{"id":"https://openalex.org/W2060700520","doi":"https://doi.org/10.14778/2021017.2021024","title":"Efficient probabilistic reverse nearest neighbor query processing on uncertain data","display_name":"Efficient probabilistic reverse nearest neighbor query processing on uncertain data","publication_year":2011,"publication_date":"2011-07-01","ids":{"openalex":"https://openalex.org/W2060700520","doi":"https://doi.org/10.14778/2021017.2021024","mag":"2060700520"},"language":"en","primary_location":{"id":"doi:10.14778/2021017.2021024","is_oa":false,"landing_page_url":"https://doi.org/10.14778/2021017.2021024","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111781610","display_name":"Thomas Bernecker","orcid":null},"institutions":[{"id":"https://openalex.org/I8204097","display_name":"Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen","ror":"https://ror.org/05591te55","country_code":"DE","type":"education","lineage":["https://openalex.org/I8204097"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Thomas Bernecker","raw_affiliation_strings":["Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen"],"affiliations":[{"raw_affiliation_string":"Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen","institution_ids":["https://openalex.org/I8204097"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019401090","display_name":"Tobias Emrich","orcid":null},"institutions":[{"id":"https://openalex.org/I8204097","display_name":"Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen","ror":"https://ror.org/05591te55","country_code":"DE","type":"education","lineage":["https://openalex.org/I8204097"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Tobias Emrich","raw_affiliation_strings":["Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen"],"affiliations":[{"raw_affiliation_string":"Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen","institution_ids":["https://openalex.org/I8204097"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109848174","display_name":"Hans\u2010Peter Kriegel","orcid":null},"institutions":[{"id":"https://openalex.org/I8204097","display_name":"Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen","ror":"https://ror.org/05591te55","country_code":"DE","type":"education","lineage":["https://openalex.org/I8204097"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Hans-Peter Kriegel","raw_affiliation_strings":["Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen"],"affiliations":[{"raw_affiliation_string":"Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen","institution_ids":["https://openalex.org/I8204097"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016653456","display_name":"Matthias Renz","orcid":"https://orcid.org/0000-0002-2024-7700"},"institutions":[{"id":"https://openalex.org/I8204097","display_name":"Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen","ror":"https://ror.org/05591te55","country_code":"DE","type":"education","lineage":["https://openalex.org/I8204097"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Matthias Renz","raw_affiliation_strings":["Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen"],"affiliations":[{"raw_affiliation_string":"Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen","institution_ids":["https://openalex.org/I8204097"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042919440","display_name":"Stefan Zankl","orcid":null},"institutions":[{"id":"https://openalex.org/I8204097","display_name":"Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen","ror":"https://ror.org/05591te55","country_code":"DE","type":"education","lineage":["https://openalex.org/I8204097"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Stefan Zankl","raw_affiliation_strings":["Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen"],"affiliations":[{"raw_affiliation_string":"Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen","institution_ids":["https://openalex.org/I8204097"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017299501","display_name":"Andreas Z\u00fcfle","orcid":"https://orcid.org/0000-0001-7001-4123"},"institutions":[{"id":"https://openalex.org/I8204097","display_name":"Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen","ror":"https://ror.org/05591te55","country_code":"DE","type":"education","lineage":["https://openalex.org/I8204097"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Andreas Z\u00fcfle","raw_affiliation_strings":["Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen"],"affiliations":[{"raw_affiliation_string":"Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen","institution_ids":["https://openalex.org/I8204097"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5111781610"],"corresponding_institution_ids":["https://openalex.org/I8204097"],"apc_list":null,"apc_paid":null,"fwci":5.9271,"has_fulltext":false,"cited_by_count":48,"citation_normalized_percentile":{"value":0.9689635,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"4","issue":"10","first_page":"669","last_page":"680"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":1.0,"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":1.0,"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":0.9950000047683716,"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/T10757","display_name":"Geographic Information Systems Studies","score":0.9829999804496765,"subfield":{"id":"https://openalex.org/subfields/3305","display_name":"Geography, Planning and Development"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.8043428659439087},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.746720016002655},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.7458357810974121},{"id":"https://openalex.org/keywords/nearest-neighbor-search","display_name":"Nearest neighbor search","score":0.6788500547409058},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.606578528881073},{"id":"https://openalex.org/keywords/best-bin-first","display_name":"Best bin first","score":0.6010036468505859},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5597509741783142},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5467877984046936},{"id":"https://openalex.org/keywords/fixed-radius-near-neighbors","display_name":"Fixed-radius near neighbors","score":0.5459824800491333},{"id":"https://openalex.org/keywords/query-optimization","display_name":"Query optimization","score":0.45233651995658875},{"id":"https://openalex.org/keywords/nearest-neighbor-chain-algorithm","display_name":"Nearest-neighbor chain algorithm","score":0.4412387013435364},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3906068205833435},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3728289306163788},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.07402288913726807}],"concepts":[{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.8043428659439087},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.746720016002655},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.7458357810974121},{"id":"https://openalex.org/C116738811","wikidata":"https://www.wikidata.org/wiki/Q608751","display_name":"Nearest neighbor search","level":2,"score":0.6788500547409058},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.606578528881073},{"id":"https://openalex.org/C161986146","wikidata":"https://www.wikidata.org/wiki/Q4896845","display_name":"Best bin first","level":3,"score":0.6010036468505859},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5597509741783142},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5467877984046936},{"id":"https://openalex.org/C46264003","wikidata":"https://www.wikidata.org/wiki/Q5456333","display_name":"Fixed-radius near neighbors","level":5,"score":0.5459824800491333},{"id":"https://openalex.org/C157692150","wikidata":"https://www.wikidata.org/wiki/Q2919848","display_name":"Query optimization","level":2,"score":0.45233651995658875},{"id":"https://openalex.org/C102164700","wikidata":"https://www.wikidata.org/wiki/Q17162702","display_name":"Nearest-neighbor chain algorithm","level":5,"score":0.4412387013435364},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3906068205833435},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3728289306163788},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.07402288913726807},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.14778/2021017.2021024","is_oa":false,"landing_page_url":"https://doi.org/10.14778/2021017.2021024","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.221.7656","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.221.7656","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.unt.edu/%7Ehuangyan/6350/paper/efficient.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.385.2806","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.385.2806","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.dbs.ifi.lmu.de/Publikationen/Papers/VLDB11_prknn.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W96373761","https://openalex.org/W586204540","https://openalex.org/W1594296362","https://openalex.org/W1608524621","https://openalex.org/W1991812589","https://openalex.org/W1997141048","https://openalex.org/W2013333366","https://openalex.org/W2045928315","https://openalex.org/W2066406253","https://openalex.org/W2076287166","https://openalex.org/W2088422262","https://openalex.org/W2107475398","https://openalex.org/W2118269922","https://openalex.org/W2119493805","https://openalex.org/W2120342618","https://openalex.org/W2125140425","https://openalex.org/W2128230033","https://openalex.org/W2133246278","https://openalex.org/W2138271690","https://openalex.org/W2138414767","https://openalex.org/W2140237757","https://openalex.org/W2144562386","https://openalex.org/W2151135734","https://openalex.org/W2153404040","https://openalex.org/W2165558283","https://openalex.org/W2169067830","https://openalex.org/W4200613059","https://openalex.org/W4244343699","https://openalex.org/W6672615329"],"related_works":["https://openalex.org/W2182477562","https://openalex.org/W1558159560","https://openalex.org/W1595303882","https://openalex.org/W2375128115","https://openalex.org/W2011582495","https://openalex.org/W4246757943","https://openalex.org/W2245581955","https://openalex.org/W2143679819","https://openalex.org/W325985789","https://openalex.org/W2169618946"],"abstract_inverted_index":{"Given":[0],"a":[1,6,13,60],"query":[2,11,54,124],"object":[3,55],"q":[4,21],",":[5],"reverse":[7,42],"nearest":[8,24,43,57],"neighbor":[9,44,58],"(RNN)":[10],"in":[12],"common":[14],"certain":[15],"database":[16],"returns":[17],"the":[18,49,53],"objects":[19,51],"having":[20,52],"as":[22,56],"their":[23],"neighbor.":[25],"A":[26],"new":[27,74],"challenge":[28],"for":[29,68,126],"databases":[30],"is":[31,98,129],"dealing":[32],"with":[33,59],"uncertain":[34,50],"objects.":[35],"In":[36,105],"this":[37],"paper":[38],"we":[39,107],"consider":[40],"probabilistic":[41],"(PRNN)":[45],"queries,":[46],"which":[47,127],"return":[48],"sufficiently":[61],"high":[62],"probability.":[63],"We":[64,82],"propose":[65],"an":[66],"algorithm":[67,85],"efficiently":[69],"answering":[70],"PRNN":[71],"queries":[72],"using":[73],"pruning":[75],"mechanisms":[76],"taking":[77],"distance":[78],"dependencies":[79],"into":[80],"account.":[81],"compare":[83],"our":[84,96,110],"to":[86,100,116],"state-of-the-art":[87],"approaches":[88],"recently":[89],"proposed.":[90],"Our":[91],"experimental":[92],"evaluation":[93],"shows":[94],"that":[95],"approach":[97,111],"able":[99],"significantly":[101],"outperform":[102],"previous":[103],"approaches.":[104],"addition,":[106],"show":[108],"how":[109],"can":[112],"easily":[113],"be":[114],"extended":[115],"PR":[117],"k":[118,121],"NN":[119],"(where":[120],"&gt;":[122],"1)":[123],"processing":[125],"there":[128],"currently":[130],"no":[131],"efficient":[132],"solution.":[133]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":6},{"year":2016,"cited_by_count":4},{"year":2015,"cited_by_count":10},{"year":2014,"cited_by_count":8},{"year":2013,"cited_by_count":6},{"year":2012,"cited_by_count":5}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
