wolfgang Lehner, Dirk Habich, Matthias Boehm, 2009, BTW.

Context-Aware Parameter Estimation for Forecast Models in the Energy Domain

Wolfgang Lehner, Matthias Boehm, Robert Schulze, 2011, SSDBM.

Anfragegetriebene Indizierung räumlicher Daten

Wolfgang Lehner, Matthias Boehm, Hannes Voigt, 2009, GI Jahrestagung.

Towards Self-Optimization of Message Transformation Processes

Wolfgang Lehner, Dirk Habich, Matthias Boehm, 2007, ADBIS Research Communications.

Indexing forecast models for matching and maintenance

Wolfgang Lehner, Matthias Boehm, Ulrike Fischer, 2010, IDEAS '10.

Resiliency-aware data management

Wolfgang Lehner, Christof Fetzer, Matthias Boehm, 2011, Proc. VLDB Endow..

An Advanced Transaction Model for Recovery Processing of Integration Processes

Wolfgang Lehner, Dirk Habich, Matthias Boehm, 2008, ADBIS.

Cost-based optimization of integration flows

Matthias Boehm, Matthias Boehm, 2010 .

Invisible Deployment of Integration Processes

Wolfgang Lehner, Dirk Habich, Matthias Boehm, 2009, ICEIS.

DIPBench Toolsuite: A Framework for Benchmarking Integration Systems

Wolfgang Lehner, Dirk Habich, Matthias Boehm, 2008, 2008 IEEE 24th International Conference on Data Engineering.

Optimizing Notifications of Subscription-Based Forecast Queries

Torben Bach Pedersen, Wolfgang Lehner, Matthias Boehm, 2012, SSDBM.

Declarative Machine Learning - A Classification of Basic Properties and Types pdf

Berthold Reinwald, Alexandre V. Evfimievski, Matthias Boehm, 2016, ArXiv.

Compressed Linear Algebra for Large-Scale Machine Learning

Peter J. Haas, Berthold Reinwald, Ahmed Elgohary, 2016, Proc. VLDB Endow..

SystemML's Optimizer: Plan Generation for Large-Scale Machine Learning Programs

Shirish Tatikonda, Frederick Reiss, Berthold Reinwald, 2014, IEEE Data Eng. Bull..

Compiling machine learning algorithms with SystemML

Shirish Tatikonda, Berthold Reinwald, Yuanyuan Tian, 2013, SoCC.

Multi-process Optimization Via Horizontal Message Queue Partitioning

Wolfgang Lehner, Dirk Habich, Matthias Boehm, 2010, ICEIS.

SPOOF: Sum-Product Optimization and Operator Fusion for Large-Scale Machine Learning

Shirish Tatikonda, Berthold Reinwald, Alexandre V. Evfimievski, 2017, CIDR.

SystemML: Declarative Machine Learning on Spark

Shirish Tatikonda, Frederick Reiss, Berthold Reinwald, 2016, Proc. VLDB Endow..

GCIP: exploiting the generation and optimization of integration processes

Wolfgang Lehner, Dirk Habich, Matthias Boehm, 2009, EDBT '09.

Message Indexing for Document-Oriented Integration Processes

Wolfgang Lehner, Dirk Habich, Matthias Boehm, 2008, ICEIS.

Session details: Research 11: Systems & Machine Learning

Matthias Boehm, 2019, SIGMOD 2019.

Ein Nachrichtentransformationsmodell für komplexe Transformationsprozesse in datenzentrischen Anwendungsszenarien

Wolfgang Lehner, Dirk Habich, Matthias Boehm, 2007, BTW.

Forcasting Evolving Time Series of Energy Demand and Supply

Wolfgang Lehner, Matthias Boehm, Lars Dannecker, 2011, ADBIS.

On optimizing machine learning workloads via kernel fusion

Shirish Tatikonda, Berthold Reinwald, P. Sadayappan, 2015, PPoPP.

DIPBench: An independent benchmark for Data-Intensive Integration Processes

Wolfgang Lehner, Dirk Habich, Matthias Boehm, 2008, 2008 IEEE 24th International Conference on Data Engineering Workshop.

Data Management in Machine Learning Systems

Jun Yang, Matthias Boehm, Arun Kumar, 2019, Data Management in Machine Learning Systems.

Efficient Integration of External Information into Forecast Models from the Energy Domain

Wolfgang Lehner, Elena Vasilyeva, Matthias Boehm, 2012, ADBIS.

ExDRa: Exploratory Data Science on Federated Raw Data

Volker Markl, Steffen Zeuch, Philipp M. Grulich, 2021, SIGMOD Conference.

SliceLine: Fast, Linear-Algebra-based Slice Finding for ML Model Debugging

Matthias Boehm, Svetlana Sagadeeva, Matthias Boehm, 2021, SIGMOD Conference.

LIMA: Fine-grained Lineage Tracing and Reuse in Machine Learning Systems

Matthias Boehm, Arnab Phani, Benjamin Rath, 2021, SIGMOD Conference.