{"id":"https://openalex.org/W7162325350","doi":"https://doi.org/10.48550/arxiv.2605.23241","title":"RelPrism: A Multi-Faceted Pre-training Framework with Self-Generated Tasks for Relational Databases","display_name":"RelPrism: A Multi-Faceted Pre-training Framework with Self-Generated Tasks for Relational Databases","publication_year":2026,"publication_date":"2026-05-22","ids":{"openalex":"https://openalex.org/W7162325350","doi":"https://doi.org/10.48550/arxiv.2605.23241"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.23241","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.23241","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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.23241","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136900888","display_name":"Jinyu Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Jinyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136899975","display_name":"Cheng Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Cheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136909634","display_name":"Junze Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Junze","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104139299","display_name":"Zedi Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Zedi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136897754","display_name":"Muhan Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Muhan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136902194","display_name":"Hanyang Peng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peng, Hanyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136956131","display_name":"Chuan Shi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shi, Chuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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/T11719","display_name":"Data Quality and Management","score":0.5328999757766724,"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"}},"topics":[{"id":"https://openalex.org/T11719","display_name":"Data Quality and Management","score":0.5328999757766724,"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"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.17229999601840973,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.06469999998807907,"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/relational-database","display_name":"Relational database","score":0.6771000027656555},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5307999849319458},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.4991999864578247},{"id":"https://openalex.org/keywords/statistical-relational-learning","display_name":"Statistical relational learning","score":0.4325000047683716},{"id":"https://openalex.org/keywords/relational-model","display_name":"Relational model","score":0.4293999969959259},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.4244999885559082},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.3700000047683716},{"id":"https://openalex.org/keywords/external-data-representation","display_name":"External Data Representation","score":0.36980000138282776}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7089999914169312},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.6771000027656555},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5307999849319458},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.4991999864578247},{"id":"https://openalex.org/C177877439","wikidata":"https://www.wikidata.org/wiki/Q7604413","display_name":"Statistical relational learning","level":3,"score":0.4325000047683716},{"id":"https://openalex.org/C40207289","wikidata":"https://www.wikidata.org/wiki/Q755662","display_name":"Relational model","level":3,"score":0.4293999969959259},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.4244999885559082},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.390500009059906},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.3700000047683716},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.36980000138282776},{"id":"https://openalex.org/C65647387","wikidata":"https://www.wikidata.org/wiki/Q1781706","display_name":"Conjunctive query","level":3,"score":0.3596999943256378},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.35280001163482666},{"id":"https://openalex.org/C24394798","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database management system","level":3,"score":0.3495999872684479},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3407999873161316},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.336899995803833},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.336899995803833},{"id":"https://openalex.org/C2781221856","wikidata":"https://www.wikidata.org/wiki/Q840247","display_name":"Hypergraph","level":2,"score":0.3368000090122223},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.30379998683929443},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.29649999737739563},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2955999970436096},{"id":"https://openalex.org/C2776207758","wikidata":"https://www.wikidata.org/wiki/Q5303302","display_name":"Downstream (manufacturing)","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.25609999895095825},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.2529999911785126},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.25200000405311584}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.23241","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.23241","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.23241","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.23241","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Relational":[0],"databases":[1],"(RDBs)":[2],"remain":[3],"the":[4,51],"cornerstone":[5],"of":[6,54],"modern":[7],"data":[8],"systems":[9],"and":[10,35,42,72,93,157,163,184,211],"support":[11],"diverse":[12],"predictive":[13],"tasks.":[14],"Recent":[15],"relational":[16,135],"deep":[17],"learning":[18,149],"(RDL)":[19],"methods":[20,120],"enable":[21],"end-to-end":[22],"prediction":[23,88],"by":[24,207,214],"converting":[25],"RDBs":[26,60],"into":[27],"graphs,":[28],"where":[29],"rows":[30],"are":[31,38],"represented":[32,39],"as":[33,40,107,129],"nodes":[34],"inter-table":[36],"interactions":[37],"edges,":[41],"then":[43],"applying":[44],"graph-based":[45],"models":[46],"for":[47,59,97,114,151,191,209,216],"representation":[48,105],"learning.":[49],"Despite":[50],"strong":[52],"capability":[53],"RDL,":[55],"effective":[56],"self-supervised":[57,148],"pre-training":[58,108],"remains":[61],"non-trivial.":[62],"RDB":[63,104],"tasks":[64,197],"often":[65],"require":[66],"multi-faceted":[67,147],"information":[68,113],"across":[69,198],"different":[70],"perspectives":[71,183],"granularities.":[73],"For":[74],"example,":[75],"user":[76,95],"churn":[77],"classification":[78,210],"may":[79],"rely":[80],"more":[81],"on":[82,195],"interaction":[83],"patterns,":[84],"whereas":[85],"consumption":[86],"value":[87],"requires":[89],"both":[90],"user-item":[91],"behaviors":[92],"intrinsic":[94,131],"attributes":[96,132,159],"fine-grained":[98],"regression.":[99],"Such":[100],"heterogeneous":[101],"needs":[102],"challenge":[103],"learning,":[106],"objectives":[109],"should":[110],"cover":[111],"comprehensive":[112],"downstream":[115,192],"adaptation.":[116,193],"However,":[117],"existing":[118],"SSL":[119],"typically":[121],"derive":[122],"supervision":[123],"from":[124,160],"a":[125,146,188],"single":[126],"facet,":[127],"such":[128],"node-level":[130],"or":[133],"subgraph-level":[134],"structures,":[136],"providing":[137],"limited":[138],"adaptability.":[139],"To":[140],"this":[141],"end,":[142],"we":[143],"propose":[144],"RelPrism,":[145],"framework":[150],"RDBs.":[152],"RelPrism":[153,204],"constructs":[154],"intrinsic,":[155],"relational,":[156],"hybrid":[158],"distinct":[161],"perspectives,":[162],"applies":[164],"multi-granularity":[165],"clustering":[166],"to":[167,170,181],"each":[168],"perspective":[169],"form":[171],"corresponding":[172],"pseudo-task":[173],"pools.":[174],"Pre-training":[175],"over":[176,218],"these":[177],"pools":[178],"exposes":[179],"representations":[180],"broader":[182],"granularity":[185],"levels,":[186],"yielding":[187],"stronger":[189],"basis":[190],"Experiments":[194],"14":[196],"5":[199],"real-world":[200],"datasets":[201],"show":[202],"that":[203],"improves":[205],"ROC-AUC":[206],"4.15%":[208],"reduces":[212],"MAE":[213],"10.75%":[215],"regression":[217],"state-of-the-art":[219],"baselines.":[220],"Our":[221],"code":[222],"is":[223],"available":[224],"at":[225],"https://anonymous.4open.science/r/RelPrism.":[226]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-26T00:00:00"}