Connectionists: CfP: 1st Workshop on Data Management for End-to-End Machine Learning

[Nedelina Teneva]

DEEM'17
The 1st Workshop on Data Management for End-to-End Machine Learning, May
14, 2017.
http://deem-workshop.org
https://twitter.com/deem_workshop

Held in conjunction with ACM SIGMOD 2017
Raleigh, NC, USA, May 14-19, 2017
http://sigmod2017.org/

----------
 WORKSHOP
----------

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.

Therefore, 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*. Submissions can either be *short papers (4
pages)* or *long papers (up to 10 pages)* following the ACM proceedings
format, as described in https://www.acm.org/publicatio
ns/proceedings-template.

Examples of data management problems in ML are as follows:

 - Simultaneously executing relational and linear algebraic operations in
data preprocessing and feature extraction
 - Choosing among popular classes of ML models (linear models, decision
trees, and deep neural networks)
 - Executing costly offline evaluation processes for choosing features and
hyperparameters
 - Deployment of models and integration into existing business workflows
 - Fast and Efficient Online Predictions from trained ML Models

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 ML Pipelines
 - Systems for Managing the Lifecycle of Machine Learning Models
 - Systems for Efficient Hyperparameter Search and Feature Selection
 - Machine Learning Services in the Cloud
 - Modeling, Storage, and Lineage of ML experimentation data
 - Integration of Machine Learning and Dataflow Systems
 - Integration of Machine Learning and ETL Processing
 - Benchmarking of Machine Learning Applications
 - Definition and Execution of Complex Ensemble Predictors
 - Architectures for Streaming Machine Learning

----------------
IMPORTANT DATES
----------------

Papers submission deadline:             February 1, 2017
Authors notification:                   March 1, 2017
Deadline for camera-ready copy:         March 20, 2017
Workshop:                               Sunday May 14th, 2017

----------------------
SUBMISSION GUIDELINES
----------------------

The workshop will have two tracks for regular research papers (including
research in progress) and industrial papers (e.g., industrial experience
reports of end-to-end ML deployments). Submissions can either be *short
papers (4 pages)* or *long papers (up to 10 pages)* following the ACM
proceedings format, as described in https://www.acm.org/publicatio
ns/proceedings-template.

----------------
PUBLICATION
----------------

The workshop proceedings will be published in ACM DL and the organizers
will prepare a SIGMOD Record report.

---------------------------
 ORGANIZERS
---------------------------

 - Sebastian Schelter (Amazon)
 - Reza Zadeh (Stanford & Matroid)
 - Markus Weimer (Microsoft)
 - Rajeev Rastogi (Amazon)
 - Volker Markl (TU Berlin)

---------------------------
 PROGRAM COMMITTEE
---------------------------

 - Sunita Sarawagi (IIT Bombay)
 - Sudip Roy (Google)
 - Rainer Gemulla (University of Mannheim)
 - Matthias Boehm (IBM Research)
 - Matthias Seeger (Amazon)
 - Evan Sparks (UC Berkeley)
 - Chris Ré (Stanford)
 - Ted Dunning (MapR Technologies)
 - Dionysios Logothetis (Facebook)
 - Nedelina Teneva (University of Chicago)
 - Vasia Kalavri (KTH Stockholm)
 - Venu Satuluri (Twitter)
 - Shannon Quinn (University of Georgia)
 - Dmitriy Lyubimov (Apache Mahout)
 - Tilmann Rabl (TU Berlin)
 - Max Heimel (Snowflake)
 - Felix Biessmann (Amazon)
 - Arun Kumar (UC San Diego)


-- 
Nedelina Teneva
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