{"id":"https://openalex.org/W4306317394","doi":"https://doi.org/10.1145/3511808.3557305","title":"Efficient Learning with Pseudo Labels for Query Cost Estimation","display_name":"Efficient Learning with Pseudo Labels for Query Cost Estimation","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4306317394","doi":"https://doi.org/10.1145/3511808.3557305"},"language":"en","primary_location":{"id":"doi:10.1145/3511808.3557305","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3511808.3557305","pdf_url":null,"source":{"id":"https://openalex.org/S4363608762","display_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://vbn.aau.dk/ws/files/800154408/Efficient_Learning_with_Pseudo_Labels_for_Query_Cost_Estimation.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066308099","display_name":"Shuncheng Liu","orcid":"https://orcid.org/0000-0003-3557-6598"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuncheng Liu","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100385692","display_name":"Xu Chen","orcid":"https://orcid.org/0000-0001-9943-6020"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xu Chen","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060365518","display_name":"Yan Zhao","orcid":"https://orcid.org/0000-0002-0242-3707"},"institutions":[{"id":"https://openalex.org/I891191580","display_name":"Aalborg University","ror":"https://ror.org/04m5j1k67","country_code":"DK","type":"education","lineage":["https://openalex.org/I891191580"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Yan Zhao","raw_affiliation_strings":["Aalborg University, Aalborg, Denmark"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aalborg University, Aalborg, Denmark","institution_ids":["https://openalex.org/I891191580"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100457200","display_name":"Jin Chen","orcid":"https://orcid.org/0009-0004-4047-8647"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jin Chen","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071191374","display_name":"Rui Zhou","orcid":"https://orcid.org/0000-0002-7955-9418"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rui Zhou","raw_affiliation_strings":["Huawei Technologies Co., Ltd., Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huawei Technologies Co., Ltd., Chengdu, China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100603979","display_name":"Kai Zheng","orcid":"https://orcid.org/0000-0002-0217-3998"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kai Zheng","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.8871,"has_fulltext":true,"cited_by_count":9,"citation_normalized_percentile":{"value":0.86002754,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1309","last_page":"1318"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10317","display_name":"Advanced Database Systems and Queries","score":0.9983000159263611,"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":0.9983000159263611,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9980999827384949,"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/T11106","display_name":"Data Management and Algorithms","score":0.9970999956130981,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.788948655128479},{"id":"https://openalex.org/keywords/query-optimization","display_name":"Query optimization","score":0.6548640727996826},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5320426225662231},{"id":"https://openalex.org/keywords/web-search-query","display_name":"Web search query","score":0.5095003247261047},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.43387675285339355},{"id":"https://openalex.org/keywords/query-expansion","display_name":"Query expansion","score":0.4285932779312134},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.41601377725601196},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3929215371608734},{"id":"https://openalex.org/keywords/search-engine","display_name":"Search engine","score":0.1827443242073059}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.788948655128479},{"id":"https://openalex.org/C157692150","wikidata":"https://www.wikidata.org/wiki/Q2919848","display_name":"Query optimization","level":2,"score":0.6548640727996826},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5320426225662231},{"id":"https://openalex.org/C164120249","wikidata":"https://www.wikidata.org/wiki/Q995982","display_name":"Web search query","level":3,"score":0.5095003247261047},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.43387675285339355},{"id":"https://openalex.org/C99016210","wikidata":"https://www.wikidata.org/wiki/Q5488129","display_name":"Query expansion","level":2,"score":0.4285932779312134},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.41601377725601196},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3929215371608734},{"id":"https://openalex.org/C97854310","wikidata":"https://www.wikidata.org/wiki/Q19541","display_name":"Search engine","level":2,"score":0.1827443242073059},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3511808.3557305","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3511808.3557305","pdf_url":null,"source":{"id":"https://openalex.org/S4363608762","display_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.atira.dk:publications/9fa74d31-a2bb-4069-83ea-0c37b0cfc6e8","is_oa":true,"landing_page_url":"https://vbn.aau.dk/da/publications/9fa74d31-a2bb-4069-83ea-0c37b0cfc6e8","pdf_url":"https://vbn.aau.dk/ws/files/800154408/Efficient_Learning_with_Pseudo_Labels_for_Query_Cost_Estimation.pdf","source":{"id":"https://openalex.org/S4306401731","display_name":"VBN Forskningsportal (Aalborg Universitet)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I891191580","host_organization_name":"Aalborg University","host_organization_lineage":["https://openalex.org/I891191580"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Liu, S, Chen, X, Zhao, Y, Chen, J, Zhou, R & Zheng, K 2022, Efficient Learning with Pseudo Labels for Query Cost Estimation. in CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management. Association for Computing Machinery (ACM), pp. 1309-1318, 31st ACM International Conference on Information and Knowledge Management, Atlanta, United States, 17/10/2022. https://doi.org/10.1145/3511808.3557305","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:oai:repository.hkust.edu.hk:1783.1-135848","is_oa":false,"landing_page_url":"http://lbdiscover.ust.hk/uresolver?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rfr_id=info:sid/HKUST:SPI&rft.genre=article&rft.issn=&rft.volume=&rft.issue=&rft.date=2022&rft.spage=1309&rft.aulast=Liu&rft.aufirst=Shuncheng&rft.atitle=Efficient+Learning+with+Pseudo+Labels+for+Query+Cost+Estimation&rft.title=International+Conference+on+Information+and+Knowledge+Management%2C+Proceedings","pdf_url":null,"source":{"id":"https://openalex.org/S4306401796","display_name":"Rare & Special e-Zone (The Hong Kong University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I200769079","host_organization_name":"Hong Kong University of Science and Technology","host_organization_lineage":["https://openalex.org/I200769079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference paper"}],"best_oa_location":{"id":"pmh:oai:pure.atira.dk:publications/9fa74d31-a2bb-4069-83ea-0c37b0cfc6e8","is_oa":true,"landing_page_url":"https://vbn.aau.dk/da/publications/9fa74d31-a2bb-4069-83ea-0c37b0cfc6e8","pdf_url":"https://vbn.aau.dk/ws/files/800154408/Efficient_Learning_with_Pseudo_Labels_for_Query_Cost_Estimation.pdf","source":{"id":"https://openalex.org/S4306401731","display_name":"VBN Forskningsportal (Aalborg Universitet)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I891191580","host_organization_name":"Aalborg University","host_organization_lineage":["https://openalex.org/I891191580"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Liu, S, Chen, X, Zhao, Y, Chen, J, Zhou, R & Zheng, K 2022, Efficient Learning with Pseudo Labels for Query Cost Estimation. in CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management. Association for Computing Machinery (ACM), pp. 1309-1318, 31st ACM International Conference on Information and Knowledge Management, Atlanta, United States, 17/10/2022. https://doi.org/10.1145/3511808.3557305","raw_type":"info:eu-repo/semantics/conferenceObject"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2270076032","display_name":null,"funder_award_id":"61972069,61836007,61832017","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3561341673","display_name":"\u9762\u5411\u9886\u57df\u5927\u6570\u636e\u7684\u4e8b\u4ef6\u77e5\u8bc6\u56fe\u8c31\u6784\u5efa\u7814\u7a76","funder_award_id":"61836007","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3634295602","display_name":null,"funder_award_id":"61832017","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G964926443","display_name":"\u7a7a\u95f4\u4f17\u5305\u4e2d\u7684\u4efb\u52a1\u9884\u6d4b\u4e0e\u5206\u914d\u6280\u672f\u7814\u7a76","funder_award_id":"61972069","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4306317394.pdf","grobid_xml":"https://content.openalex.org/works/W4306317394.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W2010149990","https://openalex.org/W2038337290","https://openalex.org/W2067191022","https://openalex.org/W2071009745","https://openalex.org/W2103702871","https://openalex.org/W2132823934","https://openalex.org/W2146732261","https://openalex.org/W2153329411","https://openalex.org/W2165698076","https://openalex.org/W2167978511","https://openalex.org/W2396309311","https://openalex.org/W2522505491","https://openalex.org/W2740924709","https://openalex.org/W2808009442","https://openalex.org/W2911464154","https://openalex.org/W2963925437","https://openalex.org/W2970148517","https://openalex.org/W2987583674","https://openalex.org/W2991530444","https://openalex.org/W2998249308","https://openalex.org/W3041133507","https://openalex.org/W3097225903","https://openalex.org/W3099273181","https://openalex.org/W3105457604","https://openalex.org/W3111141572","https://openalex.org/W3124277639","https://openalex.org/W3169933468","https://openalex.org/W3197977787","https://openalex.org/W3198024709","https://openalex.org/W4246006899","https://openalex.org/W4289706945","https://openalex.org/W4290878206"],"related_works":["https://openalex.org/W2096359267","https://openalex.org/W4256058599","https://openalex.org/W2572349046","https://openalex.org/W2901901036","https://openalex.org/W2026738364","https://openalex.org/W3125756434","https://openalex.org/W2146885082","https://openalex.org/W2538384344","https://openalex.org/W3049728138","https://openalex.org/W2184296057"],"abstract_inverted_index":{"Query":[0,22],"cost":[1,10,24,35,114,266],"estimation,":[2,31],"which":[3,45,119,168,201,237],"is":[4,15,169,180,221],"to":[5,19,70,151,163,182,223],"estimate":[6,224],"the":[7,60,82,97,100,190,194,198,205,209,225,230,240,254],"query":[8,12,20,33,41,103,113,130,137,148,166,195,231,265],"plan":[9,23,104,131,138,149,155,232],"and":[11,32,65,123,126,186,212,227,262],"execution":[13,34,191],"cost,":[14],"of":[16,44,54,87,96,102,193,207,229,259],"utmost":[17],"importance":[18],"optimizers.":[21],"estimation":[25,36,115],"heavily":[26],"relies":[27],"on":[28,40,246],"accurate":[29],"cardinality":[30,185,211,226],"gives":[37],"good":[38],"hints":[39],"latency,":[42],"both":[43],"are":[46],"challenging":[47],"in":[48,197,233,257],"database":[49],"management":[50],"systems.":[51],"Despite":[52],"decades":[53],"research,":[55],"existing":[56],"studies":[57,98],"either":[58],"over-simplify":[59],"models":[61],"only":[62],"using":[63,77,145],"histograms":[64],"polynomial":[66],"calculation":[67],"that":[68,251],"leads":[69],"inaccurate":[71],"estimates,":[72],"or":[73],"over-complicate":[74],"them":[75],"by":[76,144],"cumbersome":[78],"neural":[79],"networks":[80],"with":[81,153,189],"requirements":[83],"for":[84,128,172,264],"large":[85],"amounts":[86],"training":[88,199],"data":[89,248],"hence":[90],"poor":[91],"computational":[92],"efficiency.":[93,241],"Besides,":[94],"most":[95],"ignore":[99],"diversity":[101],"structures.":[105,132,156],"In":[106],"this":[107],"work,":[108],"we":[109],"propose":[110],"a":[111,141,146,176,216,234],"plan-based":[112],"framework,":[116],"called":[117],"Saturn,":[118],"can":[120,160,202],"e<u>S</u>timate":[121],"c<u>a</u>rdinality":[122],"la<u>t</u>ency":[124],"acc<u>ur</u>ately":[125],"efficie<u>n</u>tly,":[127],"any":[129],"Saturn":[133,252],"first":[134],"encodes":[135],"each":[136],"tree":[139],"into":[140],"compressed":[142,158],"vector":[143],"traversal-based":[147],"autoencoder":[150],"cope":[152],"diverse":[154],"The":[157],"vectors":[159],"be":[161],"leveraged":[162],"distinguish":[164],"different":[165],"types,":[167],"highly":[170],"useful":[171],"downstream":[173],"tasks.":[174],"Then":[175],"pseudo":[177],"label":[178],"generator":[179],"designed":[181],"acquire":[183],"all":[184],"latency":[187,213,228],"labels":[188],"part":[192],"plans":[196],"workload,":[200],"significantly":[203],"reduce":[204],"overhead":[206],"collecting":[208],"real":[210],"labels.":[214],"Finally,":[215],"chain-wise":[217],"transfer":[218],"learning":[219],"module":[220],"proposed":[222],"pipeline":[235],"paradigm,":[236],"further":[238],"enhances":[239],"An":[242],"extensive":[243],"empirical":[244],"study":[245],"benchmark":[247],"offers":[249],"evidence":[250],"outperforms":[253],"state-of-the-art":[255],"proposals":[256],"terms":[258],"accuracy,":[260],"efficiency,":[261],"generalizability":[263],"estimation.":[267]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":2}],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-10-10T00:00:00"}