{"id":"https://openalex.org/W2997171439","doi":"https://doi.org/10.14778/3368289.3368299","title":"Incorporating super-operators in big-data query optimizers","display_name":"Incorporating super-operators in big-data query optimizers","publication_year":2019,"publication_date":"2019-11-01","ids":{"openalex":"https://openalex.org/W2997171439","doi":"https://doi.org/10.14778/3368289.3368299","mag":"2997171439"},"language":"en","primary_location":{"id":"doi:10.14778/3368289.3368299","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3368289.3368299","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/A5026137026","display_name":"Jyoti Leeka","orcid":"https://orcid.org/0000-0003-2920-1431"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Jyoti Leeka","raw_affiliation_strings":["Microsoft"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5104063234","display_name":"Kaushik Sunder Rajan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Kaushik Rajan","raw_affiliation_strings":["Microsoft"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft","institution_ids":["https://openalex.org/I4210164937"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210164937"],"apc_list":null,"apc_paid":null,"fwci":1.9548,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.88027188,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"13","issue":"3","first_page":"348","last_page":"361"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10317","display_name":"Advanced Database Systems and Queries","score":0.9991999864578247,"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":0.9991999864578247,"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/T11106","display_name":"Data Management and Algorithms","score":0.9991999864578247,"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/T11269","display_name":"Algorithms and Data Compression","score":0.998199999332428,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8043044805526733},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.6524494886398315},{"id":"https://openalex.org/keywords/query-optimization","display_name":"Query optimization","score":0.580070972442627},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.4490705728530884},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.41417187452316284},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.41281795501708984},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.30917465686798096},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1032755970954895}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8043044805526733},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.6524494886398315},{"id":"https://openalex.org/C157692150","wikidata":"https://www.wikidata.org/wiki/Q2919848","display_name":"Query optimization","level":2,"score":0.580070972442627},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.4490705728530884},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.41417187452316284},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.41281795501708984},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30917465686798096},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1032755970954895},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C86339819","wikidata":"https://www.wikidata.org/wiki/Q407384","display_name":"Transcription factor","level":3,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C158448853","wikidata":"https://www.wikidata.org/wiki/Q425218","display_name":"Repressor","level":4,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.14778/3368289.3368299","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3368289.3368299","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":[{"id":"https://metadata.un.org/sdg/8","score":0.550000011920929,"display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W135863099","https://openalex.org/W1590564432","https://openalex.org/W1967091776","https://openalex.org/W1991069419","https://openalex.org/W1993433750","https://openalex.org/W2038692154","https://openalex.org/W2075827270","https://openalex.org/W2103337291","https://openalex.org/W2105079611","https://openalex.org/W2106771621","https://openalex.org/W2117480765","https://openalex.org/W2136195851","https://openalex.org/W2151251992","https://openalex.org/W2156000708","https://openalex.org/W2189465200","https://openalex.org/W2612261081","https://openalex.org/W2763179278","https://openalex.org/W2798457757","https://openalex.org/W2948085204","https://openalex.org/W2965018236","https://openalex.org/W4238584892","https://openalex.org/W4252813408","https://openalex.org/W4253004448","https://openalex.org/W4253046889","https://openalex.org/W6605485969"],"related_works":["https://openalex.org/W4390608645","https://openalex.org/W4247566972","https://openalex.org/W2960264696","https://openalex.org/W3090563135","https://openalex.org/W2497432351","https://openalex.org/W4206777497","https://openalex.org/W4233347783","https://openalex.org/W2910064364","https://openalex.org/W4255224757","https://openalex.org/W2499527417"],"abstract_inverted_index":{"The":[0,37],"cost":[1,194],"of":[2,25,83],"big-data":[3,176],"analytics":[4],"is":[5,77],"dominated":[6],"by":[7],"shuffle":[8,41],"operations":[9],"that":[10,28,72,78,87,131,136,147,184],"induce":[11],"multiple":[12,40],"disk":[13],"reads,":[14],"writes":[15],"and":[16,197,205],"network":[17],"transfers.":[18],"This":[19],"paper":[20],"proposes":[21],"a":[22,51,101,106,124,137,142,173],"new":[23,70,125,143,164],"class":[24],"optimization":[26,210],"rules":[27,38,71],"are":[29,80],"specifically":[30],"aimed":[31],"at":[32,180],"eliminating":[33],"shuffles":[34],"where":[35],"possible.":[36],"substitute":[39,73],"inducing":[42],"operators":[43,63],"(":[44],"Join,":[45],"UnionAll,":[46],"Spool,":[47],"GroupBy":[48],")":[49],"with":[50,68,75],"single":[52],"streaming":[53],"operator":[54,129,153,160],"which":[55],"implements":[56],"an":[57],"entire":[58],"sub-query.":[59],"We":[60,110,122,140,167,182],"call":[61],"such":[62],"super-operators.":[64,166],"A":[65],"key":[66],"challenge":[67],"adding":[69],"sub-queries":[74,135],"super-operators":[76],"there":[79],"many":[81],"variants":[82],"the":[84,95,115,158,185],"same":[85,96],"sub-query":[86],"can":[88,148],"be":[89],"implemented":[90],"via":[91],"minor":[92],"modifications":[93],"to":[94,105,114,118,162],"super-operator.":[97],"Adding":[98],"each":[99],"as":[100],"separate":[102],"rule":[103,144],"leads":[104],"search":[107,150],"space":[108],"explosion.":[109],"propose":[111,123,141],"several":[112,202],"extensions":[113],"query":[116],"optimizer":[117,177],"address":[119],"this":[120],"challenge.":[121],"abstract":[126,152],"representation":[127],"for":[128,151],"trees":[130],"captures":[132],"all":[133],"possible":[134],"super-operator":[138],"implements.":[139],"matching":[145],"algorithm":[146],"efficiently":[149],"trees.":[154],"Finally":[155],"we":[156],"extend":[157],"physical":[159],"interface":[161],"introduce":[163],"parametric":[165],"implement":[168],"our":[169],"changes":[170],"in":[171,191],"SCOPE,":[172],"state-of-the-art":[174],"production":[175,203],"used":[178],"extensively":[179],"Microsoft.":[181],"demonstrate":[183],"proposed":[186],"optimizations":[187],"provide":[188],"significant":[189],"reduction":[190],"both":[192],"resource":[193],"(average":[195,199],"1.7x)":[196],"latency":[198],"1.5x)":[200],"on":[201],"queries,":[204],"do":[206],"so":[207],"without":[208],"increasing":[209],"time.":[211]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":3}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
