{"id":"https://openalex.org/W7165738811","doi":"https://doi.org/10.48550/arxiv.2606.24204","title":"Unified Dominance Graph for Interval-Predicate Approximate Nearest Neighbor Search","display_name":"Unified Dominance Graph for Interval-Predicate Approximate Nearest Neighbor Search","publication_year":2026,"publication_date":"2026-06-23","ids":{"openalex":"https://openalex.org/W7165738811","doi":"https://doi.org/10.48550/arxiv.2606.24204"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.24204","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.24204","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.2606.24204","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139272247","display_name":"Kwun Hang Lau","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lau, Kwun Hang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139236580","display_name":"Ruiyuan Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Ruiyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122288113","display_name":"Elton Chun-Chai Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Elton Chun-Chai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123963052","display_name":"Wun Yu Chan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chan, Wun Yu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001449972","display_name":"Xiaojun Cheng","orcid":"https://orcid.org/0000-0002-3568-0603"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Xiaojun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5113903386","display_name":"X J Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Xiaofang","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/T11106","display_name":"Data Management and Algorithms","score":0.7649000287055969,"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":0.7649000287055969,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.0494999997317791,"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":0.04270000010728836,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/search-engine-indexing","display_name":"Search engine indexing","score":0.5598000288009644},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.46459999680519104},{"id":"https://openalex.org/keywords/nearest-neighbor-search","display_name":"Nearest neighbor search","score":0.45820000767707825},{"id":"https://openalex.org/keywords/interval-graph","display_name":"Interval graph","score":0.4334000051021576},{"id":"https://openalex.org/keywords/interval","display_name":"Interval (graph theory)","score":0.4325000047683716},{"id":"https://openalex.org/keywords/intersection-graph","display_name":"Intersection graph","score":0.3912999927997589},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.3490000069141388},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.31520000100135803}],"concepts":[{"id":"https://openalex.org/C75165309","wikidata":"https://www.wikidata.org/wiki/Q2258979","display_name":"Search engine indexing","level":2,"score":0.5598000288009644},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5184999704360962},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4975000023841858},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.46459999680519104},{"id":"https://openalex.org/C116738811","wikidata":"https://www.wikidata.org/wiki/Q608751","display_name":"Nearest neighbor search","level":2,"score":0.45820000767707825},{"id":"https://openalex.org/C67810366","wikidata":"https://www.wikidata.org/wiki/Q835942","display_name":"Interval graph","level":5,"score":0.4334000051021576},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.4325000047683716},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.426800012588501},{"id":"https://openalex.org/C54540088","wikidata":"https://www.wikidata.org/wiki/Q3498041","display_name":"Intersection graph","level":4,"score":0.3912999927997589},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.35600000619888306},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.3490000069141388},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3379000127315521},{"id":"https://openalex.org/C24028149","wikidata":"https://www.wikidata.org/wiki/Q7094056","display_name":"Online aggregation","level":5,"score":0.31520000100135803},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.31520000100135803},{"id":"https://openalex.org/C140146324","wikidata":"https://www.wikidata.org/wiki/Q1144319","display_name":"Predicate (mathematical logic)","level":2,"score":0.30630001425743103},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.29989999532699585},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.2874999940395355},{"id":"https://openalex.org/C24755975","wikidata":"https://www.wikidata.org/wiki/Q4943354","display_name":"Boolean conjunctive query","level":5,"score":0.28690001368522644},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.2766000032424927},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.2761000096797943},{"id":"https://openalex.org/C72169020","wikidata":"https://www.wikidata.org/wiki/Q194404","display_name":"Monotonic function","level":2,"score":0.27000001072883606},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C57691317","wikidata":"https://www.wikidata.org/wiki/Q1289248","display_name":"Scalar (mathematics)","level":2,"score":0.26339998841285706}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.24204","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.24204","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.2606.24204","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.24204","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":[{"score":0.40542125701904297,"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Approximate":[0],"Nearest":[1],"Neighbor":[2],"Search":[3],"(ANNS)":[4],"is":[5,49],"a":[6,52,59,102,116,128,139,146],"core":[7],"primitive":[8],"for":[9,69,119],"unstructured":[10],"data":[11,19],"retrieval.":[12],"Real-world":[13],"applications--such":[14],"as":[15,37,77,89],"temporal":[16],"databases,":[17],"financial":[18],"analysis,":[20],"and":[21,58,79,135,144,155,165,217,231],"retrieval-augmented":[22],"generation--often":[23],"require":[24],"hybrid":[25,236],"queries":[26],"whose":[27],"valid":[28],"objects":[29,209],"are":[30,67],"constrained":[31],"by":[32,51],"continuous":[33],"interval":[34,57,74,130,196,229],"attributes,":[35],"such":[36,76],"lifespans":[38],"or":[39],"price":[40],"ranges.":[41],"We":[42],"study":[43],"Interval-Predicate":[44],"ANNS":[45,64],"(IPANNS),":[46],"where":[47],"validity":[48],"determined":[50],"predicate":[53],"between":[54],"an":[55],"object":[56,134],"query":[60,136,225],"interval.":[61],"Existing":[62],"range-filtering":[63],"(RFANNS)":[65],"methods":[66,100],"designed":[68],"single-dimensional":[70],"scalar":[71,91],"filters,":[72,197],"but":[73],"predicates":[75,159],"containment":[78],"overlap":[80],"rely":[81],"on":[82,214],"two":[83],"coupled":[84],"endpoint":[85],"constraints.":[86],"Treating":[87],"endpoints":[88,137],"independent":[90],"attributes":[92],"can":[93],"incur":[94],"large":[95],"intersection":[96],"overhead,":[97],"while":[98,171,239],"containment-specific":[99],"lack":[101],"generalized":[103],"indexing":[104,242],"abstraction.":[105],"In":[106],"this":[107],"paper,":[108],"we":[109,198],"propose":[110],"the":[111,120,150,162],"Unified":[112],"Dominance":[113],"Graph":[114],"(UDG),":[115],"graph-indexing":[117],"framework":[118],"closed":[121],"two-bound":[122],"conjunctive":[123],"fragment":[124],"of":[125],"IPANNS.":[126],"For":[127],"chosen":[129],"predicate,":[131],"UDG":[132,173,181,222],"maps":[133],"into":[138,186],"normalized":[140],"two-dimensional":[141],"dominance":[142],"space":[143],"builds":[145],"dominance-labeled":[147],"graph":[148,192],"over":[149],"transformed":[151],"coordinates.":[152],"Containment,":[153],"overlap,":[154],"other":[156],"supported":[157],"endpoint-bound":[158],"therefore":[160],"reuse":[161],"same":[163],"construction":[164],"search":[166,193,237],"algorithms":[167],"after":[168],"semantic":[169],"mapping,":[170],"each":[172],"instance":[174],"remains":[175],"tied":[176],"to":[177],"its":[178],"selected":[179],"predicate.":[180],"compresses":[182],"query-state-specific":[183],"proximity":[184],"graphs":[185],"one":[187],"compact":[188],"index.":[189],"To":[190],"improve":[191],"under":[194],"restrictive":[195],"add":[199],"validity-preserving":[200],"patch":[201],"edges":[202],"that":[203,221],"provide":[204],"routing":[205],"choices":[206],"when":[207],"few":[208],"remain":[210],"valid.":[211],"Extensive":[212],"evaluations":[213],"standard":[215],"benchmarks":[216],"real-world":[218],"datasets":[219],"show":[220],"achieves":[223],"stable":[224],"performance":[226],"across":[227],"multiple":[228],"relations":[230],"workloads,":[232],"significantly":[233],"outperforming":[234],"existing":[235],"baselines":[238],"maintaining":[240],"low":[241],"overhead.":[243]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-25T00:00:00"}
