{"id":"https://openalex.org/W2097784484","doi":"https://doi.org/10.1109/ssdbm.2007.5","title":"Boosting k-Nearest Neighbor Queries Estimating Suitable Query Radii","display_name":"Boosting k-Nearest Neighbor Queries Estimating Suitable Query Radii","publication_year":2007,"publication_date":"2007-07-01","ids":{"openalex":"https://openalex.org/W2097784484","doi":"https://doi.org/10.1109/ssdbm.2007.5","mag":"2097784484"},"language":"en","primary_location":{"id":"doi:10.1109/ssdbm.2007.5","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssdbm.2007.5","pdf_url":null,"source":{"id":"https://openalex.org/S4210224189","display_name":"International Conference on Scientific and Statistical Database Management","issn_l":"1551-6393","issn":["1551-6393"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"19th International Conference on Scientific and Statistical Database Management (SSDBM 2007)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5103020338","display_name":"Marcos R. Vieira","orcid":"https://orcid.org/0000-0003-3502-8014"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Marcos R. Vieira","raw_affiliation_strings":["Department of Computer Science, George Mason University, Fairfax, VA, USA","George Mason Univ, Fairfax"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, George Mason University, Fairfax, VA, USA","institution_ids":["https://openalex.org/I162714631"]},{"raw_affiliation_string":"George Mason Univ, Fairfax","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020611078","display_name":"Caetano Traina","orcid":"https://orcid.org/0000-0002-6625-6047"},"institutions":[{"id":"https://openalex.org/I17974374","display_name":"Universidade de S\u00e3o Paulo","ror":"https://ror.org/036rp1748","country_code":"BR","type":"education","lineage":["https://openalex.org/I17974374"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Caetano Traina Jr.","raw_affiliation_strings":["University of Sao Paulo at Sao Carlos, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Sao Paulo at Sao Carlos, Brazil","institution_ids":["https://openalex.org/I17974374"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014783020","display_name":"Agma J. M. Traina","orcid":"https://orcid.org/0000-0003-4929-7258"},"institutions":[{"id":"https://openalex.org/I17974374","display_name":"Universidade de S\u00e3o Paulo","ror":"https://ror.org/036rp1748","country_code":"BR","type":"education","lineage":["https://openalex.org/I17974374"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Agma J. M. Traina","raw_affiliation_strings":["Computer Science Department, University of Sao Paulo, Sao Carlos, Sao Paulo, Brazil","University of S\u00e3o Paulo, S\u00e3o Carlos"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Department, University of Sao Paulo, Sao Carlos, Sao Paulo, Brazil","institution_ids":["https://openalex.org/I17974374"]},{"raw_affiliation_string":"University of S\u00e3o Paulo, S\u00e3o Carlos","institution_ids":["https://openalex.org/I17974374"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002730302","display_name":"Adriano S. Arantes","orcid":null},"institutions":[{"id":"https://openalex.org/I17974374","display_name":"Universidade de S\u00e3o Paulo","ror":"https://ror.org/036rp1748","country_code":"BR","type":"education","lineage":["https://openalex.org/I17974374"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Adriano Arantes","raw_affiliation_strings":["Computer Science Department, University of Sao Paulo, Sao Carlos, Sao Paulo, Brazil","University of S\u00e3o Paulo, S\u00e3o Carlos"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Department, University of Sao Paulo, Sao Carlos, Sao Paulo, Brazil","institution_ids":["https://openalex.org/I17974374"]},{"raw_affiliation_string":"University of S\u00e3o Paulo, S\u00e3o Carlos","institution_ids":["https://openalex.org/I17974374"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035605036","display_name":"Christos Faloutsos","orcid":"https://orcid.org/0000-0003-2996-9790"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Christos Faloutsos","raw_affiliation_strings":["Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA","Carnegie Mellon University. Pittsburgh"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"Carnegie Mellon University. Pittsburgh","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"10","last_page":"10"},"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/T11269","display_name":"Algorithms and Data Compression","score":0.9945999979972839,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9926999807357788,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.7641137838363647},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.746377170085907},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.6775258779525757},{"id":"https://openalex.org/keywords/best-bin-first","display_name":"Best bin first","score":0.6771538257598877},{"id":"https://openalex.org/keywords/nearest-neighbor-chain-algorithm","display_name":"Nearest-neighbor chain algorithm","score":0.6059509515762329},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.5641577839851379},{"id":"https://openalex.org/keywords/fixed-radius-near-neighbors","display_name":"Fixed-radius near neighbors","score":0.5582025647163391},{"id":"https://openalex.org/keywords/nearest-neighbor-graph","display_name":"Nearest neighbor graph","score":0.5488097667694092},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5450104475021362},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.4568524956703186},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4469366669654846},{"id":"https://openalex.org/keywords/nearest-neighbor-search","display_name":"Nearest neighbor search","score":0.414575457572937},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.23162052035331726},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.19142356514930725}],"concepts":[{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.7641137838363647},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.746377170085907},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.6775258779525757},{"id":"https://openalex.org/C161986146","wikidata":"https://www.wikidata.org/wiki/Q4896845","display_name":"Best bin first","level":3,"score":0.6771538257598877},{"id":"https://openalex.org/C102164700","wikidata":"https://www.wikidata.org/wiki/Q17162702","display_name":"Nearest-neighbor chain algorithm","level":5,"score":0.6059509515762329},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5641577839851379},{"id":"https://openalex.org/C46264003","wikidata":"https://www.wikidata.org/wiki/Q5456333","display_name":"Fixed-radius near neighbors","level":5,"score":0.5582025647163391},{"id":"https://openalex.org/C90988772","wikidata":"https://www.wikidata.org/wiki/Q2855103","display_name":"Nearest neighbor graph","level":3,"score":0.5488097667694092},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5450104475021362},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.4568524956703186},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4469366669654846},{"id":"https://openalex.org/C116738811","wikidata":"https://www.wikidata.org/wiki/Q608751","display_name":"Nearest neighbor search","level":2,"score":0.414575457572937},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.23162052035331726},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.19142356514930725},{"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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":2,"locations":[{"id":"doi:10.1109/ssdbm.2007.5","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssdbm.2007.5","pdf_url":null,"source":{"id":"https://openalex.org/S4210224189","display_name":"International Conference on Scientific and Statistical Database Management","issn_l":"1551-6393","issn":["1551-6393"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"19th International Conference on Scientific and Statistical Database Management (SSDBM 2007)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.491.3640","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.491.3640","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://alumni.cs.ucr.edu/~mvieira/articles/SSDBM07.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1482375083","https://openalex.org/W1861968760","https://openalex.org/W1905517305","https://openalex.org/W1930315378","https://openalex.org/W1977570286","https://openalex.org/W2046144220","https://openalex.org/W2049797265","https://openalex.org/W2065528935","https://openalex.org/W2088521060","https://openalex.org/W2104027187","https://openalex.org/W2109430544","https://openalex.org/W2148013500","https://openalex.org/W2157092487","https://openalex.org/W3003570628","https://openalex.org/W4238533680","https://openalex.org/W4242599275","https://openalex.org/W4248987599","https://openalex.org/W6663169883","https://openalex.org/W6682969285"],"related_works":["https://openalex.org/W2182477562","https://openalex.org/W1595303882","https://openalex.org/W1558159560","https://openalex.org/W2375128115","https://openalex.org/W4246757943","https://openalex.org/W2969538544","https://openalex.org/W2351157934","https://openalex.org/W2795392346","https://openalex.org/W2143679819","https://openalex.org/W2011582495"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"novel":[3],"and":[4,84,106],"effective":[5],"techniques":[6],"to":[7,11,25,42],"estimate":[8],"a":[9,37],"radius":[10],"answer":[12],"k-nearest":[13],"neighbor":[14],"queries.":[15],"The":[16,46,69],"first":[17,53],"technique":[18,48,54],"targets":[19,49],"datasets":[20,50,86],"where":[21,51,63],"it":[22],"is":[23,67],"possible":[24],"learn":[26],"the":[27,30,34,43,52,64,90,103,110,115],"distribution":[28],"about":[29],"pairwise":[31],"distances":[32],"between":[33],"elements,":[35],"generating":[36,58],"global":[38],"estimation":[39],"that":[40,60,89],"applies":[41],"whole":[44],"dataset.":[45],"second":[47],"cannot":[55],"be":[56],"employed,":[57],"estimations":[59],"depend":[61],"on":[62,81],"query":[65],"center":[66],"located.":[68],"proposed":[70,91],"k-NNF()":[71],"algorithm":[72,92,105],"combines":[73],"both":[74,82],"techniques,":[75],"achieving":[76],"remarkable":[77],"speedups.":[78],"Experiments":[79],"performed":[80],"real":[83],"synthetic":[85],"have":[87],"shown":[88],"can":[93],"accelerate":[94],"k-NN":[95],"queries":[96],"more":[97],"than":[98],"26":[99],"times":[100],"compared":[101,113],"with":[102,114],"incremental":[104],"spends":[107],"half":[108],"of":[109],"total":[111],"time":[112],"traditional":[116],"k-NN()":[117],"algorithms.":[118]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}