DEEM brings together researchers and practitioners at the intersection of applied machine learning, data management and systems research, with the goal to discuss the arising data management issues in ML application scenarios. The DEEM workshop will be held on Friday, June 27th, in conjunction with SIGMOD/PODS 2025. The workshop will be held in-person.

The workshop solicits regular research papers (8 pages plus unlimited references) describing preliminary or completed research results, as well as short papers (up to 4 pages) such as reports on applications and tools or preliminary results, interesting use cases, problems, datasets, benchmarks, visionary ideas, and descriptions of system components and tools related to end-to-end ML pipelines. Submissions should follow the guidelines as for SIGMOD, i.e. use the sigconf template for the ACM proceedings format.

Follow us on twitter @deem_workshop, bluesky @deem-workshop.bsky.social, or contact the organizers via email. We also provide archived websites of previous versions of the workshop: DEEM 2017, DEEM 2018, DEEM 2019, DEEM 2020, DEEM 2021, DEEM 2022, DEEM 2023, and DEEM 2024.

↑ top

↑ top

Applying Machine Learning (ML) in real-world scenarios is a challenging task. In recent years, the main focus of the data management community has been on creating systems and abstractions for the efficient training of ML models on large datasets. However, model training is only one of many steps in an end-to-end ML application, and a number of orthogonal data management problems arise from the large-scale use of ML and increased adoption large language models (LLMs).

For example, data preprocessing and feature extraction workloads may be complicated and require simultaneous execution of relational and linear algebraic operations. Next, model selection may involve searching many combinations of model architectures, features, and hyper-parameters to find the best-performing model. After model training, the resulting model may have to be deployed and integrated into business workflows and require lifecycle management using metadata and lineage. As a further complication, the resulting system may have to take into account a heterogeneous audience, ranging from domain experts without programming skills to data engineers and statisticians who develop custom algorithms. Many such challenges are human or engineer-centered (e.g., monitoring ML pipelines, leveraging LLMs for domain-specific tasks at scale), and DEEM uniquely encourages submissions in such topics.

Additionally, the importance of incorporating ethics and legal compliance into machine-assisted decision-making is being broadly recognized. Critical opportunities for improving data quality and representativeness, controlling for bias, and allowing humans to oversee and impact computational processes are missed if we do not consider the lifecycle stages upstream from model training and deployment. DEEM welcomes research on providing system-level support to data scientists who wish to develop and deploy responsible machine learning methods.

DEEM aims to bring together researchers and practitioners at the intersection of applied machine learning, data management and systems research, with the goal to discuss the arising data management issues in ML application scenarios.

↑ top

• Data Management in Machine Learning Applications
• Definition, Execution and Optimization of Complex Machine Learning Pipelines
• Systems for ML, e.g. for Managing the Lifecycle of ML Models, Efficient Hyper-parameter Search, or Feature Selection
• Machine Learning Services in the Cloud
• Modeling, Storage and Provenance of Machine Learning Artifacts
• Integration of Machine Learning and Dataflow Systems
• Integration of Machine Learning and ETL Processing
• Definition and Execution of Complex Ensemble Predictors
• Sourcing, Labeling, Integrating, and Cleaning Data for Machine Learning
• MLOps, Data Validation, and Model Debugging Techniques
• Privacy-preserving Machine Learning
• Benchmarking of Machine Learning Applications
• Responsible Data Management
• Transparency and Accountability of Machine-Assisted Decision Making
• Impact of Data Quality and Data Preprocessing on the Fairness of ML Predictions
• Horror stories, Anecdotes, and Lessons Learned on data management for ML
• Data management for multimodal ML
• Vector Databases for Retrieval and Systems for Retrieval Augmented Generation
• ML for data management for ML
• Data Management challenges, e.g. responsible data management, for LLMs

↑ top

We invite submissions in the following two tracks:

• Regular Papers (research and industrial papers; up to 8, plus unlimited references)
• Short Papers (preliminary results, interesting use cases, problems, datasets, benchmarks, visionary ideas, system designs, and descriptions of system components and tools; up to 4 pages)

↑ top

• Juliana Freire (New York University)
• Bill Howe (University of Washington)
• H.V. Jagadish (University of Michigan)
• Volker Markl (TU Berlin)
• Stefan Seufert (Amazon Research)
• Markus Weimer (Microsoft AI)
• Sebastian Schelter (BIFOLD & TU Berlin)

• Anna Pavlenko, Microsoft Gray Systems Lab
• Bojan Karlaš, Harvard University
• Gerardo Vitagliano, MIT CSAIL
• Haralampos Gavriilidis, TU Berlin
• Jacopo Tagliabue, Bauplan
• Joy Arulraj, Georgia Tech
• Konstantinos Kanellis, University of Wisconsin-Madison
• Manisha Luthra, TU Darmstadt and DFKI
• Matthias Boehm, Technische Universität Berlin
• Maximilian Böther, ETH Zurich
• Maximilian Schüle, University of Bamberg
• Pinar Tozun, IT University of Copenhagen
• Rainer Gemulla, Universität Mannheim
• Sebastian Schelter, BIFOLD & TU Berlin
• Shreya Shankar, University of California Berkeley
• Sivaprasad Sudhir, MIT
• Ties Robroek, IT University of Copenhagen
• Till Döhmen, MotherDuck
• Xue Li, CWI
• Yiming Lin, University of California, Berkeley
• Zezhou Huang, Columbia University

↑ top