About | Schedule | Important Dates | CfP | Topics | Submission | Accepted Papers | Invited Speakers | People

About

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 ACM proceedings format.

Follow us on twitter @deem_workshop or contact us via email at info[at]deem-workshop[dot]org. We also provide archived websites of previous versions of the workshop: DEEM 2017, DEEM 2018, DEEM 2019, DEEM 2020, DEEM 2021, and DEEM 2022.

DEEM 2022 Proceedings: ACM DL Link

Schedule
Sunday, June 18th (all times are in EDT)
TBA

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Important Dates
Submission deadline: March 15, 2023, 5pm Pacific Time
Submission website: https://cmt3.research.microsoft.com/DEEM2023
Notification of acceptance: ​April 19, 2023
Final papers due: May 10, 2023
Workshop: Sunday, June 18, 2023

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Call for Papers

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.

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Topics of Interest
Areas of particular interest for the workshop include (but are not limited to):

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Submission

We invite submissions in following two tracks:

Authors are requested to prepare submissions following the ACM proceedings format. Please use the latest ACM paper format (last update 11/2022). DEEM is a single-blind workshop, authors must include their names and affiliations on the manuscript cover page.

Submission Website: https://cmt3.research.microsoft.com/DEEM2023
Inclusion and Diversity in Writing: http://2023.sigmod.org/calls_papers_inclusion_and_diversity.shtml

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Invited Speakers
Academic Keynote: TBA
Organization / People
Workshop Chairs:
Matthias Boehm
TU Berlin, Germany

Madelon Hulsebos
University of Amsterdam, NL

Shreya Shankar
UC Berkeley, USA

Paroma Varma
Snorkel AI, USA

Steering Committee: Program Committee:

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Sponsored by

(Snorkel AI)


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