{"id":"https://openalex.org/W4318186128","doi":"https://doi.org/10.1109/bigdata55660.2022.10021056","title":"Regression-based Optimizer Statistics generation based on q-error","display_name":"Regression-based Optimizer Statistics generation based on q-error","publication_year":2022,"publication_date":"2022-12-17","ids":{"openalex":"https://openalex.org/W4318186128","doi":"https://doi.org/10.1109/bigdata55660.2022.10021056"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata55660.2022.10021056","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bigdata55660.2022.10021056","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","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":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5013377733","display_name":"Parag Paul","orcid":null},"institutions":[{"id":"https://openalex.org/I4210105678","display_name":"Microsoft (Finland)","ror":"https://ror.org/01nehjf29","country_code":"FI","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210105678"]},{"id":"https://openalex.org/I82497590","display_name":"Auburn University","ror":"https://ror.org/02v80fc35","country_code":"US","type":"education","lineage":["https://openalex.org/I82497590"]}],"countries":["FI","US"],"is_corresponding":false,"raw_author_name":"Parag Paul","raw_affiliation_strings":["Microsoft Corporation","Auburn University, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Corporation","institution_ids":["https://openalex.org/I4210105678"]},{"raw_affiliation_string":"Auburn University, USA","institution_ids":["https://openalex.org/I82497590"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101402672","display_name":"Wenlu Wang","orcid":"https://orcid.org/0000-0002-4829-1068"},"institutions":[{"id":"https://openalex.org/I96749437","display_name":"Texas A&M University \u2013 Corpus Christi","ror":"https://ror.org/01mrfdz82","country_code":"US","type":"education","lineage":["https://openalex.org/I96749437"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wenlu Wang","raw_affiliation_strings":["Texas A&M University,Corpus Christi,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Texas A&M University,Corpus Christi,USA","institution_ids":["https://openalex.org/I96749437"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5117634641","display_name":"Haiquan Chen","orcid":"https://orcid.org/0000-0003-1689-0549"},"institutions":[{"id":"https://openalex.org/I43522216","display_name":"California State University, Sacramento","ror":"https://ror.org/03e26wv14","country_code":"US","type":"education","lineage":["https://openalex.org/I43522216"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haiquan Chen","raw_affiliation_strings":["California State University,Sacramento,USA","California State University, Sacramento, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"California State University,Sacramento,USA","institution_ids":["https://openalex.org/I43522216"]},{"raw_affiliation_string":"California State University, Sacramento, USA","institution_ids":["https://openalex.org/I43522216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001457193","display_name":"Wei\u2010Shinn Ku","orcid":"https://orcid.org/0000-0001-8636-4689"},"institutions":[{"id":"https://openalex.org/I82497590","display_name":"Auburn University","ror":"https://ror.org/02v80fc35","country_code":"US","type":"education","lineage":["https://openalex.org/I82497590"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei-Shinn Ku","raw_affiliation_strings":["Auburn University,USA","Auburn University, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Auburn University,USA","institution_ids":["https://openalex.org/I82497590"]},{"raw_affiliation_string":"Auburn University, USA","institution_ids":["https://openalex.org/I82497590"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"18","issue":null,"first_page":"2346","last_page":"2351"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9995999932289124,"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":0.9995999932289124,"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/T10317","display_name":"Advanced Database Systems and Queries","score":0.9993000030517578,"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9919999837875366,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/histogram","display_name":"Histogram","score":0.7212518453598022},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6006395220756531},{"id":"https://openalex.org/keywords/multiplicative-function","display_name":"Multiplicative function","score":0.5083834528923035},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4822750389575958},{"id":"https://openalex.org/keywords/cardinality","display_name":"Cardinality (data modeling)","score":0.4508677124977112},{"id":"https://openalex.org/keywords/tuple","display_name":"Tuple","score":0.4202340841293335},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4191284775733948},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3400857150554657},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.339771568775177},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1376289427280426}],"concepts":[{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.7212518453598022},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6006395220756531},{"id":"https://openalex.org/C42747912","wikidata":"https://www.wikidata.org/wiki/Q1048447","display_name":"Multiplicative function","level":2,"score":0.5083834528923035},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4822750389575958},{"id":"https://openalex.org/C87117476","wikidata":"https://www.wikidata.org/wiki/Q362383","display_name":"Cardinality (data modeling)","level":2,"score":0.4508677124977112},{"id":"https://openalex.org/C118930307","wikidata":"https://www.wikidata.org/wiki/Q600590","display_name":"Tuple","level":2,"score":0.4202340841293335},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4191284775733948},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3400857150554657},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.339771568775177},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1376289427280426},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"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:10.1109/bigdata55660.2022.10021056","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bigdata55660.2022.10021056","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","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":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.5799999833106995,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W192214595","https://openalex.org/W1551788181","https://openalex.org/W2005188603","https://openalex.org/W2010890196","https://openalex.org/W2132823934","https://openalex.org/W2139422440","https://openalex.org/W2151203234","https://openalex.org/W2153329411","https://openalex.org/W2162870748","https://openalex.org/W2325850497","https://openalex.org/W2396309311","https://openalex.org/W2911826939","https://openalex.org/W2915063781","https://openalex.org/W3083806692","https://openalex.org/W4236226996","https://openalex.org/W4246006899","https://openalex.org/W4249653116","https://openalex.org/W6607749480","https://openalex.org/W6635154084","https://openalex.org/W6682588809","https://openalex.org/W6683306772"],"related_works":["https://openalex.org/W2045447888","https://openalex.org/W2025793054","https://openalex.org/W1993024450","https://openalex.org/W2127459735","https://openalex.org/W2117923458","https://openalex.org/W3203772198","https://openalex.org/W3158045964","https://openalex.org/W2368805953","https://openalex.org/W2785157218","https://openalex.org/W4318186128"],"abstract_inverted_index":{"Query":[0],"optimizers":[1],"in":[2,38,62,257],"Relational":[3],"Database":[4],"Management":[5],"Systems":[6],"(RDBMS)":[7],"rely":[8],"on":[9,94,159,194],"the":[10,30,60,70,75,78,83,91,95,99,103,107,115,118,128,147,160,176,191,195,203,208,216,224,234,245,249,253,258],"approximations":[11,50],"of":[12,15,25,34,51,59,64,66,74,77,80,82,102,106,114,121,127,190,198,218,237,248],"data":[13,52],"distributions":[14],"attributes,":[16],"which":[17],"are":[18],"used":[19],"for":[20,207,239],"cardinality-based":[21],"decisions":[22],"like":[23],"selectivity":[24],"a":[26,35,39,67,135,154,219,228,240],"predicate":[27],"or":[28,98,117],"finding":[29],"right":[31],"join":[32,37],"order":[33,197],"multiway":[36],"SQL":[40],"query.":[41],"The":[42,57,183],"commercial":[43],"databases":[44],"try":[45],"to":[46,139,168,212,231],"reach":[47],"best":[48],"possible":[49],"distribution":[53,236],"using":[54,85],"compressed":[55],"histograms.":[56],"state":[58],"art":[61],"measurement":[63,79],"quality":[65,81,93],"histogram":[68,143,172],"is":[69,87],"q-error.":[71],"However,":[72],"one":[73,155],"shortcomings":[76],"histograms":[84],"q-error":[86],"that":[88,123,174],"it":[89],"pivots":[90],"entire":[92],"worst":[96],"case":[97],"infinity":[100],"norm":[101],"multiplicative":[104,149,199],"errors":[105,122,150,206,238],"estimations.":[108],"This":[109],"may":[110],"not":[111],"be":[112,125],"symbolic":[113],"workload":[116],"overall":[119,148,204],"range":[120],"would":[124],"representative":[126],"system.":[129,209],"In":[130],"this":[131,213],"paper,":[132],"we":[133],"propose":[134,153],"dynamic":[136,165],"programming":[137,166],"algorithm":[138,184],"achieve":[140,169],"an":[141,170],"optimal":[142,171],"structure":[144,173],"by":[145],"keeping":[146],"low.":[151],"We":[152,210,221,243],"pass":[156],"linear":[157],"regression":[158,193],"step":[161],"function":[162],"and":[163,180,188,260],"apply":[164],"methods":[167,251],"optimizes":[175],"pair":[177,214],"(slope":[178],"\u03b2":[179],"intercept":[181,189],"\u03b1).":[182],"achieves":[185],"lower":[186],"slope":[187],"resultant":[192],"sorted":[196],"errors,":[200],"thus":[201],"reducing":[202],"estimation":[205],"refer":[211],"as":[215,227],"Q-Regression":[217],"histogram.":[220,242],"then":[222],"introduce":[223],"metric,":[225],"QRegrArea":[226],"new":[229],"means":[230],"better":[232],"quantify":[233],"cumulative":[235],"given":[241],"provide":[244],"experimental":[246],"validation":[247],"proposed":[250],"against":[252],"state-of":[254],"the-art":[255],"models":[256],"literature":[259],"industry.":[261]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}