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As core contributions to support the transfer of such a pre-trained cost model to unseen databases, we introduce a new model architecture and representation technique for encoding query workloads as input to those models. As we will show in our evaluation, zero-shot cost estimation can provide more accurate cost estimates than state-of-the-art models for a wide range of (real-world) databases without requiring any query executions on unseen databases. Furthermore, we show that zero-shot cost models can be used in a few-shot mode that further improves their quality by retraining them just with a small number of additional training queries on the unseen database.<\/jats:p>","DOI":"10.14778\/3551793.3551799","type":"journal-article","created":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T22:25:03Z","timestamp":1664490303000},"page":"2361-2374","source":"Crossref","is-referenced-by-count":61,"title":["Zero-shot cost models for out-of-the-box learned cost prediction"],"prefix":"10.14778","volume":"15","author":[{"given":"Benjamin","family":"Hilprecht","sequence":"first","affiliation":[{"name":"Technical University of Darmstadt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carsten","family":"Binnig","sequence":"additional","affiliation":[{"name":"Technical University of Darmstadt &amp; DFKI"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,9,29]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Retrieved","year":"2021","unstructured":"[n.d.]. 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