MLSkip: Data Skipping for ML Filters via Lightweight Metadata
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 default min-max metadata is enough to enable pruning. To this end, we draw connections to two lines of research: (i) the recently proposed query langua...
| Authors | Mihail Stoian, Mark Gerarts, Pascal Ginter et al. |
|---|---|
| Primary category | cs.DB |
| Published | 2 June 2026 |
| Last updated at source | 23 September 2026 |
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