{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T07:14:42Z","timestamp":1779174882090,"version":"3.51.4"},"reference-count":68,"publisher":"Association for Computing Machinery (ACM)","issue":"13","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:p>Approximate query processing (AQP) is one of the key techniques to cope with big data querying problem on account that it obtains approximate answers efficiently. To address non-trivial sample selection and heavy sampling cost issues in AQP, we propose ShadowAQP, an efficient and accurate approach based on attribute-oriented sample size allocation and data generation. We select samples according to group-by and join attributes, and determine the sample size for each group of unique value combinations to improve query accuracy. We design a conditional variational autoencoder model with automatic table data encoding and model update strategies. To further improve accuracy and efficiency, we propose a set of extensions, including parallel multi-round sampling aggregation, data outlier-aware sampling, and dimension reduction optimization. Evaluation results on diversified datasets show that, compared with SOTA approaches, ShadowAQP achieves 5.8\u00d7 query speed performance improvement on average (up to 12.8\u00d7), while reducing query error by 74% on average (up to 95%) at the same time.<\/jats:p>","DOI":"10.14778\/3625054.3625059","type":"journal-article","created":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T17:09:42Z","timestamp":1701709782000},"page":"4216-4229","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["ShadowAQP: Efficient Approximate Group-by and Join Query via Attribute-Oriented Sample Size Allocation and Data Generation"],"prefix":"10.14778","volume":"16","author":[{"given":"Rong","family":"Gu","sequence":"first","affiliation":[{"name":"State Key Laboratory for Novel, Software Technology, Nanjing University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Han","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel, Software Technology, Nanjing University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haipeng","family":"Dai","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel, Software Technology, Nanjing University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjie","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel, Software Technology, Nanjing University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Xue","sequence":"additional","affiliation":[{"name":"New York University Shanghai"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel, Software Technology, Nanjing University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaqi","family":"Zheng","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel, Software Technology, Nanjing University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoran","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yihua","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel, Software Technology, Nanjing University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guihai","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel, Software Technology, Nanjing University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,12,4]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the 25th VLDB International Conference on Very Large Data Bases. 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