About this Research Topic
The intersection of machine learning and data management has become a rapidly evolving research area, as the widespread adoption of machine learning in various applications has necessitated the development of new techniques and strategies for managing data. This interdisciplinary field aims to utilize data management methods to improve machine learning systems, making them more scalable and user-friendly, as well as leveraging machine learning techniques to solve complex data management problems such as query cost prediction and cardinality estimation.
The scope of this Research Topic includes theoretical advances, systems design, algorithmic contributions, surveys or experimental studies in the intersection of data management and machine learning fields. We solicit high-quality articles of the following types: original research, significant extensions of published articles, surveys, vision papers, or benchmarking studies. Topics of interest include but are not limited to:
● Data collection and preparation for ML applications;
● Declarative machine learning;
● DB-inspired optimization techniques for ML systems;
● Data management during the lifecycle of ML pipelines;
● ML and data debugging;
● Assisting ML pipelines construction;
● New methods and systems for applied ML;
● Learned query processing and optimization;
● Learned indexing and storage;
● Learned database design, configuration, and tuning;
● ML-enabled data exploration and discovery;
● ML-aware data cleaning, and transformation;
● Benchmarks and datasets for learned data management systems;
● Novel use of ML in data management applications.
The scope of this Research Topic includes theoretical advances, systems design, algorithmic contributions, surveys or experimental studies in the intersection of data management and machine learning fields. We solicit high-quality articles of the following types: original research, significant extensions of published articles, surveys, vision papers, or benchmarking studies. Topics of interest include but are not limited to:
● Data collection and preparation for ML applications;
● Declarative machine learning;
● DB-inspired optimization techniques for ML systems;
● Data management during the lifecycle of ML pipelines;
● ML and data debugging;
● Assisting ML pipelines construction;
● New methods and systems for applied ML;
● Learned query processing and optimization;
● Learned indexing and storage;
● Learned database design, configuration, and tuning;
● ML-enabled data exploration and discovery;
● ML-aware data cleaning, and transformation;
● Benchmarks and datasets for learned data management systems;
● Novel use of ML in data management applications.
Keywords: Data Management, Machine Learning, Data Collection, Optimization Techniques, Big Data Storage, Database Design, Data Cleaning
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
