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DEEM 2017 - 1st Workshop on Data Management for End-to-End Machine Learning

Date2017-05-14 - 2017-05-19


VenueRaleigh, NC, USA - United States USA - United States



Topics/Call fo Papers

The 1st Workshop on Data Management for End-to-End Machine Learning, May 14, 2017.
Held in conjunction with ACM SIGMOD 2017
Raleigh, NC, USA, May 14-19, 2017
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
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
Papers submission deadline: February 1, 2017
Authors notification: March 1, 2017
Deadline for camera-ready copy: March 20, 2017
Workshop: Sunday May 14th, 2017
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
The workshop proceedings will be published in ACM DL and the organizers will prepare a SIGMOD Record report.
- Sebastian Schelter (Amazon)
- Reza Zadeh (Stanford & Matroid)
- Markus Weimer (Microsoft)
- Rajeev Rastogi (Amazon)
- Volker Markl (TU Berlin)
- 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)

Last modified: 2016-10-30 22:00:29