{"id":"https://openalex.org/W3111141572","doi":"https://doi.org/10.14778/3461535.3461552","title":"Are we ready for learned cardinality estimation?","display_name":"Are we ready for learned cardinality estimation?","publication_year":2021,"publication_date":"2021-05-01","ids":{"openalex":"https://openalex.org/W3111141572","doi":"https://doi.org/10.14778/3461535.3461552","mag":"3111141572"},"language":"en","primary_location":{"id":"doi:10.14778/3461535.3461552","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3461535.3461552","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2012.06743","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Xiaoying Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I18014758","display_name":"Simon Fraser University","ror":"https://ror.org/0213rcc28","country_code":"CA","type":"education","lineage":["https://openalex.org/I18014758"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Xiaoying Wang","raw_affiliation_strings":["Simon Fraser University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Simon Fraser University","institution_ids":["https://openalex.org/I18014758"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Changbo Qu","orcid":null},"institutions":[{"id":"https://openalex.org/I18014758","display_name":"Simon Fraser University","ror":"https://ror.org/0213rcc28","country_code":"CA","type":"education","lineage":["https://openalex.org/I18014758"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Changbo Qu","raw_affiliation_strings":["Simon Fraser University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Simon Fraser University","institution_ids":["https://openalex.org/I18014758"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Weiyuan Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I18014758","display_name":"Simon Fraser University","ror":"https://ror.org/0213rcc28","country_code":"CA","type":"education","lineage":["https://openalex.org/I18014758"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Weiyuan Wu","raw_affiliation_strings":["Simon Fraser University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Simon Fraser University","institution_ids":["https://openalex.org/I18014758"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jiannan Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I18014758","display_name":"Simon Fraser University","ror":"https://ror.org/0213rcc28","country_code":"CA","type":"education","lineage":["https://openalex.org/I18014758"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jiannan Wang","raw_affiliation_strings":["Simon Fraser University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Simon Fraser University","institution_ids":["https://openalex.org/I18014758"]}]},{"author_position":"last","author":{"id":null,"display_name":"Qingqing Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingqing Zhou","raw_affiliation_strings":["Tencent Inc"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent Inc","institution_ids":["https://openalex.org/I2250653659"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I18014758"],"apc_list":null,"apc_paid":null,"fwci":10.0939,"has_fulltext":false,"cited_by_count":93,"citation_normalized_percentile":{"value":0.98480875,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"14","issue":"9","first_page":"1640","last_page":"1654"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10317","display_name":"Advanced Database Systems and Queries","score":0.5083000063896179,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10317","display_name":"Advanced Database Systems and Queries","score":0.5083000063896179,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11719","display_name":"Data Quality and Management","score":0.3025999963283539,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10286","display_name":"Information Retrieval and Search Behavior","score":0.05700000002980232,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/cardinality","display_name":"Cardinality (data modeling)","score":0.7551000118255615},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6313999891281128},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5889999866485596},{"id":"https://openalex.org/keywords/ask-price","display_name":"Ask price","score":0.5651000142097473},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5078999996185303},{"id":"https://openalex.org/keywords/workload","display_name":"Workload","score":0.4131999909877777},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.37049999833106995}],"concepts":[{"id":"https://openalex.org/C87117476","wikidata":"https://www.wikidata.org/wiki/Q362383","display_name":"Cardinality (data modeling)","level":2,"score":0.7551000118255615},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7516000270843506},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6313999891281128},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5889999866485596},{"id":"https://openalex.org/C90329073","wikidata":"https://www.wikidata.org/wiki/Q914232","display_name":"Ask price","level":2,"score":0.5651000142097473},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5078999996185303},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4788999855518341},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4447999894618988},{"id":"https://openalex.org/C2778476105","wikidata":"https://www.wikidata.org/wiki/Q628539","display_name":"Workload","level":2,"score":0.4131999909877777},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.37049999833106995},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3366999924182892},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.31369999051094055},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.30320000648498535},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30239999294281006},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2816999852657318},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.2728999853134155},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.25619998574256897}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.14778/3461535.3461552","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3461535.3461552","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2012.06743","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2012.06743","pdf_url":"https://arxiv.org/pdf/2012.06743","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2012.06743","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2012.06743","pdf_url":"https://arxiv.org/pdf/2012.06743","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":66,"referenced_works":["https://openalex.org/W1592510768","https://openalex.org/W2029798546","https://openalex.org/W2046386580","https://openalex.org/W2046437776","https://openalex.org/W2051756193","https://openalex.org/W2065840988","https://openalex.org/W2071989194","https://openalex.org/W2075712468","https://openalex.org/W2093522043","https://openalex.org/W2132823934","https://openalex.org/W2144661390","https://openalex.org/W2150779766","https://openalex.org/W2163166770","https://openalex.org/W2165717273","https://openalex.org/W2175806766","https://openalex.org/W2192203593","https://openalex.org/W2282821441","https://openalex.org/W2295598076","https://openalex.org/W2367397349","https://openalex.org/W2396309311","https://openalex.org/W2396635388","https://openalex.org/W2421547754","https://openalex.org/W2424452828","https://openalex.org/W2752189538","https://openalex.org/W2766026698","https://openalex.org/W2790625403","https://openalex.org/W2795530455","https://openalex.org/W2798277312","https://openalex.org/W2799060872","https://openalex.org/W2808009442","https://openalex.org/W2889503624","https://openalex.org/W2906910993","https://openalex.org/W2922302645","https://openalex.org/W2946026089","https://openalex.org/W2948513753","https://openalex.org/W2948791565","https://openalex.org/W2950833175","https://openalex.org/W2955798121","https://openalex.org/W2962771342","https://openalex.org/W2963853546","https://openalex.org/W2966185412","https://openalex.org/W2970148517","https://openalex.org/W2991530444","https://openalex.org/W2998032620","https://openalex.org/W2998249308","https://openalex.org/W3013555795","https://openalex.org/W3015405303","https://openalex.org/W3021702690","https://openalex.org/W3025775630","https://openalex.org/W3029535034","https://openalex.org/W3029541804","https://openalex.org/W3030994385","https://openalex.org/W3031176864","https://openalex.org/W3041888157","https://openalex.org/W3082526573","https://openalex.org/W3097225903","https://openalex.org/W4205483682","https://openalex.org/W4230112007","https://openalex.org/W4231287357","https://openalex.org/W4234171470","https://openalex.org/W4241858315","https://openalex.org/W4242142158","https://openalex.org/W4246006899","https://openalex.org/W4254407475","https://openalex.org/W4255671299","https://openalex.org/W4291713239"],"related_works":[],"abstract_inverted_index":{"Cardinality":[0],"estimation":[1],"is":[2,155],"a":[3,37,84,164,234],"fundamental":[4],"but":[5,101,153],"long":[6],"unresolved":[7],"problem":[8],"in":[9,49,193],"query":[10],"optimization.":[11],"Recently,":[12],"multiple":[13],"papers":[14],"from":[15,105],"different":[16,143],"research":[17,219,237],"groups":[18],"consistently":[19],"report":[20],"that":[21,91,129,180,241],"learned":[22,46,73,92,116,168,184,225,229,255],"models":[23,48,93,117,169,226,230],"have":[24],"the":[25,62,181,191,222],"potential":[26],"to":[27,43,206,249,252],"replace":[28],"existing":[29],"cardinality":[30,47,256],"estimators.":[31],"In":[32],"this":[33],"paper,":[34],"we":[35,41,59,112,162,215],"ask":[36],"forward-thinking":[38],"question:":[39],"Are":[40],"ready":[42,119],"deploy":[44],"these":[45,115,213],"production?":[50],"Our":[51,177],"study":[52,243],"consists":[53],"of":[54,183,224,236],"three":[55],"main":[56],"parts.":[57],"Firstly,":[58],"focus":[60],"on":[61,79,212],"static":[63],"environment":[64],"(i.e.,":[65,123],"no":[66,156],"data":[67,125,136],"updates)":[68],"and":[69,108,138,170,208,227,232,247],"compare":[70],"five":[71],"new":[72],"methods":[74,78,185],"with":[75,134],"nine":[76],"traditional":[77,99],"four":[80],"real-world":[81],"datasets":[82],"under":[83],"unified":[85],"workload":[86],"setting.":[87],"The":[88],"results":[89,178],"show":[90,179],"are":[94,118,203],"indeed":[95],"more":[96],"accurate":[97],"than":[98],"methods,":[100],"they":[102,130,149,173],"often":[103,209],"suffer":[104],"high":[106],"training":[107],"inference":[109],"costs.":[110],"Secondly,":[111],"explore":[113,171],"whether":[114],"for":[120,142],"dynamic":[121],"environments":[122],"frequent":[124,147],"updates).":[126],"We":[127,239],"find":[128],"cannot":[131],"catch":[132],"up":[133],"fast":[135],"updates":[137],"return":[139],"large":[140],"errors":[141],"reasons.":[144],"For":[145],"less":[146],"updates,":[148],"can":[150,186,244],"perform":[151],"better":[152],"there":[154],"clear":[157],"winner":[158],"among":[159],"themselves.":[160],"Thirdly,":[161],"take":[163],"deeper":[165],"look":[166],"into":[167,258],"when":[172],"may":[174],"go":[175],"wrong.":[176],"performance":[182],"be":[187],"greatly":[188],"affected":[189],"by":[190],"changes":[192],"correlation,":[194],"skewness,":[195],"or":[196],"domain":[197],"size.":[198],"More":[199],"importantly,":[200],"their":[201],"behaviors":[202],"much":[204],"harder":[205],"interpret":[207],"unpredictable.":[210],"Based":[211],"findings,":[214],"identify":[216],"two":[217],"promising":[218],"directions":[220],"(control":[221],"cost":[223],"make":[228],"trustworthy)":[231],"suggest":[233],"number":[235],"opportunities.":[238],"hope":[240],"our":[242],"guide":[245],"researchers":[246],"practitioners":[248],"work":[250],"together":[251],"eventually":[253],"push":[254],"estimators":[257],"real":[259],"database":[260],"systems.":[261]},"counts_by_year":[{"year":2026,"cited_by_count":8},{"year":2025,"cited_by_count":22},{"year":2024,"cited_by_count":21},{"year":2023,"cited_by_count":27},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":6}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2020-12-21T00:00:00"}