{"id":"https://openalex.org/W4392454229","doi":"https://doi.org/10.14778/3636218.3636229","title":"Sample-Efficient Cardinality Estimation Using Geometric Deep Learning","display_name":"Sample-Efficient Cardinality Estimation Using Geometric Deep Learning","publication_year":2023,"publication_date":"2023-12-01","ids":{"openalex":"https://openalex.org/W4392454229","doi":"https://doi.org/10.14778/3636218.3636229"},"language":"en","primary_location":{"id":"doi:10.14778/3636218.3636229","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3636218.3636229","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":true,"oa_status":"green","oa_url":"https://kops.uni-konstanz.de/server/api/core/bitstreams/ea13f6f4-7e10-461e-ab4b-cb5058210de7/content","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5094068099","display_name":"Silvan Reiner","orcid":"https://orcid.org/0009-0005-4845-4743"},"institutions":[{"id":"https://openalex.org/I189712700","display_name":"University of Konstanz","ror":"https://ror.org/0546hnb39","country_code":"DE","type":"education","lineage":["https://openalex.org/I189712700"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Silvan Reiner","raw_affiliation_strings":["University of Konstanz"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Konstanz","institution_ids":["https://openalex.org/I189712700"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000695235","display_name":"Michael Grossniklaus","orcid":"https://orcid.org/0000-0003-1609-2221"},"institutions":[{"id":"https://openalex.org/I189712700","display_name":"University of Konstanz","ror":"https://ror.org/0546hnb39","country_code":"DE","type":"education","lineage":["https://openalex.org/I189712700"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Michael Grossniklaus","raw_affiliation_strings":["University of Konstanz"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Konstanz","institution_ids":["https://openalex.org/I189712700"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I189712700"],"apc_list":null,"apc_paid":null,"fwci":2.3732,"has_fulltext":true,"cited_by_count":18,"citation_normalized_percentile":{"value":0.90853418,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"17","issue":"4","first_page":"740","last_page":"752"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9962000250816345,"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":0.9962000250816345,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.9947999715805054,"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/cardinality","display_name":"Cardinality (data modeling)","score":0.6103203296661377},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.6039181351661682},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.563431441783905},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4874610900878906},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.4690789580345154},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.45273488759994507},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43494945764541626},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3958854675292969},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.33203646540641785},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.19998258352279663},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09581133723258972},{"id":"https://openalex.org/keywords/chromatography","display_name":"Chromatography","score":0.06759360432624817}],"concepts":[{"id":"https://openalex.org/C87117476","wikidata":"https://www.wikidata.org/wiki/Q362383","display_name":"Cardinality (data modeling)","level":2,"score":0.6103203296661377},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.6039181351661682},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.563431441783905},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4874610900878906},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.4690789580345154},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.45273488759994507},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43494945764541626},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3958854675292969},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.33203646540641785},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.19998258352279663},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09581133723258972},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.06759360432624817},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.14778/3636218.3636229","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3636218.3636229","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"},{"id":"pmh:oai:kops.uni-konstanz.de:123456789/69590","is_oa":true,"landing_page_url":"http://nbn-resolving.de/urn:nbn:de:bsz:352-2-o4hxnv1cptt52","pdf_url":"https://kops.uni-konstanz.de/server/api/core/bitstreams/ea13f6f4-7e10-461e-ab4b-cb5058210de7/content","source":{"id":"https://openalex.org/S4306401487","display_name":"KOPS (University of Konstanz)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I189712700","host_organization_name":"University of Konstanz","host_organization_lineage":["https://openalex.org/I189712700"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Proceedings of the VLDB Endowment. Association for Computing Machinery (ACM). 2023, 17(4), S. 740-752. eISSN 2150-8097. Verf\u00fcgbar unter: doi: 10.14778/3636218.3636229","raw_type":"doc-type:article"}],"best_oa_location":{"id":"pmh:oai:kops.uni-konstanz.de:123456789/69590","is_oa":true,"landing_page_url":"http://nbn-resolving.de/urn:nbn:de:bsz:352-2-o4hxnv1cptt52","pdf_url":"https://kops.uni-konstanz.de/server/api/core/bitstreams/ea13f6f4-7e10-461e-ab4b-cb5058210de7/content","source":{"id":"https://openalex.org/S4306401487","display_name":"KOPS (University of Konstanz)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I189712700","host_organization_name":"University of Konstanz","host_organization_lineage":["https://openalex.org/I189712700"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Proceedings of the VLDB Endowment. Association for Computing Machinery (ACM). 2023, 17(4), S. 740-752. eISSN 2150-8097. Verf\u00fcgbar unter: doi: 10.14778/3636218.3636229","raw_type":"doc-type:article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320879","display_name":"Deutsche Forschungsgemeinschaft","ror":"https://ror.org/018mejw64"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4392454229.pdf","grobid_xml":"https://content.openalex.org/works/W4392454229.grobid-xml"},"referenced_works_count":37,"referenced_works":["https://openalex.org/W1487321909","https://openalex.org/W2025568499","https://openalex.org/W2132823934","https://openalex.org/W2396309311","https://openalex.org/W2911627187","https://openalex.org/W2946026089","https://openalex.org/W2955205099","https://openalex.org/W2955798121","https://openalex.org/W2970148517","https://openalex.org/W2991530444","https://openalex.org/W2998249308","https://openalex.org/W3013555795","https://openalex.org/W3097225903","https://openalex.org/W3099273181","https://openalex.org/W3103472792","https://openalex.org/W3111141572","https://openalex.org/W3124277639","https://openalex.org/W3157286395","https://openalex.org/W3197977787","https://openalex.org/W3198024709","https://openalex.org/W3207801254","https://openalex.org/W4221142004","https://openalex.org/W4226086155","https://openalex.org/W4281754544","https://openalex.org/W4282570649","https://openalex.org/W4283326127","https://openalex.org/W4289706945","https://openalex.org/W4311420308","https://openalex.org/W4313138291","https://openalex.org/W4317641620","https://openalex.org/W4366492480","https://openalex.org/W4366502978","https://openalex.org/W4375928354","https://openalex.org/W6602707511","https://openalex.org/W6604662147","https://openalex.org/W6677978660","https://openalex.org/W6967287017"],"related_works":["https://openalex.org/W2002177687","https://openalex.org/W2058438338","https://openalex.org/W2019471580","https://openalex.org/W2941284322","https://openalex.org/W4224920876","https://openalex.org/W2168299207","https://openalex.org/W2124475651","https://openalex.org/W2585354854","https://openalex.org/W2064478620","https://openalex.org/W4308671316"],"abstract_inverted_index":{"In":[0,13],"database":[1],"systems,":[2],"accurate":[3],"cardinality":[4,53],"estimation":[5,54],"is":[6],"a":[7,48,65,94,101],"cornerstone":[8],"of":[9,30,107,138,153],"effective":[10],"query":[11,164],"optimization.":[12],"this":[14,57],"context,":[15],"estimators":[16,33],"that":[17,55,75,104,145],"use":[18],"machine":[19],"learning":[20,73],"have":[21],"shown":[22],"significant":[23],"promise.":[24],"Despite":[25],"their":[26,37,91],"potential,":[27],"the":[28,108,115,136,150],"effectiveness":[29],"these":[31],"learned":[32,52],"strongly":[34],"depends":[35],"on":[36],"ability":[38],"to":[39],"learn":[40],"from":[41,159],"small":[42],"training":[43,116],"sets.":[44],"This":[45],"paper":[46],"presents":[47],"novel":[49],"approach":[50],"for":[51,87],"addresses":[56],"issue":[58],"by":[59,70],"enhancing":[60],"sample":[61],"efficiency.":[62],"We":[63,124],"propose":[64],"neural":[66],"network":[67],"architecture":[68],"informed":[69],"geometric":[71],"deep":[72],"principles":[74],"represents":[76],"queries":[77],"as":[78,93,160,162],"join":[79],"graphs.":[80],"Furthermore,":[81],"we":[82,99],"introduce":[83],"an":[84],"innovative":[85],"encoding":[86,92],"complex":[88],"predicates,":[89],"treating":[90],"feature":[95],"selection":[96],"problem.":[97],"Additionally,":[98],"devise":[100],"regularization":[102],"term":[103],"employs":[105],"equalities":[106],"relational":[109],"algebra":[110],"and":[111,135],"three-valued":[112],"logic,":[113],"augmenting":[114],"process":[117],"without":[118],"requiring":[119],"additional":[120],"ground":[121],"truth":[122],"cardinalities.":[123],"rigorously":[125],"evaluate":[126],"our":[127,146],"model":[128,147],"across":[129],"multiple":[130],"benchmarks,":[131],"examining":[132],"q-errors,":[133],"runtimes,":[134],"impact":[137],"workload":[139],"distribution":[140],"shifts.":[141],"Our":[142],"results":[143],"demonstrate":[144],"significantly":[148],"improves":[149],"end-to-end":[151],"runtimes":[152],"PostgreSQL,":[154],"even":[155],"with":[156],"cardinalities":[157],"gathered":[158],"little":[161],"100":[163],"executions.":[165]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":3}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
