{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T09:09:41Z","timestamp":1775639381676,"version":"3.50.1"},"reference-count":51,"publisher":"Association for Computing Machinery (ACM)","issue":"4","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:p>\n            The question of answering queries over ML predictions has been gaining attention in the database community. This question is challenging because finding high quality answers by invoking an\n            <jats:italic>oracle<\/jats:italic>\n            such as a human expert or an expensive deep neural network model on every single item in the DB and then applying the query, can be prohibitive. We develop a novel unified framework for approximate query answering by leveraging a\n            <jats:italic>proxy<\/jats:italic>\n            to minimize the oracle usage of finding high quality answers for both Precision-Target (PT) and Recall-Target (RT) queries. Our framework uses a judicious combination of invoking the expensive oracle on data samples and applying the cheap proxy on the DB objects. It relies on two assumptions. Under the P\n            <jats:sc>roxy<\/jats:sc>\n            Q\n            <jats:sc>uality<\/jats:sc>\n            assumption, we develop two algorithms: PQA that efficiently finds high quality answers with high probability and no oracle calls, and PQE, a heuristic extension that achieves empirically good performance with a small number of oracle calls. Alternatively, under the C\n            <jats:sc>ore<\/jats:sc>\n            S\n            <jats:sc>et<\/jats:sc>\n            C\n            <jats:sc>losure<\/jats:sc>\n            assumption, we develop two algorithms: CSC that efficiently returns high quality answers with high probability and minimal oracle usage, and CSE, which extends it to more general settings. Our extensive experiments on five real-world datasets on both query types, PT and RT, demonstrate that our algorithms outperform the state-of-the-art and achieve high result quality with provable statistical guarantees.\n          <\/jats:p>","DOI":"10.14778\/3574245.3574273","type":"journal-article","created":{"date-parts":[[2023,2,21]],"date-time":"2023-02-21T23:14:12Z","timestamp":1677021252000},"page":"918-931","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["On Efficient Approximate Queries over Machine Learning Models"],"prefix":"10.14778","volume":"16","author":[{"given":"Dujian","family":"Ding","sequence":"first","affiliation":[{"name":"University of British Columbia, Vancouver, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sihem","family":"Amer-Yahia","sequence":"additional","affiliation":[{"name":"CNRS, Univ. Grenoble Alpes, Grenoble, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laks","family":"Lakshmanan","sequence":"additional","affiliation":[{"name":"University of British Columbia, Vancouver, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,2,21]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019","author":"Ahmed Zeeshan","year":"2019","unstructured":"Zeeshan Ahmed , Saeed Amizadeh , Mikhail Bilenko , Rogan Carr , Wei-Sheng Chin , Yael Dekel , Xavier Dupr\u00e9 , Vadim Eksarevskiy , Senja Filipi , Tom Finley , Abhishek Goswami , Monte Hoover , Scott Inglis , Matteo Interlandi , Najeeb Kazmi , Gleb Krivosheev , Pete Luferenko , Ivan Matantsev , Sergiy Matusevych , Shahab Moradi , Gani Nazirov , Justin Ormont , Gal Oshri , Artidoro Pagnoni , Jignesh Parmar , Prabhat Roy , Mohammad Zeeshan Siddiqui , Markus Weimer , Shauheen Zahirazami , and Yiwen Zhu . 2019 . Machine Learning at Microsoft with ML.NET . In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019 , Anchorage, AK, USA , August 4-8, 2019, Ankur Teredesai, Vipin Kumar, Ying Li, R\u00f3mer Rosales, Evimaria Terzi, and George Karypis (Eds.). ACM, 2448--2458. Zeeshan Ahmed, Saeed Amizadeh, Mikhail Bilenko, Rogan Carr, Wei-Sheng Chin, Yael Dekel, Xavier Dupr\u00e9, Vadim Eksarevskiy, Senja Filipi, Tom Finley, Abhishek Goswami, Monte Hoover, Scott Inglis, Matteo Interlandi, Najeeb Kazmi, Gleb Krivosheev, Pete Luferenko, Ivan Matantsev, Sergiy Matusevych, Shahab Moradi, Gani Nazirov, Justin Ormont, Gal Oshri, Artidoro Pagnoni, Jignesh Parmar, Prabhat Roy, Mohammad Zeeshan Siddiqui, Markus Weimer, Shauheen Zahirazami, and Yiwen Zhu. 2019. Machine Learning at Microsoft with ML.NET. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, August 4-8, 2019, Ankur Teredesai, Vipin Kumar, Ying Li, R\u00f3mer Rosales, Evimaria Terzi, and George Karypis (Eds.). ACM, 2448--2458."},{"key":"e_1_2_1_2_1","volume-title":"Similarity in Patient Support Forums Using TF-IDF and Cosine Similarity Metrics. In 2015 International Conference on Healthcare Informatics. 521--522","author":"Alodadi Mohammad","year":"2015","unstructured":"Mohammad Alodadi and Vandana P. Janeja . 2015 . Similarity in Patient Support Forums Using TF-IDF and Cosine Similarity Metrics. In 2015 International Conference on Healthcare Informatics. 521--522 . 10.1109\/ICHI. 2015 .99 Mohammad Alodadi and Vandana P. Janeja. 2015. Similarity in Patient Support Forums Using TF-IDF and Cosine Similarity Metrics. In 2015 International Conference on Healthcare Informatics. 521--522. 10.1109\/ICHI.2015.99"},{"key":"e_1_2_1_3_1","volume-title":"35th IEEE International Conference on Data Engineering, ICDE 2019","author":"Anderson Michael R.","year":"2019","unstructured":"Michael R. Anderson , Michael J. Cafarella , German Ros , and Thomas F. Wenisch . 2019. Physical Representation-Based Predicate Optimization for a Visual Analytics Database . In 35th IEEE International Conference on Data Engineering, ICDE 2019 , Macao, China , April 8-11, 2019 . 1466--1477. Michael R. Anderson, Michael J. Cafarella, German Ros, and Thomas F. Wenisch. 2019. Physical Representation-Based Predicate Optimization for a Visual Analytics Database. In 35th IEEE International Conference on Data Engineering, ICDE 2019, Macao, China, April 8-11, 2019. 1466--1477."},{"key":"e_1_2_1_4_1","volume-title":"Proceedings of the 2021 International Conference on Management of Data","author":"Augustine Jees","year":"2021","unstructured":"Jees Augustine , Suraj Shetiya , Mohammadreza Esfandiari , Senjuti Basu Roy , and Gautam Das . 2021 . A Generalized Approach for Reducing Expensive Distance Calls for A Broad Class of Proximity Problems . In Proceedings of the 2021 International Conference on Management of Data ( Virtual Event, China) (SIGMOD '21). Association for Computing Machinery, New York, NY, USA, 142--154. 10.1145\/3448016.3457303 Jees Augustine, Suraj Shetiya, Mohammadreza Esfandiari, Senjuti Basu Roy, and Gautam Das. 2021. A Generalized Approach for Reducing Expensive Distance Calls for A Broad Class of Proximity Problems. In Proceedings of the 2021 International Conference on Management of Data (Virtual Event, China) (SIGMOD '21). Association for Computing Machinery, New York, NY, USA, 142--154. 10.1145\/3448016.3457303"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/0020-0190(77)90070-9"},{"key":"e_1_2_1_7_1","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.csda.2018.01.007","article-title":"A simple and fast method for computing the Poisson binomial distribution function","volume":"122","author":"Biscarri William","year":"2018","unstructured":"William Biscarri , Sihai Dave Zhao , and Robert J Brunner . 2018 . A simple and fast method for computing the Poisson binomial distribution function . Computational Statistics & Data Analysis 122 (2018), 92 -- 100 . William Biscarri, Sihai Dave Zhao, and Robert J Brunner. 2018. A simple and fast method for computing the Poisson binomial distribution function. Computational Statistics & Data Analysis 122 (2018), 92--100.","journal-title":"Computational Statistics & Data Analysis"},{"key":"e_1_2_1_8_1","volume-title":"Proceedings of the 34th International Conference on Machine Learning, ICML 2017","volume":"70","author":"Bolukbasi Tolga","year":"2017","unstructured":"Tolga Bolukbasi , Joseph Wang , Ofer Dekel , and Venkatesh Saligrama . 2017 . Adaptive Neural Networks for Efficient Inference . In Proceedings of the 34th International Conference on Machine Learning, ICML 2017 , Sydney, NSW, Australia , 6-11 August 2017 (Proceedings of Machine Learning Research), Doina Precup and Yee Whye Teh (Eds.), Vol. 70 . PMLR, 527--536. Tolga Bolukbasi, Joseph Wang, Ofer Dekel, and Venkatesh Saligrama. 2017. Adaptive Neural Networks for Efficient Inference. In Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017 (Proceedings of Machine Learning Research), Doina Precup and Yee Whye Teh (Eds.), Vol. 70. PMLR, 527--536."},{"key":"e_1_2_1_9_1","first-page":"8","article-title":"Missed opportunities for prevention in general internal medicine","volume":"160","author":"Brull R","year":"1999","unstructured":"R Brull , W A Ghali , and H Quan . 1999 . Missed opportunities for prevention in general internal medicine . CMAJ 160 , 8 (Apr 1999), 1137--1140. R Brull, W A Ghali, and H Quan. 1999. Missed opportunities for prevention in general internal medicine. CMAJ 160, 8 (Apr 1999), 1137--1140.","journal-title":"CMAJ"},{"key":"e_1_2_1_10_1","volume-title":"Dulloor","author":"Canel Christopher","year":"2019","unstructured":"Christopher Canel , Thomas Kim , Giulio Zhou , Conglong Li , Hyeontaek Lim , David G. Andersen , Michael Kaminsky , and Subramanya R . Dulloor . 2019 . Scaling Video Analytics on Constrained Edge Nodes. CoRR abs\/1905.13536 (2019). arXiv:1905.13536 http:\/\/arxiv.org\/abs\/1905.13536 Christopher Canel, Thomas Kim, Giulio Zhou, Conglong Li, Hyeontaek Lim, David G. Andersen, Michael Kaminsky, and Subramanya R. Dulloor. 2019. Scaling Video Analytics on Constrained Edge Nodes. CoRR abs\/1905.13536 (2019). arXiv:1905.13536 http:\/\/arxiv.org\/abs\/1905.13536"},{"key":"e_1_2_1_11_1","volume-title":"Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence","author":"Cao Yue","year":"2016","unstructured":"Yue Cao , Mingsheng Long , Jianmin Wang , Han Zhu , and Qingfu Wen . 2016 . Deep Quantization Network for Efficient Image Retrieval . In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence ( Phoenix, Arizona) (AAAI'16). AAAI Press, 3457--3463. Yue Cao, Mingsheng Long, Jianmin Wang, Han Zhu, and Qingfu Wen. 2016. Deep Quantization Network for Efficient Image Retrieval. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (Phoenix, Arizona) (AAAI'16). AAAI Press, 3457--3463."},{"key":"e_1_2_1_12_1","volume-title":"TVM: An Automated End-to-End Optimizing Compiler for Deep Learning. In 13th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2018","author":"Chen Tianqi","year":"2018","unstructured":"Tianqi Chen , Thierry Moreau , Ziheng Jiang , Lianmin Zheng , Eddie Q. Yan , Haichen Shen , Meghan Cowan , Leyuan Wang , Yuwei Hu , Luis Ceze , Carlos Guestrin , and Arvind Krishnamurthy . 2018 . TVM: An Automated End-to-End Optimizing Compiler for Deep Learning. In 13th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2018 , Carlsbad, CA, USA , October 8-10, 2018, Andrea C. Arpaci-Dusseau and Geoff Voelker (Eds.). USENIX Association, 578--594. Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Q. Yan, Haichen Shen, Meghan Cowan, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, and Arvind Krishnamurthy. 2018. TVM: An Automated End-to-End Optimizing Compiler for Deep Learning. In 13th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2018, Carlsbad, CA, USA, October 8-10, 2018, Andrea C. Arpaci-Dusseau and Geoff Voelker (Eds.). USENIX Association, 578--594."},{"key":"e_1_2_1_13_1","volume-title":"Lew","author":"Chen Wei","year":"2021","unstructured":"Wei Chen , Yu Liu , Weiping Wang , Erwin Bakker , Theodoros Georgiou , Paul Fieguth , Li Liu , and Michael S . Lew . 2021 . Deep Image Retrieval: A Survey . arXiv:2101.11282 [cs.CV] Wei Chen, Yu Liu, Weiping Wang, Erwin Bakker, Theodoros Georgiou, Paul Fieguth, Li Liu, and Michael S. Lew. 2021. Deep Image Retrieval: A Survey. arXiv:2101.11282 [cs.CV]"},{"key":"e_1_2_1_14_1","volume-title":"Multi-Label Image Recognition With Graph Convolutional Networks. In 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 5172--5181","author":"Chen Zhao-Min","year":"2019","unstructured":"Zhao-Min Chen , Xiu-Shen Wei , Peng Wang , and Yanwen Guo . 2019 . Multi-Label Image Recognition With Graph Convolutional Networks. In 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 5172--5181 . 10.1109\/CVPR.2019.00532 Zhao-Min Chen, Xiu-Shen Wei, Peng Wang, and Yanwen Guo. 2019. Multi-Label Image Recognition With Graph Convolutional Networks. In 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 5172--5181. 10.1109\/CVPR.2019.00532"},{"key":"e_1_2_1_15_1","volume-title":"Andy Schuetz, Walter F. Stewart, and Jimeng Sun.","author":"Choi Edward","year":"2016","unstructured":"Edward Choi , Mohammad Taha Bahadori , Andy Schuetz, Walter F. Stewart, and Jimeng Sun. 2016 . Doctor AI : Predicting Clinical Events via Recurrent Neural Networks . arXiv:1511.05942 [cs.LG] Edward Choi, Mohammad Taha Bahadori, Andy Schuetz, Walter F. Stewart, and Jimeng Sun. 2016. Doctor AI: Predicting Clinical Events via Recurrent Neural Networks. arXiv:1511.05942 [cs.LG]"},{"key":"e_1_2_1_16_1","volume-title":"Clipper: A Low-Latency Online Prediction Serving System. In 14th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2017","author":"Crankshaw Daniel","year":"2017","unstructured":"Daniel Crankshaw , Xin Wang , Giulio Zhou , Michael J. Franklin , Joseph E. Gonzalez , and Ion Stoica . 2017 . Clipper: A Low-Latency Online Prediction Serving System. In 14th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2017 , Boston, MA, USA , March 27-29, 2017, Aditya Akella and Jon Howell (Eds.). USENIX Association, 613--627. Daniel Crankshaw, Xin Wang, Giulio Zhou, Michael J. Franklin, Joseph E. Gonzalez, and Ion Stoica. 2017. Clipper: A Low-Latency Online Prediction Serving System. In 14th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2017, Boston, MA, USA, March 27-29, 2017, Aditya Akella and Jon Howell (Eds.). USENIX Association, 613--627."},{"key":"e_1_2_1_17_1","volume-title":"Proceedings of the Fourth International Workshop on Data Management for End-to-End Machine Learning","author":"Das Piali","year":"2020","unstructured":"Piali Das , Nikita Ivkin , Tanya Bansal , Laurence Rouesnel , Philip Gautier , Zohar Karnin , Leo Dirac , Lakshmi Ramakrishnan , Andre Perunicic , Iaroslav Shcherbatyi , Wilton Wu , Aida Zolic , Huibin Shen , Amr Ahmed , Fela Winkelmolen , Miroslav Miladinovic , Cedric Archembeau , Alex Tang , Bhaskar Dutt , Patricia Grao , and Kumar Venkateswar . 2020 . Amazon SageMaker Autopilot: A White Box AutoML Solution at Scale . In Proceedings of the Fourth International Workshop on Data Management for End-to-End Machine Learning ( Portland, OR, USA) (DEEM'20). Association for Computing Machinery, New York, NY, USA, Article 2, 7 pages. 10.1145\/3399579.3399870 Piali Das, Nikita Ivkin, Tanya Bansal, Laurence Rouesnel, Philip Gautier, Zohar Karnin, Leo Dirac, Lakshmi Ramakrishnan, Andre Perunicic, Iaroslav Shcherbatyi, Wilton Wu, Aida Zolic, Huibin Shen, Amr Ahmed, Fela Winkelmolen, Miroslav Miladinovic, Cedric Archembeau, Alex Tang, Bhaskar Dutt, Patricia Grao, and Kumar Venkateswar. 2020. Amazon SageMaker Autopilot: A White Box AutoML Solution at Scale. In Proceedings of the Fourth International Workshop on Data Management for End-to-End Machine Learning (Portland, OR, USA) (DEEM'20). Association for Computing Machinery, New York, NY, USA, Article 2, 7 pages. 10.1145\/3399579.3399870"},{"key":"e_1_2_1_18_1","volume-title":"2016 Federated Conference on Computer Science and Information Systems (FedCSIS). 807--816","author":"Deniziak Stanislaw","unstructured":"Stanislaw Deniziak and Tomasz Michno .2016. Content based image retrieval using query by approximate shape . In 2016 Federated Conference on Computer Science and Information Systems (FedCSIS). 807--816 . Stanislaw Deniziak and Tomasz Michno.2016. Content based image retrieval using query by approximate shape. In 2016 Federated Conference on Computer Science and Information Systems (FedCSIS). 807--816."},{"key":"e_1_2_1_19_1","unstructured":"Dujian Ding Sihem Amer-Yahia and Laks VS Lakshmanan. 2022. On Efficient Approximate Queries over Machine Learning Models. 10.48550\/ARXIV.2206.02845  Dujian Ding Sihem Amer-Yahia and Laks VS Lakshmanan. 2022. On Efficient Approximate Queries over Machine Learning Models. 10.48550\/ARXIV.2206.02845"},{"key":"e_1_2_1_20_1","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The Pascal Visual Object Classes (VOC) Challenge","volume":"88","author":"Everingham Mark","year":"2010","unstructured":"Mark Everingham , Luc Van Gool , Christopher K. I. Williams , John Winn , and Andrew Zisserman . 2010 . The Pascal Visual Object Classes (VOC) Challenge . International Journal of Computer Vision 88 , 2 (2010), 303 -- 338 . Mark Everingham, Luc Van Gool, Christopher K. I. Williams, John Winn, and Andrew Zisserman. 2010. The Pascal Visual Object Classes (VOC) Challenge. International Journal of Computer Vision 88, 2 (2010), 303--338.","journal-title":"International Journal of Computer Vision"},{"key":"e_1_2_1_21_1","volume-title":"Proceedings of the 19th International Database Engineering & Applications Symposium","author":"Fernandes S\u00e9rgio","year":"2015","unstructured":"S\u00e9rgio Fernandes and Jorge Bernardino . 2015 . What is BigQuery? . In Proceedings of the 19th International Database Engineering & Applications Symposium ( Yokohama, Japan) (IDEAS '15). Association for Computing Machinery, New York, NY, USA, 202--203. 10.1145\/2790755.2790797 S\u00e9rgio Fernandes and Jorge Bernardino. 2015. What is BigQuery?. In Proceedings of the 19th International Database Engineering & Applications Symposium (Yokohama, Japan) (IDEAS '15). Association for Computing Machinery, New York, NY, USA, 202--203. 10.1145\/2790755.2790797"},{"key":"e_1_2_1_22_1","volume-title":"Efficiently Answering Durability Prediction Queries. In SIGMOD '21: International Conference on Management of Data","author":"Gao Junyang","year":"2021","unstructured":"Junyang Gao , Yifan Xu , Pankaj K. Agarwal , and Jun Yang . 2021 . Efficiently Answering Durability Prediction Queries. In SIGMOD '21: International Conference on Management of Data , Virtual Event, China , June 20-25, 2021. 591--604. Junyang Gao, Yifan Xu, Pankaj K. Agarwal, and Jun Yang. 2021. Efficiently Answering Durability Prediction Queries. In SIGMOD '21: International Conference on Management of Data, Virtual Event, China, June 20-25, 2021. 591--604."},{"key":"e_1_2_1_23_1","volume-title":"Probability: An Introduction","author":"Grimmett Geoffrey R.","year":"1986","unstructured":"Geoffrey R. Grimmett . 1986 . Probability: An Introduction . Oxford University Press . Geoffrey R. Grimmett. 1986. Probability: An Introduction. Oxford University Press."},{"key":"e_1_2_1_24_1","volume-title":"Girshick","author":"He Kaiming","year":"2017","unstructured":"Kaiming He , Georgia Gkioxari , Piotr Doll\u00e1r , and Ross B . Girshick . 2017 . Mask R-CNN. CoRR abs\/1703.06870 (2017). arXiv:1703.06870 http:\/\/arxiv.org\/abs\/1703.06870 Kaiming He, Georgia Gkioxari, Piotr Doll\u00e1r, and Ross B. Girshick. 2017. Mask R-CNN. CoRR abs\/1703.06870 (2017). arXiv:1703.06870 http:\/\/arxiv.org\/abs\/1703.06870"},{"key":"e_1_2_1_25_1","volume-title":"Deep Residual Learning for Image Recognition. CoRR abs\/1512.03385","author":"He Kaiming","year":"2015","unstructured":"Kaiming He , Xiangyu Zhang , Shaoqing Ren , and Jian Sun . 2015. Deep Residual Learning for Image Recognition. CoRR abs\/1512.03385 ( 2015 ). arXiv:1512.03385 http:\/\/arxiv.org\/abs\/1512.03385 Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015. Deep Residual Learning for Image Recognition. CoRR abs\/1512.03385 (2015). arXiv:1512.03385 http:\/\/arxiv.org\/abs\/1512.03385"},{"key":"e_1_2_1_26_1","volume-title":"Focus: Querying Large Video Datasets with Low Latency and Low Cost. arXiv:1801.03493 [cs.DB]","author":"Hsieh Kevin","year":"2018","unstructured":"Kevin Hsieh , Ganesh Ananthanarayanan , Peter Bodik , Paramvir Bahl , Matthai Philipose , Phillip B. Gibbons , and Onur Mutlu . 2018 . Focus: Querying Large Video Datasets with Low Latency and Low Cost. arXiv:1801.03493 [cs.DB] Kevin Hsieh, Ganesh Ananthanarayanan, Peter Bodik, Paramvir Bahl, Matthai Philipose, Phillip B. Gibbons, and Onur Mutlu. 2018. Focus: Querying Large Video Datasets with Low Latency and Low Cost. arXiv:1801.03493 [cs.DB]"},{"key":"e_1_2_1_27_1","doi-asserted-by":"crossref","first-page":"160035","DOI":"10.1038\/sdata.2016.35","article-title":"MIMIC-III, a freely accessible critical care database","volume":"3","author":"Johnson Alistair E. W.","year":"2016","unstructured":"Alistair E. W. Johnson , Tom J. Pollard , Lu Shen , Li-wei H. Lehman , Mengling Feng , Mohammad Ghassemi , Benjamin Moody , Peter Szolovits , Leo Anthony Celi , and Roger G. Mark . 2016 . MIMIC-III, a freely accessible critical care database . Scientific Data 3 , 1 (2016), 160035 . Alistair E. W. Johnson, Tom J. Pollard, Lu Shen, Li-wei H. Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G. Mark. 2016. MIMIC-III, a freely accessible critical care database. Scientific Data 3, 1 (2016), 160035.","journal-title":"Scientific Data"},{"key":"e_1_2_1_28_1","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1016\/j.ins.2020.09.024","article-title":"LIG-Doctor: Efficient patient trajectory prediction using bidirectional minimal gated-recurrent networks","volume":"545","author":"Rodrigues Jos\u00e9 F.","year":"2021","unstructured":"Jos\u00e9 F. Rodrigues Jr ., Marco Antonio Gutierrez , Gabriel Spadon , Bruno Brandoli , and Sihem Amer-Yahia . 2021 . LIG-Doctor: Efficient patient trajectory prediction using bidirectional minimal gated-recurrent networks . Inf. Sci. 545 (2021), 813 -- 827 . Jos\u00e9 F. Rodrigues Jr., Marco Antonio Gutierrez, Gabriel Spadon, Bruno Brandoli, and Sihem Amer-Yahia. 2021. LIG-Doctor: Efficient patient trajectory prediction using bidirectional minimal gated-recurrent networks. Inf. Sci. 545 (2021), 813--827.","journal-title":"Inf. Sci."},{"key":"e_1_2_1_29_1","doi-asserted-by":"crossref","first-page":"1586","DOI":"10.14778\/3137628.3137664","article-title":"NoScope: Optimizing Deep CNN-Based Queries over Video Streams at Scale","volume":"10","author":"Kang Daniel","year":"2017","unstructured":"Daniel Kang , John Emmons , Firas Abuzaid , Peter Bailis , and Matei Zaharia . 2017 . NoScope: Optimizing Deep CNN-Based Queries over Video Streams at Scale . Proc. VLDB Endow. 10 , 11 (2017), 1586 -- 1597 . Daniel Kang, John Emmons, Firas Abuzaid, Peter Bailis, and Matei Zaharia. 2017. NoScope: Optimizing Deep CNN-Based Queries over Video Streams at Scale. Proc. VLDB Endow. 10, 11 (2017), 1586--1597.","journal-title":"Proc. VLDB Endow."},{"key":"e_1_2_1_30_1","doi-asserted-by":"crossref","first-page":"1990","DOI":"10.14778\/3407790.3407804","article-title":"Approximate Selection with Guarantees using Proxies","volume":"13","author":"Kang Daniel","year":"2020","unstructured":"Daniel Kang , Edward Gan , Peter Bailis , Tatsunori Hashimoto , and Matei Zaharia . 2020 . Approximate Selection with Guarantees using Proxies . Proc. VLDB Endow. 13 , 11 (2020), 1990 -- 2003 . Daniel Kang, Edward Gan, Peter Bailis, Tatsunori Hashimoto, and Matei Zaharia. 2020. Approximate Selection with Guarantees using Proxies. Proc. VLDB Endow. 13, 11 (2020), 1990--2003.","journal-title":"Proc. VLDB Endow."},{"key":"e_1_2_1_31_1","volume-title":"10th Conference on Innovative Data Systems Research, CIDR","author":"Karanasos Konstantinos","year":"2020","unstructured":"Konstantinos Karanasos , Matteo Interlandi , Fotis Psallidas , Rathijit Sen , Kwanghyun Park , Ivan Popivanov , Doris Xin , Supun Nakandala , Subru Krishnan , Markus Weimer , Yuan Yu , Raghu Ramakrishnan , and Carlo Curino . 2020. Extending Relational Query Processing with ML Inference . In 10th Conference on Innovative Data Systems Research, CIDR 2020 , Amsterdam, The Netherlands , January 12-15, 2020, Online Proceedings . www.cidrdb.org. Konstantinos Karanasos, Matteo Interlandi, Fotis Psallidas, Rathijit Sen, Kwanghyun Park, Ivan Popivanov, Doris Xin, Supun Nakandala, Subru Krishnan, Markus Weimer, Yuan Yu, Raghu Ramakrishnan, and Carlo Curino. 2020. Extending Relational Query Processing with ML Inference. In 10th Conference on Innovative Data Systems Research, CIDR 2020, Amsterdam, The Netherlands, January 12-15, 2020, Online Proceedings. www.cidrdb.org."},{"key":"e_1_2_1_32_1","volume-title":"2016 4th International Conference on Cyber and IT Service Management. 1--6. 10","author":"Lahitani Alfirna Rizqi","year":"2016","unstructured":"Alfirna Rizqi Lahitani , Adhistya Erna Permanasari , and Noor Akhmad Setiawan . 2016 . Cosine similarity to determine similarity measure: Study case in online essay assessment . In 2016 4th International Conference on Cyber and IT Service Management. 1--6. 10 .1109\/CITSM.2016.7577578 Alfirna Rizqi Lahitani, Adhistya Erna Permanasari, and Noor Akhmad Setiawan. 2016. Cosine similarity to determine similarity measure: Study case in online essay assessment. In 2016 4th International Conference on Cyber and IT Service Management. 1--6. 10.1109\/CITSM.2016.7577578"},{"key":"e_1_2_1_33_1","volume-title":"Proceedings of the 2021 International Conference on Management of Data","author":"Lai Ziliang","year":"2021","unstructured":"Ziliang Lai , Chenxia Han , Chris Liu , Pengfei Zhang , Eric Lo , and Ben Kao . 2021 . Top-K Deep Video Analytics: A Probabilistic Approach . In Proceedings of the 2021 International Conference on Management of Data ( Virtual Event, China) (SIGMOD '21). Association for Computing Machinery, New York, NY, USA, 1037--1050. 10.1145\/3448016.3452786 Ziliang Lai, Chenxia Han, Chris Liu, Pengfei Zhang, Eric Lo, and Ben Kao. 2021. Top-K Deep Video Analytics: A Probabilistic Approach. In Proceedings of the 2021 International Conference on Management of Data (Virtual Event, China) (SIGMOD '21). Association for Computing Machinery, New York, NY, USA, 1037--1050. 10.1145\/3448016.3452786"},{"key":"e_1_2_1_34_1","first-page":"379","article-title":"Approximate Query Processing: What is New and Where to Go? - A Survey on Approximate Query Processing. Data Sci","volume":"3","author":"Li Kaiyu","year":"2018","unstructured":"Kaiyu Li and Guoliang Li . 2018 . Approximate Query Processing: What is New and Where to Go? - A Survey on Approximate Query Processing. Data Sci . Eng. 3 , 4 (2018), 379 -- 397 . Kaiyu Li and Guoliang Li. 2018. Approximate Query Processing: What is New and Where to Go? - A Survey on Approximate Query Processing. Data Sci. Eng. 3, 4 (2018), 379--397.","journal-title":"Eng."},{"key":"e_1_2_1_35_1","doi-asserted-by":"crossref","first-page":"7155","DOI":"10.1038\/s41598-020-62922-y","article-title":"BEHRT: Transformer for Electronic Health Records","volume":"10","author":"Li Yikuan","year":"2020","unstructured":"Yikuan Li , Shishir Rao , Jos\u00e9Roberto Ayala Solares , Abdelaali Hassaine , Rema Ramakrishnan , Dexter Canoy , Yajie Zhu , Kazem Rahimi , and Gholamreza Salimi-Khorshidi . 2020 . BEHRT: Transformer for Electronic Health Records . Scientific Reports 10 , 1 (2020), 7155 . Yikuan Li, Shishir Rao, Jos\u00e9Roberto Ayala Solares, Abdelaali Hassaine, Rema Ramakrishnan, Dexter Canoy, Yajie Zhu, Kazem Rahimi, and Gholamreza Salimi-Khorshidi. 2020. BEHRT: Transformer for Electronic Health Records. Scientific Reports 10, 1 (2020), 7155.","journal-title":"Scientific Reports"},{"key":"e_1_2_1_36_1","volume-title":"Computer Vision - ECCV","author":"Lin Tsung-Yi","year":"2014","unstructured":"Tsung-Yi Lin , Michael Maire , Serge Belongie , James Hays , Pietro Perona , Deva Ramanan , Piotr Doll\u00e1r , and C. Lawrence Zitnick . 2014. Microsoft COCO: Common Objects in Context . In Computer Vision - ECCV 2014 , David Fleet, Tomas Pajdla , Bernt Schiele, and Tinne Tuytelaars (Eds.). Springer International Publishing , Cham, 740--755. Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll\u00e1r, and C. Lawrence Zitnick. 2014. Microsoft COCO: Common Objects in Context. In Computer Vision - ECCV 2014, David Fleet, Tomas Pajdla, Bernt Schiele, and Tinne Tuytelaars (Eds.). Springer International Publishing, Cham, 740--755."},{"key":"e_1_2_1_37_1","doi-asserted-by":"crossref","first-page":"1418","DOI":"10.1002\/1097-0142(19940815)74:4+<1418::AID-CNCR2820741604>3.0.CO;2-5","article-title":"Cancer prevention through health promotion: Defining the role of physicians in public health","volume":"74","author":"Love Richard R","year":"1994","unstructured":"Richard R Love . 1994 . Cancer prevention through health promotion: Defining the role of physicians in public health . Cancer 74 , S4 (1994), 1418 -- 1422 . Richard R Love. 1994. Cancer prevention through health promotion: Defining the role of physicians in public health. Cancer 74, S4 (1994), 1418--1422.","journal-title":"Cancer"},{"key":"e_1_2_1_38_1","volume-title":"Proceedings of the 2018 International Conference on Management of Data, SIGMOD Conference 2018","author":"Lu Yao","year":"2018","unstructured":"Yao Lu , Aakanksha Chowdhery , Srikanth Kandula , and Surajit Chaudhuri . 2018 . Accelerating Machine Learning Inference with Probabilistic Predicates . In Proceedings of the 2018 International Conference on Management of Data, SIGMOD Conference 2018 , Houston, TX, USA , June 10-15, 2018. 1493--1508. Yao Lu, Aakanksha Chowdhery, Srikanth Kandula, and Surajit Chaudhuri. 2018. Accelerating Machine Learning Inference with Probabilistic Predicates. In Proceedings of the 2018 International Conference on Management of Data, SIGMOD Conference 2018, Houston, TX, USA, June 10-15, 2018. 1493--1508."},{"key":"e_1_2_1_39_1","first-page":"49","article-title":"Methods for Similarity Query on Uncertain Data with Cosine Similarity Constraints","volume":"12","author":"Mingdong ZHU","year":"2018","unstructured":"ZHU Mingdong , XU Lixin , SHEN Derong , KOU Yue , and NIE Tiezheng . 2018 . Methods for Similarity Query on Uncertain Data with Cosine Similarity Constraints . Journal of Frontiers of Computer Science & Technology 12 , 1 (2018), 49 . ZHU Mingdong, XU Lixin, SHEN Derong, KOU Yue, and NIE Tiezheng. 2018. Methods for Similarity Query on Uncertain Data with Cosine Similarity Constraints. Journal of Frontiers of Computer Science & Technology 12, 1 (2018), 49.","journal-title":"Journal of Frontiers of Computer Science & Technology"},{"key":"e_1_2_1_41_1","unstructured":"N. Unnikrishnan Nair P. G. Sankaran and N. Balakrishnan. 2013. Stochastic Orders in Reliability. Springer New York New York NY 281--326. 10.1007\/978-0-8176-8361-0_8  N. Unnikrishnan Nair P. G. Sankaran and N. Balakrishnan. 2013. Stochastic Orders in Reliability. Springer New York New York NY 281--326. 10.1007\/978-0-8176-8361-0_8"},{"key":"e_1_2_1_42_1","doi-asserted-by":"crossref","first-page":"180178","DOI":"10.1038\/sdata.2018.178","article-title":"The eICU Collaborative Research Database, a freely available multi-center database for critical care research","volume":"5","author":"Pollard Tom J.","year":"2018","unstructured":"Tom J. Pollard , Alistair E. W. Johnson , Jesse D. Raffa , Leo A. Celi , Roger G. Mark , and Omar Badawi . 2018 . The eICU Collaborative Research Database, a freely available multi-center database for critical care research . Scientific Data 5 , 1 (2018), 180178 . Tom J. Pollard, Alistair E. W. Johnson, Jesse D. Raffa, Leo A. Celi, Roger G. Mark, and Omar Badawi. 2018. The eICU Collaborative Research Database, a freely available multi-center database for critical care research. Scientific Data 5, 1 (2018), 180178.","journal-title":"Scientific Data"},{"key":"e_1_2_1_43_1","doi-asserted-by":"crossref","unstructured":"Joseph Redmon Santosh Divvala Ross Girshick and Ali Farhadi. 2016. You Only Look Once: Unified Real-Time Object Detection. arXiv:1506.02640 [cs.CV]  Joseph Redmon Santosh Divvala Ross Girshick and Ali Farhadi. 2016. You Only Look Once: Unified Real-Time Object Detection. arXiv:1506.02640 [cs.CV]","DOI":"10.1109\/CVPR.2016.91"},{"key":"e_1_2_1_44_1","doi-asserted-by":"crossref","unstructured":"Joseph Redmon and Ali Farhadi. 2016. YOLO9000: Better Faster Stronger. arXiv:1612.08242 [cs.CV]  Joseph Redmon and Ali Farhadi. 2016. YOLO9000: Better Faster Stronger. arXiv:1612.08242 [cs.CV]","DOI":"10.1109\/CVPR.2017.690"},{"key":"e_1_2_1_45_1","volume-title":"An Extensive Investigation of Machine Learning Techniques for Sleep Apnea Screening. In CIKM '20: The 29th ACM International Conference on Information and Knowledge Management","author":"Rodrigues Jose F.","year":"2020","unstructured":"Jose F. Rodrigues , Jean Louis P\u00e9pin , Lorraine Goeuriot , and Sihem Amer-Yahia . 2020 . An Extensive Investigation of Machine Learning Techniques for Sleep Apnea Screening. In CIKM '20: The 29th ACM International Conference on Information and Knowledge Management , Virtual Event, Ireland , October 19-23, 2020. 2709--2716. Jose F. Rodrigues, Jean Louis P\u00e9pin, Lorraine Goeuriot, and Sihem Amer-Yahia. 2020. An Extensive Investigation of Machine Learning Techniques for Sleep Apnea Screening. In CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, October 19-23, 2020. 2709--2716."},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.09.024"},{"key":"e_1_2_1_47_1","first-page":"5","article-title":"Time allocation in primary care office visits","volume":"42","author":"Tai-Seale Ming","year":"2007","unstructured":"Ming Tai-Seale , Thomas G McGuire , and Weimin Zhang . 2007 . Time allocation in primary care office visits . Health Serv Res 42 , 5 (Oct 2007), 1871--1894. Ming Tai-Seale, Thomas G McGuire, and Weimin Zhang. 2007. Time allocation in primary care office visits. Health Serv Res 42, 5 (Oct 2007), 1871--1894.","journal-title":"Health Serv Res"},{"key":"e_1_2_1_48_1","volume-title":"VLDB","author":"Theobald Martin","year":"2004","unstructured":"Martin Theobald , Gerhard Weikum , and Ralf Schenkel . 2004. Top-k Query Evaluation with Probabilistic Guarantees. In (e)Proceedings of the Thirtieth International Conference on Very Large Data Bases , VLDB 2004 , Toronto, Canada, August 31 - September 3 2004, Mario A. Nascimento, M. Tamer \u00d6zsu, Donald Kossmann, Ren\u00e9e J. Miller, Jos\u00e9 A. Blakeley, and K. Bernhard Schiefer (Eds.). Morgan Kaufmann , 648--659. Martin Theobald, Gerhard Weikum, and Ralf Schenkel. 2004. Top-k Query Evaluation with Probabilistic Guarantees. In (e)Proceedings of the Thirtieth International Conference on Very Large Data Bases, VLDB 2004, Toronto, Canada, August 31 - September 3 2004, Mario A. Nascimento, M. Tamer \u00d6zsu, Donald Kossmann, Ren\u00e9e J. Miller, Jos\u00e9 A. Blakeley, and K. Bernhard Schiefer (Eds.). Morgan Kaufmann, 648--659."},{"key":"e_1_2_1_49_1","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1001\/archinte.1959.00270010170027","article-title":"Preventive Medicine for the Doctor in His Community: An Epidemiologic Approach","volume":"103","author":"Top Franklin H","year":"1959","unstructured":"Franklin H Top . 1959 . Preventive Medicine for the Doctor in His Community: An Epidemiologic Approach . AMA Archives of Internal Medicine 103 , 1 (1959), 164 -- 165 . Franklin H Top. 1959. Preventive Medicine for the Doctor in His Community: An Epidemiologic Approach. AMA Archives of Internal Medicine 103, 1 (1959), 164--165.","journal-title":"AMA Archives of Internal Medicine"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1017\/9781108231596"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.14778\/3547305.3547310"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2709749"},{"key":"e_1_2_1_53_1","first-page":"1","article-title":"Database Meets Artificial Intelligence: A Survey","volume":"1","author":"Zhou Xuanhe","year":"2020","unstructured":"Xuanhe Zhou , Chengliang Chai , Guoliang Li , and JI SUN. 2020 . Database Meets Artificial Intelligence: A Survey . IEEE Transactions on Knowledge and Data Engineering 1 , 1 (2020), 1 -- 18 . Xuanhe Zhou, Chengliang Chai, Guoliang Li, and JI SUN. 2020. Database Meets Artificial Intelligence: A Survey. IEEE Transactions on Knowledge and Data Engineering 1, 1 (2020), 1--18.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3574245.3574273","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,21]],"date-time":"2023-02-21T23:22:00Z","timestamp":1677021720000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3574245.3574273"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12]]},"references-count":51,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["10.14778\/3574245.3574273"],"URL":"https:\/\/doi.org\/10.14778\/3574245.3574273","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2022,12]]},"assertion":[{"value":"2023-02-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}