{"id":"https://openalex.org/W2948489526","doi":"https://doi.org/10.1145/3375395.3387648","title":"Fair Near Neighbor Search: Independent Range Sampling in High Dimensions","display_name":"Fair Near Neighbor Search: Independent Range Sampling in High Dimensions","publication_year":2020,"publication_date":"2020-05-29","ids":{"openalex":"https://openalex.org/W2948489526","doi":"https://doi.org/10.1145/3375395.3387648","mag":"2948489526"},"language":"en","primary_location":{"id":"doi:10.1145/3375395.3387648","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3375395.3387648","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 39th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://pure.itu.dk/portal/da/publications/b7839de0-0ab0-4683-adc9-7ef088229d27","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Martin Aum\u00fcller","orcid":null},"institutions":[{"id":"https://openalex.org/I83467386","display_name":"IT University of Copenhagen","ror":"https://ror.org/02309jg23","country_code":"DK","type":"education","lineage":["https://openalex.org/I83467386"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Martin Aum\u00fcller","raw_affiliation_strings":["IT University of Copenhagen, Copenhagen, Denmark"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IT University of Copenhagen, Copenhagen, Denmark","institution_ids":["https://openalex.org/I83467386"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Rasmus Pagh","orcid":null},"institutions":[{"id":"https://openalex.org/I83467386","display_name":"IT University of Copenhagen","ror":"https://ror.org/02309jg23","country_code":"DK","type":"education","lineage":["https://openalex.org/I83467386"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Rasmus Pagh","raw_affiliation_strings":["BARC and IT University of Copenhagen, Copenhagen, Denmark"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"BARC and IT University of Copenhagen, Copenhagen, Denmark","institution_ids":["https://openalex.org/I83467386"]}]},{"author_position":"last","author":{"id":null,"display_name":"Francesco Silvestri","orcid":null},"institutions":[{"id":"https://openalex.org/I138689650","display_name":"University of Padua","ror":"https://ror.org/00240q980","country_code":"IT","type":"education","lineage":["https://openalex.org/I138689650"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Francesco Silvestri","raw_affiliation_strings":["University of Padova, Padova, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Padova, Padova, Italy","institution_ids":["https://openalex.org/I138689650"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6834,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.7655059,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"191","last_page":"204"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9991999864578247,"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"}},"topics":[{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9991999864578247,"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/T12288","display_name":"Optimization and Search Problems","score":0.9925000071525574,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9876000285148621,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/locality-sensitive-hashing","display_name":"Locality-sensitive hashing","score":0.800000011920929},{"id":"https://openalex.org/keywords/nearest-neighbor-search","display_name":"Nearest neighbor search","score":0.7193999886512756},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.6628000140190125},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5680999755859375},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.5515999794006348},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5490999817848206},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.5065000057220459},{"id":"https://openalex.org/keywords/hash-function","display_name":"Hash function","score":0.4957999885082245},{"id":"https://openalex.org/keywords/locality","display_name":"Locality","score":0.47760000824928284},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.4422999918460846}],"concepts":[{"id":"https://openalex.org/C74270461","wikidata":"https://www.wikidata.org/wiki/Q1625299","display_name":"Locality-sensitive hashing","level":4,"score":0.800000011920929},{"id":"https://openalex.org/C116738811","wikidata":"https://www.wikidata.org/wiki/Q608751","display_name":"Nearest neighbor search","level":2,"score":0.7193999886512756},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.6628000140190125},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5680999755859375},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5515999794006348},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5490999817848206},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5098000168800354},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.5065000057220459},{"id":"https://openalex.org/C99138194","wikidata":"https://www.wikidata.org/wiki/Q183427","display_name":"Hash function","level":2,"score":0.4957999885082245},{"id":"https://openalex.org/C2779808786","wikidata":"https://www.wikidata.org/wiki/Q6664603","display_name":"Locality","level":2,"score":0.47760000824928284},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47350001335144043},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44760000705718994},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.4422999918460846},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4309000074863434},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.4101000130176544},{"id":"https://openalex.org/C136736807","wikidata":"https://www.wikidata.org/wiki/Q818943","display_name":"Range query (database)","level":5,"score":0.40369999408721924},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.39649999141693115},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.3935999870300293},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.364300012588501},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.359499990940094},{"id":"https://openalex.org/C182964748","wikidata":"https://www.wikidata.org/wiki/Q208216","display_name":"Triangle inequality","level":2,"score":0.352400004863739},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.3499999940395355},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.3474999964237213},{"id":"https://openalex.org/C65236422","wikidata":"https://www.wikidata.org/wiki/Q173740","display_name":"Cartesian product","level":2,"score":0.3433000147342682},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.3400999903678894},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.32829999923706055},{"id":"https://openalex.org/C143271835","wikidata":"https://www.wikidata.org/wiki/Q254515","display_name":"Similitude","level":2,"score":0.30649998784065247},{"id":"https://openalex.org/C2639959","wikidata":"https://www.wikidata.org/wiki/Q1344778","display_name":"Distance measures","level":2,"score":0.3012999892234802},{"id":"https://openalex.org/C193319292","wikidata":"https://www.wikidata.org/wiki/Q272172","display_name":"Hamming distance","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C178635117","wikidata":"https://www.wikidata.org/wiki/Q747499","display_name":"RADIUS","level":2,"score":0.2727000117301941},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C53661774","wikidata":"https://www.wikidata.org/wiki/Q13108095","display_name":"Cover tree","level":5,"score":0.25929999351501465},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.2581000030040741},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.2547000050544739}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1145/3375395.3387648","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3375395.3387648","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 39th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.atira.dk:openaire/b7839de0-0ab0-4683-adc9-7ef088229d27","is_oa":true,"landing_page_url":"https://pure.itu.dk/portal/da/publications/b7839de0-0ab0-4683-adc9-7ef088229d27","pdf_url":null,"source":{"id":"https://openalex.org/S4377196680","display_name":"IT University Of Copenhagen (IT University of Copenhagen)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I83467386","host_organization_name":"IT University of Copenhagen","host_organization_lineage":["https://openalex.org/I83467386"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Aum\u00fcller, M, Pagh, R & Silvestri, F 2020, Fair Near Neighbor Search: Independent Range Sampling in High Dimensions. PODS. in PODS'20: Proceedings of the 39th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems. Association for Computing Machinery, pp. 191\u2013204. https://doi.org/10.1145/3375395.3387648","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:arXiv.org:1906.01859","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1906.01859","pdf_url":"https://arxiv.org/pdf/1906.01859","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:www.research.unipd.it:11577/3344588","is_oa":true,"landing_page_url":"https://arxiv.org/abs/1906.01859","pdf_url":null,"source":{"id":"https://openalex.org/S4306402547","display_name":"Padua Research Archive (University of Padova)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I138689650","host_organization_name":"University of Padua","host_organization_lineage":["https://openalex.org/I138689650"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"pmh:oai:pure.atira.dk:openaire/b7839de0-0ab0-4683-adc9-7ef088229d27","is_oa":true,"landing_page_url":"https://pure.itu.dk/portal/da/publications/b7839de0-0ab0-4683-adc9-7ef088229d27","pdf_url":null,"source":{"id":"https://openalex.org/S4377196680","display_name":"IT University Of Copenhagen (IT University of Copenhagen)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I83467386","host_organization_name":"IT University of Copenhagen","host_organization_lineage":["https://openalex.org/I83467386"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Aum\u00fcller, M, Pagh, R & Silvestri, F 2020, Fair Near Neighbor Search: Independent Range Sampling in High Dimensions. PODS. in PODS'20: Proceedings of the 39th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems. Association for Computing Machinery, pp. 191\u2013204. https://doi.org/10.1145/3375395.3387648","raw_type":"info:eu-repo/semantics/publishedVersion"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4422840520","display_name":null,"funder_award_id":"16582","funder_id":"https://openalex.org/F4320310490","funder_display_name":"Villum Fonden"}],"funders":[{"id":"https://openalex.org/F4320310490","display_name":"Villum Fonden","ror":"https://ror.org/007ww2d15"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1430582609","https://openalex.org/W2012833704","https://openalex.org/W2019336343","https://openalex.org/W2025051251","https://openalex.org/W2042558865","https://openalex.org/W2054141820","https://openalex.org/W2059141064","https://openalex.org/W2084358005","https://openalex.org/W2084732238","https://openalex.org/W2140431670","https://openalex.org/W2147717514","https://openalex.org/W2158194525","https://openalex.org/W2170209906","https://openalex.org/W2508919161","https://openalex.org/W2613423924","https://openalex.org/W2741172101","https://openalex.org/W2769182151","https://openalex.org/W2889169527","https://openalex.org/W2897154134","https://openalex.org/W2951087162","https://openalex.org/W2964013013","https://openalex.org/W3098556943","https://openalex.org/W3104626761"],"related_works":[],"abstract_inverted_index":{"Similarity":[0],"search":[1,21,133,210],"is":[2,29],"a":[3,36,40,46,57,141,179,188,199,204,234],"fundamental":[4],"algorithmic":[5],"primitive,":[6],"widely":[7],"used":[8],"in":[9,75,83,134,157,195,233],"many":[10],"computer":[11],"science":[12],"disciplines.":[13],"There":[14],"are":[15,92,160],"several":[16],"variants":[17,249],"of":[18,25,42,78,86,198,250],"the":[19,26,30,72,76,84,97,101,108,127,164,173,196,242,251],"similarity":[20,132,209],"problem,":[22],"and":[23,39,119,170,218,240],"one":[24],"most":[27,63],"relevant":[28],"r-near":[31],"neighbor":[32],"(r-NN)":[33],"problem:":[34],"given":[35,52,183],"radius":[37],"r>0":[38],"set":[41],"points":[43,90,156,194],"S,":[44],"construct":[45],"data":[47,150,189,205],"structure":[48,190,206],"that,":[49,182],"for":[50,152,191,207],"any":[51,184],"query":[53,98],"point":[54,58],"q,":[55],"returns":[56],"p":[59],"within":[60,93],"distance":[61,94],"at":[62],"r":[64,95],"from":[65,96],"q.":[66],"In":[67,107],"this":[68,111],"paper,":[69],"we":[70,147,176,202],"study":[71],"r-NN":[73,153],"problem":[74,112],"light":[77],"fairness.":[79],"We":[80],"consider":[81],"fairness":[82,142],"sense":[85],"equal":[87],"opportunity:":[88],"all":[89,155],"that":[91,159,214,230],"should":[99],"have":[100,163],"same":[102,165],"probability":[103,166],"to":[104,131,167],"be":[105,168],"returned.":[106],"low-dimensional":[109],"case,":[110],"was":[113],"first":[114,177],"studied":[115],"by":[116,172,246],"Hu,":[117],"Qiao,":[118],"Tao":[120],"(PODS":[121],"2014).":[122],"Locality":[123],"sensitive":[124,221],"hashing":[125],"(LSH),":[126],"theoretically":[128],"strongest":[129],"approach":[130,181],"high":[135],"dimensions,":[136],"does":[137],"not":[138],"provide":[139],"such":[140],"guarantee.":[143],"To":[144],"address":[145],"this,":[146],"propose":[148,178],"efficient":[149],"structures":[151],"where":[154],"S":[158],"near":[161],"q":[162],"selected":[169],"returned":[171],"query.":[174,200],"Specifically,":[175],"black-box":[180],"LSH":[185],"scheme,":[186],"constructs":[187],"uniformly":[192],"sampling":[193],"neighborhood":[197],"Then,":[201],"develop":[203],"fair":[208],"under":[211],"inner":[212],"product":[213],"requires":[215],"nearly-linear":[216],"space":[217],"exploits":[219],"locality":[220],"filters.":[222],"The":[223],"paper":[224],"concludes":[225],"with":[226],"an":[227],"experimental":[228],"evaluation":[229],"highlights":[231],"(un)fairness":[232],"recommendation":[235],"setting":[236],"on":[237],"real-world":[238],"datasets":[239],"discusses":[241],"inherent":[243],"unfairness":[244],"introduced":[245],"solving":[247],"other":[248],"problem.":[252]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3}],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2019-06-14T00:00:00"}
