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About

The DEEM workshop will be held on Sunday, 30th of June in Amsterdam, NL in conjunction with SIGMOD/PODS 2019. 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 describing preliminary and ongoing research results. In addition, the workshop encourages the submission of industrial experience reports of end-to-end ML deployments. Submissions can be short papers (4 pages) or long papers (up to 10 pages) following 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.

Important Dates
Submission Deadline: 11th of March, 5pm Pacific Time
Submission Website: tba
Notification of Acceptance: ​15th​ ​of​ ​April
Final papers due: 29th​ ​of​ ​April
Workshop: Sunday, 30th of June

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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 database 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, which require the​ attention​ of​ the​ data​ management​ community.

For example, data preprocessing and feature extraction workloads result in complex pipelines that often require the simultaneous execution of relational and linear algebraic operations. Next, the class of the ML model to use needs to be chosen, for that often a set of popular approaches such as linear models, decision trees and deep neural networks have to be tried out on the problem at hand. The prediction quality of such ML models heavily depends on the choice of features and hyperparameters, which are typically selected in a costly offline evaluation process, that poses huge opportunities for parallelization and optimization. Afterwards, the resulting models must be deployed and integrated into existing business workflows in a way that enables fast and efficient predictions, while still allowing for the lifecycle of models (that become stale over time) to be managed. Managing this lifecycle requires careful bookkeeping of metadata and lineage (“which data was used to train this model?”, “which models are affected by changes in this feature?”) and involves methods for continuous analysis, validation, and monitoring of data and models in production. As a further complication, the resulting systems need to take the target audience of ML applications into account; this audience is very heterogeneous, ranging from analysts without programming skills that possibly prefer an easy-to-use cloud-based solution on the one hand, to teams of data processing experts and statisticians developing and deploying custom-tailored algorithms​ on​ the​ other​ hand.

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. The workshop solicits regular research papers describing preliminary and ongoing research results. In addition, the workshop encourages the submission of industrial experience reports of end-to-end ML deployments.

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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

The workshop will have two tracks for regular research papers and industrial papers. Submissions can be short papers (4 pages) or long papers (up to 10 pages). Authors are requested to prepare submissions following the ACM proceedings format. DEEM is a single-blind workshop, authors must include their names and affiliations on the manuscript cover page.

Submission Website: tba

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Organisation / People
Workshop Chairs:
Sebastian Schelter
New York University

Neoklis Polyzotis
Google AI

Stefan Seufert
Amazon Research

Manasi Vartak
MIT

Steering Committee:

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