{"id":"https://openalex.org/W7162464701","doi":"https://doi.org/10.48550/arxiv.2605.24207","title":"Incorporating Deep Learning Design in Database Queries","display_name":"Incorporating Deep Learning Design in Database Queries","publication_year":2026,"publication_date":"2026-05-22","ids":{"openalex":"https://openalex.org/W7162464701","doi":"https://doi.org/10.48550/arxiv.2605.24207"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.24207","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24207","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.24207","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5093106062","display_name":"Yuval Lev Lubarsky","orcid":"https://orcid.org/0009-0002-8062-9046"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lubarsky, Yuval Lev","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079549430","display_name":"Dean Light","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Light, Dean","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137051142","display_name":"Boaz Berger","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Berger, Boaz","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018879650","display_name":"Shunit Agmon","orcid":"https://orcid.org/0000-0001-9605-4131"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Agmon, Shunit","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5006706357","display_name":"Benny Kimelfeld","orcid":"https://orcid.org/0000-0002-7156-1572"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kimelfeld, Benny","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"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":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.8761000037193298,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.8761000037193298,"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/T12292","display_name":"Graph Theory and Algorithms","score":0.08889999985694885,"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/T11719","display_name":"Data Quality and Management","score":0.01080000028014183,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/relational-database","display_name":"Relational database","score":0.6628000140190125},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6389999985694885},{"id":"https://openalex.org/keywords/tuple","display_name":"Tuple","score":0.5659000277519226},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.48260000348091125},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.46709999442100525},{"id":"https://openalex.org/keywords/graph-database","display_name":"Graph database","score":0.44839999079704285},{"id":"https://openalex.org/keywords/statistical-relational-learning","display_name":"Statistical relational learning","score":0.4422000050544739},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4334000051021576},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.40459999442100525}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8206999897956848},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.6628000140190125},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6389999985694885},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5867000222206116},{"id":"https://openalex.org/C118930307","wikidata":"https://www.wikidata.org/wiki/Q600590","display_name":"Tuple","level":2,"score":0.5659000277519226},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.48260000348091125},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46709999442100525},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4569000005722046},{"id":"https://openalex.org/C176225458","wikidata":"https://www.wikidata.org/wiki/Q595971","display_name":"Graph database","level":3,"score":0.44839999079704285},{"id":"https://openalex.org/C177877439","wikidata":"https://www.wikidata.org/wiki/Q7604413","display_name":"Statistical relational learning","level":3,"score":0.4422000050544739},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4334000051021576},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.40459999442100525},{"id":"https://openalex.org/C99436015","wikidata":"https://www.wikidata.org/wiki/Q1722436","display_name":"Relational calculus","level":4,"score":0.36959999799728394},{"id":"https://openalex.org/C510870499","wikidata":"https://www.wikidata.org/wiki/Q47607","display_name":"SQL","level":2,"score":0.36070001125335693},{"id":"https://openalex.org/C40207289","wikidata":"https://www.wikidata.org/wiki/Q755662","display_name":"Relational model","level":3,"score":0.3562999963760376},{"id":"https://openalex.org/C148840519","wikidata":"https://www.wikidata.org/wiki/Q1049878","display_name":"Database design","level":2,"score":0.3528999984264374},{"id":"https://openalex.org/C146380142","wikidata":"https://www.wikidata.org/wiki/Q1137726","display_name":"Directed graph","level":2,"score":0.33410000801086426},{"id":"https://openalex.org/C106891557","wikidata":"https://www.wikidata.org/wiki/Q4148051","display_name":"Wait-for graph","level":4,"score":0.3260999917984009},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.32260000705718994},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3127000033855438},{"id":"https://openalex.org/C72634772","wikidata":"https://www.wikidata.org/wiki/Q386824","display_name":"Data integration","level":2,"score":0.3125999867916107},{"id":"https://openalex.org/C24394798","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database management system","level":3,"score":0.3095000088214874},{"id":"https://openalex.org/C95916125","wikidata":"https://www.wikidata.org/wiki/Q840540","display_name":"Relational algebra","level":3,"score":0.3005000054836273},{"id":"https://openalex.org/C157170001","wikidata":"https://www.wikidata.org/wiki/Q4781507","display_name":"Applications of artificial intelligence","level":2,"score":0.2922999858856201},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29120001196861267},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.2883000075817108},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.288100004196167},{"id":"https://openalex.org/C5968703","wikidata":"https://www.wikidata.org/wiki/Q267136","display_name":"Database model","level":3,"score":0.27950000762939453},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.25619998574256897}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.24207","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24207","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.24207","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24207","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Deep":[0],"learning":[1,30,92],"over":[2,74,218],"relational":[3,58,75,143],"databases":[4,219],"is":[5,65,99],"conventionally":[6],"realized":[7],"by":[8,57,177],"translating":[9],"data":[10,76,121],"into":[11],"graph":[12,40,183,187],"representations":[13],"and":[14,27,47,122,152,169,192,201],"applying":[15],"graph-based":[16],"neural":[17,41,72,190,216],"networks":[18,42,191,217],"within":[19],"external":[20,28],"frameworks.":[21],"This":[22,136],"round-trip":[23],"between":[24],"the":[25,54,173,199,212],"database":[26,94,149],"machine":[29],"(ML)":[31],"systems":[32],"introduces":[33],"non-trivial":[34],"engineering":[35],"overhead.":[36],"In":[37],"effect,":[38],"these":[39],"operate":[43,118],"on":[44,120,165],"tuple":[45,103],"embeddings":[46],"manipulate":[48],"them":[49],"in":[50],"ways":[51],"that":[52,88],"capture":[53],"interactions":[55],"induced":[56],"joins.":[59],"Given":[60],"this":[61,162],"natural":[62],"correspondence,":[63],"there":[64],"no":[66],"fundamental":[67],"reason":[68],"why":[69],"specifying":[70],"a":[71,108,139,158,207,224],"network":[73],"should":[77],"be":[78],"substantially":[79],"harder":[80],"than":[81],"querying":[82],"it.":[83],"We":[84,155,171],"propose":[85],"an":[86],"approach":[87,137,163],"naturally":[89],"integrates":[90],"deep":[91,144,193],"with":[93,104,111,127,133,148],"queries.":[95],"The":[96,196],"key":[97],"idea":[98],"to":[100,117,130],"associate":[101],"each":[102],"provenance,":[105],"represented":[106],"as":[107,220,222],"vector":[109],"embedding":[110],"learnable":[112],"parameters.":[113],"Queries":[114],"are":[115],"lifted":[116],"jointly":[119],"embeddings,":[123],"mapping":[124],"input":[125],"relations":[126,132],"embedded":[128,134],"tuples":[129],"output":[131],"tuples.":[135],"provides":[138],"declarative":[140],"foundation":[141],"for":[142],"learning,":[145],"facilitating":[146],"integration":[147],"systems,":[150],"optimization,":[151],"wide":[153],"adoption.":[154],"describe":[156],"RelaNN,":[157],"proof-of-concept":[159],"implementation":[160,213],"of":[161,167,175,198,214],"built":[164],"top":[166],"PyTorch":[168],"cuDF.":[170],"illustrate":[172],"utility":[174],"RelaNN":[176],"implementing":[178],"various":[179],"graph-learning":[180],"models,":[181],"including":[182],"convolutional":[184],"networks,":[185],"heterogeneous":[186],"transformers,":[188],"hypergraph":[189],"homomorphism":[194],"networks.":[195],"simplicity":[197],"programs":[200],"their":[202],"competitive":[203],"runtime":[204],"performance":[205],"demonstrate":[206],"concrete":[208],"path":[209],"toward":[210],"making":[211],"state-of-the-art":[215],"simple":[221],"writing":[223],"query.":[225]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-27T00:00:00"}