{"id":"https://openalex.org/W4416385809","doi":"https://doi.org/10.48550/arxiv.2510.08489","title":"Implementing Semantic Join Operators Efficiently","display_name":"Implementing Semantic Join Operators Efficiently","publication_year":2025,"publication_date":"2025-10-09","ids":{"openalex":"https://openalex.org/W4416385809","doi":"https://doi.org/10.48550/arxiv.2510.08489"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2510.08489","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.08489","pdf_url":"https://arxiv.org/pdf/2510.08489","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2510.08489","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5087259526","display_name":"Immanuel Trummer","orcid":"https://orcid.org/0000-0002-7203-2349"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Trummer, Immanuel","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5087259526"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10317","display_name":"Advanced Database Systems and Queries","score":0.7369999885559082,"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":0.7369999885559082,"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/T10215","display_name":"Semantic Web and Ontologies","score":0.051100000739097595,"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/T11106","display_name":"Data Management and Algorithms","score":0.030300000682473183,"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/joins","display_name":"Joins","score":0.8234999775886536},{"id":"https://openalex.org/keywords/join","display_name":"Join (topology)","score":0.5741000175476074},{"id":"https://openalex.org/keywords/row","display_name":"Row","score":0.5198000073432922},{"id":"https://openalex.org/keywords/row-and-column-spaces","display_name":"Row and column spaces","score":0.4607999920845032},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4417000114917755},{"id":"https://openalex.org/keywords/hash-join","display_name":"Hash join","score":0.42320001125335693},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.40689998865127563},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.392300009727478},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.38839998841285706}],"concepts":[{"id":"https://openalex.org/C2778692605","wikidata":"https://www.wikidata.org/wiki/Q4041866","display_name":"Joins","level":2,"score":0.8234999775886536},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7883999943733215},{"id":"https://openalex.org/C2776124973","wikidata":"https://www.wikidata.org/wiki/Q3183033","display_name":"Join (topology)","level":2,"score":0.5741000175476074},{"id":"https://openalex.org/C135598885","wikidata":"https://www.wikidata.org/wiki/Q1366302","display_name":"Row","level":2,"score":0.5198000073432922},{"id":"https://openalex.org/C104140500","wikidata":"https://www.wikidata.org/wiki/Q2088159","display_name":"Row and column spaces","level":3,"score":0.4607999920845032},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4417000114917755},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.43470001220703125},{"id":"https://openalex.org/C188805328","wikidata":"https://www.wikidata.org/wiki/Q4060691","display_name":"Hash join","level":3,"score":0.42320001125335693},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.40689998865127563},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.392300009727478},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.38839998841285706},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.3546999990940094},{"id":"https://openalex.org/C68859911","wikidata":"https://www.wikidata.org/wiki/Q1503724","display_name":"Pattern matching","level":2,"score":0.34299999475479126},{"id":"https://openalex.org/C26713055","wikidata":"https://www.wikidata.org/wiki/Q245962","display_name":"Implementation","level":2,"score":0.34279999136924744},{"id":"https://openalex.org/C1306188","wikidata":"https://www.wikidata.org/wiki/Q4060687","display_name":"Nested loop join","level":2,"score":0.3391999900341034},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31690001487731934},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.3165000081062317},{"id":"https://openalex.org/C157692150","wikidata":"https://www.wikidata.org/wiki/Q2919848","display_name":"Query optimization","level":2,"score":0.31119999289512634},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.29019999504089355},{"id":"https://openalex.org/C2129575","wikidata":"https://www.wikidata.org/wiki/Q54837","display_name":"Semantic Web","level":2,"score":0.2863999903202057},{"id":"https://openalex.org/C203570394","wikidata":"https://www.wikidata.org/wiki/Q4060688","display_name":"Sort-merge join","level":3,"score":0.2689000070095062},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.2662999927997589},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.2651999890804291},{"id":"https://openalex.org/C85407183","wikidata":"https://www.wikidata.org/wiki/Q1045785","display_name":"Semantic network","level":2,"score":0.25949999690055847},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.25440001487731934},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.25380000472068787},{"id":"https://openalex.org/C2777946921","wikidata":"https://www.wikidata.org/wiki/Q7449044","display_name":"Semantic analysis (machine learning)","level":2,"score":0.25290000438690186},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2524000108242035}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2510.08489","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.08489","pdf_url":"https://arxiv.org/pdf/2510.08489","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2510.08489","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.08489","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2510.08489","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.08489","pdf_url":"https://arxiv.org/pdf/2510.08489","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416385809.pdf","grobid_xml":"https://content.openalex.org/works/W4416385809.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Semantic":[0],"query":[1,41,199],"processing":[2,42,173,200],"engines":[3,43],"often":[4],"support":[5],"semantic":[6,40,45,198],"joins,":[7],"enabling":[8],"users":[9],"to":[10,53,103,121,155,166,192],"match":[11],"rows":[12,87],"that":[13,31,117,181],"satisfy":[14],"conditions":[15,22],"specified":[16],"in":[17,77,109,157],"natural":[18],"language.":[19],"Such":[20],"join":[21,56,74,193],"can":[23,118],"be":[24,119],"evaluated":[25],"using":[26],"large":[27],"language":[28],"models":[29],"(LLMs)":[30],"solve":[32],"novel":[33,66],"tasks":[34],"without":[35],"task-specific":[36],"training.":[37],"Currently,":[38],"many":[39],"implement":[44],"joins":[46],"via":[47],"nested":[48,72],"loops,":[49],"invoking":[50],"the":[51,55,70,99,110,123,126,134,137,151,159,162,182],"LLM":[52,100,138],"evaluate":[54],"condition":[57],"on":[58,133],"row":[59,107,127],"pairs.":[60],"Instead,":[61],"this":[62],"paper":[63,114],"proposes":[64],"a":[65,93],"algorithm,":[67],"inspired":[68],"by":[69,196],"block":[71],"loops":[73],"operator":[75],"implementation":[76],"traditional":[78],"database":[79],"systems.":[80],"The":[81,96,113],"proposed":[82,152,183],"algorithm":[83,153],"integrates":[84],"batches":[85],"of":[86,98,125,136,150,161,171],"from":[88],"both":[89,142],"input":[90,143],"tables":[91],"into":[92,130],"single":[94],"prompt.":[95],"goal":[97],"invocation":[101],"is":[102,164],"identify":[104],"all":[105],"matching":[106],"pairs":[108],"current":[111],"input.":[112],"introduces":[115],"formulas":[116],"used":[120,195],"optimize":[122],"size":[124,135,160],"batches,":[128],"taking":[129],"account":[131],"constraints":[132],"context":[139],"window":[140],"(limiting":[141],"and":[144,188],"output":[145,163],"size).":[146],"An":[147],"adaptive":[148],"variant":[149],"refers":[154],"cases":[156],"which":[158],"difficult":[165],"estimate.":[167],"A":[168],"formal":[169],"analysis":[170],"asymptotic":[172],"costs,":[174],"as":[175,177],"well":[176,190],"empirical":[178],"results,":[179],"demonstrates":[180],"approach":[184],"reduces":[185],"costs":[186],"significantly":[187],"performs":[189],"compared":[191],"implementations":[194],"recent":[197],"engines.":[201]},"counts_by_year":[],"updated_date":"2026-03-14T08:43:22.919905","created_date":"2025-10-11T00:00:00"}