AI RESEARCH
MLSkip: Data Skipping for ML Filters via Lightweight Metadata
arXiv CS.LG
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ArXi:2606.03946v1 Announce Type: cross 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.