The VLDB Journal - Call for Papers: Special Issue on Machine Learning and Databases
Aims and Scope
Recent advances in machine learning (ML) techniques have led to an explosion in their adoption across all fields of computer science, including database (DB) systems and data management. Meanwhile, end-to-end ML pipelines built for diverse data-driven applications are becoming increasingly more data-centric, presenting new challenges and opportunities in data science and engineering. From data collection and preparation to model training and deployment, efficient access to high-quality data and models form a critical component of the iterative lifecycle of these pipelines. Furthermore, new synergies arise in applying ML techniques for improving DB system internals as well as specializing their functionality and performance to new data and query workload characteristics – a critical need in increasingly more complex deployment environments, such as disaggregated cloud data centers or heterogenous hardware settings.
In this special issue, we solicit innovative research articles that explore data management problems spanning broadly across machine learning and databases (both ML techniques for addressing challenges in data management systems and applications as well as DB techniques for addressing challenges in machine learning systems and applications).
Topics of interest include, but are not limited to, the following:
- Learned query processing and optimization
- Learned index structures and storage layouts
- Learned algorithms for sorting, compressing, encoding data
- Learned data exploration, discovery, and integration
- Self-tuning and instance-optimized database systems
- Learned data systems on emerging hardware and cloud platforms
- New datasets, benchmarks, and evaluation methods for learned databases
- Novel use of ML techniques in big data applications (e.g., digital biology, video analytics)
- Novel use of natural language models and interfaces in data management
- Building and managing large-scale knowledge bases
- Data and model management in ML applications and complex ML pipelines
- Data integration, alignment, and preparation of multi-modal training datasets
- Data cleaning/debugging techniques and data quality management
- Data augmentation techniques, pipelines, and algorithm integration
- Data flow optimizations in ML systems (e.g., rewrites, operator fusion)
- Data- and task-parallel execution strategies for ML pipelines
- Data access methods in ML systems (e.g., indexing, compression, partitioning)
- New data system infrastructures and tools for applied ML
Guest Editors
Matthias Boehm, Technische Universität Berlin, Germany (matthias.boehm@tu-berlin.de (this opens in a new tab))
Nesime Tatbul, Intel Labs and MIT, USA (tatbul@csail.mit.edu)
Important Dates
Initial Submissions: February 15, 2023
First-round Decisions: May 15, 2023
Revised Versions: July 15, 2023
Final Decisions: September 15, 2023
Submission Guidelines:
Authors should prepare their manuscript according to the Instructions for Authors available from the VLDB website (this opens in a new tab). Authors should submit through the online submission site at https://www.editorialmanager.com/vldb/default2.aspx (this opens in a new tab) and select “SI Special Issue on Machine Learning and Databases" when they reach the “Article Type” step in the submission process. Submitted papers should present original, unpublished work, relevant to the topics of the special issue. All submitted papers will be evaluated on the basis of relevance, significance of contribution, technical quality, scholarship, and quality of presentation, by at least three independent reviewers. It is the policy of the journal that no submission, or substantially overlapping submission, be published or be under review at another journal or conference at any time during the review process. Final decisions on all papers are made by the Editor in Chief.