The DEEM workshop will be held on Sunday, June 12th, in conjunction with SIGMOD/PODS 2022. 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 (up to 10 pages, plus unlimited references) describing preliminary and ongoing research results, including industrial experience reports of end-to-end ML deployments, related to DEEM topics. In addition, DEEM 2022 establishes a new paper category for reports on applications and tools (4 pages) as a 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 (last update 11/2020).

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DEEM 2022 Proceedings: ACM DL Link

Sunday, June 12th (all times are in EDT)





 


 


 


 





 


 






 








 

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Submission deadline: March 01 14 (extended), 2022, 5pm Pacific Time
Submission website: https://cmt3.research.microsoft.com/DEEM2022
Notification of acceptance: ​April 05 15, 2022
Final papers due: May 03 10, 2022
Workshop: Sunday, June 12, 2022

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

• 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
• 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

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We invite submissions in following two tracks:

• Regular Papers (research and industrial papers; up to 10 pages, plus unlimited references)
• Reports on Applications and Tools (interesting use cases, problems, datasets, benchmarks, visionary ideas, system designs, and descriptions of system components and tools; 4 pages)

Authors are requested to prepare submissions following the ACM proceedings format. Please use the latest ACM paper format (last update 11/2020). 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/DEEM2022
Inclusion and Diversity in Writing: http://2022.sigmod.org/calls_papers_inclusion_and_diversity.shtml

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Academic Keynote: Julia Stoyanovich (New York University)

Julia Stoyanovich is an Institute Associate Professor of Computer Science & Engineering at the Tandon School of Engineering, Associate Professor of Data Science at the Center for Data Science, and Director of the Center for Responsible AI at New York University (NYU). Her research focuses on responsible data management and analysis: on operationalizing fairness, diversity, transparency, and data protection in all stages of the data science lifecycle. She established the "Data, Responsibly" consortium and served on the New York City Automated Decision Systems Task Force, by mayoral appointment. Julia developed and has been teaching courses on Responsible Data Science at NYU, and is a co-creator of an award-winning comic book series on this topic. In addition to data ethics, Julia works on the management and analysis of preference and voting data, and on querying large evolving graphs. She holds M.S. and Ph.D. degrees in Computer Science from Columbia University, and a B.S. in Computer Science and in Mathematics & Statistics from the University of Massachusetts at Amherst. She is a recipient of an NSF CAREER award and a Senior Member of the ACM.

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Workshop Chairs:
Matthias Boehm
Graz University of Technology, Austria

Paroma Varma
Snorkel AI, USA

Doris Xin
UC Berkeley & Linea, 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)

Program Committee:

• Khaled Ammar (University of Waterloo, Thomson Reuters Labs)
• Abolfazl Asudeh (University of Illinois at Chicago)
• Srikanta Bedathur (IIT Delhi)
• Renata Borovica-Gajic (University of Melbourne)
• Patrick Damme (TU Graz, Know-Center GmbH)
• Ahmed Elgohary (Microsoft)
• Edward Gan (Stanford University)
• Rainer Gemulla (University of Mannheim)
• Chris Jermaine (Rice University)
• Zoi Kaoudi (TU Berlin)
• Sanjay Krishnan (University of Chicago)
• Arun Kumar (UC San Diego)
• Milos Nikolic (University of Edinburgh)
• Tilmann Rabl (HPI, University of Potsdam)
• Berthold Reinwald (IBM Research - Almaden)
• Maximilian Schleich (University of Washington)
• Christin Seifert (University of Duisburg-Essen)
• Vraj Shah (UC San Diego)
• Nesime Tatbul (Intel Labs and MIT)
• Shirish Tatikonda (Target Corporation)
• Ce Zhang (ETH Zurich)

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(Snorkel AI)

(Linea)

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