arXiv:2606.03946v1 Announce Type: cross Abstract: Database vendors recently released AI functions that can be used in filter predicates. As such functions often rely on costly, black-box ML models, they unveil new data management challenges. Concretely, traditional data skipping techniques for integer and string data fail to be applicable to the new filter type. Indeed, there is no known mechanism for pruning non-qualifying row groups, e.g., when reading files from blob storage. In this work, we initiate the study of data skipping techniques for ML filters. We make the case that Parquet's defa
The increasing use of AI functions within databases necessitates new data management techniques to handle their computational cost and black-box nature, departing from traditional data skipping methods.
This work directly addresses the efficiency bottleneck of integrating costly ML models into database filter predicates, which is critical for scaling AI-driven data processing and reducing operational costs.
The proposed 'MLSkip' mechanism introduces a method for intelligent data skipping specifically tailored for ML filters, improving performance and resource utilization for database queries involving AI functions.
- · Database vendors integrating AI
- · Cloud storage providers
- · Data scientists and ML engineers
- · Enterprises with large datasets
- · Inefficient data processing systems
- · Traditional data skipping methods
Reduced query latency and computational costs for databases leveraging AI filters.
Increased adoption of AI functions within databases due to improved performance and efficiency.
New standards and best practices for data management and storage evolving around ML-driven data processing paradigms.
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