{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T07:15:15Z","timestamp":1779174915200,"version":"3.51.4"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T00:00:00Z","timestamp":1737504000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T00:00:00Z","timestamp":1737504000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Front. Comput. Sci."],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s11704-024-40624-2","type":"journal-article","created":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T15:24:15Z","timestamp":1737559455000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["GaussDB-AISQL: a composable cloud-native SQL system with AI capabilities"],"prefix":"10.1007","volume":"19","author":[{"given":"Cheng","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenlong","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Congli","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenliang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Ye","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yueguo","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyong","family":"Du","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,22]]},"reference":[{"key":"40624_CR1","volume-title":"Machine Learning with BigQuery ML: Create, execute, and improve machine learning models in BigQuery using standard SQL queries","author":"Marrandino","year":"2021","unstructured":"Marrandino, Alessandro. Machine Learning with BigQuery ML: Create, execute, and improve machine learning models in BigQuery using standard SQL queries. Packt Publishing Ltd, 2021."},{"key":"40624_CR2","volume-title":"Amazon redshift machine learning","author":"Amazon Web Services, Inc.","year":"2024","unstructured":"Amazon Web Services, Inc. Amazon redshift machine learning. See docs.aws.amazoncom\/redshift\/latest\/dg\/machine_learning website, 2024"},{"key":"40624_CR3","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1145\/3514221.3526141","volume-title":"Proceedings of 2022 International Conference on Management of Data, SIGMOD\u2019 22","author":"K Park","year":"2022","unstructured":"Park K, Saur K, Banda D, Sen R, Interlandi M, Karanasos K. End-to-end optimization of machine learning prediction queries. In: Proceedings of 2022 International Conference on Management of Data, SIGMOD\u2019 22. 2022, 587\u2013601"},{"key":"40624_CR4","volume-title":"MindsDB","author":"MindsDB","year":"2024","unstructured":"MindsDB. MindsDB. See mariadbcom\/about-us\/partners\/mindsdb\/ website, 2024"},{"key":"40624_CR5","first-page":"1","volume-title":"Proceedings of 2013 ACM SIGMOD International Conference on Management of Data","author":"B Huang","year":"2013","unstructured":"Huang B, Babu S, Yang J. Cumulon: optimizing statistical data analysis in the cloud. In: Proceedings of 2013 ACM SIGMOD International Conference on Management of Data. 2013, 1\u201312"},{"issue":"2","key":"40624_CR6","doi-asserted-by":"publisher","first-page":"1481","DOI":"10.14778\/1687553.1687576","volume":"2","author":"J Cohen","year":"2009","unstructured":"Cohen J, Dolan B, Dunlap M, Hellerstein J M, Welton C. MAD skills: new analysis practices for big data. Proceedings of the VLDB Endowment, 2009, 2(2): 1481\u20131492","journal-title":"Proceedings of the VLDB Endowment"},{"key":"40624_CR7","first-page":"1794","volume-title":"Proceedings of the 38th IEEE International Conference on Data Engineering (ICDE)","author":"Q Lin","year":"2022","unstructured":"Lin Q, Wu S, Zhao J, Dai J, Li F, Chen G. A comparative study of in-database inference approaches. In: Proceedings of the 38th IEEE International Conference on Data Engineering (ICDE). 2022, 1794\u20131807"},{"key":"40624_CR8","volume-title":"SQLFLow: a bridge between SQL and machine learning","author":"Y Wang","year":"2020","unstructured":"Wang Y, Yang Y, Zhu W, Wu Y, Yan X, Liu Y, Wang Y, Xie L, Gao Z, Zhu W, Chen X, Yan W, Tang M, Tang Y. SQLFLow: a bridge between SQL and machine learning. 2020, arXiv preprint arXiv: 2001.06846"},{"key":"40624_CR9","volume-title":"Oracle machine learning","author":"Oracle Corporation","year":"2024","unstructured":"Oracle Corporation. Oracle machine learning. See Docs.oracle.com\/en\/database\/oracle\/machine-learning\/ website, 2024"},{"key":"40624_CR10","first-page":"79","volume-title":"Proceedings of 2021 CHI Conference on Human Factors in Computing Systems","author":"D Wang","year":"2021","unstructured":"Wang D, Andres J, Weisz J D, Oduor E, Dugan C. AutoDS: towards human-centered automation of data science. In: Proceedings of 2021 CHI Conference on Human Factors in Computing Systems. 2021, 79"},{"issue":"6245","key":"40624_CR11","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1126\/science.aaa8415","volume":"349","author":"M I Jordan","year":"2015","unstructured":"Jordan M I, Mitchell T M. Machine learning: trends, perspectives, and prospects. Science, 2015, 349(6245): 255\u2013260","journal-title":"Science"},{"issue":"10","key":"40624_CR12","doi-asserted-by":"publisher","first-page":"10295","DOI":"10.1109\/TKDE.2023.3269592","volume":"35","author":"M Paganelli","year":"2023","unstructured":"Paganelli M, Sottovia P, Park K, Interlandi M, Guerra F. Pushing ML predictions into DBMSs. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(10): 10295\u201310308","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"40624_CR13","unstructured":"Substrait. See Github.com\/substrait-io website, 2024"},{"key":"40624_CR14","volume-title":"The predictive model markup language","author":"Group T D M","year":"2024","unstructured":"Group T D M. The predictive model markup language. See dmg.org\/pmml\/pmml-v4-4-1.html website, 2024"},{"key":"40624_CR15","unstructured":"ONNX. See Onnx.ai\/ website, 2024"},{"key":"40624_CR16","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1145\/3580305.3599326","volume-title":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"C Chai","year":"2023","unstructured":"Chai C, Wang J, Tang N, Yuan Y, Liu J, Deng Y, Wang G. Efficient coreset selection with cluster-based methods. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2023, 167\u2013178"},{"key":"40624_CR17","doi-asserted-by":"publisher","first-page":"1969","DOI":"10.1145\/2723372.2723713","volume-title":"Proceedings of 2015 ACM SIGMOD International Conference on Management of Data","author":"A Kumar","year":"2015","unstructured":"Kumar A, Naughton J, Patel J M. Learning generalized linear models over normalized data. In: Proceedings of 2015 ACM SIGMOD International Conference on Management of Data. 2015, 1969\u20131984"},{"key":"40624_CR18","volume-title":"The state of data science","author":"Kaggle","year":"2020","unstructured":"Kaggle. The state of data science. See www.kaggle.com\/kaggle-survey-2020 website, 2020"},{"key":"40624_CR19","volume-title":"Data science through the looking glass and what we found there","author":"F Psallidas","year":"2019","unstructured":"Psallidas F, Zhu Y, Karlas B, Interlandi M, Floratou A, Karanasos K, Wu W, Zhang C, Krishnan S, Curino C, Weimer M. Data science through the looking glass and what we found there. 2019, arXiv preprint arXiv: 1912.09536"},{"key":"40624_CR20","first-page":"37","volume-title":"Proceedings of the 36th International Conference on Neural Information Processing Systems","author":"L Grinsztajn","year":"2022","unstructured":"Grinsztajn L, Oyallon E, Varoquaux G. Why do tree-based models still outperform deep learning on typical tabular data? In: Proceedings of the 36th International Conference on Neural Information Processing Systems. 2022, 37"},{"key":"40624_CR21","volume-title":"Apache arrow","author":"The Apache Software Foundation","year":"2016","unstructured":"The Apache Software Foundation. Apache arrow. See Arrow.apache website, 2016"},{"key":"40624_CR22","volume-title":"ClickHouse","author":"ClickHouse","year":"2024","unstructured":"ClickHouse. ClickHouse. See githubcom\/ClickHouse\/ClickHouse website, 2024"},{"key":"40624_CR23","volume-title":"Apache\u00ae druid","author":"Apache Druid","year":"2024","unstructured":"Apache Druid. Apache\u00ae druid. See druidapache.org\/ website, 2024"},{"key":"40624_CR24","unstructured":"MySQL. See www.mysql.com\/ website, 2024"},{"key":"40624_CR25","doi-asserted-by":"publisher","first-page":"1463","DOI":"10.1145\/3318464.3386129","volume-title":"Proceedings of 2020 ACM SIGMOD International Conference on Management of Data","author":"A Depoutovitch","year":"2020","unstructured":"Depoutovitch A, Chen C, Chen J, Larson P, Lin S, Ng J, Cui W, Liu Q, Huang W, Xiao Y, He Y. Taurus database: how to be fast, available, and frugal in the cloud. In: Proceedings of 2020 ACM SIGMOD International Conference on Management of Data. 2020, 1463\u20131478"},{"key":"40624_CR26","doi-asserted-by":"publisher","first-page":"2177","DOI":"10.1145\/3514221.3526043","volume-title":"Proceedings of 2022 International Conference on Management of Data","author":"Y Ma","year":"2022","unstructured":"Ma Y, Xie S, Zhong H, Lee L, Lv K. HiEngine: how to architect a cloud-native memory-optimized database engine. In: Proceedings of 2022 International Conference on Management of Data. 2022, 2177\u20132190"},{"key":"40624_CR27","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1145\/3600006.3613144","volume-title":"Proceedings of the 29th Symposium on Operating Systems Principles","author":"J Shen","year":"2023","unstructured":"Shen J, Zuo P, Luo X, Su Y, Gu J, Feng H, Zhou Y, Lyu M R. Ditto: an elastic and adaptive memory-disaggregated caching system. In: Proceedings of the 29th Symposium on Operating Systems Principles. 2023, 675\u2013691"},{"key":"40624_CR28","volume-title":"GPT-4 technical report","author":"J Achiam","year":"2023","unstructured":"Achiam J, Adler S, Agarwal S, Ahmad L, Akkaya I, et al. GPT-4 technical report. 2023, arXiv preprint arXiv: 2303.08774"},{"key":"40624_CR29","volume-title":"PanGu-\u03a3: Towards trillion parameter language model with sparse heterogeneous computing","author":"X Ren","year":"2023","unstructured":"Ren X, Zhou P, Meng X, Huang X, Wang Y, Wang W, Li P, Zhang X, Podolskiy A, Arshinov G, Bout A, Piontkovskaya I, Wei J, Jiang X, Su T, Liu Q, Yao J. PanGu-\u03a3: Towards trillion parameter language model with sparse heterogeneous computing. 2023, arXiv preprint arXiv: 2303.10845"},{"key":"40624_CR30","volume-title":"IP network traffic flows labeled with 75 apps","author":"J S Rojas","year":"2018","unstructured":"Rojas J S. IP network traffic flows labeled with 75 apps. See Kaggle.com\/datasets\/jsrojas\/ip-network-traffic-flows-labeled-with-87-apps website, 2018"},{"key":"40624_CR31","volume-title":"Census income-UCI Machine Learning Repository","author":"R Kohavi","year":"1996","unstructured":"Kohavi R. Census income-UCI Machine Learning Repository. See Archive.ics.uci.edu\/dataset\/20\/census+income website, 1996"},{"key":"40624_CR32","volume-title":"The airlines dataset","author":"A Bifet","year":"2009","unstructured":"Bifet A, Ikonomovska E. The airlines dataset. See www.openml.org\/d\/1169 website, 2009"},{"key":"40624_CR33","volume-title":"Connect-4- UCI Machine Learning Repository","author":"J Tromp","year":"1995","unstructured":"Tromp J. Connect-4- UCI Machine Learning Repository. See Archive.ics.uci.edu\/dataset\/26\/connect+4 website, 1995"},{"key":"40624_CR34","volume-title":"Bank marketing- UCI Machine Learning Repository","author":"S Moro","year":"2012","unstructured":"Moro S, Rita P, Cortez P. Bank marketing- UCI Machine Learning Repository. See Archive.ics.uci.edu\/dataset\/222\/bank+marketing website, 2012"},{"key":"40624_CR35","volume-title":"The black Friday dataset","author":"M Raabe","year":"2019","unstructured":"Raabe M. The black Friday dataset. See www.openml.org website, 2019"},{"key":"40624_CR36","volume-title":"The diamonds dataset","author":"A Mueller","year":"2019","unstructured":"Mueller A. The diamonds dataset. See www.openml.org\/data\/download\/21792853\/dataset website, 2019"},{"key":"40624_CR37","volume-title":"New York city taxi tip prediction","author":"Taxi N Y C","year":"2016","unstructured":"Taxi N Y C. New York city taxi tip prediction. See www.openml.org\/d\/44065 website, 2016"},{"key":"40624_CR38","volume-title":"Mercedes-Benz greener manufacturing","author":"Group Mercedes Benz","year":"2017","unstructured":"Group Mercedes Benz. Mercedes-Benz greener manufacturing. See Github.com\/MezbanS\/Mercedes-Benz-Greener-Manufacturing website, 2017"},{"issue":"2","key":"40624_CR39","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1145\/3375661","volume":"45","author":"M A Khamis","year":"2020","unstructured":"Khamis M A, Ngo H Q, Nguyen X, Olteanu D, Schleich M. Learning models over relational data using sparse tensors and functional dependencies. ACM Transactions on Database Systems, 2020, 45(2): 7","journal-title":"ACM Transactions on Database Systems"},{"key":"40624_CR40","first-page":"1832","volume-title":"Proceedings of the 35th International Conference on Neural Information Processing Systems","author":"A Kadra","year":"2021","unstructured":"Kadra A, Lindauer M, Hutter F, Grabocka J. Well-tuned simple nets excel on tabular datasets. In: Proceedings of the 35th International Conference on Neural Information Processing Systems. 2021, 1832"},{"issue":"2","key":"40624_CR41","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1007\/s10994-020-05913-4","volume":"110","author":"S Bej","year":"2021","unstructured":"Bej S, Davtyan N, Wolfien M, Nassar M, Wolkenhauer O. LoRAS: an oversampling approach for imbalanced datasets. Machine Learning, 2021, 110(2): 279\u2013301","journal-title":"Machine Learning"},{"key":"40624_CR42","first-page":"725","volume-title":"Proceedings of the 40th International Conference on Machine Learning","author":"A Kotelnikov","year":"2023","unstructured":"Kotelnikov A, Baranchuk D, Rubachev I, Babenko A. TabDDPM: modelling tabular data with diffusion models. In: Proceedings of the 40th International Conference on Machine Learning. 2023, 725"},{"key":"40624_CR43","first-page":"2755","volume-title":"Proceedings of the 28th International Conference on Neural Information Processing Systems","author":"M Feurer","year":"2015","unstructured":"Feurer M, Klein A, Eggensperger K, Springenberg J T, Blum M, Hutter F. Efficient and robust automated machine learning. In: Proceedings of the 28th International Conference on Neural Information Processing Systems. 2015, 2755\u20132763"},{"issue":"12","key":"40624_CR44","doi-asserted-by":"publisher","first-page":"3166","DOI":"10.14778\/3415478.3415542","volume":"13","author":"A Yakovlev","year":"2020","unstructured":"Yakovlev A, Moghadam H F, Moharrer A, Cai J, Chavoshi N, Varadarajan V, Agrawal S R, Idicula S, Karnagel T, Jinturkar S, Agarwal N. Oracle AutoML: a fast and predictive AutoML pipeline. Proceedings of the VLDB Endowment, 2020, 13(12): 3166\u20133180","journal-title":"Proceedings of the VLDB Endowment"},{"issue":"2","key":"40624_CR45","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1007\/s00778-022-00752-2","volume":"32","author":"Y Li","year":"2023","unstructured":"Li Y, Shen Y, Zhang W, Zhang C, Cui B. VolcanoML: speeding up end-to-end AutoML via scalable search space decomposition. The VLDB Journal, 2023, 32(2): 389\u2013413","journal-title":"The VLDB Journal"},{"key":"40624_CR46","volume-title":"Scalable AutoML in H2O-3 open source","author":"H2O.ai","year":"2023","unstructured":"H2O.ai. Scalable AutoML in H2O-3 open source. See H2o.ai\/platform\/h2o-automl\/ website, 2023"},{"key":"40624_CR47","first-page":"399","volume-title":"Proceedings of 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","author":"N Patki","year":"2016","unstructured":"Patki N, Wedge R, Veeramachaneni K. The synthetic data vault. In: Proceedings of 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA). 2016, 399\u2013410"},{"issue":"10","key":"40624_CR48","doi-asserted-by":"publisher","first-page":"2679","DOI":"10.14778\/3603581.3603604","volume":"16","author":"P Pedreira","year":"2023","unstructured":"Pedreira P, Erling O, Karanasos K, Schneider S, McKinney W, Valluri S R, Zait M, Nadeau J. The composable data management system manifesto. Proceedings of the VLDB Endowment, 2023, 16(10): 2679\u20132685","journal-title":"Proceedings of the VLDB Endowment"},{"key":"40624_CR49","volume-title":"Proceedings of the 1st International Workshop on Composable Data Management Systems","author":"D Wilhite","year":"2022","unstructured":"Wilhite D. GoogleSQL: A SQL language as a component. In: Proceedings of the 1st International Workshop on Composable Data Management Systems. 2022"},{"key":"40624_CR50","volume-title":"Proceedings of the 13th Conference on Innovative Data Systems Research","author":"B Chattopadhyay","year":"2023","unstructured":"Chattopadhyay B, Pedreira P, Agarwal S, Sun Y, Vakharia S, Li P, Liu W, Narayanan S. Shared foundations: modernizing Meta\u2019s data lakehouse. In: Proceedings of the 13th Conference on Innovative Data Systems Research. 2023"},{"key":"40624_CR51","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1145\/3183713.3190662","volume-title":"Proceedings of 2018 International Conference on Management of Data","author":"E Begoli","year":"2018","unstructured":"Begoli E, Camacho-Rodr\u00edguez J, Hyde J, Mior M J, Lemire D. Apache calcite: a foundational framework for optimized query processing over heterogeneous data sources. In: Proceedings of 2018 International Conference on Management of Data. 2018, 221\u2013230"},{"key":"40624_CR52","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1145\/2588555.2595637","volume-title":"Proceedings of 2014 ACM SIGMOD International Conference on Management of Data","author":"M A Soliman","year":"2014","unstructured":"Soliman M A, Antova L, Raghavan V, El-Helw A, Gu Z, Shen E, Caragea G C, Garcia-Alvarado C, Rahman F, Petropoulos M, Waas F, Narayanan S, Krikellas K, Baldwin R. Orca: a modular query optimizer architecture for big data. In: Proceedings of 2014 ACM SIGMOD International Conference on Management of Data. 2014, 337\u2013348"},{"issue":"12","key":"40624_CR53","doi-asserted-by":"publisher","first-page":"3372","DOI":"10.14778\/3554821.3554829","volume":"15","author":"P Pedreira","year":"2022","unstructured":"Pedreira P, Erling O, Basmanova M, Wilfong K, Sakka L, Pai K, He W, Chattopadhyay B. Velox: Meta\u2019s unified execution engine. Proceedings of the VLDB Endowment, 2022, 15(12): 3372\u20133384","journal-title":"Proceedings of the VLDB Endowment"},{"key":"40624_CR54","volume-title":"Microsoft SQL server machine learning services","author":"Microsoft","year":"2024","unstructured":"Microsoft. Microsoft SQL server machine learning services. website, 2024"},{"key":"40624_CR55","volume-title":"Proceedings of the 10th Conference on Innovative Data Systems Research (CIDR 2020)","author":"K Karanasos","year":"2020","unstructured":"Karanasos K, Interlandi M, Psallidas F, Sen R, Park K, Popivanov I, Xin D, Nakandal S, Krishnan S, Weimer M, Yu Y, Ramakrishnan R, Curino C. Extending relational query processing with ML inference. In: Proceedings of the 10th Conference on Innovative Data Systems Research (CIDR 2020). 2020"},{"key":"40624_CR56","volume-title":"IBM db2 machine learning","author":"Corporation I","year":"2024","unstructured":"Corporation I. IBM db2 machine learning. website, 2024"},{"issue":"12","key":"40624_CR57","doi-asserted-by":"publisher","first-page":"4140","DOI":"10.14778\/3611540.3611639","volume":"16","author":"F Li","year":"2023","unstructured":"Li F. Modernization of databases in the cloud era: building databases that run like Legos. Proceedings of the VLDB Endowment, 2023, 16(12): 4140\u20134151","journal-title":"Proceedings of the VLDB Endowment"},{"key":"40624_CR58","volume-title":"SAP HANA predictive analysis library (PAL)","author":"AP","year":"2024","unstructured":"AP. SAP HANA predictive analysis library (PAL). See Help.sap.com website, 2024"},{"issue":"12","key":"40624_CR59","doi-asserted-by":"publisher","first-page":"1700","DOI":"10.14778\/2367502.2367510","volume":"5","author":"J M Hellerstein","year":"2012","unstructured":"Hellerstein J M, R\u00e9 C, Schoppmann F, Wang D Z, Fratkin E, Gorajek A, Ng K S, Welton C, Feng X, Li K, Kumar A. The MADlib analytics library: or MAD skills, the SQL. Proceedings of the VLDB Endowment, 2012, 5(12): 1700\u20131711","journal-title":"Proceedings of the VLDB Endowment"},{"key":"40624_CR60","doi-asserted-by":"publisher","first-page":"2696","DOI":"10.1145\/3448016.3452771","volume-title":"Proceedings of 2021 International Conference on Management of Data","author":"F Del Buono","year":"2021","unstructured":"Del Buono F, Paganelli M, Sottovia P, Interlandi M, Guerra F. Transforming ML predictive pipelines into SQL with MASQ. In: Proceedings of 2021 International Conference on Management of Data. 2021, 2696\u20132700"},{"key":"40624_CR61","first-page":"25","volume-title":"Proceedings of the 33rd International Conference on Scientific and Statistical Database Management, SSDBM\u2019 21","author":"M Schule","year":"2021","unstructured":"Schule M, Lang H, Springer M, Kemper A, Neumann T, Gunnemann S. In-database machine learning with SQL on GPUs. In: Proceedings of the 33rd International Conference on Scientific and Statistical Database Management, SSDBM\u2019 21. 2021, 25\u201336"},{"issue":"12","key":"40624_CR62","doi-asserted-by":"publisher","first-page":"3502","DOI":"10.14778\/3415478.3415572","volume":"13","author":"D Olteanu","year":"2020","unstructured":"Olteanu D. The relational data Borg is learning. Proceedings of the VLDB Endowment, 2020, 13(12): 3502\u20133515","journal-title":"Proceedings of the VLDB Endowment"},{"key":"40624_CR63","volume-title":"Proceedings of the 13th Conference on Innovative Data Systems Research","author":"A Gandhi","year":"2023","unstructured":"Gandhi A, Asada Y, Fu V, Gemawat A, Zhang L, Sen R, Curino C, Camacho-Rodr\u00edguez J, Interlandi M. The tensor data platform: towards an AI-centric database system. In: Proceedings of the 13th Conference on Innovative Data Systems Research. 2023"},{"issue":"12","key":"40624_CR64","doi-asserted-by":"publisher","first-page":"3970","DOI":"10.14778\/3611540.3611598","volume":"16","author":"M Ghorbani","year":"2023","unstructured":"Ghorbani M, Shaikhha A. Demonstration of OpenDBML, a framework for democratizing in-database machine learning. Proceedings of the VLDB Endowment, 2023, 16(12): 3970\u20133973","journal-title":"Proceedings of the VLDB Endowment"},{"key":"40624_CR65","first-page":"571","volume-title":"Proceedings of the IEEE 33rd International Conference on Data Engineering (ICDE)","author":"H Miao","year":"2017","unstructured":"Miao H, Li A, Davis L S, Deshpande A. Towards unified data and lifecycle management for deep learning. In: Proceedings of the IEEE 33rd International Conference on Data Engineering (ICDE). 2017, 571\u2013582"},{"key":"40624_CR66","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1145\/2723372.2750549","volume-title":"Proceedings of 2015 ACM SIGMOD International Conference on Management of Data","author":"X Wang","year":"2015","unstructured":"Wang X, Dong X L, Meliou A. Data x-ray: a diagnostic tool for data errors. In: Proceedings of 2015 ACM SIGMOD International Conference on Management of Data. 2015, 1231\u20131245"},{"key":"40624_CR67","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1145\/3183713.3196934","volume-title":"Proceedings of 2018 International Conference on Management of Data","author":"M Vartak","year":"2018","unstructured":"Vartak M, da Trindade J M F, Madden S, Zaharia M. MISTIQUE: a system to store and query model intermediates for model diagnosis. In: Proceedings of 2018 International Conference on Management of Data. 2018, 1285\u20131300"}],"container-title":["Frontiers of Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11704-024-40624-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11704-024-40624-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11704-024-40624-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T15:33:54Z","timestamp":1737560034000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11704-024-40624-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,22]]},"references-count":67,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["40624"],"URL":"https:\/\/doi.org\/10.1007\/s11704-024-40624-2","relation":{},"ISSN":["2095-2228","2095-2236"],"issn-type":[{"value":"2095-2228","type":"print"},{"value":"2095-2236","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,22]]},"assertion":[{"value":"23 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Competing interests The authors declare that they have no competing interests or financial conflicts to disclose.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics"}}],"article-number":"199608"}}