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DBML 2022 : International Workshop on Databases and Machine Learning


When May 9, 2022 - May 9, 2022
Where Virtual event
Submission Deadline Jan 14, 2022
Notification Due Feb 22, 2022
Final Version Due Mar 8, 2022

Call For Papers

Call for Papers
International Workshop on Databases and Machine Learning in conjunction with ICDE 2022

May 9 2022 - Virtual event

About the workshop
After the increased adoption of machine learning (ML) in various applications and disciplines, a synergy between the
database (DB) systems and ML communities emerged. Steps involved in an ML pipeline, such as data preparation and cleaning,
feature engineering and management of the ML lifecycle, can benefit from research conducted by the data management community.
For example, the management of the ML lifecycle requires mechanisms for modeling, storing and querying ML artifacts.
Moreover, in many use cases pipelines require a mixture of relational and linear algebra operators raising the question
of whether a seamless integration between the two algebras is possible.
In the opposite direction, ML techniques are explored in core components of database systems, e.g., query optimization,
indexing and monitoring. Traditionally hard problems in databases, such as cardinality estimation, or problems with high
human supervision like DB administration, might benefit more from learning algorithms than from rule-based or
cost-based approaches.

The workshop aims at bringing together researchers and practitioners in the intersection of DB and ML research,
providing a forum for DB-inspired or ML-inspired approaches addressing challenges encountered
in each of the two areas. In particular, we welcome new research topics combining the strengths of both fields.

Call for papers
Topics of particular interest in the workshop include, but are not limited to:

* Data collection and preparation for ML applications
* Declarative machine learning on databases, data warehouses or data lakes
* Hybrid optimization techniques for databases and machine learning
* Model-aware data discovery, cleaning, and transformation
* Benchmarking ML-oriented data management systems (data augmentation, data cleaning, etc)
* Data management during the life cycle of ML models
* Novel data management systems for accelerating training and inference of ML models
* DB-inspired techniques for modeling, storage and provenance of ML artifacts
* Learned database design, configuration and tuning
* Machine learning for query optimization
* Applied machine learning/deep learning for data integration
* ML-enabled data exploration and discovery in data lakes
* ML functionality inside DBMS

The workshop will accept both regular papers (8 pages) and short papers (4 pages - work in progress, vision/outrageous ideas).
Submission Website:

Important Dates
Paper submission deadline: Jan 14, 2022

Authors notification: Feb 22, 2022

Camera ready version: Mar 08, 2022

Workshop day: May 9, 2022

Workshop chairs
Rihan Hai (TU Delft)
Nantia Makrynioti (CWI)
Ioana Manolescu (INRIA)

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