{"id":"https://openalex.org/W4413978051","doi":"https://doi.org/10.14778/3749646.3749712","title":"LIMAO: A Framework for Lifelong Modular Learned Query Optimization","display_name":"LIMAO: A Framework for Lifelong Modular Learned Query Optimization","publication_year":2025,"publication_date":"2025-07-01","ids":{"openalex":"https://openalex.org/W4413978051","doi":"https://doi.org/10.14778/3749646.3749712"},"language":"en","primary_location":{"id":"doi:10.14778/3749646.3749712","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3749646.3749712","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/A5110335200","display_name":"Q. Zhang","orcid":"https://orcid.org/0009-0005-5785-8766"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Qihan Zhang","raw_affiliation_strings":["University of Southern California, Los Angeles, USA"],"affiliations":[{"raw_affiliation_string":"University of Southern California, Los Angeles, USA","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057886296","display_name":"Sheng Xie","orcid":"https://orcid.org/0000-0003-0754-4134"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shaolin Xie","raw_affiliation_strings":["University of Southern California, Los Angeles, USA"],"affiliations":[{"raw_affiliation_string":"University of Southern California, Los Angeles, USA","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062053667","display_name":"Ibrahim Sabek","orcid":"https://orcid.org/0009-0006-2102-5241"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ibrahim Sabek","raw_affiliation_strings":["University of Southern California, Los Angeles, USA"],"affiliations":[{"raw_affiliation_string":"University of Southern California, Los Angeles, USA","institution_ids":["https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5110335200"],"corresponding_institution_ids":["https://openalex.org/I1174212"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13609688,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"18","issue":"11","first_page":"4546","last_page":"4559"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9940999746322632,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9940999746322632,"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":0.9933000206947327,"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/T12288","display_name":"Optimization and Search Problems","score":0.988099992275238,"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/modular-design","display_name":"Modular design","score":0.690988302230835},{"id":"https://openalex.org/keywords/query-optimization","display_name":"Query optimization","score":0.6667932271957397},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6545799970626831},{"id":"https://openalex.org/keywords/process-management","display_name":"Process management","score":0.3268146514892578},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.24105224013328552},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.23102715611457825},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.21786051988601685}],"concepts":[{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.690988302230835},{"id":"https://openalex.org/C157692150","wikidata":"https://www.wikidata.org/wiki/Q2919848","display_name":"Query optimization","level":2,"score":0.6667932271957397},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6545799970626831},{"id":"https://openalex.org/C195094911","wikidata":"https://www.wikidata.org/wiki/Q14167904","display_name":"Process management","level":1,"score":0.3268146514892578},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.24105224013328552},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.23102715611457825},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.21786051988601685}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.14778/3749646.3749712","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3749646.3749712","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":55,"referenced_works":["https://openalex.org/W135863099","https://openalex.org/W1501284393","https://openalex.org/W2020555424","https://openalex.org/W2063599951","https://openalex.org/W2153329411","https://openalex.org/W2282866165","https://openalex.org/W2396309311","https://openalex.org/W2396635388","https://openalex.org/W2560647685","https://openalex.org/W2617837836","https://openalex.org/W2743151379","https://openalex.org/W2790634852","https://openalex.org/W2803673185","https://openalex.org/W2911464154","https://openalex.org/W2945486614","https://openalex.org/W2962771342","https://openalex.org/W2964118342","https://openalex.org/W2964262254","https://openalex.org/W2970148517","https://openalex.org/W2991530444","https://openalex.org/W2998249308","https://openalex.org/W3013821552","https://openalex.org/W3035622927","https://openalex.org/W3082526573","https://openalex.org/W3097225903","https://openalex.org/W3099273181","https://openalex.org/W3102944959","https://openalex.org/W3105457604","https://openalex.org/W3118844182","https://openalex.org/W3147852756","https://openalex.org/W3158109590","https://openalex.org/W3167537398","https://openalex.org/W3197977787","https://openalex.org/W3212604410","https://openalex.org/W4206830372","https://openalex.org/W4221142004","https://openalex.org/W4226124281","https://openalex.org/W4246297465","https://openalex.org/W4281972940","https://openalex.org/W4282546806","https://openalex.org/W4285412424","https://openalex.org/W4285451014","https://openalex.org/W4286447321","https://openalex.org/W4289706945","https://openalex.org/W4313138291","https://openalex.org/W4317641620","https://openalex.org/W4366492480","https://openalex.org/W4366502978","https://openalex.org/W4375928354","https://openalex.org/W4383749444","https://openalex.org/W4385245566","https://openalex.org/W4387559950","https://openalex.org/W4389609916","https://openalex.org/W4396571425","https://openalex.org/W4399208444"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Query":[0,81],"optimizers":[1,14],"are":[2],"crucial":[3],"for":[4,85],"the":[5,56,63,151,167],"performance":[6,19,152],"of":[7,26,88,153,169],"database":[8],"systems.":[9],"Recently,":[10],"many":[11],"learned":[12],"query":[13,35,46,162,218],"(LQOs)":[15],"have":[16],"demonstrated":[17],"significant":[18],"improvements":[20],"over":[21,66,197],"traditional":[22],"optimizers.":[23],"However,":[24],"most":[25],"them":[27,40],"operate":[28],"under":[29,176],"a":[30,33,83,102,158,181,206],"limited":[31],"assumption:":[32],"static":[34],"environment.":[36],"This":[37],"limitation":[38,74],"prevents":[39],"from":[41],"effectively":[42,187],"handling":[43],"complex,":[44],"dynamic":[45,177],"environments":[47],"in":[48,132,161,216],"real-world":[49,217],"scenarios.":[50],"Extensive":[51],"retraining":[52],"can":[53,93],"lead":[54],"to":[55,119,126,141,157,174,200,205],"well-known":[57],"catastrophic":[58,189],"forgetting":[59],"problem":[60],"which":[61],"reduces":[62],"LQO":[64],"generalizability":[65],"time.":[67,198],"In":[68],"this":[69,73],"paper,":[70],"we":[71],"address":[72],"and":[75,113,165,183,193],"introduce":[76],"LIMAO":[77,100,131,148,186,202],"(Lifelong":[78],"Modular":[79],"Learned":[80],"Optimizer),":[82],"framework":[84],"lifelong":[86,104],"learning":[87,105],"plan":[89,195],"cost":[90],"prediction":[91],"that":[92,136,147],"be":[94],"seamlessly":[95],"integrated":[96],"into":[97],"existing":[98],"LQOs.":[99],"leverages":[101],"modular":[103],"technique,":[106],"an":[107,114],"attention-based":[108],"neural":[109],"network":[110],"composition":[111],"architecture,":[112],"efficient":[115],"training":[116],"paradigm":[117],"designed":[118],"retain":[120],"prior":[121],"knowledge":[122],"while":[123],"continuously":[124],"adapting":[125],"new":[127],"environments.":[128],"We":[129],"implement":[130],"two":[133],"LQOs,":[134,154],"showing":[135],"our":[137],"approach":[138],"is":[139],"agnostic":[140],"underlying":[142],"engines.":[143],"Experimental":[144],"results":[145],"show":[146],"significantly":[149],"enhances":[150],"achieving":[155],"up":[156,173,204],"40%":[159],"improvement":[160],"execution":[163,170],"time":[164,171],"reducing":[166],"variance":[168],"by":[172],"60%":[175],"workloads.":[178],"By":[179],"leveraging":[180],"precise":[182],"self-consistent":[184],"design,":[185],"mitigates":[188],"forgetting,":[190],"ensuring":[191],"stable":[192],"reliable":[194],"quality":[196],"Compared":[199],"Postgres,":[201],"achieves":[203],"4\u00d7":[207],"speedup":[208],"on":[209],"selected":[210],"benchmarks,":[211],"highlighting":[212],"its":[213],"practical":[214],"advantages":[215],"optimization.":[219]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}