{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T09:50:19Z","timestamp":1773481819651,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":82,"publisher":"ACM","license":[{"start":{"date-parts":[[2017,5,9]],"date-time":"2017-05-09T00:00:00Z","timestamp":1494288000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1629397"],"award-info":[{"award-number":["1629397"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1553169"],"award-info":[{"award-number":["1553169"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1544844"],"award-info":[{"award-number":["1544844"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2017,5,9]]},"DOI":"10.1145\/3035918.3064013","type":"proceedings-article","created":{"date-parts":[[2017,5,10]],"date-time":"2017-05-10T18:09:00Z","timestamp":1494439740000},"page":"587-602","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":49,"title":["Database Learning"],"prefix":"10.1145","author":[{"given":"Yongjoo","family":"Park","sequence":"first","affiliation":[{"name":"University of Michigan, Ann Arbor, Ann Arbor, MI, USA"}]},{"given":"Ahmad Shahab","family":"Tajik","sequence":"additional","affiliation":[{"name":"University of Michigan, Ann Arbor, Ann Arbor, MI, USA"}]},{"given":"Michael","family":"Cafarella","sequence":"additional","affiliation":[{"name":"University of Michigan, Ann Arbor, Ann Arbor, MI, USA"}]},{"given":"Barzan","family":"Mozafari","sequence":"additional","affiliation":[{"name":"University of Michigan, Ann Arbor, Ann Arbor, MI, USA"}]}],"member":"320","published-online":{"date-parts":[[2017,5,9]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"https:\/\/db.apache.org\/derby\/docs\/10.6\/tuning\/ctuntransform36368.html.  https:\/\/db.apache.org\/derby\/docs\/10.6\/tuning\/ctuntransform36368.html."},{"key":"e_1_3_2_1_2_1","volume-title":"VLDB","author":"Acharya S.","year":"1999","unstructured":"S. Acharya , P. B. Gibbons , and V. Poosala . Aqua: A fast decision support system using approximate query answers . In VLDB , 1999 . S. Acharya, P. B. Gibbons, and V. Poosala. Aqua: A fast decision support system using approximate query answers. In VLDB, 1999."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/304181.304207"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/304182.304581"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2588555.2593667"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2465351.2465355"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.14778\/2367502.2367533"},{"key":"e_1_3_2_1_8_1","volume-title":"VLDB","author":"Agrawal S.","year":"2000","unstructured":"S. Agrawal , S. Chaudhuri , and V. R. Narasayya . Automated selection of materialized views and indexes in sql databases . In VLDB , 2000 . S. Agrawal, S. Chaudhuri, and V. R. Narasayya. Automated selection of materialized views and indexes in sql databases. In VLDB, 2000."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/1055558.1055598"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/2723372.2742797"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/872757.872822"},{"key":"e_1_3_2_1_12_1","volume-title":"Dynamic prefetching of data tiles for interactive visualization","author":"Battle L.","year":"2015","unstructured":"L. Battle , R. Chang , and M. Stonebraker . Dynamic prefetching of data tiles for interactive visualization . 2015 . L. Battle, R. Chang, and M. Stonebraker. Dynamic prefetching of data tiles for interactive visualization. 2015."},{"key":"e_1_3_2_1_13_1","volume-title":"A maximum entropy approach to natural language processing. Computational linguistics","author":"Berger A. L.","year":"1996","unstructured":"A. L. Berger , V. J. D. Pietra , and S. A. D. Pietra . A maximum entropy approach to natural language processing. Computational linguistics , 1996 . A. L. Berger, V. J. D. Pietra, and S. A. D. Pietra. A maximum entropy approach to natural language processing. Computational linguistics, 1996."},{"key":"e_1_3_2_1_14_1","volume-title":"Machine Learning","author":"Bishop C. M.","year":"2006","unstructured":"C. M. Bishop . Pattern recognition . Machine Learning , 2006 . C. M. Bishop. Pattern recognition. Machine Learning, 2006."},{"key":"e_1_3_2_1_15_1","volume-title":"Machine learning.","author":"Carbonell J. G.","year":"1983","unstructured":"J. G. Carbonell , R. S. Michalski , and T. M. Mitchell . An overview of machine learning . In Machine learning. 1983 . J. G. Carbonell, R. S. Michalski, and T. M. Mitchell. An overview of machine learning. In Machine learning. 1983."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.5555\/2898607.2898816"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/1242524.1242526"},{"key":"e_1_3_2_1_18_1","volume-title":"NSDI","author":"Condie T.","year":"2010","unstructured":"T. Condie , N. Conway , P. Alvaro , J. M. Hellerstein , K. Elmeleegy , and R. Sears . Mapreduce online . In NSDI , 2010 . T. Condie, N. Conway, P. Alvaro, J. M. Hellerstein, K. Elmeleegy, and R. Sears. Mapreduce online. In NSDI, 2010."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.5555\/977401.978068"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.14778\/1687627.1687675"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.14778\/1687553.1687556"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/2745754.2745771"},{"key":"e_1_3_2_1_23_1","volume-title":"Statistics","author":"Freedman D.","year":"2007","unstructured":"D. Freedman , R. Pisani , and R. Purves . Statistics . 2007 . D. Freedman, R. Pisani, and R. Purves. Statistics. 2007."},{"key":"e_1_3_2_1_24_1","volume-title":"VLDB","author":"Ganti V.","year":"2000","unstructured":"V. Ganti , M.-L. Lee , and R. Ramakrishnan . Icicles: Self-tuning samples for approximate query answering . In VLDB , 2000 . V. Ganti, M.-L. Lee, and R. Ramakrishnan. Icicles: Self-tuning samples for approximate query answering. In VLDB, 2000."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.14778\/2735479.2735494"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDEW.2010.5452743"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/s007780100054"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/253262.253291"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/IDEAS.2006.17"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/1516360.1516487"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-011-0131-7"},{"key":"e_1_3_2_1_32_1","volume-title":"CIDR","author":"Idreos S.","year":"2007","unstructured":"S. Idreos , M. L. Kersten , and S. Manegold . Database cracking . In CIDR , 2007 . S. Idreos, M. L. Kersten, and S. Manegold. Database cracking. In CIDR, 2007."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/1559845.1559878"},{"key":"e_1_3_2_1_34_1","unstructured":"J. S. R. Jang. General formula: Matrix inversion lemma. http:\/\/www.cs.nthu.edu.tw\/ jang\/book\/addenda\/matinv\/matinv\/.  J. S. R. Jang. General formula: Matrix inversion lemma. http:\/\/www.cs.nthu.edu.tw\/ jang\/book\/addenda\/matinv\/matinv\/."},{"key":"e_1_3_2_1_35_1","unstructured":"Y. Jia. Running tpc-h queries on hive. https:\/\/issues.apache.org\/jira\/browse\/HIVE-600.  Y. Jia. Running tpc-h queries on hive. https:\/\/issues.apache.org\/jira\/browse\/HIVE-600."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2016.7498300"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2007.190664"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-008-0095-0"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/2882903.2882940"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-03793-1_6"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.14778\/2735479.2735485"},{"key":"e_1_3_2_1_42_1","volume-title":"NIPS","author":"Lawrence N.","year":"2003","unstructured":"N. Lawrence , M. Seeger , R. Herbrich , Fast sparse gaussian process methods: The informative vector machine . NIPS , 2003 . N. Lawrence, M. Seeger, R. Herbrich, et al. Fast sparse gaussian process methods: The informative vector machine. NIPS, 2003."},{"key":"e_1_3_2_1_43_1","volume-title":"UCI machine learning repository","author":"Lichman M.","year":"2013","unstructured":"M. Lichman . UCI machine learning repository , 2013 . M. Lichman. UCI machine learning repository, 2013."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-04898-2"},{"key":"e_1_3_2_1_45_1","volume-title":"IPSN","author":"Meliou A.","year":"2009","unstructured":"A. Meliou , C. Guestrin , and J. M. Hellerstein . Approximating sensor network queries using in-network summaries . In IPSN , 2009 . A. Meliou, C. Guestrin, and J. M. Hellerstein. Approximating sensor network queries using in-network summaries. In IPSN, 2009."},{"key":"e_1_3_2_1_46_1","volume-title":"JMLR","author":"Micchelli C. A.","year":"2006","unstructured":"C. A. Micchelli , Y. Xu , and H. Zhang . Universal kernels . JMLR , 2006 . C. A. Micchelli, Y. Xu, and H. Zhang. Universal kernels. JMLR, 2006."},{"key":"e_1_3_2_1_47_1","volume-title":"Biennial Conference on Innovative Data Systems","author":"Mozafari B.","year":"2015","unstructured":"B. Mozafari . Verdict : A system for stochastic query planning. In CIDR , Biennial Conference on Innovative Data Systems , 2015 . B. Mozafari. Verdict: A system for stochastic query planning. In CIDR, Biennial Conference on Innovative Data Systems, 2015."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3035918.3056098"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/2723372.2749454"},{"key":"e_1_3_2_1_50_1","volume-title":"A handbook for building an approximate query engine","author":"Mozafari B.","year":"2015","unstructured":"B. Mozafari and N. Niu . A handbook for building an approximate query engine . IEEE Data Eng. Bull ., 2015 . B. Mozafari and N. Niu. A handbook for building an approximate query engine. IEEE Data Eng. Bull., 2015."},{"key":"e_1_3_2_1_51_1","volume-title":"CIDR","author":"Mozafari B.","year":"2017","unstructured":"B. Mozafari , J. Ramnarayan , S. Menon , Y. Mahajan , S. Chakraborty , H. Bhanawat , and K. Bachhav . Snappydata: A unified cluster for streaming, transactions, and interactive analytics . In CIDR , 2017 . B. Mozafari, J. Ramnarayan, S. Menon, Y. Mahajan, S. Chakraborty, H. Bhanawat, and K. Bachhav. Snappydata: A unified cluster for streaming, transactions, and interactive analytics. In CIDR, 2017."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2010.5447867"},{"key":"e_1_3_2_1_53_1","volume-title":"CIDR","author":"Olston C.","year":"2009","unstructured":"C. Olston , E. Bortnikov , K. Elmeleegy , F. Junqueira , and B. Reed . Interactive Analysis of Web-Scale Data . In CIDR , 2009 . C. Olston, E. Bortnikov, K. Elmeleegy, F. Junqueira, and B. Reed. Interactive Analysis of Web-Scale Data. In CIDR, 2009."},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2010.5447826"},{"key":"e_1_3_2_1_55_1","volume-title":"Online aggregation for large mapreduce jobs. PVLDB, 4","author":"Pansare N.","year":"2011","unstructured":"N. Pansare , V. R. Borkar , C. Jermaine , and T. Condie . Online aggregation for large mapreduce jobs. PVLDB, 4 , 2011 . N. Pansare, V. R. Borkar, C. Jermaine, and T. Condie. Online aggregation for large mapreduce jobs. PVLDB, 4, 2011."},{"key":"e_1_3_2_1_56_1","volume-title":"CIDR","author":"Park Y.","year":"2017","unstructured":"Y. Park . Active database learning . In CIDR , 2017 . Y. Park. Active database learning. In CIDR, 2017."},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.14778\/2850583.2850589"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2016.7498287"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/3035918.3064013"},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/2938503.2938526"},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/2723372.2723719"},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/1066157.1066224"},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.14778\/2777598.2777599"},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1145\/2882903.2899408"},{"key":"e_1_3_2_1_65_1","volume-title":"Scalable analytics model calibration with online aggregation","author":"Rusu F.","year":"2015","unstructured":"F. Rusu , C. Qin , and M. Torres . Scalable analytics model calibration with online aggregation . IEEE Data Eng. Bull ., 2015 . F. Rusu, C. Qin, and M. Torres. Scalable analytics model calibration with online aggregation. IEEE Data Eng. Bull., 2015."},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1147\/rd.33.0210"},{"key":"e_1_3_2_1_67_1","volume-title":"VLDB","author":"Sarawagi S.","year":"2000","unstructured":"S. Sarawagi . User-adaptive exploration of multidimensional data . In VLDB , 2000 . S. Sarawagi. User-adaptive exploration of multidimensional data. In VLDB, 2000."},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSST.2010.5496972"},{"key":"e_1_3_2_1_69_1","volume-title":"CIDR","author":"Sidirourgos L.","year":"2011","unstructured":"L. Sidirourgos , M. L. Kersten , and P. A. Boncz . SciBORQ: Scientific data management with Bounds On Runtime and Quality . In CIDR , 2011 . L. Sidirourgos, M. L. Kersten, and P. A. Boncz. SciBORQ: Scientific data management with Bounds On Runtime and Quality. In CIDR, 2011."},{"key":"e_1_3_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.5555\/550312"},{"key":"e_1_3_2_1_71_1","volume-title":"Data Analysis: A Bayesian Tutorial","author":"Skilling J.","year":"2006","unstructured":"J. Skilling . Data Analysis: A Bayesian Tutorial . Oxford University Press , 2006 . J. Skilling. Data Analysis: A Bayesian Tutorial. Oxford University Press, 2006."},{"key":"e_1_3_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2013.6544869"},{"key":"e_1_3_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.14778\/2831360.2831371"},{"key":"e_1_3_2_1_74_1","volume-title":"All of Nonparametric Statistics","author":"Wasserman L.","year":"2006","unstructured":"L. Wasserman . All of Nonparametric Statistics . Springer , 2006 . L. Wasserman. All of Nonparametric Statistics. Springer, 2006."},{"key":"e_1_3_2_1_75_1","volume-title":"NIPS","author":"Williams C. K.","year":"2000","unstructured":"C. K. Williams and M. Seeger . Using the nystr\u00f6m method to speed up kernel machines . In NIPS , 2000 . C. K. Williams and M. Seeger. Using the nystr\u00f6m method to speed up kernel machines. In NIPS, 2000."},{"key":"e_1_3_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.14778\/2732951.2732964"},{"key":"e_1_3_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.1145\/1807167.1807238"},{"key":"e_1_3_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.1145\/1386118.1386122"},{"key":"e_1_3_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.1145\/2723372.2735381"},{"key":"e_1_3_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.1145\/2882903.2915240"},{"key":"e_1_3_2_1_81_1","doi-asserted-by":"publisher","DOI":"10.1145\/2588555.2594532"},{"key":"e_1_3_2_1_82_1","doi-asserted-by":"publisher","DOI":"10.1145\/2588555.2588579"}],"event":{"name":"SIGMOD\/PODS'17: International Conference on Management of Data","location":"Chicago Illinois USA","acronym":"SIGMOD\/PODS'17","sponsor":["SIGMOD ACM Special Interest Group on Management of Data"]},"container-title":["Proceedings of the 2017 ACM International Conference on Management of Data"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3035918.3064013","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3035918.3064013","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3035918.3064013","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T03:36:41Z","timestamp":1750217801000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3035918.3064013"}},"subtitle":["Toward a Database that Becomes Smarter Every Time"],"short-title":[],"issued":{"date-parts":[[2017,5,9]]},"references-count":82,"alternative-id":["10.1145\/3035918.3064013","10.1145\/3035918"],"URL":"https:\/\/doi.org\/10.1145\/3035918.3064013","relation":{},"subject":[],"published":{"date-parts":[[2017,5,9]]},"assertion":[{"value":"2017-05-09","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}