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The DEEM workshop will be held on Sunday, June 18th, in conjunction with SIGMOD/PODS 2023. The workshop will be held in hybrid (in-person and virtual) form. 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 workshop solicits regular research papers (10 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. With this new paper category (introduced in 2022) on applications and tools, the DEEM workshop aims to establish a broader forum for sharing interesting use cases, problems, datasets, benchmarks, visionary ideas, system designs, 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 or contact us via email at . We also provide archived websites of previous versions of the workshop: DEEM 2017, DEEM 2018, DEEM 2019, DEEM 2020, DEEM 2021, and DEEM 2022.
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DEEM 2022 Proceedings: ACM DL Link
Marius Schlegel (TU Ilmenau), Kai-Uwe Sattler (TU Ilmenau)
Ties Robroek (IT University of Copenhagen), Aaron Duane (IT University of Copenhagen), Ehsan Yousefzadeh-Asl-Miandoab (IT University of Copenhagen), Pinar Tozun (IT University of Copenhagen)
Gaurav Tarlok Kakkar (Georgia Institute of Technology), Jiashen Cao (Georgia Tech), Pramod Chunduri (Georgia Institute of Technology), Zhuangdi Xu (Georgia Tech), Suryatej Reddy Vyalla (Georgia Institute of Technology), Anirudh Prabakaran (Georgia Institute of Technology), Jaeho Bang (Georgia Institute of Technology), Kaushik Ravichandran (Georgia Institute of Technology), Ishwarya Sivakumar (Georgia Institute of Technology), Aryan Rajoria (Georgia Institute of Technology), Ashmita Raju (Georgia Institute of Technology), Tushar Aggarwal (Georgia Institute of Technology), Shashank Suman (Georgia Institute of Technology), Myna Prasanna Kalluraya (Georgia Institute of Technology), Subrata Mitra (Adobe Research), Ali Payani (Cisco Systems Inc.), Yao Lu (Microsoft Research), Umakishore Ramachandran (Georgia Institute of Technology), Joy Arulraj (Georgia Tech)
Cheng Zhen (Oregon State University), Amandeep Sing Chabada (Oregon State University), Arash Termehchy (Oregon State University)
Haoxiang Zhang (New York University), Roque Enrique López Condori (New York University), Aécio Santos (New York University), Jorge H Piazentin Ono (NYU), Aline Bessa (New York University), Juliana Freire (New York University)
Yordan Grigorov (Technische Universität Berlin), Haralampos Gavriilidis (Technische Universität Berlin), Sergey Redyuk (TU Berlin), Kaustubh Beedkar (IIT Delhi), Volker Markl (Technische Universität Berlin)
Clemens Ruck (TUM); Maximilian E Schüle (University of Bamberg)* 1
Supawit Chockchowwat (University of Illinois at Urbana-Champaign), Zhaoheng Li (University of Illinois at Urbana-Champaign), Yongjoo Park (University of Illinois at Urbana-Champaign)
Aditya Parameswaran (University of California, Berkeley)
Abstract: A large fraction of the data science and machine learning workflow is performed in computational notebooks such as Jupyter with libraries such as pandas, NumPy, and scikit-learn in an ad-hoc, highly iterative manner. However, this process is not without its challenges. We describe three open-source tools that we've built that address scalability, interactivity, and reproducibility challenges along the way -- and have been adopted widely by data scientists. We also reflect on how our recipe -- of enhancing existing tools as opposed to replacing them -- may need revisiting in the exciting arena of LLM-powered data work, which forms the focus of our new EPIC Data lab at Berkeley.
Benjamin Hilprecht (TU Darmstadt), Christian Hammacher (Software AG), Eduardo S Reis (TU Darmstadt), Mohamed Abdelaal (Software AG), Carsten Binnig (TU Darmstadt)
TBA
Submission website: https://cmt3.research.microsoft.com/DEEM2023
Notification of acceptance: April
Final papers due: May 10, 2023
Workshop: Sunday, June 18, 2023
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.
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.
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.
- Data Management in Machine Learning Applications
- Definition, Execution and Optimization of Complex Machine Learning Pipelines
- Systems for Managing the Lifecycle of Machine Learning Models
- Systems for Efficient Hyper-parameter Search and 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
- War stories, Anecdotes, and Lessons Learned on Data Management for ML
We invite submissions in following two tracks:
- Regular Papers (research and industrial papers; up to 10 pages, 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)
Submission Website: https://cmt3.research.microsoft.com/DEEM2023
Inclusion and Diversity in Writing: https://2023.sigmod.org/calls_papers_inclusion_and_diversity.shtml
Matthias BoehmTU Berlin, Germany
Madelon HulsebosUniversity of Amsterdam, NL
Shreya ShankarUC Berkeley, USA
Paroma VarmaSnorkel AI, USA
Steering Committee:
- 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)
- Raul Castro Fernandez (University of Chicago)
- Patrick Damme (TU Berlin)
- Rainer Gemulla (University of Mannheim)
- Stefan Grafberger (University of Amsterdam)
- Nezihe Merve Gürel (ETH Zürich)
- Matteo Interlandi (Microsoft Research)
- Zoi Kaoudi (TU Berlin)
- Bojan Karlaš (Harvard)
- Asterios Katsifodimos (TU Delft)
- Arun Kumar (University of California San Diego)
- Nantia Makrynioti (RelationalAI)
- Laurel Orr (Stanford University)
- Tilmann Rabl (HPI and University of Potsdam)
- Berthold Reinwald (IBM)
- Sebastian Schelter (University of Amsterdam)
- Nesime Tatbul (Intel Labs and MIT)
- Eugene Wu (Columbia University)
- Xiaozhe Yao (ETH Zürich)
- Chi Zhang (Brandeis University)
We are very pleased that we can award one talented researcher a travel grant of $1000, with the help of our sponsors. Applications for this travel award are due 26 April 2023 to enable early-bird registration by 1 May. Please find more information (e.g. eligibility criteria) and apply through the below form: Application Form Travel Award DEEM 2023
We will also award the best paper as well as the best presentation during the workshop!
