{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T04:58:22Z","timestamp":1781326702477,"version":"3.54.1"},"reference-count":50,"publisher":"Association for Computing Machinery (ACM)","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Manag. Data"],"published-print":{"date-parts":[[2025,9,22]]},"abstract":"<jats:p>Data quality issues have been a long-standing challenge in the database community. Erroneous data can lead to incorrect query results, which in turn affect the credibility of the data-driven decisions. To circumvent this issue, a common practice is to discovery integrity constraints and enforce them on the data to ensure its quality. For instance, one can use constraints entailed by functional dependencies (FDs) to detect violations in the data. However, existing approaches fail to effectively discover them from noisy data.<\/jats:p>\n                  <jats:p>\n                    In this paper, we present a novel form of integrity constraints as a program under a domain-specific language (DSL) that can be used to detect and rectify errors in the data. On top of DSL, we propose an efficient synthesis algorithm that leverages the statistical structural properties of the data to generate the sketch of the program that significantly reduces the search space and speedup the synthesis process. To demonstrate the usefulness of our approach, we evaluate it on 12 real-world datasets for error detection. Then, we show that the synthesized integrity constraints can be used to solidify ML-integrated SQL queries over 48 queries, leading to an average reduction of 87% in the error rates. Our open-source artifact, including the G\n                    <jats:sc>uardrail<\/jats:sc>\n                    framework and the datasets, is available for the community to use [2].\n                  <\/jats:p>","DOI":"10.1145\/3749166","type":"journal-article","created":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T17:17:03Z","timestamp":1758647823000},"page":"1-26","source":"Crossref","is-referenced-by-count":0,"title":["G\n                    <scp>uardrail<\/scp>\n                    : Automated Integrity Constraint Synthesis From Noisy Data"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7680-2817","authenticated-orcid":false,"given":"Pingchuan","family":"Ma","sequence":"first","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6892-1264","authenticated-orcid":false,"given":"Zhaoyu","family":"Wang","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3167-0480","authenticated-orcid":false,"given":"Zhenlan","family":"Ji","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9897-4086","authenticated-orcid":false,"given":"Zongjie","family":"Li","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5369-624X","authenticated-orcid":false,"given":"Ao","family":"Sun","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0866-0308","authenticated-orcid":false,"given":"Shuai","family":"Wang","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,9,23]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"https:\/\/github.com\/pckennethma\/guardrail\/blob\/main\/docs\/full.pdf","author":"Extended","year":"2025","unstructured":"Extended version of the paper with appendix. https:\/\/github.com\/pckennethma\/guardrail\/blob\/main\/docs\/full.pdf, 2025."},{"key":"e_1_2_1_2_1","volume-title":"https:\/\/github.com\/pckennethma\/guardrail","author":"Research","year":"2025","unstructured":"Research artifact. https:\/\/github.com\/pckennethma\/guardrail, 2025."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.14778\/2850578.2850579"},{"key":"e_1_2_1_4_1","first-page":"507","volume-title":"Proceedings of the 2014 ACM International Conference on Object Oriented Programming Systems Languages & Applications","author":"Barowy Daniel W","year":"2014","unstructured":"Daniel W Barowy, Dimitar Gochev, and Emery D Berger. Checkcell: Data debugging for spreadsheets. In Proceedings of the 2014 ACM International Conference on Object Oriented Programming Systems Languages & Applications, pages 507-523, 2014."},{"key":"e_1_2_1_5_1","volume-title":"Openml benchmarking suites. arXiv:1708.03731v2 [stat.ML]","author":"Bischl Bernd","year":"2019","unstructured":"Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Frank Hutter, Michel Lang, Rafael G. Mantovani, Jan N. van Rijn, and Joaquin Vanschoren. Openml benchmarking suites. arXiv:1708.03731v2 [stat.ML], 2019."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-46681-0_14"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/342009.335388"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.14778\/2536258.2536262"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/564691.564719"},{"key":"e_1_2_1_10_1","volume-title":"Autogluon-tabular: Robust and accurate automl for structured data. arXiv preprint arXiv:2003.06505","author":"Erickson Nick","year":"2020","unstructured":"Nick Erickson, Jonas Mueller, Alexander Shirkov, Hang Zhang, Pedro Larroy, Mu Li, and Alexander Smola. Autogluon-tabular: Robust and accurate automl for structured data. arXiv preprint arXiv:2003.06505, 2020."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2010.154"},{"key":"e_1_2_1_12_1","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1145\/3448016.3452795","volume-title":"Proceedings of the 2021 International Conference on Management of Data","author":"Fariha Anna","year":"2021","unstructured":"Anna Fariha, Ashish Tiwari, Arjun Radhakrishna, Sumit Gulwani, and Alexandra Meliou. Conformance constraint discovery: Measuring trust in data-driven systems. In Proceedings of the 2021 International Conference on Management of Data, pages 499-512, 2021."},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3140587.3062351"},{"key":"e_1_2_1_14_1","volume-title":"A survey on concept drift adaptation. ACM computing surveys (CSUR), 46(4):1-37","author":"Gama Jo","year":"2014","unstructured":"Jo ao Gama, Indr.e \u017dliobait.e, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia. A survey on concept drift adaptation. ACM computing surveys (CSUR), 46(4):1-37, 2014."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.14778\/2536360.2536363"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3389721"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/1925844.1926423"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1561\/2500000010"},{"key":"e_1_2_1_19_1","volume-title":"Inductive program synthesis over noisy datasets using abstraction refinement based optimization. arXiv preprint arXiv:2104.13315","author":"Handa Shivam","year":"2021","unstructured":"Shivam Handa and Martin Rinard. Inductive program synthesis over noisy datasets using abstraction refinement based optimization. arXiv preprint arXiv:2104.13315, 2021."},{"key":"e_1_2_1_20_1","volume-title":"Program synthesis over noisy data with guarantees. arXiv preprint arXiv:2103.05030","author":"Handa Shivam","year":"2021","unstructured":"Shivam Handa and Martin Rinard. Program synthesis over noisy data with guarantees. arXiv preprint arXiv:2103.05030, 2021."},{"key":"e_1_2_1_21_1","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1145\/3368089.3409732","volume-title":"Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering","author":"Handa Shivam","year":"2020","unstructured":"Shivam Handa and Martin C Rinard. Inductive program synthesis over noisy data. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pages 87-98, 2020."},{"key":"e_1_2_1_22_1","volume-title":"A baseline for detecting misclassified and out-of-distribution examples in neural networks. arXiv preprint arXiv:1610.02136","author":"Hendrycks Dan","year":"2016","unstructured":"Dan Hendrycks and Kevin Gimpel. A baseline for detecting misclassified and out-of-distribution examples in neural networks. arXiv preprint arXiv:1610.02136, 2016."},{"key":"e_1_2_1_23_1","volume-title":"Tane: An efficient algorithm for discovering functional and approximate dependencies. The computer journal, 42(2):100-111","author":"Huhtala Yka","year":"1999","unstructured":"Yka Huhtala, Juha K\u00e4rkk\u00e4inen, Pasi Porkka, and Hannu Toivonen. Tane: An efficient algorithm for discovering functional and approximate dependencies. The computer journal, 42(2):100-111, 1999."},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/1007568.1007641"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3035918.3064034"},{"key":"e_1_2_1_26_1","first-page":"392","volume-title":"Proceedings of the international conference on very large data bases","author":"Knox Edwin M","year":"1998","unstructured":"Edwin M Knox and Raymond T Ng. Algorithms for mining distancebased outliers in large datasets. In Proceedings of the international conference on very large data bases, pages 392-403. Citeseer, 1998."},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510091"},{"key":"e_1_2_1_28_1","volume-title":"The cost of representation by subset repairs. arXiv preprint arXiv:2410.16501","author":"Liu Yuxi","year":"2024","unstructured":"Yuxi Liu, Fangzhu Shen, Kushagra Ghosh, Amir Gilad, Benny Kimelfeld, and Sudeepa Roy. The cost of representation by subset repairs. arXiv preprint arXiv:2410.16501, 2024."},{"key":"e_1_2_1_29_1","volume-title":"Data sanity check for deep learning systems via learnt assertions. arXiv preprint arXiv:1909.03835","author":"Lu Haochuan","year":"2019","unstructured":"Haochuan Lu, Huanlin Xu, Nana Liu, Yangfan Zhou, and Xin Wang. Data sanity check for deep learning systems via learnt assertions. arXiv preprint arXiv:1909.03835, 2019."},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/2771783.2771792"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/2813885.2737982"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.14778\/2794367.2794377"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.14778\/2856318.2856325"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/581339.581378"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3290350"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/1065010.1065045"},{"key":"e_1_2_1_37_1","doi-asserted-by":"crossref","first-page":"1678","DOI":"10.1145\/3448016.3457250","volume-title":"Proceedings of the 2021 International Conference on Management of Data","author":"Song Jie","year":"2021","unstructured":"Jie Song and Yeye He. Auto-validate: Unsupervised data validation using data-domain patterns inferred from data lakes. In Proceedings of the 2021 International Conference on Management of Data, pages 1678-1691, 2021."},{"key":"e_1_2_1_38_1","volume-title":"prediction, and search","author":"Spirtes Peter","year":"2000","unstructured":"Peter Spirtes, Clark N Glymour, Richard Scheines, and David Heckerman. Causation, prediction, and search. MIT press, 2000."},{"key":"e_1_2_1_39_1","first-page":"4991","volume-title":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Tu Dezhan","year":"2023","unstructured":"Dezhan Tu, Yeye He, Weiwei Cui, Song Ge, Haidong Zhang, Shi Han, Dongmei Zhang, and Surajit Chaudhuri. Auto-validate by-history: Auto-program data quality constraints to validate recurring data pipelines. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 4991-5003, 2023."},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3062341.3062365"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/2588555.2610494"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3299869.3319855"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i10.26451"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3389696"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.14778\/3641204.3641211"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3380568"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313599"},{"key":"e_1_2_1_48_1","first-page":"2327","volume-title":"30th USENIX Security Symposium (USENIX Security 21)","author":"Yang Limin","year":"2021","unstructured":"Limin Yang, Wenbo Guo, Qingying Hao, Arridhana Ciptadi, Ali Ahmadzadeh, Xinyu Xing, and Gang Wang. {CADE}: Detecting and explaining concept drift samples for security applications. In 30th USENIX Security Symposium (USENIX Security 21), pages 2327-2344, 2021."},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11610"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3389749"}],"container-title":["Proceedings of the ACM on Management of Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3749166","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T04:40:49Z","timestamp":1781325649000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3749166"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,22]]},"references-count":50,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,9,22]]}},"alternative-id":["10.1145\/3749166"],"URL":"https:\/\/doi.org\/10.1145\/3749166","relation":{},"ISSN":["2836-6573"],"issn-type":[{"value":"2836-6573","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,22]]}}}