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We highlight that a robust imputation strategy should properly take three aspects of variety into consideration: source of imputed value, the types of tables involved, and the data types of the missing value. Existing imputation methods rely on estimation-based approaches (using a model trained on data from the same table to estimate missing values) or search-based approaches (retrieving values from other tables). Unfortunately, none of these approaches effectively incorporate all three aspects of variety. To address this gap, we propose , a novel framework that uses a\n                    <jats:underline>C<\/jats:underline>\n                    ombination of\n                    <jats:underline>E<\/jats:underline>\n                    stimation-based and\n                    <jats:underline>S<\/jats:underline>\n                    earch-based methods for missing value\n                    <jats:underline>I<\/jats:underline>\n                    mputation in\n                    <jats:underline>D<\/jats:underline>\n                    ata lakes.  contains three core modules: (1) the , which efficiently discovers candidate values from tables by exploiting the contextual information; (2) the , which introduces an influence function and a sampling-based exploration strategy to yield accurate estimated values; (3) the , which determines the most suitable method based on table-level and column-level statistics. Extensive experiments conducted on three data lakes demonstrate that  effectively and efficiently addresses the missing value problem.\n                  <\/jats:p>","DOI":"10.1007\/s00778-025-00957-1","type":"journal-article","created":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T11:59:15Z","timestamp":1768823955000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Missing Value Imputation in Tabular Data Lakes Unleashed: A Hybrid Approach"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-0448-3462","authenticated-orcid":false,"given":"Feng","family":"Luo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4433-9232","authenticated-orcid":false,"given":"Hai","family":"Lan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7299-031X","authenticated-orcid":false,"given":"Hui","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2477-381X","authenticated-orcid":false,"given":"Zhifeng","family":"Bao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1902-9087","authenticated-orcid":false,"given":"J. 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