{"id":"https://openalex.org/W3197977787","doi":"https://doi.org/10.14778/3476249.3476259","title":"Flow-loss","display_name":"Flow-loss","publication_year":2021,"publication_date":"2021-07-01","ids":{"openalex":"https://openalex.org/W3197977787","doi":"https://doi.org/10.14778/3476249.3476259","mag":"3197977787"},"language":"en","primary_location":{"id":"doi:10.14778/3476249.3476259","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3476249.3476259","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":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5018121640","display_name":"Parimarjan Negi","orcid":"https://orcid.org/0000-0002-8442-9159"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Parimarjan Negi","raw_affiliation_strings":["MIT CSAIL"],"affiliations":[{"raw_affiliation_string":"MIT CSAIL","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025731013","display_name":"Ryan Marcus","orcid":"https://orcid.org/0000-0002-1279-1124"},"institutions":[{"id":"https://openalex.org/I1343180700","display_name":"Intel (United States)","ror":"https://ror.org/01ek73717","country_code":"US","type":"company","lineage":["https://openalex.org/I1343180700"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ryan Marcus","raw_affiliation_strings":["MIT CSAIL and Intel Labs"],"affiliations":[{"raw_affiliation_string":"MIT CSAIL and Intel Labs","institution_ids":["https://openalex.org/I1343180700"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046188245","display_name":"Andreas Kipf","orcid":"https://orcid.org/0000-0003-3463-0564"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Andreas Kipf","raw_affiliation_strings":["MIT CSAIL"],"affiliations":[{"raw_affiliation_string":"MIT CSAIL","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004578457","display_name":"Hongzi Mao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hongzi Mao","raw_affiliation_strings":["MIT CSAIL"],"affiliations":[{"raw_affiliation_string":"MIT CSAIL","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002085554","display_name":"Nesime Tatbul","orcid":"https://orcid.org/0000-0002-0416-7022"},"institutions":[{"id":"https://openalex.org/I1343180700","display_name":"Intel (United States)","ror":"https://ror.org/01ek73717","country_code":"US","type":"company","lineage":["https://openalex.org/I1343180700"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nesime Tatbul","raw_affiliation_strings":["MIT CSAIL and Intel Labs"],"affiliations":[{"raw_affiliation_string":"MIT CSAIL and Intel Labs","institution_ids":["https://openalex.org/I1343180700"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034086130","display_name":"Tim Kraska","orcid":"https://orcid.org/0009-0003-2414-2759"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tim Kraska","raw_affiliation_strings":["MIT CSAIL"],"affiliations":[{"raw_affiliation_string":"MIT CSAIL","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101669321","display_name":"Mohammad Alizadeh","orcid":"https://orcid.org/0000-0002-2002-2632"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mohammad Alizadeh","raw_affiliation_strings":["MIT CSAIL"],"affiliations":[{"raw_affiliation_string":"MIT CSAIL","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5018121640"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.0921,"has_fulltext":false,"cited_by_count":58,"citation_normalized_percentile":{"value":0.96326285,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":100},"biblio":{"volume":"14","issue":"11","first_page":"2019","last_page":"2032"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11106","display_name":"Data Management and Algorithms","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12535","display_name":"Machine Learning and Data Classification","score":0.991100013256073,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9904000163078308,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/computer-science","display_name":"Computer science","score":0.7896474599838257},{"id":"https://openalex.org/keywords/cardinality","display_name":"Cardinality (data modeling)","score":0.7389519214630127},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5579396486282349},{"id":"https://openalex.org/keywords/joins","display_name":"Joins","score":0.5031456351280212},{"id":"https://openalex.org/keywords/query-optimization","display_name":"Query optimization","score":0.4650230407714844},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4514275789260864},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.4399433135986328},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.42406165599823},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.41601982712745667},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.40638935565948486},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.39968374371528625},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3585168719291687},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3340788185596466},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1351454257965088}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7896474599838257},{"id":"https://openalex.org/C87117476","wikidata":"https://www.wikidata.org/wiki/Q362383","display_name":"Cardinality (data modeling)","level":2,"score":0.7389519214630127},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5579396486282349},{"id":"https://openalex.org/C2778692605","wikidata":"https://www.wikidata.org/wiki/Q4041866","display_name":"Joins","level":2,"score":0.5031456351280212},{"id":"https://openalex.org/C157692150","wikidata":"https://www.wikidata.org/wiki/Q2919848","display_name":"Query optimization","level":2,"score":0.4650230407714844},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4514275789260864},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.4399433135986328},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.42406165599823},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.41601982712745667},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.40638935565948486},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39968374371528625},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3585168719291687},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3340788185596466},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1351454257965088},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.14778/3476249.3476259","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3476249.3476259","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"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W1575641978","https://openalex.org/W1581547316","https://openalex.org/W2009281933","https://openalex.org/W2073019257","https://openalex.org/W2089059416","https://openalex.org/W2091202128","https://openalex.org/W2120108467","https://openalex.org/W2132823934","https://openalex.org/W2153441719","https://openalex.org/W2161453954","https://openalex.org/W2167064216","https://openalex.org/W2168865746","https://openalex.org/W2295766446","https://openalex.org/W2396309311","https://openalex.org/W2421547754","https://openalex.org/W2576152334","https://openalex.org/W2585664214","https://openalex.org/W2766026698","https://openalex.org/W2795239330","https://openalex.org/W2798499404","https://openalex.org/W2889503624","https://openalex.org/W2893787142","https://openalex.org/W2906910993","https://openalex.org/W2911540814","https://openalex.org/W2922302645","https://openalex.org/W2939293933","https://openalex.org/W2946026089","https://openalex.org/W2946246678","https://openalex.org/W2950833175","https://openalex.org/W2952433032","https://openalex.org/W2955798121","https://openalex.org/W2963751092","https://openalex.org/W2963853546","https://openalex.org/W2970148517","https://openalex.org/W2991530444","https://openalex.org/W2998249308","https://openalex.org/W3013555795","https://openalex.org/W3015439172","https://openalex.org/W3021702690","https://openalex.org/W3024738030","https://openalex.org/W3030994385","https://openalex.org/W3035622927","https://openalex.org/W3082526573","https://openalex.org/W3097225903","https://openalex.org/W3099273181","https://openalex.org/W3100077023","https://openalex.org/W3100925961","https://openalex.org/W3111141572","https://openalex.org/W3124277639","https://openalex.org/W3150455880","https://openalex.org/W4241858315","https://openalex.org/W4254407475"],"related_works":["https://openalex.org/W2088925915","https://openalex.org/W2382891957","https://openalex.org/W2393491644","https://openalex.org/W2378924333","https://openalex.org/W2362446711","https://openalex.org/W4323323165","https://openalex.org/W1495801388","https://openalex.org/W2551308855","https://openalex.org/W4386066345","https://openalex.org/W2745033168"],"abstract_inverted_index":{"Recently":[0],"there":[1],"has":[2,19],"been":[3],"significant":[4],"interest":[5],"in":[6,115],"using":[7],"machine":[8],"learning":[9,68],"to":[10,48,57,87,105,120,153,229],"improve":[11,49],"the":[12,44,50,54,74,95,130,137,170,188,194,206,224,244],"accuracy":[13,180],"of":[14,97,102,143],"cardinality":[15,69],"estimation.":[16],"This":[17],"work":[18],"focused":[20],"on":[21,110,213,240],"improving":[22],"average":[23],"estimation":[24,70,179],"error,":[25],"but":[26,219],"not":[27],"all":[28],"estimates":[29,51],"matter":[30],"equally":[31],"for":[32,67,90,141],"downstream":[33],"tasks":[34],"like":[35],"query":[36,92,103,122,174,221],"optimization.":[37],"Since":[38],"learned":[39],"models":[40,197],"inevitably":[41],"make":[42,53],"mistakes,":[43],"goal":[45],"should":[46],"be":[47],"that":[52,158],"biggest":[55],"difference":[56],"an":[58],"optimizer.":[59],"We":[60,156],"introduce":[61,129],"a":[62,100,106,111,164,182],"new":[63],"loss":[64,201],"function,":[65],"Flow-Loss,":[66],"models.":[71],"Flow-Loss":[72,98,168],"approximates":[73],"optimizer's":[75],"cost":[76],"model":[77,165,183,208,226,246],"and":[78,162,173,248],"search":[79],"algorithm":[80],"with":[81,151,167,185,199,243],"analytical":[82],"functions,":[83],"which":[84,116,135],"it":[85],"uses":[86],"optimize":[88],"explicitly":[89],"better":[91,228,236],"plans.":[93,123],"At":[94],"heart":[96],"is":[99],"reduction":[101],"optimization":[104],"flow":[107],"routing":[108],"problem":[109],"certain":[112],"\"plan":[113],"graph\",":[114],"different":[117,121,160,215],"paths":[118],"correspond":[119],"To":[124],"evaluate":[125],"our":[126],"approach,":[127],"we":[128],"Cardinality":[131],"Estimation":[132],"Benchmark":[133],"(CEB)":[134],"contains":[136],"ground":[138],"truth":[139],"cardinalities":[140],"sub-plans":[142],"over":[144],"16":[145],"K":[146],"queries":[147,191,216],"from":[148],"21":[149],"templates":[150,242],"up":[152],"15":[154],"joins.":[155],"show":[157],"across":[159],"architectures":[161],"databases,":[163],"trained":[166,184,198],"improves":[169],"plan":[171],"costs":[172],"runtimes":[175,239],"despite":[176],"having":[177],"worse":[178],"than":[181],"Q-Error.":[186],"When":[187],"test":[189],"set":[190],"closely":[192],"match":[193],"training":[195,249],"queries,":[196],"both":[200],"functions":[202],"perform":[203],"well.":[204],"However,":[205],"Q-Error-trained":[207],"degrades":[209],"significantly":[210],"when":[211],"evaluated":[212],"slightly":[214],"(e.g.,":[217],"similar":[218],"unseen":[220,241],"templates),":[222],"while":[223],"Flow-Loss-trained":[225],"generalizes":[227],"such":[230],"situations,":[231],"achieving":[232],"4":[233],"--":[234],"8\u00d7":[235],"99th":[237],"percentile":[238],"same":[245],"architecture":[247],"data.":[250]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":22},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":3}],"updated_date":"2026-04-04T08:04:53.788161","created_date":"2021-09-13T00:00:00"}