{"id":"https://openalex.org/W4385568101","doi":"https://doi.org/10.1145/3580305.3599953","title":"Rover: An Online Spark SQL Tuning Service via Generalized Transfer Learning","display_name":"Rover: An Online Spark SQL Tuning Service via Generalized Transfer Learning","publication_year":2023,"publication_date":"2023-08-04","ids":{"openalex":"https://openalex.org/W4385568101","doi":"https://doi.org/10.1145/3580305.3599953"},"language":"en","primary_location":{"id":"doi:10.1145/3580305.3599953","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580305.3599953","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-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/A5100770388","display_name":"Yu Shen","orcid":"https://orcid.org/0000-0001-6503-6504"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yu Shen","raw_affiliation_strings":["Peking University &amp; ByteDance Inc., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University &amp; ByteDance Inc., Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025604819","display_name":"Xinyuyang Ren","orcid":"https://orcid.org/0009-0008-2887-3125"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xinyuyang Ren","raw_affiliation_strings":["ByteDance Inc., Beijing, China"],"affiliations":[{"raw_affiliation_string":"ByteDance Inc., Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003916389","display_name":"Yupeng Lu","orcid":"https://orcid.org/0009-0001-0819-6445"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yupeng Lu","raw_affiliation_strings":["Peking University &amp; ByteDance Inc., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University &amp; ByteDance Inc., Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086112587","display_name":"Huaijun Jiang","orcid":"https://orcid.org/0000-0003-1566-4849"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huaijun Jiang","raw_affiliation_strings":["Peking University &amp; ByteDance Inc., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University &amp; ByteDance Inc., Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042832465","display_name":"Huanyong Xu","orcid":"https://orcid.org/0009-0006-6735-0816"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huanyong Xu","raw_affiliation_strings":["ByteDance Inc., Beijing, China"],"affiliations":[{"raw_affiliation_string":"ByteDance Inc., Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038006673","display_name":"Di Peng","orcid":"https://orcid.org/0009-0007-8969-3702"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Di Peng","raw_affiliation_strings":["ByteDance Inc., Beijing, China"],"affiliations":[{"raw_affiliation_string":"ByteDance Inc., Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100745269","display_name":"Yang Li","orcid":"https://orcid.org/0000-0001-5249-1807"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Li","raw_affiliation_strings":["Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008772211","display_name":"Wentao Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210164802","display_name":"Mila - Quebec Artificial Intelligence Institute","ror":"https://ror.org/05c22rx21","country_code":"CA","type":"facility","lineage":["https://openalex.org/I4210164802"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Wentao Zhang","raw_affiliation_strings":["Mila - Qu\u00e9bec AI Institute, Montr\u00e9al, Canada"],"affiliations":[{"raw_affiliation_string":"Mila - Qu\u00e9bec AI Institute, Montr\u00e9al, Canada","institution_ids":["https://openalex.org/I4210164802"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062357883","display_name":"Bin Cui","orcid":"https://orcid.org/0000-0003-1681-4677"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Cui","raw_affiliation_strings":["Peking University &amp; Peking University (Qingdao), Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University &amp; Peking University (Qingdao), Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5100770388"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":2.2468,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.90308058,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"4800","last_page":"4812"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9936000108718872,"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"}},"topics":[{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9936000108718872,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.982699990272522,"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/T10317","display_name":"Advanced Database Systems and Queries","score":0.9822999835014343,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/spark","display_name":"SPARK (programming language)","score":0.8576427698135376},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7864872217178345},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.6748459339141846},{"id":"https://openalex.org/keywords/sql","display_name":"SQL","score":0.6580178141593933},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5810291767120361},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5224301218986511},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.5090317726135254},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4749346375465393},{"id":"https://openalex.org/keywords/production","display_name":"Production (economics)","score":0.44571685791015625},{"id":"https://openalex.org/keywords/performance-tuning","display_name":"Performance tuning","score":0.42355674505233765},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3554219603538513},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.35509344935417175},{"id":"https://openalex.org/keywords/industrial-engineering","display_name":"Industrial engineering","score":0.34005337953567505},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12347909808158875},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.11897420883178711}],"concepts":[{"id":"https://openalex.org/C2781215313","wikidata":"https://www.wikidata.org/wiki/Q3493345","display_name":"SPARK (programming language)","level":2,"score":0.8576427698135376},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7864872217178345},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.6748459339141846},{"id":"https://openalex.org/C510870499","wikidata":"https://www.wikidata.org/wiki/Q47607","display_name":"SQL","level":2,"score":0.6580178141593933},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5810291767120361},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5224301218986511},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.5090317726135254},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4749346375465393},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.44571685791015625},{"id":"https://openalex.org/C2777138346","wikidata":"https://www.wikidata.org/wiki/Q1714153","display_name":"Performance tuning","level":2,"score":0.42355674505233765},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3554219603538513},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.35509344935417175},{"id":"https://openalex.org/C13736549","wikidata":"https://www.wikidata.org/wiki/Q4489420","display_name":"Industrial engineering","level":1,"score":0.34005337953567505},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12347909808158875},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.11897420883178711},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C139719470","wikidata":"https://www.wikidata.org/wiki/Q39680","display_name":"Macroeconomics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3580305.3599953","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580305.3599953","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.6100000143051147,"id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G1691401027","display_name":null,"funder_award_id":"U22B2037","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4253300795","display_name":null,"funder_award_id":"61832001","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4387417602","display_name":null,"funder_award_id":"No. 61832001","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8208342437","display_name":null,"funder_award_id":"1 and","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":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W60686164","https://openalex.org/W95608104","https://openalex.org/W605727707","https://openalex.org/W1834532152","https://openalex.org/W2038412523","https://openalex.org/W2110086534","https://openalex.org/W2114896543","https://openalex.org/W2142031898","https://openalex.org/W2155072926","https://openalex.org/W2244094084","https://openalex.org/W2318383848","https://openalex.org/W2542459869","https://openalex.org/W2570373436","https://openalex.org/W2613206411","https://openalex.org/W2732547613","https://openalex.org/W2760770811","https://openalex.org/W2776147949","https://openalex.org/W2783447917","https://openalex.org/W2792529086","https://openalex.org/W2799015892","https://openalex.org/W2927589612","https://openalex.org/W2948513753","https://openalex.org/W2949762319","https://openalex.org/W2963745734","https://openalex.org/W2970851599","https://openalex.org/W3007086929","https://openalex.org/W3022630129","https://openalex.org/W3097528317","https://openalex.org/W3098844916","https://openalex.org/W3104631761","https://openalex.org/W3119448165","https://openalex.org/W3167899070","https://openalex.org/W3170664780","https://openalex.org/W4200154784","https://openalex.org/W4221117392","https://openalex.org/W4288057686","https://openalex.org/W4291033390","https://openalex.org/W4319653510","https://openalex.org/W6849955676"],"related_works":["https://openalex.org/W2558523485","https://openalex.org/W4379407450","https://openalex.org/W2905107896","https://openalex.org/W2895375519","https://openalex.org/W4289533904","https://openalex.org/W2026893489","https://openalex.org/W3112487524","https://openalex.org/W317776562","https://openalex.org/W23253958","https://openalex.org/W3208869747"],"abstract_inverted_index":{"Distributed":[0],"data":[1,12],"analytic":[2],"engines":[3],"like":[4],"Spark":[5,19,41,99],"are":[6],"common":[7],"choices":[8],"to":[9,76,105,129],"process":[10],"massive":[11],"in":[13,69,133],"industry.":[14],"However,":[15],"the":[16,24,29,34,62,78,107],"performance":[17,109,132],"of":[18,26,102],"SQL":[20,42],"highly":[21],"depends":[22],"on":[23,91],"choice":[25],"configurations,":[27],"where":[28,124],"optimal":[30],"ones":[31,127],"vary":[32],"with":[33],"executed":[35],"workloads.":[36],"Among":[37],"various":[38],"alternatives":[39],"for":[40],"tuning,":[43],"Bayesian":[44],"optimization":[45],"(BO)":[46],"is":[47,66,101,111],"a":[48,130],"popular":[49],"framework":[50],"that":[51],"finds":[52],"near-optimal":[53],"configurations":[54],"given":[55],"sufficient":[56],"budget,":[57],"but":[58,110],"it":[59],"suffers":[60],"from":[61,98],"re-optimization":[63],"issue":[64],"and":[65],"not":[67,112],"practical":[68],"real":[70],"production.":[71,134],"When":[72],"applying":[73],"transfer":[74],"learning":[75],"accelerate":[77],"tuning":[79,93,108],"process,":[80],"we":[81],"notice":[82],"two":[83],"domain-specific":[84],"challenges:":[85],"1)":[86],"most":[87],"previous":[88],"work":[89],"focus":[90],"transferring":[92],"history,":[94],"while":[95],"expert":[96],"knowledge":[97],"engineers":[100],"great":[103],"potential":[104],"improve":[106],"well":[113],"studied":[114],"so":[115],"far;":[116],"2)":[117],"history":[118],"tasks":[119],"should":[120],"be":[121],"carefully":[122],"utilized,":[123],"using":[125],"dissimilar":[126],"lead":[128],"deteriorated":[131]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":2}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
